Zorba Sorting

Abstract

The material handling system utilizes a combination of spectroscopy sensors (e.g., X-ray fluorescence) and a vision system implementing an artificial intelligence system to sort mixed scrap materials and identify or classify each of the materials, which are then sorted into distinct groups based on such identification or classification. The system is capable of sorting between materials typically found in Zorba, Zebra, and Twitch.

Description

[Technical Field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 487,583, which is incorporated herein by reference. This application is a continuation-in-part of U.S. Patent Application No. 17 / 495,291 (issued as U.S. Patent No. 11,975,365), which is a continuation-in-part of U.S. Patent Application No. 17 / 491,415 (issued as U.S. Patent No. 11,278,937), which is a continuation-in-part of U.S. Patent Application No. 17 / 380,928, which is a continuation-in-part of U.S. Patent Application No. 17 / 227,245 (issued as U.S. Patent No. 11,964,304), which is a continuation-in-part of U.S. Patent Application No. 16 / 939,011 (issued as U.S. Patent No. 11,471,916). No. 16 / 375,675 (issued as U.S. Patent No. 10,722,922), which is a continuation-in-part of U.S. Patent Application No. 15 / 963,755 (issued as U.S. Patent No. 10,710,119), which is a continuation-in-part of U.S. Patent Application No. 15 / 213,129 (issued as U.S. Patent No. 10,207,296), which claims priority to U.S. Provisional Patent Application No. 62 / 193,332, all of which are incorporated herein by reference. U.S. Patent Application No. 17 / 491,415 (issued as U.S. Patent No. 11,278,937) is a continuation-in-part of U.S. Patent Application No. 16 / 852,514 (issued as U.S. Patent No. 11,260,426), which is a divisional application of U.S. Patent Application No. 16 / 358,374 (issued as U.S. Patent No. 10,625,304), which is a continuation-in-part of U.S. Patent Application No. 15 / 963,755 (issued as U.S. Patent No. 10,710,119), which claims priority to U.S. Provisional Patent Application No. 62 / 490,219, all of which are incorporated herein by reference.

[0002] Government License Rights This disclosure was made with U.S. Government support under Grant No. DE-AR0000422 awarded by the U.S. Department of Energy. The U.S. Government may have certain rights in this disclosure.

[0003] FIELD OF THE DISCLOSURE This disclosure relates generally to handling mixtures of materials, and more particularly to sorting and separating materials typically found in Zorba, Zebra, and / or Twitch containers. [Background technology]

[0004] This section is intended to introduce various aspects of the art that may be related to exemplary embodiments of the present disclosure. The discussion is believed to help provide a framework to facilitate a better understanding of certain aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.

[0005] Recycling benefits local communities and the environment because it reduces the amount of waste sent to landfills and incinerators, conserves natural resources, increases economic security by utilizing domestic sources of materials, prevents pollution by reducing the need to collect new raw materials, and saves energy.

[0006] Recycling aluminum (Al) scrap is a very attractive proposition, as it can save up to 95% of the energy costs associated with its production when compared to the labor-intensive extraction of more costly primary aluminum. Primary aluminum (or virgin aluminum) is defined as aluminum derived from aluminum-rich ores (e.g., bauxite). At the same time, demand for aluminum has steadily increased in markets such as automotive manufacturing due to its lightweight properties. As a result, certain economies exist that the aluminum industry can exploit by developing a well-planned yet simple recycling program or system. The use of recycled materials represents a cheaper metal resource than primary aluminum sources. As the amount of aluminum sold to the automotive industry (and other industries) increases, it will become increasingly necessary to use recycled aluminum to supplement primary aluminum supplies.

[0007] Correspondingly, efficient separation of aluminum scrap metal into alloy families is particularly desirable because mixed aluminum scrap of the same alloy family is much more valuable than indiscriminately mixed alloys. For example, in blending processes used to recycle aluminum, any quantity of scrap of consistent quality composed of similar (or the same) alloys will be more valuable than scrap composed of mixed aluminum alloys. In such aluminum alloys, aluminum will always be the majority of the material. However, components such as copper, magnesium, silicon, iron, chromium, zinc, manganese, and other alloying elements provide different properties to the alloyed aluminum and provide a means to distinguish one aluminum alloy from another.

[0008] The Aluminum Association is the organization that defines the allowable limits for the chemical composition of aluminum alloys. Data regarding the chemical composition of aluminum wrought alloys is published by the Aluminum Association in "International Alloy Designations and Chemical Composition Limits for Wrought Aluminum and Wrought Aluminum Alloys," which was updated in January 2015 and is incorporated herein by reference. Generally, according to the Aluminum Association, the 1xxx series of wrought aluminum alloys consists essentially of pure aluminum with a minimum aluminum content of 99% by weight; the 2xxx series is wrought aluminum alloyed primarily with copper (Cu); the 3xxx series is wrought aluminum alloyed primarily with manganese (Mn); the 4xxx series is wrought aluminum alloyed primarily with silicon (Si); the 5xxx series is wrought aluminum alloyed primarily with magnesium (Mg); the 6xxx series is wrought aluminum alloyed primarily with magnesium and silicon; the 7xxx series is wrought aluminum alloyed primarily with zinc (Zn); and the 8xxx series is other categories.

[0009] The Aluminum Association also has a similar document for designating cast aluminum alloys. The 1xx series of cast aluminum alloys consists essentially of pure aluminum with a minimum aluminum content of 99% by weight; the 2xx series is cast aluminum alloyed primarily with copper; the 3xx series is cast aluminum alloyed primarily with silicon plus copper and / or magnesium; the 4xx series is cast aluminum alloyed primarily with silicon; the 5xx series is cast aluminum alloyed primarily with magnesium; the 6xx series is virgin; the 7xx series is cast aluminum alloyed primarily with zinc; the 8xx series is cast aluminum alloyed primarily with tin; and the 9xx series is cast aluminum alloyed with other elements. Examples of cast alloys utilized for automotive parts include 38x (e.g., 380, 383, 384, 356, 360, and 319).

[0010] Generally, wrought aluminum alloys have higher magnesium concentrations than cast aluminum alloys, and cast aluminum alloys have higher silicon concentrations than wrought aluminum alloys.

[0011] Moreover, the presence of mixed alloy pieces within the body of scrap limits the ability of the scrap to be usefully recycled unless the different alloys (or at least alloys belonging to different compositional families, such as those designated by the Aluminum Association) can be separated prior to remelting. This is because when scrap of mixed alloy compositions or compositional families is remelted, the resulting molten mixture contains too high a proportion of major alloys and elements (or different compositions) to meet the compositional constraints required in any particular commercial alloy.

[0012] The automotive industry is a critical sector for the U.S. economy in terms of revenue generation and employment. Over the past 50 years, Detroit's automotive industry has suffered many hardships due to low-cost imports. The recent emergence of fuel-efficient, lightweight electric vehicles, particularly in light of the huge investments by Ford and General Motors to capture significant market share, offers an opportunity for Detroit to regain global leadership in automotive manufacturing. One result is that the materials used to manufacture automobiles are changing from heavy steel to lightweight aluminum for the body and battery tray, and copper for the electric motor. For example, a Tesla Model 3 electric vehicle (EV) contains approximately 660 pounds of aluminum, compared to approximately 250 pounds in the average internal combustion engine ("ICE") vehicle. There is currently consensus that by 2025, there will be a 2 billion pound shortage of aluminum for manufacturing electric vehicles. Russia and China are currently two of the world's largest suppliers of primary aluminum. A similar situation exists with regard to the need for copper for electric motors, which has caused the price of copper to rise significantly. Hence, there is a need to develop a reliable and sustainable domestic supply chain for these critical materials.

[0013] Zorba is mixed non-ferrous scrap, typically containing 90-95% metal content, and is a by-product of the current steel manufacturing process. Currently, there are approximately 300 auto shredders in the United States, which shred 12-15 million end-of-life ("EOL") automobiles annually, in addition to white goods (e.g., washers, dryers, refrigerators, and other appliances) and construction scrap (e.g., aluminum siding, window, and door frames). Shredders typically operate continuously, producing steel (ferrous) scrap (e.g., steel rebar for use in the construction of buildings, bridges, and factories). After shredding, the steel scrap is removed by giant electromagnets and sent to a steel mill. The shredder's by-product is the remaining mixed non-ferrous scrap, which contains everything except magnetic steel. The next step has traditionally involved the use of eddy current sorters to separate low-value non-metals (typically half the weight of a car sent to a landfill) and recover mixed metal scrap (i.e., zorba). However, such eddy current sorters are inefficient and very expensive to operate. As a result, large amounts of zorba remain unsorted. Currently, over 10 billion pounds of zorba are produced in the United States each year, with over 40% of that shipped overseas to be sorted by hand.

[0014] Moreover, as evidenced by the production and sale of the Ford F-150 pickup truck, which has a significant increase in its body and frame parts constructed of aluminum instead of steel, it is additionally desirable to recycle sheet metal scrap (e.g., wrought aluminum of certain alloy compositions), including that generated in the manufacture of automotive components from sheet aluminum (often referred to in the industry as "clips"). Recycling scrap involves remelting the scrap to provide molten metal bodies that can be cast and / or rolled into aluminum parts useful for further production of such vehicles. However, automotive manufacturing scrap (and metal scrap from other sources, such as aircraft and commercial and household appliances) often contains a mixture of wrought and cast scrap pieces and / or two or more aluminum alloys that differ substantially from each other in composition. Thus, those skilled in the art of aluminum alloys will recognize the difficulty of separating aluminum alloys, particularly processed alloys (e.g., cast, forged, extruded, rolled, and generally wrought alloys), into reusable or recyclable processed products.

[0015] Currently, the only existing technology that separates cast from wrought materials in a cost-effective manner is X-ray transmission ("XRT") technology. Because cast materials are heavier than wrought materials due to their higher silicon concentration, cast alloys have a higher density than wrought alloys. X-ray transmission technology can measure the heavier density of cast aluminum alloys and then separate the cast alloys from the wrought alloys. However, this method is not perfect. For example, cast alloys 319 and 380 / 383 have relatively high zinc concentrations (e.g., about 3%), giving these cast alloys their higher respective densities. However, cast alloy 360 has a lower relative zinc concentration (e.g., about 0.5%) and therefore a lower density. The lower density of cast alloy 360 causes the X-ray transmission method to classify this alloy as a wrought alloy rather than a cast alloy. Therefore, X-ray transmission technology does not correctly classify all cast alloys due to the large variation in the respective densities of the cast alloys. Therefore, such casting alloys end up being sorted with wrought aluminum alloys, which results in too much relative silicon in the molten mixture. [Prior art documents] [Patent documents]

[0016] [Patent Document 1] US Patent Application Publication No. 2022 / 0161298 [Patent Document 2] U.S. Patent No. 10,207,296 [Patent Document 3] U.S. Patent Application Serial No. 18 / 491,692 [Patent Document 4] US Patent Application Publication No. 2022 / 0371057 [Patent Document 5] U.S. Patent No. 11,471,916 [Patent Document 6] U.S. Patent No. 11,278,937 [Patent Document 7] U.S. Patent Application Publication No. 2021 / 0346916 [Patent Document 8] US Patent Application Publication No. 2021 / 0229133 [Patent Document 9] US Patent Application Publication No. 2022 / 0016675 [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a schematic diagram of a material handling system configured in accordance with various embodiments of the present disclosure. [Figure 2] FIG. 1 is a flow chart diagram configured in accordance with certain embodiments of the present disclosure. [Figure 3] FIG. 1 is a flow chart diagram configured in accordance with certain embodiments of the present disclosure. [Figure 4] 1A-1C are diagrams illustrating schematically various sortations that can be performed on scrap. [Figure 5] 1A-1C are diagrams that schematically illustrate exemplary techniques that may be utilized to sort and separate zebra material. [Figure 6] 1A-1C are diagrams that schematically illustrate exemplary techniques that may be utilized to sort and separate twitch materials. [Figure 7A] FIG. 1 is a flow chart diagram arranged in accordance with various embodiments of the present disclosure. [Figure 7B] FIG. 1 is a flow chart diagram arranged in accordance with various embodiments of the present disclosure. [Figure 7C] FIG. 1 is a flow chart diagram arranged in accordance with various embodiments of the present disclosure. [Figure 8] FIG. 1 illustrates a system and process for sorting materials according to certain embodiments of the present disclosure. [Figure 9A] FIG. 1 illustrates a system and process for sorting materials according to certain embodiments of the present disclosure. [Figure 9B] FIG. 1 illustrates a system and process for sorting materials according to certain embodiments of the present disclosure. [Figure 10] FIG. 1 illustrates a linkage of a serial material handling system in accordance with certain embodiments of the present disclosure. [Figure 11] FIG. 1 is a block diagram of a data processing system configured in accordance with various embodiments of the present disclosure. [Figure 12A] FIG. 1 is a flow chart diagram arranged in accordance with various embodiments of the present disclosure. [Figure 12B] FIG. 1 is a flow chart diagram arranged in accordance with various embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0018] Various detailed embodiments of the present disclosure are disclosed herein. However, it should be understood that the disclosed embodiments are merely exemplary of the present disclosure, which may be embodied in various and alternative forms. The figures are not necessarily to scale, and some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein should not be construed as limiting, but merely as representative basis for teaching those skilled in the art to use various embodiments of the present disclosure.

[0019] It is desired to recover high value materials from the solver with high robustness, throughput, efficiency, accuracy, and precision. As will be further described herein, after removing the low value steel with a magnetic separator, various higher value non-ferrous materials (e.g., aluminum, copper, brass, zinc, stainless steel, PCBs, lead, etc.) are sorted according to various embodiments of the present disclosure to generate high purity "spec feedstock" that can be used to manufacture products.

[0020] Sorting / recovering various high-value materials from the solver is more complex and requires both spectroscopic and vision (implementing AI systems) based sensors. Embodiments of the present disclosure accomplish such a task by essentially measuring the chemical composition of each type of material found in the solver. Signals from the vision system and sensor system (e.g., a spectroscopic system (e.g., XRF, LIBS, etc.)) are combined for each individual material to generate a "fingerprint" representing image and compositional data within the material, which is then utilized to sort among the various materials in the solver.

[0021] As used herein, "material" can include any item or object, including but not limited to metals (ferrous and / or non-ferrous), metal alloys (including but not limited to aluminum alloys), heavy, zorba, zebra, twitch, pieces of metal embedded within another, different material, plastics / polymers (including but not limited to any of the plastics / polymers disclosed herein, known in the art, or developed in the future), rubber, foam, printed circuit boards ("PCBs"), glass (including but not limited to borosilicate glass or soda lime glass, and various colored glasses), ceramic, paper, cardboard, Teflon, PE, bundled wire, wire covered with insulation, rare earth elements, leaves, wood, plants, plant parts, fibers, biowaste, packaging, electronic waste Including, but not limited to, refuse, batteries and accumulators, scrap from end-of-life ("EOL") products (e.g., vehicles, aircraft, and / or appliances), mining, construction, and demolition waste, agricultural crop waste, forest residues, purpose-grown grasses, woody energy crops, microalgae, food waste, hazardous chemical and biomedical waste, construction debris, agricultural waste, biogenic items, non-biogenic items, objects with a particular carbon content, any other object that may be found in municipal solid waste, and any other object, item, or material disclosed herein, including any of the foregoing that may be distinguished from one another by further types or classes, including, but not limited to, by one or more sensor systems, including, but not limited to, any of the sensor technologies disclosed herein.

[0022] In a more general sense, a "material" can include any item or object composed of a chemical element, a compound or mixture of chemical elements, or a compound or mixture of compounds or mixtures of chemical elements, and the complexity of the compound or mixture can range from simple to complex (all of which can also be referred to herein as materials having a particular "chemical composition" (also referred to herein as a particular "material composition"). "Chemical element" means a chemical element of the periodic table of chemical elements, including chemical elements that may be discovered after the filing date of this application. In this disclosure, the terms "scrap," "scrap piece," "material," "material piece," and "material scrap piece" can be used interchangeably. As used herein, a material piece or scrap piece referred to as having a metal alloy composition is a metal alloy having a particular chemical composition that distinguishes it from other metal alloys. As used herein, a "contaminant" is any material (or component of a material piece) that is to be excluded from a group of sorted materials.

[0023] As used herein, the term "predetermined" refers to something that is established or determined in advance (e.g., by a user of an embodiment of the present disclosure).

[0024] As used herein, the term "chemical signature" refers to a unique pattern (e.g., a fingerprint spectrum) produced by one or more analytical instruments that indicates the presence of one or more specific elements or molecules (including polymers) in a sample. The elements or molecules can be organic and / or inorganic. Such analytical instruments include any of the sensor systems disclosed herein (and in U.S. Patent Application Publication No. 2022 / 0161298), which is incorporated herein by reference. According to embodiments of the present disclosure, one or more such sensor systems can be configured to produce a chemical signature of a piece of material.

[0025] As defined in the Guidelines for Nonferrous Scrap issued by the Institute of Scrap Recycling Industries, Inc. ("ISRI"), the term "zorba" is a collective term for shredded nonferrous metals, including, but not limited to, those resulting from end-of-life products (e.g., vehicles, aircraft, appliances) or waste electrical and electronic equipment ("WEEE"). ISRI has established specifications for zorba, where each scrap piece may be composed of a combination of nonferrous metals (aluminum, copper, lead, magnesium, stainless steel, nickel, tin, and zinc) in elemental or alloyed (solid) form. Additionally, the term "twitch" refers to fragmented aluminum scrap. Twitch is traditionally produced by media separation techniques (e.g., float processes), whereby aluminum scrap floats to the top as heavier metal scrap pieces sink (e.g., some processes may include sand mixed in to vary the density of the water in which the scrap is immersed). The term "zebra" is intended to mean the high density non-ferrous metals typically produced by such processes.

[0026] As is well known in the art, a "polymer" is a substance or material composed of very large molecules (or macromolecules) made up of many repeating subunits. Polymers can be natural polymers found in nature or synthetic polymers. A "multilayer polymer film" is composed of two or more different compositions, and can have up to about 7.5-8 x 10 -4 The layers may have a thickness of 1 / 2 m. The layers are at least partially contiguous and preferably (but optionally) coextensive. As used herein, the terms "plastic," "plastic piece," and "piece of plastic material" (all of which may be used interchangeably) refer to any object that includes or is constructed from one or more polymers and / or polymer compositions of multilayer polymer films.

[0027] As used herein, "fraction" refers to any specified combination of organic and / or inorganic elements or molecules, polymer type, plastic type, polymer composition, chemical signature of a plastic, physical properties of a plastic piece (e.g., color, clarity, strength, melting point, density, shape, size, manufacturing type, uniformity, response to stimuli, etc.), etc., including any and all of the various classes and types of plastics disclosed herein. Non-limiting examples of fractions are one or more different types of plastic pieces containing the following: LDPE with a relatively high percentage of aluminum; LDPE and PP with a relatively low percentage of iron; PP with zinc; combinations of PE, PET, and HDPE; red LDPE plastic pieces of any type; any combination of plastic pieces excluding PVC; black plastic pieces; combinations of #3 to #7 type plastics containing specified combinations of organic and inorganic molecules; combinations of one or more different types of multilayer polymer films; combinations of specified plastics that do not contain specified contaminants or additives; any type of plastic with a melting point greater than a specified threshold; any thermosetting plastic of multiple specified types; specified plastics that do not contain chlorine; combinations of plastics with similar densities; combinations of plastics with similar polarity; plastic bottles without attached caps or vice versa.

[0028] As used herein, the term "image data" refers to packets of digital data relating to a captured visual image of an individual piece of material.

[0029] As used herein, the term "sort" and any derivatives thereof refer to the physical separation of particular pieces of material (e.g., specifically classified pieces of material) from other pieces of material.

[0030] As used herein, the terms "identify" and "classify," "identification" and "classification," and any derivatives of the foregoing, can be used interchangeably. As used herein, to "classify" a piece of material is to assign or determine (i.e., identify) the type or class of material to which the piece of material belongs. For example, according to certain embodiments of the present disclosure, a vision system (as further described herein) and / or a sensor system (as further described herein) can be configured to capture (collect) and analyze any type of information to classify materials and distinguish such classified materials from other materials, which classification can be utilized in a material handling system to selectively sort material pieces according to a set of one or more physical and / or chemical characteristics (e.g., which can be user-defined) including, but not limited to, color, texture, hue, shape, brightness, weight, density, chemical composition, size, uniformity, manufacturing type, chemical signature, predetermined fraction, radioactive signature, transmittance to light, sound, or other signals, and response to stimuli (e.g., various fields, etc.) including the emitted and / or reflected electromagnetic radiation (“EM”) of the material pieces.

[0031] The type or class (i.e., classification) of the material pieces can be user-definable (e.g., predetermined) and is not limited to any known classification of materials. The granularity of the type or class can range from very coarse to very fine. For example, the types or classes can include plastics, ceramics, glass, metals, foams, wood, and other materials (where the granularity of such types or classes is relatively coarse); different metals and metal alloys (e.g., zinc, copper, brass, lead, chrome plate, nickel plate, stainless steel, aluminum, etc.) (where the granularity of such types or classes is finer); or between certain types of aluminum alloys (where the granularity of such types or classes is relatively fine). Thus, the types or classes can be configured to distinguish between materials of significantly different chemical compositions (e.g., plastics and metal alloys, etc.) or between materials of nearly identical chemical compositions (e.g., different types of aluminum alloys, etc.). It should be appreciated that the methods and systems discussed herein can be applied to accurately identify / classify pieces of material whose chemical composition is completely unknown before being classified.

[0032] As used herein, "manufacturing type" refers to the type of manufacturing process by which a piece of material is produced, such as, for example, a metal part formed by a wrought process, cast (including, but not limited to, expendable die casting, permanent die casting, and powder metallurgy), extruded, forged, material removal process, etc.

[0033] As referred to herein, a "conveyor system" can be any known piece of mechanical handling equipment that moves material from one location to another, including, but not limited to, aeromechanical conveyors, vehicle conveyors, conveyor belts, belt-driven live roller conveyors, bucket conveyors, chain conveyors, chain-driven live roller conveyors, drag conveyors, dust-tight conveyors, electric track vehicle systems, flexible conveyors, gravity conveyors, gravity skate wheel conveyors, line shaft roller conveyors, electrically driven roller conveyors, overhead I-beam conveyors, overland conveyors, pharmaceutical conveyors, plastic belt conveyors, pneumatic conveyors, screw or auger conveyors, spiral conveyors, tubular gallery conveyors, vertical conveyors, vibrating conveyors, wire mesh conveyors, and conveying material pieces in a fluid past a vision and / or sensor system (including, but not limited to, very small particles suspended in the fluid).

[0034] The systems and methods described herein, according to certain embodiments of the present disclosure, accept a heterogeneous mixture of multiple material pieces (e.g., EOL scrap, Zorba, Heavy, Zebra, and / or Twitch), where at least one material piece in the heterogeneous mixture has a different chemical composition than one or more other material pieces, and / or is physically distinguishable from the other material pieces, and / or is of a different class or type of material from the other material pieces in the mixture, and the systems and methods are configured to identify / classify / separate / sort the one material piece into a group separate from such other material pieces. Embodiments of the present disclosure can be utilized to sort any type or class of material, as defined herein. In contrast, a homogeneous set or group of materials all fall into the same identifiable class or type of material.

[0035] Certain embodiments of the present disclosure will be described herein as classifying and sorting material pieces into user-defined or predetermined groupings or collections (e.g., material piece classifications) by sorting (e.g., physically depositing (e.g., ejecting or diverting) the material pieces into separate receptacles or bins or onto another conveyor system) the material pieces according to such separate groups or collections. By way of example, in certain embodiments of the present disclosure, material pieces are sorted into separate receptacles to separate specific material compositions, or material pieces comprised of compositions, from other material pieces comprised of different material compositions.

[0036] It should be noted that the material to be sorted may have irregular sizes and shapes. For example, such material (e.g., Zorba, Zebra, and / or Twich) may have previously been passed through some type of shredding mechanism that chopped the material into such irregularly shaped and sized pieces (creating scrap pieces), which can then be fed or diverted onto a conveyor system.

[0037] Certain embodiments of the present disclosure can be configured to sort aluminum alloy material pieces into separate receptacles, such that substantially all of the aluminum alloy material pieces having a material composition that falls within one of the aluminum alloy series published by the Aluminum Association are sorted into a single receptacle (e.g., a receptacle can correspond to one or more specific aluminum alloy series (e.g., 1xxx, 2xxx, 3xxx, 4xxx, 5xxx, 6xxx, 7xxx, 8xxx, 1xx, 2xx, 3xx, 4xx, 5xx, 6xx, 7xx, 8xx, 9xx)). Moreover, as will be described herein, certain embodiments of the present disclosure can be configured to sort metal alloys into separate receptacles according to the classification of their metal alloy compositions, even when such metal alloy compositions fall within the same alloy series (e.g., as defined by the Aluminum Association). As a result, material handling systems configured in accordance with certain embodiments of the present disclosure are capable of sorting and separating aluminum alloy material pieces having compositions that would all fall into a single aluminum alloy series (e.g., the 3xx series or the 5xx series) into separate receptacles according to their aluminum alloy compositions. For example, certain embodiments of the present disclosure are capable of sorting and separating aluminum alloy material pieces classified as cast aluminum alloy 360 into separate receptacles from aluminum alloy material pieces classified as cast aluminum alloy 380 (or other similar cast aluminum alloys, such as 383).

[0038] 1 illustrates a non-limiting example of a material handling system 100 configured in accordance with various embodiments of the present disclosure. A conveyor system 103 can be implemented to transport individual material pieces 101 through the material handling system 100 so that each of the individual material pieces 101 can be tracked, classified, segregated, and / or sorted into predetermined desired groups (e.g., material classifications). Such a conveyor system 103 can be implemented with one or more conveyor belts, over which the material pieces 101 typically move at a predetermined constant speed. However, certain embodiments of the present disclosure can be implemented with other types of conveyor systems, including systems in which the material pieces free fall through one or more of the various components of the material handling system 100 (or any other type of vertical sorter), or any of the other conveyor systems disclosed herein. Hereinafter, where applicable, the conveyor system 103 may also be referred to as a conveyor belt 103. In one or more embodiments, some or all of the acts or functions of conveying, capturing, stimulating, detecting, classifying, differentiating, and sorting may be performed automatically (i.e., without human intervention). For example, in material handling system 100, one or more cameras, one or more vision systems, one or more sensor systems, one or more sources of stimuli, one or more emission detectors, one or more classification modules, sorting apparatus, one or more sorting devices, and / or other system components may be configured to perform these and other operations automatically.

[0039] Moreover, although the simplified illustration in FIG. 1 shows a single stream of material pieces 101 on the conveyor belt 103, embodiments of the present disclosure can also be implemented in which multiple such streams of material pieces pass parallel to one another through various components of the material handling system 100. According to certain embodiments of the present disclosure, some suitable feeder mechanism (e.g., another conveyor system, a bowl feeder, or a hopper 102) can be utilized to feed the material pieces 101 onto the conveyor system 103, which then transports the material pieces 101 through various components in the material handling system 100. According to certain embodiments of the present disclosure, a tumbler and / or a vibrator can be utilized to separate individual material pieces from a collection (e.g., a physical stack) of material pieces. According to certain embodiments of the present disclosure, the material pieces can be positioned into one or more singulated (i.e., single-file) streams, which can be performed by an active or passive singulator 106. Examples of passive singulators are further described in US Pat. No. 10,207,296.

[0040] As such, certain embodiments of the present disclosure can simultaneously track, classify, differentiate, and / or sort such a moving stream of material pieces. Alternatively, a conveyor system (e.g., conveyor belt 103) can simply transport a collection of material pieces deposited on conveyor belt 103 in a random manner. As such, according to certain embodiments of the present disclosure, singulation of material pieces 101 is not required to track, classify, differentiate, and / or sort the material pieces.

[0041] In certain embodiments of the present disclosure, the conveyor system 103 is operated to move at a predetermined speed by a conveyor system motor 104. This predetermined speed may be programmable and / or adjustable by an operator in any known manner. In certain embodiments of the present disclosure, control of the conveyor system motor 104 and / or position detector 105 may be performed by an automated control system 108. Such an automated control system 108 may be operated under the control of a computer system 107, and / or functionality for performing the automated control may be implemented in software within the computer system 107. When the conveyor system 103 is a conveyor belt, it may be a conventional endless belt conveyor using a conventional drive motor 104 suitable for moving the conveyor belt 103 at the predetermined speed.

[0042] A position detector 105 (e.g., a conventional encoder) is operably coupled to the conveyor belt 103 and the automation control system 108 and can provide information corresponding to the movement (e.g., speed) of the conveyor belt 103. Thus, as will be described further herein, through the use of control over the conveyor belt drive motor 104 and / or the automation control system 108 (and, alternatively, including the position detector 105), as each of the material pieces 101 moving on the conveyor belt 103 are identified, they can be tracked by location and time (relative to various components of the material handling system 100) and various components of the material handling system 100 can be activated / deactivated as each material piece 101 passes nearby. As a result, the automation control system 108 can track the location of each of the material pieces 101 as they move along the conveyor belt 103.

[0043] The vision system 110 can be configured to perform a particular type of identification (e.g., classification) (also referred to herein as a “vision check”) of all or a portion of the material pieces 101, as will be further described herein. For example, such a vision system 110 can be utilized to capture or acquire information about each of the material pieces 101. For example, the vision system 110 can be configured to capture or gather any type of information from the material pieces 101 (e.g., with an artificial intelligence (“AI”) system, as will be further described herein), which information can be utilized in the material handling system 100 to classify the material pieces 101 according to a set of one or more characteristics (e.g., physical characteristics, chemical characteristics, radioactive characteristics, etc.), as described herein. According to certain embodiments of the present disclosure, the vision system 110 can be configured to capture visual images (including one-dimensional, two-dimensional, three-dimensional, or holographic imaging) of each of the material pieces 101, for example, by using optical sensors such as those utilized in typical digital cameras and video equipment. Such visual images captured by the optical sensor are then stored as image data in a memory device (e.g., formatted as image data packets). According to certain embodiments of the present disclosure, such image data may represent images captured within optical wavelengths of light (i.e., wavelengths of light observable by a typical human eye). However, alternative embodiments of the present disclosure may utilize vision systems configured to capture images of materials composed of wavelengths of light outside the visible wavelengths of the human eye.

[0044] According to an alternative embodiment of the present disclosure, the vision system 110 can also be utilized as a means for tracking each of the material pieces 101 as they move on the conveyor system 103, which can utilize one or more still or live action cameras 109 to record the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103.

[0045] According to an alternative embodiment of the present disclosure, the vision system 110 may implement a machine vision system (e.g., such as may be implemented in LabVIEW) for analyzing and / or determining the shape (or relative shape) of each of the material pieces 101.

[0046] According to certain embodiments of the present disclosure, the material handling system 100 may be implemented with one or more sensor systems 120, which may be utilized alone or in combination with the vision system 110 to classify / identify / distinguish material pieces 101 (e.g., as described with respect to Figures 7A-7C and 12A-12B). The sensor system 120 may be any type of sensor technology (utilizing emitted or reflected electromagnetic radiation (e.g., infrared (“IR”), Fourier transform IR (“FTIR”), forward-looking infrared (“FLIR”), very near infrared (“VNIR”), near infrared (“NIR”), short wavelength infrared (“SWIR”), long wavelength infrared (“LWIR”), mid-wavelength infrared (“MWIR” or “MIR”), ultraviolet (“UV”), X-ray transmission (“XRT”) spectroscopy, X-ray fluorescence (“XRF”) spectroscopy, laser-induced breakdown spectroscopy (“LIBS”), laser spark spectroscopy (“LSS”), laser-induced optical emission spectroscopy (“LIOES”), Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, gamma-ray spectroscopy, hyperspectral spectroscopy (e.g., any wavelength beyond the visible wavelengths), The sensor system 120 may be configured by sensors utilizing techniques such as, but not limited to, optical, acoustic, NMR, microwave, terahertz, and fluorometric (e.g., XRF), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy, differential scanning calorimetry ("DSC"), thermogravimetric analysis ("TGA"), optical and scanning electron microscopy ("SEM"), and chromatography (e.g., LC-PDA, LC-MS, LC-LS, GC-MS, GC-FID, HS-GC), (including one-dimensional, two-dimensional, or three-dimensional imaging by any of the foregoing), or by any other type of sensor technology (including, but not limited to, chemical or radioactive sensors), all of which are to be distinguished herein from vision system implementations that utilize AI techniques (e.g., AI models) to analyze visual images. An exemplary XRF spectroscopy system implementation (e.g., for use herein as sensor system 120) is further described in U.S. Pat. No. 10,207,296.XRF can also be used in alternative embodiments of the present disclosure to identify inorganic materials in plastic pieces (eg, for inclusion in a chemical signature).

[0047] As used herein, the terms "sensor system" and "sensor technology" refer to any implementation of a sensor system disclosed herein for classifying / identifying / differentiating material pieces (also referred to herein as "sensor system classification"), as distinguished from the use of a vision system that utilizes AI technology to classify / identify / differentiate material pieces.

[0048] The following sensor systems can also be used in certain embodiments of the present disclosure to determine the chemical signature of plastic pieces and / or classify plastic pieces for sorting. Various forms of infrared spectroscopy (e.g., IR, FTIR, FLIR, VNIR, NIR, SWIR, LWIR, MWIR, and / or MIR) previously disclosed can be utilized to obtain a unique chemical signature for each plastic piece, which provides information about the base polymer of any plastic material and other components present in the material (mineral fillers, copolymers, polymer blends, etc.). DSC is a thermal analysis technique that obtains thermal transitions produced during heating of the analyzed material that are unique to each material. TGA is another thermal analysis technique that results in quantitative information about the composition of plastic materials in terms of polymer percentage, other organic components, mineral fillers, carbon black, etc. Capillary and rotational rheometry can determine the rheological properties of polymer materials by measuring their creep and deformation resistance. Optical microscopy and SEM can provide information about the structure of the analyzed material in terms of the number and thickness of layers in multilayer materials (e.g., multilayer polymer films), the dispersion size of pigment or filler particles in a polymer matrix, coating defects, interphase morphology between components, etc. Chromatography can quantify trace components of plastic materials, e.g., UV stabilizers, antioxidants, plasticizers, anti-slip agents, etc., as well as residual monomers, residual solvents from inks or adhesives, degradation products, etc.

[0049] 1 is illustrated as including one or more sensor systems 120, implementation of such sensor systems is optional in certain embodiments of the present disclosure. In certain embodiments of the present disclosure, a combination of one or more vision systems and one or more sensor systems may be used to classify the material pieces 101. In certain embodiments of the present disclosure, any combination of one or more of the different sensor technologies disclosed herein may be used to classify the material pieces 101 without the use of a vision system 110.

[0050] According to certain embodiments of the present disclosure, one or more vision systems and / or one or more sensor systems can be configured to identify which of the material pieces 101 contain contaminants (e.g., steel or iron pieces containing copper; plastic pieces containing particular contaminants, additives, or undesirable physical characteristics (e.g., attached container caps formed from a different type of plastic than the container)) and send a signal to separate (sort) such material pieces (e.g., from those that do not contain contaminants). In such a configuration, the identified material pieces 101 can be diverted / ejected (sorted) using one of the mechanisms described below for physically sorting material pieces into individual receptacles.

[0051] In certain embodiments of the present disclosure, a material piece tracking device 111 (or a commercially available profilometer) and associated control system 112 can be utilized and configured to measure the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103, as well as the size and / or shape of each of the material pieces 101 as they pass near the material piece tracking device 111. Exemplary operation of such a material piece tracking device 111 and control system 112 is further described in U.S. Patent No. 10,207,296. Exemplary operation of profilometers and similar devices is described in U.S. Patent Application No. 18 / 491,692, which is incorporated herein by reference.

[0052] Alternatively, as disclosed herein, the vision system 110 can be utilized to track the position (i.e., location and timing) of each of the material pieces 101 as they are transported by the conveyor system 103. As such, certain embodiments of the present disclosure can be implemented without a material piece tracking device (e.g., material piece tracking device 111) for tracking the material pieces.

[0053] In certain embodiments of the present disclosure implementing one or more sensor systems 120, the sensor system 120 can be configured to identify the chemical composition, relative chemical composition (including, but not limited to, measuring the amount of specific elements in the material piece), and / or manufacturing type of each of the material pieces 101 as the material pieces 101 pass near the sensor system 120. The sensor system 120 can include, for example, an energy emission source 121 (which can be powered by a power source 122) to stimulate a response from each of the material pieces 101. In certain embodiments of the present disclosure, the sensor system 120 can emit an appropriate sensing signal toward the material pieces 101 as each material piece 101 passes near the emission source 121. One or more detectors 124 can be positioned and configured to sense / detect one or more properties from the material pieces 101 in a manner appropriate for the type of sensor technology employed. One or more detectors 124 and associated detector electronics 125 capture these received sensed characteristics, perform signal processing thereon, and produce digitized information representing the sensed characteristics (e.g., spectroscopy data (e.g., XRF spectral data, etc.)), which is then analyzed to classify each of the material pieces 101.

[0054] It should be noted that while FIG. 1 is illustrated with a combination of a vision system 110 and one or more sensor systems 120, embodiments of the present disclosure may be implemented with any combination of sensor systems utilizing any of the sensor technologies disclosed herein or any other sensor technology currently available or developed in the future.

[0055] In certain embodiments of the present disclosure, material piece tracking devices 111 and associated control systems 112 may be utilized and configured to determine the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103, as well as the size and / or shape of each of the material pieces 101 as they pass near the material piece tracking devices 111. Exemplary operation of such material piece tracking devices 111 and control systems 112 is further described in U.S. Pat. No. 10,207,296.

[0056] According to certain embodiments of the present disclosure, the material tracking device 111 can be implemented before the vision system 110 and / or the sensor system 120 (e.g., upstream on a conveyor system) such that when a material piece 101 is detected by the material tracking system 111, it triggers the material handling system 100 as to when the vision system 110 and / or the sensor system 120 should capture characteristics of the material piece. Moreover, the order in which the vision system 110 and the sensor system 120 are implemented in the material handling system 100 can be interchanged.

[0057] The classification of the material pieces (which may be implemented in the computer system 107) may then be utilized by the automated control system 108 to activate one of the N (N>1) sorting devices 126...129 of the sorting apparatus to sort (e.g., divert / discharge) the material pieces 101 (e.g., into one or more N (N>1) sorting receptacles 136...139 or onto one or more other conveyor belts) according to the determined classification. Four sorting devices 126...129 and four sorting receptacles 136...139 associated with the sorting devices are illustrated in FIG. 1 by way of non-limiting example only.

[0058] The sorting apparatus may include any known mechanism for redirecting selected material pieces 101 toward a desired location (including, but not limited to, diverting the material pieces 101 from a conveyor belt system into multiple sortation receptacles). For example, the sorting device may utilize air jets, each of which is assigned to one or more of the classifications. When one or more of the air jets (e.g., 127) receives a signal from the automated control system 108, the air jet emits a stream of air that causes the material pieces 101 to be diverted / discharged from the conveyor system 103 into the sortation receptacle (e.g., 137) corresponding to that air jet (or onto another conveyor system).

[0059] 1 uses air jets to deflect / eject the material pieces, other mechanisms can also be used to deflect / eject the material pieces, such as, for example, robotically removing the material pieces from the conveyor belt, pushing the material pieces from the conveyor belt (e.g., with a paintbrush-type plunger), creating an opening (e.g., a trap door) in the conveyor system 103 through which the material pieces can fall, or using air jets to separate the material pieces into separate receptacles as they are thrown / dropped off the edge of the conveyor belt. A pusher device, as that term is used herein, can refer to any form of device that can be activated to dynamically displace an object onto or from a conveyor system / device (e.g., with pneumatic, mechanical, or other means to do so (e.g., any suitable type of mechanical pushing mechanism (e.g., ACME screw drive), pneumatic pushing mechanism, air jet pushing mechanism, etc.)).

[0060] In addition to the N sorting receptacles 136...139 into which the material pieces 101 are diverted / discharged, the material handling system 100 may also include a receptacle 140 that receives material pieces 101 that have not been diverted / discharged from the conveyor system 103 into one of the N sorting receptacles 136...139. For example, a material piece 101 may not be diverted / discharged from the conveyor system 103 into one of the N sorting receptacles 136...139 when the classification of the material piece 101 cannot be determined (or simply because the sorting device was unable to properly divert / discharge the piece). Thus, the receptacle 140 may serve as a default receptacle, into which unclassified or unsorted material pieces are dumped. Alternatively, the receptacle 140 may be used to receive one or more classifications of material pieces that have not been intentionally assigned to any of the N sorting receptacles 136...139. These such material pieces can then be further sorted according to other characteristics and / or by another material handling system.

[0061] Depending on the various classifications of the material pieces desired, multiple classifications can be mapped to a single sorting device and / or associated sorting receptacles. In other words, there need not be a one-to-one correlation between classifications and sorting devices and / or receptacles. For example, a user may desire to sort specific classifications of material into the same sorting receptacles. To accomplish this sorting, when material pieces 101 are classified to fall into a predetermined classification grouping, the same sorting device can be activated to sort them into the same sorting receptacles (or onto separate conveyor belts). Such combinatorial sorting can be applied to create any desired combination of sorted material pieces. The classification mapping can be programmed by a user (e.g., using any of the sorting algorithms as described herein operated by the computer system 107) to create such desired combinations. Additionally, the classifications of the material pieces are user-definable and are not limited to any particular known classification of material pieces.

[0062] The systems and methods described herein can be applied to sorting and / or separating individual material pieces having any of a variety of sizes. Although the systems and methods described herein are primarily described with respect to sorting individual material pieces of a singulated stream one at a time, the systems and methods described herein are not limited thereto. Such systems and methods can be used to simultaneously stimulate and / or detect emissions from multiple materials. For example, in contrast to a stream of singulated material being transported serially along one or more conveyor belts, multiple singulated streams can be transported in parallel. Each stream can be on the same belt or on a different belt arranged in parallel. Furthermore, the pieces can be randomly distributed on one or more conveyor belts (e.g., across and along one or more conveyor belts). Thus, the systems and methods described herein can be used to simultaneously stimulate and / or detect emissions from multiple these material pieces. In other words, multiple material pieces can be treated as a single piece, as opposed to each material piece being considered individually. Thus, multiple material pieces can be sorted and separated (eg, diverted / discharged from a conveyor system) together.

[0063] Conveyor system 103 may include a recycle loop (not shown) such that unsorted material pieces are rerouted through material handling system 100 for rescanning and resorting into predetermined categories. Moreover, because material handling system 100 can specifically track each material piece 101 as it moves on conveyor system 103, some sorting device (e.g., sorting device 129) may be implemented to direct / eject material pieces 101 that material handling system 100 was unable to sort (or that have collected in receptacle 140) after a predetermined number of cycles through material handling system 100.

[0064] With material handling system 100 implementing an XRF system for sensor system 120, the signal representing the detected / captured XRF spectrum can be converted, for example, on a per-channel (i.e., element) basis, as further described herein, into a discrete energy histogram, which can be utilized to determine the amount of a particular element in the material piece. Such a conversion process can be implemented in control system 123 or computer system 107. In certain embodiments of the present disclosure, such control system 123 or computer system 107 can include a commercially available spectral acquisition module (e.g., a commercially available Amptech MCA 5000 acquisition card and software programmed to operate the card). Such a spectral acquisition module (or other software implemented in material handling system 100) can be configured to implement multiple channels for dispersing X-rays into such discrete energy spectra (i.e., histograms) having multiple energy levels, whereby each energy level corresponds to an element that material handling system 100 is configured to detect. The material handling system 100 can be configured so that there are enough channels corresponding to specific elements in the chemical periodic table that are important to distinguish between different materials. The energy counts for each energy level can be stored in a separate collection storage register. The computer system 107 then reads each collection register to determine the number of counts for each energy level during the collection interval and constructs an energy histogram.As will be described in more detail herein, a sorting algorithm (e.g., a PCA algorithm) configured in accordance with certain embodiments of the present disclosure can then utilize this collected histogram of energy levels to classify at least certain of the material pieces 101 (and / or assist the vision system 110 in classifying the material pieces 101).

[0065] As previously mentioned, certain embodiments of the present disclosure may implement one or more vision systems (e.g., vision system 110) configured to classify and / or differentiate material pieces (e.g., in combination with an AI system). Such AI systems may include any known AI system (e.g., specific artificial intelligence ("ANI"), general artificial intelligence ("AGI"), artificial superintelligence ("ASI")), machine learning systems including those implementing neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, reinforcement learning, etc.), supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature representation learning, sparse dictionary learning, anomaly detection, robot learning, association rule learning, fuzzy logic, deep learning algorithms, deep structured learning hierarchical learning algorithms, decision tree learning (e.g., classification and regression), and the like. It is possible to implement a machine learning system that implements tree ("CART"), ensemble methods (e.g., ensemble learning, random forests, bagging and pasting, patch and subspace, boosting, stacking, etc.), dimensionality reduction (e.g., projection, manifold learning, principal component analysis, etc.), and / or deep machine learning algorithms (e.g., those described and publicly available at the deeplearning.net website (including all software, publications, and hyperlinks to available software referenced within this website)) (the deeplearning.net website is incorporated herein by reference).Non-limiting examples of publicly available machine learning software and libraries that may be utilized in embodiments of the present disclosure include Python, OpenCV, Inception, Theano, Torch, PyTorch, Pylearn2, Numpy, Blocks, TensorFlow, MXNet, Caffe, Lasagne, Keras, Qt, Ubuntu, NVIDIA drivers, Pandas, Matplotlib, github, Chainer, Matlab® Deep Learning, CNTK, MatConvNet (a MATLAB® toolbox that implements convolutional neural networks for computer vision applications), DeepLearn Toolbox (a Matlab® toolbox for deep learning (from Rasmus Berg Palm)), BigDL, Cuda-Convnet (a fast C++ / CUDA implementation of convolutional (or, more generally, feed-forward) neural networks), Deep Belief Networks, including RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factored RBM and mcRBM, mPoT (Python code that uses CUDAMat and Gnumpy to train a model on natural images), ConvNet, Elektronn, OpenNN, NeuralDesigner, Theano Generalized Hebbian Learning, Apache Singa, Lightnet, and SimpleDNN.

[0066] According to certain embodiments of the present disclosure, certain types of machine learning can be performed in stages. For example, training occurs (which can be performed offline, in that the material handling system 100 (or similar equipment) is not utilized to perform the actual sorting / sorting of material pieces). For example, the material handling system 100 (or similar equipment) can be utilized to train the machine learning system, in that a control sample (e.g., a homogenous set) of material pieces (i.e., having the same type or class of material or falling into the same predetermined fraction) is passed through the material handling system 100 (e.g., by conveyor system 103); then, all such material pieces can be collected in a common receptacle (e.g., receptacle 140) rather than sorted. Alternatively, training can be performed at another location remote from the material handling system 100, which involves using some other mechanism to collect sensed information (characteristics) of the control set of material pieces. During this training phase, algorithms in the machine learning system extract features from the captured information (e.g., using image processing techniques well known in the art). Non-limiting examples of training algorithms include, but are not limited to, linear regression, gradient descent, feedforward, polynomial regression, learning curve, regularized learning model, and logistic regression. During the training phase, the algorithms in the machine learning system learn the relationships between materials and their features / characteristics (e.g., as captured by the vision system and / or sensor system) to generate a knowledge base for subsequent classification of heterogeneous mixtures of material pieces received by the material handling system 100, which can then be sorted according to the desired classification.Such a knowledge base may include one or more libraries, each library containing parameters (e.g., neural network parameters) for use by the machine learning system in classifying material pieces. For example, one particular library may contain parameters configured by a training phase to recognize and classify a particular type or class of material, or one or more materials that fall within a predetermined fraction. According to certain embodiments of the present disclosure, such libraries may be input into the machine learning system, and a user of the material handling system 100 may then be able to adjust certain of the parameters to adjust the operation of the material handling system 100 (e.g., adjust the threshold effectiveness of how well the machine learning system recognizes (identifies, classifies) a particular material piece from a heterogeneous mixture of materials).

[0067] Additionally, the inclusion of specific chemical elements in a material piece results in a distinguishable physical characteristic (e.g., a visually distinguishable property) in the material. As a result, when multiple material pieces containing such specific chemical compositions are passed through the training phase described above, the machine learning system can learn how to distinguish such material pieces from others. Consequently, a machine learning system (or any AI system) configured according to certain embodiments of the present disclosure can be configured to sort among material pieces according to their respective material / chemical compositions. It can be readily appreciated that embodiments of the present disclosure can be configured to utilize image data (e.g., visual images) of a material piece as a surrogate for a representation of one or more various physical and / or chemical attributes of the material piece (e.g., ductility, malleability, brittleness, hardness, gloss, tensile strength, reactivity with various materials, etc.).

[0068] For example, twich includes cast aluminum alloy and wrought aluminum alloy. These two alloys have the same color. The difference between these two alloys is their chemical composition. Cast aluminum alloy has a higher concentration of silicon as an alloying element, while wrought aluminum alloy does not have a high concentration of silicon as an alloying element, thus commanding a premium price. An AI system implemented in a vision system (e.g., vision system 110) can be configured to classify these pieces at a high speed with an accuracy of over 95%. The AI ​​system can accurately classify these materials because the difference in silicon content results in the alloys appearing physically different from one another. Due to its higher silicon concentration, cast aluminum alloy fractures after shredding and does not bend or fold. However, wrought aluminum alloy is much more malleable because it does not have a high silicon concentration. Wrought aluminum alloy bends and folds during the shredding process and therefore has a visual characteristic resembling torn cloth. These visual features can be trained in an AI system and ultimately arise from the alloy's chemistry.

[0069] During the training phase, multiple material pieces (which are control samples) of one or more specific types, classes, or fractions of material can be delivered (e.g., by a conveyor system) through the vision system and / or one or more sensor systems so that algorithms in the machine learning system detect, extract, and learn what features are representative of such types or classes of material. For example, each of the material pieces in the control sample can first be passed through such a training phase so that algorithms in the machine learning system “learn” (are trained) how to detect, recognize, and classify such material pieces, and in the case of training a vision system (e.g., vision system 110), so that it is trained to visually discriminate (distinguish) between material pieces. This generates a library of parameters specific to such homogeneous classes of material pieces. The same process can be performed on images of any classification of material pieces to generate a library of parameters specific to such classification of material pieces. For each type of material to be classified by the vision system, any number of example material pieces of that classification can be passed by the vision system. Given the captured sensed information as input data, an algorithm in a machine learning system can use N classifiers, each of which tests one of N different material types. Note that a machine learning system can be "taught" (trained) to detect any type, class, or fraction of material, including any of the material types, classes, or fractions disclosed herein.

[0070] After the algorithms are established and the machine learning system has sufficiently learned (trained) the differences (e.g., visually distinguishable differences) for material classifications (e.g., within a user-defined level of statistical confidence), the library for different material classifications is then implemented into the material classification / sorting system (e.g., material handling system 100) and used to identify, distinguish, and / or classify material pieces from a heterogeneous mixture of material pieces, and then potentially sort such classified material pieces if sorting is to be performed.

[0071] One point to mention here is that, according to certain embodiments of the present disclosure, the detected / captured features / characteristics (e.g., visual images) of the material pieces may not necessarily be specifically identifiable or distinguishable physical characteristics; they may be abstract formulations that can only be expressed mathematically, or may not be expressed mathematically at all; nevertheless, the AI ​​system can be configured to analyze the spectral data to look for patterns that allow the control samples to be classified during the training phase. Moreover, the AI ​​system can take subsections of the captured information of the material pieces and attempt to find correlations between predefined classifications.

[0072] According to certain embodiments of the present disclosure, instead of utilizing a training phase in which control (homogeneous) samples of material pieces are passed by a vision system, training of the AI ​​system can be performed utilizing labeling / annotation techniques (or any other supervised learning technique), whereby as the data / information for the material pieces is captured by the vision system, a user inputs a label or annotation identifying each material piece, which is then used to generate a library for use by the AI ​​system in classifying material pieces within a heterogeneous mixture of material pieces. In other words, a previously generated knowledge base of properties captured from one or more samples of a class of material can be achieved by any of the techniques disclosed herein, whereby such knowledge base is then utilized to automatically classify materials.

[0073] Thus, as disclosed herein, certain embodiments of the present disclosure provide for the identification / classification of one or more different materials to determine which material pieces should be diverted from a conveyor system or device. According to certain embodiments, machine learning techniques can be utilized to train (i.e., configure) a neural network to identify one or more different classes or types of material. Images or other types of sensed information can be captured from the material (e.g., moving on the conveyor system), and based on such material identification / classification, the systems described herein can determine which material pieces should be allowed to remain on the conveyor system and which should be diverted / removed from the conveyor system (e.g., either diverted into a collection receptacle or diverted onto another conveyor system).

[0074] FIG. 2 illustrates a flowchart diagram depicting an exemplary embodiment of a process 200 for sorting / categorizing material pieces utilizing a vision system according to certain embodiments of the present disclosure. Process 200 can be implemented to sort a heterogeneous mixture of material pieces into any combination of predetermined types, classes, and / or fractions. Process 200 can be configured to operate within any of the embodiments of the present disclosure described herein (including material handling system 100 of FIG. 1 and those related to the "vision check" described with respect to FIGS. 7A-7C and 12A-12B). Operations of process 200 can be implemented by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 11) that controls a system (e.g., computer system 107 and / or vision system 110). In process block 201, material pieces are fed past a vision system (e.g., on a conveyor system). At process block 202, the location of each material piece on the conveyor system can be detected to track each material piece as it moves through the material handling system 100. This can be performed by the vision system 110 (e.g., by distinguishing the material piece from the underlying conveyor system material while communicating with a conveyor system position detector (e.g., position detector 105)). Alternatively, a material piece tracking device 111 can be used to track the piece. Or, any system capable of generating a light source (including, but not limited to, visible light, UV, and IR) and having a detector that can be used to locate the material piece. At process block 203, sensed information / characteristics of the material piece are captured / acquired as the material piece moves in proximity to one or more of the vision systems.At process block 204, the vision system may perform preprocessing of the captured information, which may be used to detect (extract) information about each of the material pieces (e.g., from the background (e.g., conveyor belt)); in other words, preprocessing may be used to identify differences between the material pieces and the background). Well-known image processing techniques (e.g., dilation, thresholding, contouring, etc.) may be used to identify the material pieces as distinct from the background. At process block 205, segmentation may be performed. For example, the captured information may include information about one or more material pieces. Additionally, a particular material piece may be located over a seam in a conveyor belt when its image is captured. Therefore, in such cases, it may be desirable to isolate the image of the individual material piece from the image background. In an exemplary technique for process block 205, the first step is to apply high contrast to the image, in which background pixels are reduced to substantially all black pixels and at least some of the pixels related to the material piece are brightened to substantially all white pixels. The image pixels of the white material piece are then dilated to cover the entire size of the material piece. After this step, the location of the material piece is a high-contrast image of all white pixels on a black background. A contour processing algorithm can then be used to detect the boundary of the material piece. The boundary information is saved, and the boundary location is then transferred to the original image. Segmentation is then performed on the original image in an area larger than the previously defined boundary. In this manner, the material piece is identified and separated from the background.

[0075] In optional process block 206, the material pieces can be transported along a conveyor system within the vicinity of a material piece tracking device (or profilometer) to determine the size and / or shape of the material pieces (which may be useful in assisting in classification of certain materials due to their shape or size, or may also be useful if an XRF system or some other spectroscopy sensor is implemented in the material handling system). In process block 207, post-processing can be performed. Post-processing can involve resizing the captured information / data and preparing it for use in a neural network. This can also include modifying certain characteristics (e.g., enhancing image contrast, changing the image background, or applying a filter) in a manner that will produce enhancements to the AI ​​system's ability to classify the material pieces. After image post-processing, normalization can be performed in process block 208. In process block 209, the data can be resized. Data resizing can be desired under certain circumstances to meet the data input requirements for a particular AI system (e.g., neural network, etc.). For example, a neural network may require an image size (e.g., 225x255 pixels or 299x299 pixels) that is much smaller than the size of an image captured by a typical digital camera. Moreover, the smaller the input data size, the less processing time is required to perform the classification. Therefore, smaller data sizes can ultimately increase the throughput and value of the material handling system 100.

[0076] In process blocks 210 and 211, identification / classification is performed on each material piece based on the sensed / detected features. For example, process block 210 can be configured with a neural network that uses one or more algorithms to compare the extracted features with those stored in a previously generated knowledge base (e.g., generated during a training phase) and assign each material piece the classification with the highest degree of match based on such comparison. The algorithm can process the captured information / data in a hierarchical manner by using automatically trained filters. The filter responses are then successfully combined at the next level of the algorithm until a probability is obtained in the final step. In process block 211, these probabilities can be used for each of the N classifications to determine how each material piece should be sorted. For example, each of the N classifications can be assigned to one sorting receptacle (or on another identified conveyor belt), and the material piece under consideration is sorted into that receptacle (or another identified conveyor belt) corresponding to the classification that returns the highest probability greater than a predefined threshold. In embodiments of the present disclosure, such predefined thresholds can be preset by a user, and a particular piece of material can be sorted into an outlier receptacle (or onto another specified conveyor belt) if none of the probabilities are greater than the predefined thresholds.

[0077] Next, at process block 212, a sorting device corresponding to one or more classifications of the material pieces can be activated (e.g., instructions are sent to the sorting device to sort the material pieces). As will be described with respect to various embodiments of Figures 7A-7C and 12A-12B, such sorting instructions can be based solely on classifications by the vision system (also referred to herein as a "vision check") or can be in response to a combination of classifications from the vision system and the sensor system (also referred to herein as a "sensor system classification"). Between the time the image of the material piece is captured and the time the sorting device is activated, the material piece has moved from the vicinity of the vision system to a downstream location on the conveyor system (e.g., at the rate of transport of the conveyor system). In an embodiment of the present disclosure, activation of the sorting devices is timed so that when a material piece passes a sorting device mapped to the classification of the material piece, the sorting device is activated and the material piece is diverted / discharged (sorted) from the conveyor system into its associated sorting receptacle (or, in some cases, onto another conveyor belt). In an embodiment of the present disclosure, activation of the sorting devices can be timed by respective position detectors that detect when a material piece passes in front of the sorting device and send a signal to enable activation of the sorting device. At process block 213, the sorting receptacle (or other conveyor belt) corresponding to the activated sorting device receives the sorted material piece.

[0078] According to alternative embodiments of the present disclosure, the AI ​​system utilized to classify the material pieces can be periodically or even continuously updated with newly acquired image data collected by the vision system as it classifies the material pieces. In other words, as new image data is collected by the vision system, it is utilized to update one or more AI models in the AI ​​system, such as, for example, if classification accuracy increases.

[0079] 3 illustrates a flowchart diagram depicting an exemplary embodiment of a process 300 for sorting and / or separating material pieces utilizing a sensor system 120 according to certain embodiments of the present disclosure. Process 300 can be configured to operate within any of the embodiments of the present disclosure described herein, including the material handling system 100 of FIG. 1 and aspects of process 700 described with respect to FIGS. 7A-7C or aspects of process 1200 described with respect to FIGS. 12A-12B. According to certain embodiments of the present disclosure, process 300 can be configured to operate in conjunction with process 200. For example, process blocks 303-305 can be incorporated into process 200 (e.g., operating in series or parallel with process blocks 203-210) to combine the efforts of one or more vision systems (e.g., vision system 110) with one or more sensor systems (e.g., sensor system 120).

[0080] The operations of process 300 can be implemented by hardware and / or software, including within a computer system (e.g., computer system 3400 of FIG. 11 ) that controls a system (e.g., sensor control unit 123 and / or computer system 107 of FIG. 1 ). At process block 301, material pieces are fed along a conveyor system. Then, at optional process block 302, the material pieces can be transported along the conveyor system within the vicinity of a material piece tracking device, a profilometer, and / or an optical imaging system to track each material piece and / or determine the size and / or shape of the material piece. At process block 303, as the material pieces move into the vicinity of the sensor system, they can be interrogated (or stimulated) with EM energy (waves) or some other type of stimulus appropriate for the particular type of sensor technology utilized by the sensor system. At process block 304, a physical property of the material piece (e.g., a captured XRF spectrum) is sensed / detected and captured by the sensor system. At process block 305, the type of material is identified / classified based on the captured characteristics, which can be combined with classification by an AI system in conjunction with vision system 110 (e.g., as described with respect to various embodiments of Figures 7A-7C and 12A-12B).

[0081] Next, if sorting of the material piece is to be performed, a sorting device corresponding to the material piece's classification is activated at process block 306. Between the time the material piece is sensed and the time the sorting device is activated, the material piece is moving from the vicinity of the sensor system to a downstream location on the conveyor system at the rate of transport of the conveyor system. In certain embodiments of the present disclosure, the activation of the sorting device is timed so that when the material piece passes a sorting device mapped to the material piece's classification, the sorting device is activated and the material piece is diverted / discharged from the conveyor system into its associated sorting receptacle (or onto another conveyor belt). In certain embodiments of the present disclosure, the activation of the sorting device can be timed by a respective position detector that detects when the material piece passes in front of the sorting device and sends a signal to enable activation of the sorting device. In process block 307, the sorting receptacle (or another conveyor belt) corresponding to the activated sorting device receives the diverted / discharged material piece.

[0082] As can be readily appreciated, and as will be further disclosed with respect to Figures 7A-7C and 12A-12B, process blocks 203-211 can be performed in parallel with process blocks 303-305 (and, if necessary, process block 302) in material handling system 100 to achieve the various classifications / sorting described with respect to Figures 7A-7C and 12A-12B.

[0083] 4-6, systems and processes configured in accordance with certain embodiments of the present disclosure are illustrated in which materials (e.g., scrap pieces) may be sorted. Such materials may originate from shredded end-of-life products (e.g., vehicles, aircraft, and / or appliances). Referring to FIG. 4, the materials (which may have been shredded into scrap) may be sorted between ferrous and non-ferrous materials. For example, magnets may be utilized to remove the ferrous material pieces. The remaining non-ferrous materials may typically include non-ferrous metals (often referred to as "zorba") and other "junk" or "fluff" materials (e.g., fabric, leather, foam rubber, rubber, plastic, wood, PCBs, glass, coins, and any other non-metallic material).

[0084] According to certain embodiments of the present disclosure, for example, as disclosed with reference to Figures 7A-7C and 12A-12B, the solvers can then be separated (sorted / classified) from the junk material (see, e.g., process blocks 701-703). The solvers can include one or more of various metals or metal alloys (e.g., copper, brass, zinc, stainless steel, lead, nickel alloys, gold or silver (e.g., located in PCBs), aluminum (cast, wrought, and / or extruded alloys, including but not limited to high-Z (high zinc content) cast aluminum alloys (e.g., cast aluminum alloys 319 and 380 / 383) and / or low-Z (low zinc content) cast aluminum alloys (e.g., cast aluminum alloys 356 and 360)).

[0085] According to certain embodiments of the present disclosure, the zorbers can be classified / sorted to separate heavy metals (also referred to as zebras or "heavies") from lighter metals (e.g., twitches) (see, e.g., process blocks 704-721 of Figures 7A-7B).

[0086] Certain alternative embodiments of the present disclosure can be configured to separate "meatballs" and / or airbag canisters from a material stream. See, for example, U.S. Patent Application Publication No. 2022 / 0371057, which is incorporated herein by reference.

[0087] According to certain embodiments of the present disclosure, Figure 5 schematically illustrates how one or more various combinations of one or more vision systems (e.g., implementing an AI system) and / or one or more sensor systems (e.g., an XRF system) can be utilized to separately extract various metals (e.g., copper, zinc, brass, stainless steel, nickel-plated, lead, etc.) from a stream of conveyed material pieces so that the zebras can be classified / sorted. Alternatively, any other of the disclosed sensor systems 120 (e.g., LIBS, XRT, etc.) can be utilized in place of an XRF system.

[0088] According to certain embodiments of the present disclosure, Figure 6 schematically illustrates how one or more various combinations of one or more vision systems (e.g., implementing an AI system) and / or one or more sensor systems (e.g., an XRF system) can be utilized such that the twitch can be separated into various desired classifications of cast, extruded, and / or wrought aluminum alloys. Alternatively, any other of the disclosed sensor systems 120 (e.g., LIBS, XRT, etc.) can be utilized in place of an XRF system.

[0089] 7A-7C illustrate a flowchart diagram of a process 700 configured in accordance with one or more embodiments of the present disclosure, in which a material handling system utilizes a unique combination of classification by one or more vision systems (e.g., implementing an AI system) and classification by one or more sensor systems (e.g., an XRF or LIBS system) to sort and classify various materials (e.g., a stream of material pieces transported by a conveyor system). Process 700 (or any aspect or portion thereof) can be implemented in any material handling system (including, but not limited to, the material handling systems described with respect to FIGS. 1, 8, 9A-9B, and 10) suitably configured to perform one or more of the various described sorting and sorting operations, utilizing the sorting and / or sorting functions, operations, systems, apparatus, and devices described with respect to FIGS. 1-3. Although certain process blocks are described as utilizing an XRF system, any one or more of such process blocks can be implemented with any of the sensor systems as described herein (e.g., LIBS, XRT, etc.) It should be noted that one or more of the various process blocks in process 700 can be optional or omitted according to certain embodiments of the present disclosure.

[0090] In the flow chart diagrams of FIGS. 7A-7C (and also for certain embodiments of FIGS. 12A-12B), the following legend applies with respect to measurements made by the XRF system (resulting in a captured XRF spectrum of each material piece):

[0091] Variable Legend M-Total = Sum of raw counts from measurements of Ti, Cr, Mn, Fe, Ni, Cu, Zn, Sn, and Pb N-Total = total count after normalizing by length and height (or mass) of the material piece Calculated XRF elemental percentages for each material piece: TI = 100 × Titanium raw counts / M-Total CR = 100 x chromium raw count / M-Total MN = 100 × manganese raw count / M-Total FE = 100 x raw iron count / M-Total NI = 100 x Nickel Raw Counts / M-Total CU = 100 x raw copper count / M-Total ZN = 100 x zinc raw count / M-Total SN = 100 × tin raw count / M-Total PB = 100 × lead raw count M-Total Pre-determined set points (values) for XRF elemental percentages for each material piece: TIK, CRK, MNK, FEK, NIK, CUK, ZNK, SNK, and PBK represent the XRF classification percentage constant for each element (which can be any predetermined value set between 0.0 and 100.0). Other predetermined set points (values): AK = 0.4 x N-Total for 319 standard cast alloys (constant value for N-Total calculated from 319 standard to determine cast vs. wrought category; 1 to 100,000) BK = 0.1 x Cu / ZN ratio for 319 standard (319 / 38 x ratio value calculated from 319 standard to determine casting category vs. die-cast zinc category; 0.0 to 1.0 ratio) CURB = the ratio chosen to select between copper and brass (it can be a value between 0.8 and 1.0 fractions (it can be set (predetermined) to a non-limiting exemplary default value of 0.9). CUYB = Ratio chosen for yellow brass to red brass (it can be a value between 0.3 and 0.8 fractions (it can be set (predetermined) to a non-limiting exemplary default value of 0.5) MNSS = A set (predetermined) value for classifying the manganese content in 2xx series stainless steels (it can be a value between 10 and 30).

[0092] The resulting spectral data from the XRF system can be normalized for piece size. This step can be optional. It can be performed by determining the XRF signal level as a function of piece size and then calibrating the classification to normalize for piece size.

[0093] Process 700 is described as operating on a piece-by-piece basis; it should be understood that when one material piece in a stream of conveyed material pieces has been operated on by a particular process block, subsequent material pieces can then be handled by that process block (e.g., when one material piece has been analyzed / classified by a vision system and / or an XRF system, subsequent material pieces can then be analyzed / classified by a vision system and / or an XRF system). Note that the flowchart diagrams of FIGS. 7A-7C depict multiple process blocks in which a "vision check" is performed, with other diamond-shaped process blocks representing analysis of material pieces by an XRF system. Note that the "vision check" can be performed by any suitable vision system, including, but not limited to, a vision system implementing appropriately configured AI techniques. Each vision check classifies a material piece in response to processing a visual image captured from each material piece through an AI system. Additionally, it should be noted that one or more of the "vision checks" implemented in process 700 can be performed on image data captured from a piece of material by a single vision system implementing one or more different AI algorithms (models) for each vision check. In a similar manner, one or more of the XRF sensor system classifications implemented in process 700 can be performed on spectral data captured from a piece of material by a single XRF system.

[0094] While any number of vision systems and / or XRF systems may be implemented to perform the operations in these process blocks, they may all be performed by a single vision system and / or a single XRF system (or any combination of one or more vision systems and one or more XRF systems) implemented in the material handling system (see, e.g., FIG. 8). For example, as each material piece passes by the single implemented vision system, image data of the material piece captured by the single implemented vision system may be analyzed (e.g., substantially simultaneously or in parallel) according to one or more of process blocks 701, 704, 718, and 721. In other words, algorithms associated with one or more of process blocks 701, 704, 718, and 721 may process the captured image data (e.g., by one or more AI models substantially simultaneously or in parallel with each other), and the results are utilized for their respective classification. Similarly, XRF spectral data captured by a single implemented XRF system can be analyzed according to one or more of process blocks 705, 707, 709, 710, 712, 715, 717, 722, 725, 727, and 729 (e.g., substantially simultaneously or in parallel by one or more algorithms).

[0095] Additionally, it should be noted that each vision check described in process 700 represents an attempt by that particular described algorithm to perform its classification on each material piece in the material stream; however, the vision check on a particular material piece may produce a “null” result, meaning that the particular vision check was unable to produce a classification (e.g., a vision check by process block 718 or 721 may produce a “null” output because the material piece is neither a cast aluminum alloy nor a wrought aluminum alloy). Similarly, it should be noted that each classification using XRF spectral data described in process 700 represents an attempt by that particular described algorithm to perform its classification on each material piece in the material stream; however, the classification on a particular material piece may produce a “null” result, meaning that the particular XRF sensor system classification was unable to produce a classification (e.g., an XRF sensor system classification by any of process blocks 705, 707, or 722 may produce a “null” output because the material piece did not have a measurable amount of zinc).

[0096] It should be noted that any of the vision checks described in Figures 7A-7C can be performed in accordance with process blocks 203-211 of process 200 described with respect to Figure 3. Any of the classifications performed by the XRF system (or any other suitable sensor system) in Figures 7A-7C can be performed in accordance with process blocks 303-305 of process 300 described with respect to Figure 4.

[0097] As will be described further herein, certain aspects of process 700 (i.e., the classification and / or sorting as implemented in one or more process blocks) may need to be performed in one or more particular sequences in order to more efficiently and / or accurately perform those aspects of process 700. It should be noted that while material pieces may be analyzed / classified by a single vision system and a single XRF system (including being analyzed / classified substantially simultaneously or in parallel), the sorting of various material pieces may need to be performed in a particular sequence (e.g., along a conveyor belt), as will be described further herein.

[0098] Although process 700 is described with respect to sorting and separating a stream of conveyed material including a zorba, a zebra, and / or a twitch, aspects of process 700 may also be applicable to sorting and / or separating other types of material pieces.

[0099] At process block 701, a vision check may be performed on the material piece to determine whether it should be classified as some predetermined specific material (e.g., "junk" material associated with a solver). If the vision check classifies the material piece as the predetermined specific material, then at process block 702, process 700 will send instructions to an identified sorting device to separate the material piece (e.g., from the stream of conveyed material pieces) according to its classification (e.g., material piece composed of or containing a PCB). Process block 703 indicates that process blocks 700-702 may be performed for any other type of material piece (e.g., other "junk" material) that a user desires to separate from the stream of material pieces, which may be classified by a vision system (e.g., a vision check using any of the vision-related techniques described herein, including, but not limited to, artificial intelligence ("AI") techniques). This can be achieved by a series of different vision systems, or a single vision system can be configured to perform classification in a substantially simultaneous manner or in parallel for any multiple predetermined types of material pieces (e.g., image data captured by a vision system for a single material piece is then analyzed by one or more AI algorithms (substantially simultaneously and / or in parallel with each other) to classify whether the material piece belongs to one of multiple predetermined types of material pieces (e.g., a predetermined set of "junk" material)).

[0100] It should be noted that the implementation of process blocks 701-703 in process 700 may be optional. If process blocks 701-703 are implemented, it may be important to perform the sorting indicated by these process blocks before the sorting indicated by one or more of the other process blocks of process 700 to improve the efficiency of the subsequent sorting / sorting of material pieces. For example, sorting various materials (as sorted in process blocks 701, 703) from the material stream may be important before performing the sorting of other metals and / or metal alloys (e.g., removing "junk" material before the remaining sorter sorting / sorting) as sorted in one or more subsequent process blocks of process 700. For example, removing "junk" material from the conveyed material stream before other materials can reduce the likelihood of such "junk" material contaminating subsequently sorted materials. In a non-limiting example, PCBs very often contain layers of copper, and an XRF sensor may erroneously classify such PCBs as copper scrap pieces. However, a vision system can be configured to distinguish green PCBs from red copper metal with considerable accuracy. Consequently, classification of such PCBs by an XRF system may result in them being sorted as copper scrap pieces, so it may be advantageous to use a vision check to classify such PCBs so that they can be separated from the material stream.

[0101] Process blocks 704-731 will now be described with respect to sorting and separating materials that may typically be found in a zorba (also referred to herein as "zorba materials"). According to certain embodiments of the present disclosure, process blocks 704-716 can be configured to sort / separate zebra materials from a zorba.

[0102] At process block 704, a vision check may be performed on the material piece to determine whether the material piece should be classified as consisting essentially of or containing copper and / or brass. This may be accomplished by a vision system (e.g., using any of the vision-related techniques described herein, including but not limited to AI techniques) because copper and brass pieces often have distinctive colors and / or shapes (e.g., a vision system may be trained to classify a particular pipe-shaped material as consisting of or containing copper).

[0103] If the material piece is classified by process block 704 as being composed of or containing copper and / or brass, then process 700 can further utilize the XRF sensor system classification of the material piece at process block 705 to distinguish between copper and brass pieces. Because brass typically contains a specific ratio of the amount of copper (Cu) to the amount of zinc (Zn) (e.g., about 60% Cu, about 40% Zn), a material piece will be classified as a copper piece when the ratio of the relative amounts of copper to zinc is greater than a predetermined value (e.g., when the ratio of the values ​​Cu / Zn is greater than a predetermined value (Curb), which, according to certain non-limiting embodiments of the present disclosure, has been empirically determined to be between 0.8 and 1.0). In other words, if, in the captured XRF spectrum of the material piece, the ratio of the calculated Cu value to the calculated Z value is greater than the Curb value, then the material piece is classified as copper rather than brass. Thus, if CU / ZN>CURB, then at process block 706, the process 700 sends instructions to the identified sorting device to sort the material piece from the material stream as a copper piece.

[0104] However, if the material piece is classified as a brass piece by process block 705, then at process block 707, process 700 may further determine whether the material piece is composed of yellow brass or red brass based on the captured XRF spectrum of the material piece. This may be performed by analyzing whether the ratio of the relative amount of copper to zinc is greater than a second predetermined value (e.g., a value CU / ZN relative to a predetermined CUYB value; the CUYB value has been empirically determined to be between 0.3 and 0.8, according to certain non-limiting embodiments of the present disclosure). If the material piece is classified as being composed of or containing red brass, then at process block 708, process 700 sends instructions to the identified sorting device to sort the material piece as a red brass material. If the material piece is classified as being composed of or containing yellow brass, then at process block 790, process 700 sends instructions to the identified sorting device to sort the material piece as a yellow brass material.

[0105] It can be readily seen that by combining process blocks 705-708 and 790, a piece of material can be classified as being comprised of yellow brass if it contains a certain amount of zinc relative to copper. Red brass is typically comprised of about 85% Cu and about 15% Zn, while yellow brass is typically comprised of about 60-70% Cu and about 30-40% Zn.

[0106] It can be further readily recognized that (1) the vision check of process block 704 in combination with the XRF sensor system classification of process block 706 can be utilized to separate copper scrap pieces from a material stream, (2) the vision check of process block 704 in combination with the XRF sensor system classification of process block 706 can be utilized to separate copper and brass scrap pieces from a material stream, (3) the vision check of process block 704 in combination with the XRF sensor system classification of process block 707 can be utilized to separate brass scrap pieces from a material stream, (4) the vision check of process block 704 in combination with the XRF sensor system classification of process blocks 706 and 707 can be utilized to separately sort red and yellow brass scrap pieces from a material stream, and (5) the vision check of process block 704 in combination with the XRF sensor system classification of process blocks 706 and 707 can be utilized to separate copper and red / yellow brass scrap pieces from a material stream.

[0107] It should be noted that process blocks 705-708, 790 may optionally be implemented in process 700 (e.g., when the pieces of material to be sorted are known not to contain copper and / or brass). Moreover, one or more of process blocks 705-708, 790 may optionally be omitted (or appropriately modified in a manner consistent with embodiments of the present disclosure as described herein) when the pieces of material to be sorted are known not to contain any one or more of copper, red brass, or yellow brass. Additionally, it should be noted that process 700 can be configured (e.g., process block 704 modified accordingly) so that non-copper / brass materials that have a copper or brass color (or may be shaped like copper tubing) are not classified / sorted as copper / brass pieces because they would not be classified as such as a result of implementation of process blocks 705-708, 790, which utilize XRF sensor system classification to achieve classification of copper / brass pieces for sorting from the solver.

[0108] Additionally, it may be important to perform the sorting of materials from the material stream as directed by process blocks 706, 708, and / or 790 before the sorting as directed by one or more of process blocks 723-724, because these process blocks also rely on the classification of material pieces as a function of CU / ZN ratio (process block 722) to prevent such material pieces from contaminating the sorted material resulting from either or both of process blocks 723 and 724. Also, for similar reasons (e.g., because copper-containing material pieces are more likely to contaminate 2xxx pieces sorted by process block 727, and because zinc-containing material pieces are more likely to contaminate 7xxx pieces sorted by process block 728), it may be important to perform the sorting as directed by process blocks 706, 708, and / or 790 before either or both of the sorting as directed by process blocks 726 and / or 728.

[0109] According to certain embodiments of the present disclosure, various combinations of process blocks 709-714 can be implemented to sort and separate nickel (Ni)-plated and / or stainless steel (“SS”) materials from a stream of material pieces. Process 700 can be configured to do so because these materials have relatively higher concentrations of nickel than the aluminum alloys to be subsequently sorted. Note that process block 709 is implemented as a sorting technique utilizing a sensor system other than a vision check (e.g., XRF) due to the possibility that nickel Ni-plated and / or SS material pieces may visually appear similar to certain types of aluminum material (cast and / or wrought). Moreover, because certain shredded pieces of such Ni-plated or stainless steel material may visually appear similar to certain cast and / or wrought aluminum pieces, it can be important to screen these material pieces prior to sorting of the aluminum alloys so that such nickel-plated and / or stainless steel materials do not contaminate the sorting / sorting of one or more various aluminum alloys.

[0110] At process block 709, a determination is made whether the captured XRF spectrum of the material piece indicates that the material piece contains an amount of chromium (Cr) greater than a predetermined threshold. This can be performed when the aluminum alloy to be sorted by process 700 is known to have a chromium concentration less than a known or predetermined value (e.g., a predetermined CRK value). Thus, based on the known or predetermined CRK value, the material piece can be separated from the material stream when the value CR is greater than the value CRK. According to certain embodiments of the present disclosure, and depending on the material to be sorted downstream, material classified as having a predetermined amount (i.e., concentration) of chromium can be separated from the material piece stream. It should be noted that this can be implemented with respect to any element known to be present in certain of the material pieces to be sorted.

[0111] If the result of process block 709 is positive, then process 700 can further utilize the XRF sensor system classification of the material piece to further distinguish between Ni-plated and SS materials by process block 710. Process block 710 determines whether the relative content of nickel to chromium in the material piece is greater than a predetermined ratio, which in this non-limiting example is determined from a calculation of NI / CR>1. This is because typical XRF systems are only effective at measuring x-ray fluorescence a relatively small depth into the surface of the material piece, and therefore the XRF system will read a much greater content of nickel in the nickel plating on the surface of the material piece, and because 2xx and 3xx stainless steels contain substantially less nickel than chromium.

[0112] If the piece of material is classified as Ni-plated, then instructions will be sent to the identified sorting device to sort the piece of material accordingly at process block 711. If not, then process 700 can proceed to process block 712 to determine if the manganese (Mn) content is greater than a predetermined or known amount, which in this non-limiting example is a value between 10 and 30, which can be set to classify the manganese content (Mn) in 2xx series stainless steels.

[0113] It should be noted that the determining factor for whether a piece of material is comprised of a 2xx series stainless steel or a 3xx series stainless steel is the amount of manganese. Thus, if MN>MNSS, then the piece of material is classified as a 2xx series stainless steel (e.g., 201, 202); otherwise, the piece of material is classified as a 3xx series stainless steel (e.g., 301, 302, 304, 316). At process block 713, if the piece of material is classified as a 2xx series stainless steel, then instructions will be sent by process 700 to the identified sorting device to sort the piece of material accordingly. At process block 714, if the piece of material is classified as a 3xx series stainless steel, then instructions will be sent by process 700 to the identified sorting device to sort the piece of material accordingly.

[0114] (1) The XRF sensor system classification combination of process blocks 709 and 710 can be utilized to separate the Ni plated pieces from the material stream, (2) the XRF sensor system classification combination of process blocks 709 and 710 can be utilized to separate the SS pieces from the material stream, (3) the XRF sensor system classification combination of process blocks 709 and 710 can be utilized to separate the Ni plated pieces and the SS pieces from the material stream, and (4) the XRF sensor system classification combination of process blocks 709, 710, and 712 can be utilized to separate the SS pieces from the material stream. It can be readily appreciated that (5) the combination of XRF sensor system classification of process blocks 709, 710, and 712 can be utilized to separate the SS pieces from the material stream into separate groups of SS201 / 202 and SS301, 302, 304, 316, and (6) the combination of XRF sensor system classification of process blocks 709, 710, and 712 can be utilized to separate the Ni-plated pieces and the SS pieces (which are further separated into groups of SS201 / 202 and SS301, 302, 304, 316) from the material stream. Additionally, all of the foregoing combinations can also include (1) the vision check of process block 704, and / or (2) the vision check of process blocks 701 / 703.

[0115] At process block 715, a determination is made whether the captured XRF spectrum of the material piece indicates that the material piece contains an amount of lead (Pb) greater than a predetermined threshold. This can be performed when the material piece to be sorted by process 700 is known to have a lead concentration less than a known or predetermined value (e.g., a predetermined PBK value). Thus, based on the known or predetermined PBK value, the material piece can be sorted when the value PB is greater than the value PBK. At process block 716, process 700 will send instructions to the identified sorting device to sort the material piece (e.g., from the stream of conveyed material pieces) according to the lead classification. Note that according to certain embodiments of the present disclosure, the sorting resulting from process blocks 704, 709, and 715 can be performed in a different order relative to one another (e.g., if it is known that doing so will improve the accuracy and / or efficiency of sorting one or more of the materials). According to an embodiment of the present disclosure, implementation of process blocks 715-716 is optional.

[0116] Also, the material stream to be classified / sorted may contain other materials that can be classified based on containing a unique (specific) chemical composition or signature (e.g., containing specific elements, metals, and / or alloying elements). In such cases, the sorting of these materials may be performed before the sorting of materials comprised of more complex chemical compositions or signatures, where such unique chemical compositions or signatures may contaminate (and thus reduce the accuracy or efficiency of) the sorting of materials comprised of more complex chemical compositions or signatures. Therefore, it may be important to separate out these materials with such unique chemical compositions or signatures so that they do not inadvertently sort into (contaminate) a receptacle for materials comprised of more complex chemical compositions or signatures. For example, process 700 can be configured to separate any material known to be present in the zebra prior to sorting of the twitch material (e.g., if it is known that doing so in that order will improve the accuracy and / or efficiency of sorting one or more particular twitch materials). Alternatively, according to certain embodiments of the present disclosure, process 700 can be configured to separate any material known to be present in the twitch prior to sorting and separating a particular zebra material (e.g., if it is known that doing so in that order will improve the accuracy and / or efficiency of sorting one or more particular zebra materials).

[0117] According to certain embodiments of the present disclosure, it may be advantageous or desirable to perform sorting based on process blocks 717-731 after one or more of the previously described process blocks. This is because XRF technology is known to be very poor at discriminating between certain aluminum alloys (e.g., the presence of any one or more of the various zebra materials (e.g., the copper, brass, Ni-plated materials, and SS materials mentioned above) may negatively or adversely affect the XRF measurements in process blocks 717-731). For example, pieces of die-cast zinc appear very similar to certain aluminum alloys and therefore cannot be visually distinguished from one another based solely on a vision check. However, XRF systems are able to easily distinguish between die-cast zinc and aluminum. Therefore, it may be advantageous to sort and separate die-cast zinc (see process block 724) before processing other aluminum pieces (e.g., any one or more of process blocks 725-731). Additionally, material pieces that are substantially larger (e.g., in size and / or mass) than other material pieces in the stream may have a relatively large zinc peak in the captured XRF spectrum, which may result in such material pieces being misclassified as 7xxx series aluminum alloys. Therefore, it may be advantageous or desirable to perform sorting according to process blocks 721-724 before sorting according to process blocks 727-728.

[0118] It should be noted that the captured XRF spectrum of a wrought aluminum piece (also referred to as sheet aluminum) will have a relatively lower total captured raw count than a similarly sized cast aluminum piece because the cast aluminum contains a relatively higher amount of copper and zinc, resulting in a relatively higher total number of captured raw counts. An exception to the foregoing is the 356 and 360 series cast aluminum alloys, which have relatively lower concentrations of copper and zinc. Therefore, process blocks 717-720 are configured to identify (sort) and separate the 356 and / or 360 series cast aluminum pieces, which, like wrought aluminum, have relatively lower amounts of copper and zinc. Otherwise, the 356 and / or 360 series cast aluminum pieces may be misclassified and therefore sorted as wrought aluminum (e.g., in process block 721). Therefore, it may be advantageous to perform sorting according to process blocks 717-719 before sorting according to process block 721.

[0119] At process block 717, a determination is made whether the total captured XRF spectral counts (N-Total) for the material piece (after normalization by the length and height (or mass) of the piece) are greater than a predetermined constant value (AK). According to certain embodiments of the present disclosure, this constant value may be set as 0.4×N-Total for 319 standard cast aluminum alloy (e.g., as determined by the Aluminum Association). According to certain embodiments of the present disclosure, process block 717 may determine whether the measured amount of copper and zinc in the scrap piece is greater than a predetermined value. For classifications that do not meet the standard set forth by the classification at process block 717 (i.e., the total captured XRF counts are relatively low or the measured amount of copper and zinc in the scrap piece is less than a predetermined value), then process 700 may utilize a vision check at process block 718 to determine whether the material piece is comprised of wrought aluminum or cast aluminum. If the material piece is not classified as a wrought aluminum piece, then at process block 719 it will be classified as a 356 and / or 360 cast aluminum piece and instructions are sent to the identified sorting device to sort the material piece accordingly.

[0120] Thus, according to an embodiment of the present disclosure, process 700 can be configured such that if process block 717 determines that N-Total≦AK and the vision check classifies the material piece as a cast aluminum piece, then instructions sent by process block 719 cause the material piece to be sorted for classification as 356 / 360 cast aluminum material; otherwise, the material piece remains on the main conveyor belt for subsequent classification / sorting by process blocks 721-731. Thus, it can be readily appreciated that process 700 can be configured such that a combination of process blocks 717 and 719 sorts the material piece for classification as 356 / 360 cast aluminum material. Additionally, the foregoing combination can also include (1) the vision check of process block 704, and / or (2) the vision check of process blocks 701 / 703, and / or (3) XRF classification by process block 709.

[0121] According to an alternative embodiment of the present disclosure, if the material piece is classified as a wrought aluminum piece by process block 718, then at process block 720, instructions can be sent by process 700 to the identified sorting device to sort the material piece accordingly.

[0122] According to further alternative embodiments of the present disclosure, such material pieces classified as wrought aluminum pieces may instead be redirected and / or returned in any suitable manner to the main conveyor belt at process block 720 for classification / sorting by subsequent process blocks in process 700. This may be accomplished by depositing these material pieces back onto the beginning of the material handling system and then deactivating process block 717, or may be accomplished by a return conveyor belt or carousel belt that returns these material pieces to a location on the conveyor belt downstream from the sorting locations associated with process blocks 719 and 720.

[0123] According to alternative embodiments of the present disclosure, the AK value may be predetermined (including predetermined on an empirical basis) to sort certain (but not all) wrought aluminum alloys at process block 717. In other words, the AK value may be adjusted as needed to achieve a desired sorting / sorting result. According to certain embodiments of the present disclosure, the AK value may be set such that a predetermined percentage of wrought aluminum pieces are passed by process block 717 to process block 721.

[0124] It should be noted that according to alternative embodiments of the present disclosure, further classification and sorting (e.g., by XRF or LIBS systems) can be performed to classify / sort between 356 cast aluminum alloy and 360 cast aluminum alloy (e.g., as a modification and / or addition to process block 719 according to embodiments disclosed herein). For example, process 700 can be modified to include process blocks 1216-1218 as described with respect to Figures 12A-12B (e.g., process block 719 is modified by or replaced by process blocks 1216-1218).

[0125] Because 319 and 38x cast aluminum alloys have material compositions represented by relatively large copper peaks compared to zinc as captured in XRF spectra (copper concentration approximately 3-4%; zinc concentration <1%), process blocks 721-724 can be implemented in process 700 to separate these alloys from the die-cast zinc metal pieces (or any other cast aluminum alloys remaining in the stream of material pieces). Note that, according to an alternative embodiment of the present disclosure, the material handling system can be configured such that all cast aluminum alloy pieces not classified as either 319 / 38x and / or die-cast zinc metal pieces are collected in a catch-all receptacle (e.g., in response to classification by process block 722, the process sends instructions to a sorting device to separate such cast aluminum alloy pieces from the 319 / 38x and / or die-cast zinc metal pieces).

[0126] At process block 721, the vision check can be configured to perform a classification between cast aluminum alloy and wrought aluminum alloy. For those material pieces classified as cast aluminum, process 700 can be configured to perform a further classification of each such material piece per process block 722, whereby a determination is made whether a captured XRF spectrum of the material piece indicates that the material piece has a copper concentration that is relatively greater than a zinc concentration (e.g., as previously described with respect to process blocks 705, 707). For example, a determination can be made whether the ratio of the value CU / ZN is greater than a predetermined value BK, which in this non-limiting example can be predetermined to be 0.1 × CU / ZN.

[0127] If yes, then the material piece is classified as a 319 / 38x cast aluminum piece, and at process block 723, process 700 sends instructions to the identified sorting device to sort the material piece accordingly. Otherwise, the material piece is classified as a die-cast zinc piece, and at process block 724, process 700 sends instructions to the identified sorting device to sort the material piece accordingly. Thus, it can be readily recognized that (1) the combination of the vision check of process block 721 and the XRF sensor system classification of process block 722 can result in the sorting of die cast zinc and 319 / 38x cast aluminum scrap pieces from the material stream, (2) the combination of the vision check of process block 721 and the XRF sensor system classification of process block 722 can result in the sorting of die cast zinc pieces from the material stream, and (3) the combination of the vision check of process block 721 and the XRF sensor system classification of process block 722 can result in the sorting of 319 / 38x cast aluminum scrap pieces from the material stream. Additionally, any of the foregoing combinations can also include (1) any one or more of the vision checks of process blocks 701 / 703 and 704, and / or (2) any one or more of the XRF classifications per process blocks 709, 715, and 717.

[0128] According to an alternative embodiment of the present disclosure, the process may divert the material pieces classified as cast aluminum alloy pieces at process block 721 onto a separate conveyor belt (or any suitable conveyor system; or diverted onto a different portion of the conveyor belt) whereby sorting / separation occurs between the 319 / 38x cast alloy pieces and the die-cast zinc pieces.

[0129] It should be noted that according to alternative embodiments of the present disclosure, further classification and sorting (e.g., by XRF or LIBS systems) can be performed to classify / sort between 319 and 38x cast aluminum alloys (e.g., as a modification and / or addition to process block 723 according to embodiments disclosed herein). For example, process 700 can be modified to include process blocks 1221-1224 as described with respect to Figures 12A-12B.

[0130] According to an alternative embodiment of the present disclosure, the vision check implemented in process block 721 can be configured to classify between cast, wrought, and extruded aluminum alloys, such as those described in U.S. Pat. No. 11,471,916, which is incorporated herein by reference.

[0131] According to an alternative embodiment of the present disclosure, optional process block 740 can be implemented in process 700 to perform negative classification / sorting on those material pieces and remove from the material stream any cast aluminum alloy pieces classified as wrought aluminum by process block 721. Such classification can be implemented by an appropriately configured vision check (or any of the sensor technologies described herein).

[0132] According to embodiments of the present disclosure, material pieces classified as wrought aluminum alloy pieces by the vision check of process block 721 may be classified / sorted according to various combinations of one or more of process blocks 725-731. According to embodiments of the present disclosure, sorting of these wrought aluminum alloy pieces into these various classifications is performed after one or more of the sorting previously described with respect to Figures 7A-7B. It should be noted that according to certain embodiments of the present disclosure, any one or more of these classifications / sorting may be optional or omitted.

[0133] At process block 725, a determination is made using the captured XRF spectrum of the material piece as to whether the percentage amount of copper (CU value) in the material piece is greater than a predetermined value (CUK). If so, the material piece is then classified by process block 725 as belonging to the 2xxx series of wrought aluminum alloys, and at process block 726, instructions are sent by process 700 to the identified sorting device to sort the material piece accordingly. Note that the 2xxx aluminum alloy series is known to contain a certain amount of more copper than other wrought aluminum alloy series. Therefore, the CUK value can be set (predetermined) to effectively sort these 2xxx series aluminum alloys from other material pieces in the material stream (as predetermined by the user). It can be readily appreciated that the operation (or implementation) of process block 726 may be beneficially performed after the sorting of any other material having a relatively high copper content (e.g., process blocks 706, 708, 790, and / or 723).

[0134] At process block 727, using the captured XRF spectrum of the material piece, a determination is made as to whether the percentage amount of zinc in the material piece (the ZN value) is greater than a predetermined value (ZNK). If so, the material piece is then classified as belonging to the 7xxx series of wrought aluminum alloys by process block 727, and at process block 728, instructions are sent by process 700 to the identified sorting device to sort the material piece accordingly. Note that the 7xxx series aluminum alloys are known to contain a certain amount more zinc than other wrought aluminum alloy series. Therefore, the ZNK value can be set (predetermined) to effectively sort these 7xxx series aluminum alloys from other material pieces in the material stream (as predetermined by a user).

[0135] Using the captured XRF spectrum of the material piece, a determination is made whether the percentage amount of manganese in the material piece (MN value) is greater than a predetermined value (MNK) and whether the percentage amount of iron in the material piece (FE value) is less than a predetermined value (FEK) at process block 729. If so, the material piece is classified by process block 729 as belonging to the 3xxx series of wrought aluminum alloys, and at process block 730, instructions are sent by process 700 to the identified sorting device to sort the material piece accordingly.

[0136] Process block 731 may indicate that any material pieces not classified / sorted as 3xxx series wrought aluminum alloys are classified as 5xxx / 6xxx series wrought aluminum alloys. This may be achieved by simply allowing all such remaining material pieces in the material stream to collect in a sorting receptacle. According to an alternative embodiment of the present disclosure, further sorting / sorting may be performed between 5xxx series aluminum alloys and 6xxx series aluminum alloys. As is known, 5xxx series wrought aluminum alloys are aluminum alloyed with magnesium, while 6xxx series wrought aluminum alloys are aluminum alloyed with magnesium and silicon. However, it should be noted that the concentrations of these aluminum alloy components are relatively very low; therefore, it may be important to configure process 700 so that the sorting / sorting at process block 731 occurs after other sorting / sorting associated with larger peaks measured in the captured XRF spectrum (which may obscure the ability of process 700 to classify / sort 5xxx / 6xxx wrought aluminum alloy pieces).

[0137] According to an alternative embodiment of the present disclosure, process block 731 may be configured to implement a principal component analysis (“PCA”) algorithm (or any other suitably configured algorithm) of the captured XRF spectra to classify / sort between 5xxx series aluminum alloys and 6xxx series aluminum alloys.

[0138] According to certain embodiments of the present disclosure, process blocks 726, 728, 730, and / or 731 may be performed in any order, however, the effectiveness of one or more such classifications / sorting may be affected thereby. Moreover, these classifications / sortings may be interchanged / reordered to achieve particular effectiveness / efficiency for one or more of these classifications / sortings.

[0139] Additionally, according to alternative embodiments of the present disclosure, further classification / sorting can be performed after any one or more of sorting 726, 728, 730, or 731 to further separate these into finer alloy classifications (e.g., within that particular wrought aluminum series) by utilizing any suitable sensor technology (e.g., XRF, LIBS, etc.), such as those described in U.S. Pat. No. 11,278,937, U.S. Patent Application Publication No. 2021 / 0346916, and U.S. Patent Application Publication No. 2021 / 0229133, which are incorporated herein by reference.

[0140] According to certain embodiments of the present disclosure, process 700 can be configured such that material pieces classified as cast aluminum alloy are sorted from the stream of material pieces, and then any material pieces not classified as belonging to a specified wrought aluminum alloy by any of process blocks 725, 727, and 729 are collected in an identified receptacle (see, for example, receptacle 140 in FIG. 1 ). According to certain embodiments of the present disclosure, process 700 can be configured such that both the classified cast aluminum alloy and wrought aluminum alloy are sorted from the stream of material pieces after process block 721, and then process blocks 722-724 are performed on the sorted cast aluminum alloy and process blocks 725-731 are performed on the sorted wrought aluminum alloy. According to certain embodiments of the present disclosure, process 700 can be configured such that both the sorted 3xxx series wrought aluminum alloys and the sorted 5xxx / 6xxx series wrought aluminum alloys are separated from the stream of material pieces after process block 729, with any unsorted material pieces being collected in a receiver (see, e.g., receiver 140 in FIG. 1 ).

[0141] It can be further readily recognized that (1) the combination of the vision check in process block 721 and the XRF sensor system classification in process block 725 can be utilized to sort 2xxx series wrought aluminum alloy pieces from a material stream, (2) the combination of the vision check in process block 721 and the XRF sensor system classification in process block 727 can be utilized to sort 7xxx series wrought aluminum alloy pieces from a material stream, (3) the combination of the vision check in process block 721 and the XRF sensor system classification in process block 729 can be utilized to sort 3xxx series wrought aluminum alloy pieces from a material stream, and (4) the combination of the vision check in process block 721 and the XRF sensor system classification in process block 729 can be utilized to sort 5xxx / 6xxx series wrought aluminum alloy pieces from a material stream. Additionally, any of the foregoing combinations may also include (1) any one or more of the vision checks of process blocks 701 / 703 and 704, and / or (2) any one or more of the XRF classifications of process blocks 709, 715, and 717.

[0142] 12A-12B illustrate a flowchart diagram of a process 1200 configured in accordance with one or more alternative embodiments of the present disclosure, in which a material handling system utilizes a unique combination of classification by one or more vision systems (e.g., implementing an AI system) and classification by one or more sensor systems (e.g., spectroscopic sensor systems, such as XRF or LIBS systems) to classify and sort various materials (e.g., a stream of material pieces transported by a conveyor system). Process 1200 (or any aspect or portion thereof) can be implemented in any material handling system (including, but not limited to, the material handling systems described with respect to FIGS. 1, 8, 9A-9B, and 10) suitably configured to perform one or more of the various described sorting and sorting operations using the sorting and / or sorting functions, operations, systems, apparatus, and devices described with respect to FIGS. 1-3. Although certain process blocks are described as utilizing an XRF system, any one or more of such process blocks can be implemented by any of the spectroscopic sensor systems as described herein (e.g., LIBS, XRT, etc.) It should be noted that one or more of the various process blocks in process 1200 can be optional or omitted according to certain embodiments of the present disclosure.

[0143] Process 1200 is described as operating on a piece-by-piece basis; it should be understood that when one material piece in a stream of conveyed material pieces has been operated on by a particular process block, subsequent material pieces can then be handled by that process block (e.g., when one material piece has been analyzed / classified by a vision system and / or an XRF system, subsequent material pieces can then be analyzed / classified by a vision system and / or an XRF system). Note that the flowchart diagrams of FIGS. 12A-12B depict multiple process blocks in which a "vision check" is performed, while other diamond-shaped process blocks represent analysis of material pieces by an XRF system (also referred to as XRF sensor system classification). Note that a "vision check" can be performed by any suitable vision system, including, but not limited to, a vision system implementing appropriately configured AI techniques. Each vision check classifies a material piece in response to processing a visual image captured from each material piece through an AI system.

[0144] While any number of vision systems and / or XRF systems can be implemented to perform the operations in these process blocks, they can all be performed by a single vision system and / or a single XRF system (or any combination of one or more vision systems and one or more XRF systems) implemented in a material handling system (see, e.g., FIG. 8 ). Thus, one or more of the “vision checks” implemented in process 1200 can be performed on image data captured from a material piece by a single vision system implementing one or more different AI algorithms (models) for each vision check. In a similar manner, one or more of the XRF sensor system classifications implemented in process 1200 can be performed on spectral data captured from a material piece by a single XRF system. For example, as each material piece passes by the single implemented vision system, image data of the material piece captured by the single vision system can be analyzed (e.g., substantially simultaneously or in parallel) according to one or more of process blocks 1201, 1205, 1211, and 1213. In other words, algorithms associated with one or more of process blocks 1201, 1205, 1211, and 1213 can process captured image data (e.g., by one or more AI models substantially simultaneously or in parallel with one another) and the results utilized for their respective classification. Similarly, XRF spectral data captured by a single implemented XRF system can be analyzed according to one or more of process blocks 1206, 1208, 1215, 1216, 1219, 1221, and 1223 (e.g., by one or more algorithms substantially simultaneously or in parallel).

[0145] It should be noted that any of the vision checks described in Figures 12A-12B can be performed in accordance with process blocks 203-211 of process 200 described with respect to Figure 3. Any of the classifications performed by the XRF system (or any other suitable sensor system) in Figures 12A-12B can be performed in accordance with process blocks 303-305 of process 300 described with respect to Figure 4.

[0146] As will be described further herein, certain aspects of process 1200 (i.e., the sorting and / or classification as implemented in one or more process blocks) may need to be performed in one or more particular sequences in order to more efficiently and / or accurately perform those aspects of process 1200. It should be noted that while material pieces may be analyzed / classified by a single vision system and a single XRF system (including being analyzed / classified substantially simultaneously or in parallel), as will be described further herein, it may be advantageous to sort various material pieces in a particular sequence (e.g., along a conveyor belt).

[0147] Although process 1200 is described with respect to sorting and separating a stream of conveyed material including a zorba, a zebra, and / or a twitch, aspects of process 1200 may also be applicable to sorting and / or separating other types of material pieces.

[0148] At process block 1201, a vision check may be performed on the material piece to determine whether it should be classified as some predetermined specific material (e.g., "junk" or "fluff" material associated with a solver). If the vision check classifies the material piece as a predetermined specific material, then at process block 1202, process 1200 will send instructions to an identified sorting device to separate the material piece (e.g., from a stream of conveyed material pieces) according to its classification (e.g., material pieces comprised of or containing PCBs). Process block 1203 indicates that process blocks 1200-1202 may be performed for any other type of material piece (e.g., other "junk" or "fluff" material) that a user desires to separate from the stream of material pieces, which can be classified by a vision system (e.g., a vision check using any of the vision-related techniques described herein, including, but not limited to, AI techniques). This can be achieved by a series of different vision systems, or a single vision system can be configured to perform classification in a substantially simultaneous manner or in parallel for any multiple predetermined types of material pieces (e.g., image data captured by a vision system for a single material piece is then analyzed by one or more AI algorithms (substantially simultaneously and / or in parallel with each other) to classify the material piece as belonging to one of multiple predetermined types of material pieces (e.g., a predetermined set of "junk" or "fluff" material).

[0149] It should be noted that the implementation of process blocks 1201-1203 may be optional. If process blocks 1201-1203 are implemented, it may be important to perform the sorting indicated by these process blocks before the sorting indicated by one or more of the other process blocks of process 1200 to improve the efficiency of the subsequent sorting / sorting of material pieces. For example, sorting various materials (as sorted in process blocks 1201, 1203) from the material stream may be important before performing the sorting of other metals and / or metal alloys (e.g., removing “junk” or “fluff” material before the remaining solver sorting / sorting) as sorted in one or more subsequent process blocks of process 1200. For example, removing “junk” or “fluff” material from the conveyed material stream before other materials may reduce the likelihood of such “junk” or “fluff” material contaminating subsequently sorted materials. In a non-limiting example, PCBs very often contain layers of copper, and an XRF sensor may erroneously classify such PCBs as copper scrap pieces. However, a vision system can be configured to distinguish green PCBs from red copper metal with considerable accuracy. Consequently, classification of such PCBs by an XRF system may result in them being sorted as copper scrap pieces, so it may be advantageous to use a vision check to classify such PCBs so that they can be separated from the material stream.

[0150] Process blocks 1204-1225 will now be described with respect to classifying and sorting materials that may typically be found in a solver (also referred to herein as "solver materials").

[0151] At process block 1204, the resulting data from the XRF system can be normalized for piece size. This step can be optional. It can be performed by determining the XRF signal level as a function of piece size and then calibrating the classification to normalize for piece size.

[0152] According to embodiments such as those described with respect to Figures 12A-12B, process 1200 can be configured to sort / separate wrought and / or extruded aluminum alloys prior to sorting / sorting certain cast aluminum alloys. According to embodiments of the present disclosure, process 1200 can be configured to sort / separate wrought aluminum alloys based on a combination of one or more vision checks and one or more sensor system classifications based on the measured amounts of copper and zinc in the material pieces. According to embodiments of the present disclosure, process 1200 can be configured to sort / separate cast aluminum alloys based on a combination of one or more vision checks and one or more sensor system classifications based on the measured amounts of copper and zinc in the material pieces.

[0153] At process block 1205, a vision check is performed to classify the wrought aluminum alloy pieces in the stream of material. According to embodiments of the present disclosure, material pieces classified as wrought aluminum alloy pieces by process block 1205 may be further classified and sorted according to various combinations of one or more of process blocks 1206-1210. It should be noted that according to certain embodiments of the present disclosure, any one or more of these classifications / sorting may be optional or omitted.

[0154] At process block 1206, a determination is made using the captured XRF spectrum of the piece of material whether the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is greater than a predetermined value (which, according to certain non-limiting embodiments of the present disclosure, has been empirically determined to be 10) and whether the measured amount of copper in the piece of material is greater than a predetermined value "A." According to certain embodiments of the present disclosure, the ratio of values ​​CU / Zn as described with respect to process 700 may be utilized. Also, according to certain embodiments of the present disclosure, the determination of CU>CUK as described with respect to process block 725 may be utilized to determine CU>A.

[0155] If both determinations at process block 1206 are affirmative, then the material piece is classified as belonging to the 2xxx series of wrought aluminum alloys, and instructions are sent by process 1200 to the identified sorting device to sort the material piece accordingly at process block 1207. Note that the 2xxx aluminum alloy series is known to contain a certain amount more copper than other wrought aluminum alloy series. Thus, the value "A" can be set (predetermined) to effectively sort these 2xxx series aluminum alloys from other material pieces in the material stream (as predetermined by a user).

[0156] At process block 1208, a determination is made using the captured XRF spectrum of the piece of material whether the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is less than a predetermined value (which, according to certain non-limiting embodiments of the present disclosure, has been empirically determined to be 2) and whether the measured amount of zinc in the piece of material is greater than a predetermined value "B." According to certain embodiments of the present disclosure, the ratio of values ​​C U / Z N as described with respect to process 700 may be utilized. Also, according to certain embodiments of the present disclosure, the determination of Z N > Z N K as described with respect to process block 727 may be utilized to determine Z N > B.

[0157] If both determinations at process block 1208 are affirmative, then the material piece is classified as belonging to the 7xxx series of wrought aluminum alloys, and instructions are sent by process 1200 to the identified sorting device to sort the material piece accordingly at process block 1209. Note that the 7xxx series aluminum alloys are known to contain a certain amount more zinc than other wrought aluminum alloy series. Thus, the value "B" can be set (predetermined) to effectively sort these 7xxx series aluminum alloys from other material pieces in the material stream (as predetermined by a user).

[0158] According to embodiments of the present disclosure, process 1200 may send instructions (process block 1210) to the identified sorting device to sort into a receiver any remaining material pieces classified as wrought aluminum but not sorted as either 2xxx or 7xxx series wrought aluminum pieces (and, according to embodiments of the present disclosure, these material pieces may be designated as 3xxx, 5xxx, and / or 6xxx series aluminum pieces). Thus, process block 1210 may indicate that any material pieces not classified / sorted as 2xxx or 7xxx series wrought aluminum alloys will be classified as 3xxx / 5xxx / 6xxx series wrought aluminum alloys. According to alternative embodiments of the present disclosure, further sorting / sorting may be performed among 3xxx, 5xxx, and / or 6xxx series aluminum alloys, such as those described with respect to process blocks 729-731.

[0159] According to certain embodiments of the present disclosure, the sorting by process block 1209 may be performed before the sorting by process block 1207, however, the effectiveness of these sorts may be affected thereby. Moreover, these classifications / sortings may be interchanged / reordered to achieve particular effectiveness / efficiency for one or more of these classifications / sortings.

[0160] Additionally, according to alternative embodiments of the present disclosure, any one or more of classifications 1207, 1209, or 1210 can be further modified by further classification / sorting to further separate these into finer alloy classifications (e.g., within that particular wrought aluminum series) by utilizing classification based on any suitable sensor technology (e.g., XRF, LIBS, etc.), such as those described in U.S. Pat. No. 11,278,937, U.S. Patent Application Publication No. 2021 / 0346916, and U.S. Patent Application Publication No. 2021 / 0229133, which are incorporated herein by reference.

[0161] According to certain embodiments of the present disclosure, process 1200 can be configured such that material pieces classified as cast aluminum alloy are separated from the stream of material pieces, and then any material pieces not classified as belonging to the wrought aluminum alloy specified by either process block 1206 or 1208 are collected in an identified receiver (see, for example, receiver 140 in FIG. 1 ).

[0162] (1) the combination of the vision check of process block 1205 and the XRF sensor system classification of process block 1206 can be utilized to separate 2xxx wrought aluminum alloy pieces from a material stream; (2) the combination of the vision check of process block 1205 and the XRF sensor system classification of process block 1206 can be utilized to separate 2xxx and / or 7xxx, 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from a material stream; and (3) the combination of the vision check of process block 1205 and the XRF sensor system classification of process blocks 1206 and 1208 can be utilized to separate 7xxx and 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from a material stream. It can be readily recognized that (1) the vision check of process block 1205 in combination with the XRF sensor system classification of process blocks 1206 and 1208 can be utilized to separate 6xxx series wrought aluminum alloy pieces from a material stream, (2) the vision check of process block 1205 in combination with the XRF sensor system classification of process blocks 1206 and 1208 can be utilized to separate 7xxx or 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from a material stream, and (3) the vision check of process block 1205 in combination with the XRF sensor system classification of process blocks 1206 and 1208 can be utilized to separate 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from a material stream. Additionally, all of the foregoing combinations can also include the vision check of process blocks 1201 / 1203.

[0163] It can also be appreciated that it may be advantageous to configure process 1200 such that the vision check for wrought aluminum alloys per process block 1205 is performed in combination with the sensor system classification per process block 1206 to sort out 2xxx series aluminum alloys before sorting out certain cast aluminum alloys known to contain relatively high amounts of copper. It can also be appreciated that it may be advantageous to configure process 1200 such that the vision check for wrought aluminum alloys per process block 1205 is performed in combination with the sensor system classification per process block 1208 to sort out 7xxx series aluminum alloys before sorting out certain cast aluminum alloys known to contain relatively high amounts of zinc.

[0164] At process block 1211, a vision check can be performed to classify extruded aluminum alloy pieces within the stream of material (see, e.g., U.S. Pat. No. 11,471,916, which is incorporated herein by reference). According to non-limiting embodiments of the present disclosure, such extruded aluminum alloy pieces can be classified as 6061, 6063, or other types of extrusions. Process 1200 can then send instructions (process block 1212) to the identified sorting device to separate the material pieces accordingly. Additionally, according to alternative embodiments of the present disclosure, after sorting 1212, further classification / sorting can be performed to further separate these into finer alloy classifications (e.g., within their particular extruded aluminum series) by utilizing any suitable sensor technology (e.g., XRF, LIBS, etc.).

[0165] At process block 1213, a vision check can be performed to classify the cast aluminum alloy pieces within the stream of material. Process block 1214 indicates that any material pieces remaining in the stream of material that are not classified as cast aluminum can be sorted from the stream as belonging to the "other" material classification (i.e., not wrought, extruded, or cast aluminum). Additionally, process block 1213 can be optionally implemented in that all remaining material pieces that are not sorted are then considered as cast aluminum alloy pieces. Process block 1213 can also be optionally implemented with an assumption within process 1200 that all material pieces that were not classified as wrought aluminum alloy by process block 1205 and / or not classified as extruded aluminum alloy by process block 1211 will subsequently be considered as cast aluminum alloy within process 1200.

[0166] It should be noted that, according to alternative embodiments of the present disclosure, negative classification / sorting can be performed after any one or more of process blocks 1205, 1211, 1213.

[0167] At process block 1215, a determination is made whether the measured amount of copper and zinc combined in the piece of material is greater than a predetermined value "C." According to certain embodiments of the present disclosure, such a determination may be made utilizing the C-U and Z-N values ​​described with respect to process 700. Additionally, according to certain embodiments of the present disclosure, such a determination may be made utilizing the total captured XRF spectral counts (N-Total) for the piece of material (after being normalized by the length and height (or mass) of the piece) with respect to a value A-K, such as that disclosed with respect to process block 717. According to certain embodiments of the present disclosure, this A-K value may be set as 0.4 x N-Total for 319 standard cast aluminum alloy (e.g., as determined by the Aluminum Association).

[0168] If the determination by process block 1215 is not affirmative, then a determination is made at process block 1216 as to whether the measured amount of iron in the material piece is greater than a predetermined value "D." According to certain embodiments of the present disclosure, the determination of FE relative to the value FEK, as also described with respect to process block 729, can be utilized to determine FE>D. If FE>D, the material piece will be classified as a 360 cast aluminum piece, and instructions can be sent by process 1200 to the identified sorting device at process block 1217 to sort the material piece accordingly. Otherwise, the material piece will be classified as a 356 cast aluminum piece, and instructions can be sent by process 1200 to the identified sorting device at process block 1218 to sort the material piece accordingly.

[0169] Thus, it can be readily appreciated that process 1200 can be configured such that a piece of material is sorted to be classified as 356 / 360 cast aluminum material by the combination of process blocks 1215 and 1216. Additionally, the foregoing combination can also include any one or more of the vision checks of process blocks 1201 / 1203, 1205, 1211, and / or 1213.

[0170] Because 319 and 38x cast aluminum alloys have material compositions represented by relatively large copper peaks compared to zinc as captured in XRF spectra (copper concentration approximately 3-4%; zinc concentration <1%), process blocks 1219-1224 can be implemented in process 1200 to separate these alloys from the die-cast zinc metal pieces (or any other cast aluminum alloys remaining in the stream of material pieces). Note that, according to an alternative embodiment of the present disclosure, the material handling system can be configured such that all cast aluminum alloy pieces not classified as either 319 / 38x and / or die-cast zinc metal pieces are collected in a catch-all receptacle (see process block 1225).

[0171] At process block 1219, a classification of each such piece of material is performed, whereby a determination is made as to whether the captured XRF spectrum of the piece of material indicates that the piece of material has a copper concentration that is relatively greater than a zinc concentration, as previously described. For example, a determination can be made as to whether the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is greater than a predetermined value "E." According to certain embodiments of the present disclosure, a determination as to whether the ratio of the value CU / Zn is greater than a predetermined value BK, as described with respect to process block 722, can be utilized.

[0172] If the ratio of the measured amount of copper to the measured amount of zinc in the material piece is not greater than a predetermined value "E," the material piece is classified as a die-cast zinc piece, and at process block 1220, process 1200 sends instructions to the identified sorting device to sort the material piece accordingly.

[0173] At process block 1221, a classification of each such piece of material is performed whereby a determination is made as to whether the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is greater than a predetermined value E and less than a predetermined value F. According to certain embodiments of the present disclosure, a determination of whether the ratio of the values ​​CU / ZN is greater than a predetermined value E and less than a predetermined value F can be utilized.

[0174] If yes, the material piece is classified as a 38x cast aluminum piece, and at process block 1222, the process 1200 sends instructions to the identified sorting device to sort the material piece accordingly.

[0175] At process block 1223, a classification of each such piece of material is performed whereby a determination is made as to whether the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is greater than a predetermined value F and less than a predetermined value G. According to certain embodiments of the present disclosure, a determination of whether the ratio of the values ​​CU / ZN is greater than a predetermined value F and less than a predetermined value G can be utilized.

[0176] If yes, the material piece is classified as a 319 cast aluminum piece, and at process block 1224, the process 1200 sends instructions to the identified sorting device to sort the material piece accordingly.

[0177] According to an embodiment of the present disclosure, process 1200 can be configured such that any other material pieces not sorted from the material stream are collected in a receiver, as described in process block 1225.

[0178] (1) the XRF sensor system classification combination of process blocks 1215 and 1219 can be utilized to separate die cast zinc pieces from a material stream; (2) the XRF sensor system classification combination of process blocks 1219 and 1221 can be utilized to separate 38x cast aluminum alloy pieces from a material stream; (3) the XRF sensor system classification combination of process blocks 1221 and 1223 can be utilized to separate 319 cast aluminum alloy pieces from a material stream; It can be readily recognized that (4) the combination of XRF sensor system classifications of process blocks 1219, 1221, and 1223 can be utilized to separately sort die cast zinc, 38x cast aluminum alloy, and 319 cast aluminum alloy pieces from a material stream, and (5) the combination of XRF sensor system classifications of process blocks 1215, 1219, 1221, and 1223 can be utilized to separately sort 356, 360, 38x, and 319 cast aluminum alloy and die cast zinc pieces from a material stream. Additionally, the foregoing combinations can also include any one or more of the vision checks of process blocks 1201 / 1203, 1205, 1211, and / or 1213.

[0179] 8 illustrates a simplified schematic diagram of a non-limiting example of a material handling system 800 configured in accordance with embodiments of the present disclosure. Label 808 represents a conveyor system, which can be configured with any combination of conveyor devices as described herein, including, but not limited to, one or more conveyor belts, for transporting a plurality of material pieces (not shown) past a vision system and an XRF system 801 (or any other appropriately configured sensor technology disclosed herein) and N (N≧1) sorting devices 802...804. The N sorting devices 802...804 can be configured to sort specifically classified material pieces into N corresponding receivers or onto N other conveyor systems 805...807, respectively. According to certain embodiments of the present disclosure, one or more of the N conveyor systems 805...807 may be configured to transport their respective sorted material pieces to another sorting device (not shown) for further sorting based on one or more classifications derived from captured information from the vision system and / or XRF system 801 (see, e.g., the discussion regarding FIG. 10).

[0180] 7A-7C, whereby each sorting is based on one or more classifications derived from captured information from the vision system and / or XRF system 801. For example, various configurations of the system and process 800 can be configured to implement one or more combinations of process blocks 704-708, 790, one or more combinations of process blocks 709-714, one or more combinations of process blocks 704, 709, 715, 717, and / or 721, or one or more combinations of process blocks 725-731.

[0181] 12A-12B, whereby each sorting is based on one or more classifications derived from captured information from the vision system and / or XRF system 801. For example, various configurations of the system and process 800 can be configured to implement one or more combinations of process blocks 1205-1210, one or more combinations of process blocks 1215-1218, one or more combinations of process blocks 1205, 1211, 1213, 1215, 1219, 1221, and / or 1223, or one or more combinations of process blocks 1219-1225.

[0182] According to non-limiting exemplary embodiments of the present disclosure, at least a portion of the components of material handling system 100 can be linked together (e.g., serially or in parallel) to perform multiple iterations or layers of sorting / sorting. Such linking can be a physical / mechanical linking between systems or a linking between sorting / sorting processes in any desired manner with separate material handling systems (or the same material handling system separately performing various sorting / sorting processes). For example, when two or more systems 100 are linked in such a manner, the conveyor system can be implemented by a single conveyor belt (or multiple conveyor belts) that transports material pieces of a heterogeneous mixture of a first set of materials past a first vision system (and, according to certain embodiments, a sensor system) configured to sort / sort material pieces of a heterogeneous mixture of a first set of materials into a first set of one or more receptacles (e.g., sorting receptacles 136...139) by a sorting device (e.g., first automated control system 108 and associated sorting device(s) 126...129), and then transports the material pieces past a second vision system (and, according to certain embodiments, another sensor system) configured to sort / sort material pieces of a heterogeneous mixture of a second set of materials into a second set of one or more sorting receptacles by a second sorting device. Further discussion of such multi-stage sorting is found in U.S. Patent Application Publication No. 2022 / 0016675, which is incorporated herein by reference.

[0183] As described further herein, such a series of material handling systems 100 can contain any number of such systems linked together in such a manner. According to certain embodiments of the present disclosure, each successive material handling system can be configured to sort / sort a different category or type of material than the previous system (e.g., as described with respect to Figures 7A-7C and 12A-12B).

[0184] 9A-9B illustrate simplified schematic diagrams of a system and process 1600 configured in accordance with certain non-limiting exemplary embodiments of the present disclosure for sorting / categorizing multiple material pieces (e.g., as described with respect to various aspects of process 700 of FIGS. 7A-7C and process 1200 of FIGS. 12A-12B). FIG. 9A illustrates an exemplary non-limiting schematic diagram of a side view of such system and process 1600, while FIG. 9B illustrates a top view.

[0185] A plurality of material pieces 1601 can be transported (e.g., by conveyor belt 1602) or deposited into a hopper and picked up by inclined conveyor system 1603. Note that material pieces 1601 are not depicted in FIG. 9B for simplicity. Conveyor system 1603 transports material pieces 1601 through an AI and / or XRF system to classify the material pieces for sorting. Alternatively, any other of the disclosed sensor systems 120 (e.g., LIBS, XRT, etc.) can be utilized in place of the XRF system.

[0186] For example, various configurations of system and process 1600 can be configured to implement one or more combinations of process blocks 705-708, 790, one or more combinations of process blocks 709-714, one or more combinations of process blocks 704, 709, 715, 717, and / or 721, or one or more combinations of process blocks 725-731.

[0187] Take, for example, the combination of process blocks 725-731. As a non-limiting example, an XRF or vision system implementing AI system 1610 can be configured to classify which of material pieces 1601 are comprised of a 2xxx series wrought aluminum alloy. Conveyor system 1603 can be configured to operate at a speed sufficient to "dump" material pieces not classified as a 2xxx series wrought aluminum alloy onto a subsequent inclined conveyor system 1604. Material pieces classified as comprising a 2xxx series wrought aluminum alloy are ejected by a sorting device 1620 onto a lower-positioned conveyor system 1606. For example, such a sorting device 1620 can be an air jet nozzle, such as those described herein, that is operated to eject material pieces classified as a 2xxx series wrought aluminum alloy from the normal trajectory of material pieces "dumped" from the end of conveyor system 1603 onto conveyor system 1604. A piece of material classified as a 2xxx series wrought aluminum alloy can be delivered into the receiver 1630 .

[0188] Material pieces not classified as 2xxx series wrought aluminum alloys may be conveyed through an XRF or AI system 1611, which may be configured to identify and classify those material pieces comprised of a 7xxx series wrought aluminum alloy. The conveyor system 1604 may be configured to operate at a speed sufficient to "dump" material pieces not classified as a 7xxx series wrought aluminum alloy onto a subsequent inclined conveyor system 1605. Material pieces classified as comprised of a 7xxx series wrought aluminum alloy may be discharged by a sorting device 1621 onto a lower positioned conveyor system 1607. For example, such a sorting device 1621 may be an air jet nozzle, such as those described herein, that is operated to discharge material pieces classified as a 7xxx series wrought aluminum alloy from the normal trajectory of material pieces "dumped" from the end of the conveyor system 1604 onto the conveyor system 1605. The sorted material pieces can be transported into a receiver 1631 .

[0189] Pieces of material that are not classified as 7xxx series wrought aluminum alloys can be conveyed through an XRF or AI system 1612, which can be configured to identify and classify the pieces of material as 3xxx series wrought aluminum alloys.

[0190] Conveyor system 1605 can be configured to operate at a sufficient speed to “dump” material pieces not classified as 3xxx series wrought aluminum alloy onto yet another conveyor system (not shown) or into receiver 1633. Material pieces classified as 3xxx series wrought aluminum alloy can be discharged by sorting device 1622 onto a lower positioned conveyor system 1608. For example, such sorting device 1622 can be an air jet nozzle, such as those described herein, that is operated to eject material pieces classified as 3xxx series wrought aluminum alloy, for example, from the normal trajectory of material pieces “dumped” from the end of conveyor system 1605. These classified material pieces can be conveyed into receiver 1632. The remaining material pieces dumped from the end of conveyor belt 1605 can be considered to be classified as either or both of 5xxx and 6xxx series wrought aluminum alloy.

[0191] It should be noted that system and process 1600 is not limited to one line of a conveyor system, but can be expanded to multiple lines that each discharge the classified material pieces onto multiple conveyor systems (e.g., conveyor systems 1606...1608). Similarly, one or more of conveyor systems 1606...1608 can be implemented with additional XRF or AI systems to further classify those material pieces. For example, material pieces classified as being composed of 5xxx and 6xxx series wrought aluminum alloys (and collected in receiver 1633) can instead be transported (by a conveyor system, not shown) through another XRF and / or AI system (or other sensor system 120) for sorting and / or identification among those wrought aluminum alloys.

[0192] Thus, according to certain embodiments of the present disclosure, a sorting / sorting system such as that described with respect to one or more of process blocks 717-721 may first separate the cast aluminum material pieces, and then the remaining material pieces may be sorted / sorted among the various remaining wrought aluminum alloys.

[0193] Similarly, material handling system 1600 can be configured to implement one or more aspects of process 1200 described with respect to Figures 12A-12B, whereby each sorting is based on one or more classifications derived from captured information from vision system and / or XRF system 801. For example, various configurations of system and process 800 can be configured to implement one or more combinations of process blocks 1205-1210, one or more combinations of process blocks 1215-1218, one or more combinations of process blocks 1205, 1211, 1213, 1215, 1219, 1221, and / or 1223, or one or more combinations of process blocks 1219-1225.

[0194] Referring to FIG. 10 , a schematic diagram of a non-limiting example of a linkage of sequential material handling systems (either physically linked or sequentially implemented by a plurality of appropriately configured material handling systems 100 (or the same material handling systems appropriately configured for each sequential sorting / sorting)) according to certain embodiments of the present disclosure is illustrated, which can be implemented by any material handling system utilizing one or more vision systems and / or one or more sensor systems 120 (e.g., utilizing artificial intelligence (“AI”)) implementing (e.g., to implement one or more various aspects as described with respect to FIGS. 7A-7C or 12A-12B ). For simplicity, with respect to the following discussion of FIG. 10 , such a combination of one or more vision systems and / or one or more sensor systems can be referred to simply as a material sorting system. In FIG. 10 , arrows generally depict how various material pieces are transported along such an exemplary material handling system. In this non-limiting example, four separate material handling systems are illustrated, but any number of such material handling systems can be combined in any manner to separate and sort a variety of different classes of material. While the example of Figure 10 illustrates various classes of material to be sorted (e.g., such as those typically contained by Zorba, Zebra, and Twich), embodiments of the present disclosure are applicable to sorting / sorting any combination of heterogeneous mixtures of material pieces.

[0195] In this particular example, a group of materials including a heterogeneous mixture of material 3801a (e.g., Zorba and "junk" or "fluff" materials (e.g., aluminum, stainless steel, plastic, wood, rubber, brass, copper, PCBs, e-scrap, copper wire, etc.)) is fed, for example, from a ramp or chute 3802a (e.g., from a ramp or chute 102, e.g., from a hopper) onto a first conveyor system 3803a (identified in FIG. 10 as conveyor belt #1). Conveyor system 3803a transports material pieces 3801a through material sorting system 3810a, which may be configured to utilize sorter 3826a to sort / separate material pieces (e.g., "junk" or "fluff" material) from the remainder of the material pieces (identified as Sort #1), which sorter 3826a may utilize any of the sorting devices described herein for deposition into one or more receivers 3836a (see, e.g., process blocks 701-703 of FIG. 7A or process blocks 1201-1203 of FIG. 12A).

[0196] The remaining heterogeneous mixture of material pieces 3801b (e.g., Zorba material) may then be conveyed along the same conveyor system or deposited 3802b onto conveyor system 3803b (identified as conveyor belt #2 in FIG. 10). Conveyor system 3803b passes these material pieces 3801b through material sorting system 3810b, which may be configured to identify and separate zebra pieces from twitch pieces (identified as Sort #2) using sorter 3826b, which may include one or more combinations of sorting / sorting (e.g., see process blocks 704, 709, and 715 in FIGS. 7A-7B).

[0197] In this particular, non-limiting example, the copper and brass material pieces 3801c can then be deposited 3802c onto a conveyor system 3803c (identified in FIG. 10 as conveyor belt #3) and sorted by a sorter 3826c (identified as sort #3) for identification by a material sorting system 3810c. This section of the material handling system can be configured to separate and sort material pieces made from copper and copper wire from brass (see, e.g., process blocks 705-706), which can be deposited into one or more receivers or onto the conveyor system for further sorting / sorting. According to certain embodiments of the present disclosure, each of the material pieces classified as copper, copper wire, yellow brass, and red brass material pieces can be individually sorted and deposited into separate receivers for copper 3836c and copper wire 3837c. The heterogeneous mixture of remaining material pieces (yellow brass and red brass) can then be deposited into receiver 3840 or can be further processed by a sorting / sorting system (not shown) as previously described (see, e.g., process blocks 707, 708, 790).

[0198] Embodiments of the present disclosure are not limited to a linear series of such material handling systems, but can include combinations of branching such material handling systems for further classification and sorting of a particular class or classes of material. For example, FIG. 10 illustrates how material pieces classified as aluminum alloy (twitch) material pieces 3836b sorted in sort #2 are then deposited 3802d onto conveyor system 3803d (identified in FIG. 10 as conveyor belt #4). For example, sorter 3826b could physically sort such twich material pieces onto a conveyor system (e.g., conveyor system 3803d), or receiver 3836b into which the twich material pieces are deposited could be a ramp or chute for depositing the twich material pieces onto conveyor system 3803d, or the receiver containing the twich material pieces could simply be manipulated to deposit the twich material pieces onto conveyor system 3803d. Material sorting system 3810d can then be configured to sort these twitch material pieces into cast aluminum alloys and wrought aluminum alloys (e.g., such as described herein with respect to process block 721 of FIG. 7B ), or into wrought aluminum alloys, extruded aluminum alloys, and cast aluminum alloys (e.g., such as described herein with respect to process blocks 1205, 1211, and 1213 of FIG. 12A ). In this sort #4, sorter 3826d can then be configured to separate the cast aluminum alloys from the wrought aluminum alloys based on the classification by material sorting system 3810d, whereby the cast aluminum alloys can be deposited into receiver 3837d and the wrought aluminum alloys can be deposited into receiver 3836d or on a conveyor system (not shown) for further sorting / sorting.

[0199] Variations on the system of FIG. 10 can include further classifying / sorting the cast aluminum alloys into different predefined cast aluminum alloys using one or more sensor systems 120 (including, but not limited to, an XRF system (such as those described with respect to process blocks 722-724 of FIG. 7B and also with respect to process blocks 1215-1225 of FIG. 12B)). Another variation on the system of FIG. 10 can include further classifying / sorting the wrought aluminum alloys into different predefined wrought aluminum alloys using one or more sensor systems 120 (including, but not limited to, an XRF system (such as those described with respect to process blocks 725-731 of FIG. 7C (and also with respect to process blocks 1206-1210 of FIG. 12A)).

[0200] As can be readily seen, the material handling system illustrated in Figure 10 can be modified into any combination of sorting / categorizing systems to separate materials as desired.

[0201] According to various embodiments of the present disclosure, different types or classes of material can each be classified by different types of sensors for use with an AI system and can be combined to classify material pieces in a scrap or waste stream.

[0202] According to various embodiments of the present disclosure, data from two or more sensors can be combined using a single or multiple AI systems to perform classification of material pieces.

[0203] According to various embodiments of the present disclosure, multiple sensor systems can be mounted on a single conveyor system, each sensor system utilizing a different AI system. According to various embodiments of the present disclosure, multiple sensor systems can be mounted on different conveyor systems, each sensor system utilizing a different AI system.

[0204] According to various embodiments of the present disclosure, different types or classes of material can each be classified by different types of sensors for use with an AI system and can be combined to classify material pieces in a scrap or waste stream.

[0205] According to various embodiments of the present disclosure, data (e.g., spectral data or XRF spectral data) from two or more sensors can be combined using a single or multiple AI systems to perform classification of material pieces.

[0206] Referring now to FIG. 11 , a block diagram illustrating a data processing (“computer”) system 3400 is depicted in which aspects of embodiments of the present disclosure may be implemented. (The terms “computer,” “system,” “computer system,” and “data processing system” may be used interchangeably herein.) Aspects of the computer system 107, automation control system 108, sensor system 120, and / or vision system 110 may similarly be configured as the computer system 3400. The computer system 3400 may use a local bus 3405 (e.g., a peripheral component interconnect (“PCI”) local bus architecture). Any suitable bus architecture may be utilized, such as Accelerated Graphics Port (“AGP”) and Industry Standard Architecture (“ISA”), among others. One or more processors 3415, volatile memory 3420, and non-volatile memory 3435 may be connected to the local bus 3405 (e.g., through a PCI bridge (not shown)). The integrated memory controller and cache memory can be coupled to one or more processors 3415. The one or more processors 3415 can include one or more central processor units, one or more graphics processor units, and / or one or more tensor processing units. Additional connections to the local bus 3405 can be made through direct component interconnects or through add-in boards. In the depicted example, a communications (e.g., network (LAN)) adapter 3425, an I / O (e.g., small computer system interface ("SCSI") host bus) adapter 3430, and an expansion bus interface (not shown) can be connected to the local bus 3405 by direct component connections.An audio adapter (not shown), a graphics adapter (not shown), and a display adapter 3416 (coupled to a display 3440) may be connected to the local bus 3405 (e.g., by an add-in board inserted into an expansion slot).

[0207] User interface adapter 3412 may provide connections for a keyboard 3413 and mouse 3414, a modem (not shown), and additional memory (not shown). I / O adapter 3430 may provide connections for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).

[0208] An operating system may run on the one or more processors 3415 and be used to coordinate and provide control of various components within the computer system 3400. The operating system may be a commercially available operating system. An object-oriented programming system may run in conjunction with the operating system and provide calls to the operating system from programs or programs (e.g., Java, Python, etc.) executing on the system 3400. Instructions for the operating system, object-oriented operating system, and programs may be located on a non-volatile memory 3435 storage device (e.g., hard disk drive 3431, etc.) and loaded into volatile memory 3420 for execution by the processor 3415.

[0209] Those skilled in the art will recognize that the hardware in FIG. 11 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash ROM (or equivalent non-volatile memory) or optical disk drives, may be used in addition to or in place of the hardware depicted in FIG. 11. Also, any of the processes of this disclosure may be applied to a multi-processor computer system or may be performed by multiple such systems 3400. For example, training of vision system 110 may be performed by a first computer system 3400, while operation of vision system 110 for sorting may be performed by a second computer system 3400.

[0210] As another example, computer system 3400 can be a stand-alone system configured to be bootable without relying on any type of network communication interface, regardless of whether computer system 3400 includes any type of network communication interface. As a further example, computer system 3400 can be an embedded controller configured with ROM and / or flash ROM that provide non-volatile memory for storing operating system files or user-created data.

[0211] 11 and described above are not meant to imply architectural limitations. Additionally, computer program forms of aspects of the present disclosure may reside on any computer-readable storage medium (i.e., floppy disk, compact disc, hard disk, tape, ROM, RAM, etc.) for use by a computer system.

[0212] As described herein, embodiments of the present disclosure can be implemented to perform various functions described for identifying, tracking, classifying, and / or sorting material pieces. Such functionality can be implemented in hardware and / or software (e.g., in one or more data processing systems (e.g., data processing system 3400 of FIG. 11 )), such as aspects of the previously described computer system 107, vision system 110, sensor system 120, and / or automation control system 108. Nevertheless, the functionality described herein is not limited with respect to implementation in any particular hardware / software platform.

[0213] As will be recognized by those skilled in the art, aspects of the present disclosure may be embodied as a system, process, method, and / or program product. Accordingly, various aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, which may be generally referred to herein as a "circuit," "circuitry," "module," or "system." Moreover, aspects of the present disclosure may take the form of a program product embodied in one or more computer-readable storage medium(s) having computer-readable program code embodied thereon. (However, any combination of one or more computer-readable media may be utilized. The computer-readable medium(s) may be a computer-readable signal medium or a computer-readable storage medium.)

[0214] A computer-readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, biological, atomic, or semiconductor system, apparatus, controller, or device, or any suitable combination of the foregoing; the computer-readable storage medium is not itself a transitory signal. More specific examples (a non-exhaustive list) of computer-readable storage media can include an electrical connection having one or more wires, a portable computer diskette, a hard disk, random access memory (“RAM”) (e.g., RAM 3420 in FIG. 11), read-only memory (“ROM”) (e.g., ROM 3435 in FIG. 11), erasable programmable read-only memory (“EPROM” or flash memory), fiber optics, a portable compact disc read-only memory (“CD-ROM”), an optical storage device, a magnetic storage device (e.g., hard drive 3431 in FIG. 11), or any suitable combination of the foregoing. In the context of this document, a computer-readable storage medium can be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, controller, or device. Program code embodied on a computer-readable signal medium can be transmitted using any appropriate medium (including, but not limited to, wireless, wireline, fiber optic cable, RF, etc., or any suitable combination of the foregoing).

[0215] A computer-readable signal medium may include a propagated data signal with computer-readable program code embodied therein (e.g., in baseband or as part of a carrier wave). Such a propagated signal may take any of a variety of forms, including, but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium is not a computer-readable storage medium and may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, controller, or device.

[0216] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, processes, and program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, which includes one or more executable program instructions for implementing the specified logical function(s). It should also be noted that in some implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently, or the blocks may possibly be executed in the reverse order, depending on the functionality involved.

[0217] Modules implemented in software for execution by various types of processors (e.g., GPU 3401, CPU 3415) can include, for example, one or more physical or logical blocks of computer instructions, which can be organized, for example, as an object, a procedure, or a function. Nevertheless, the executable files of identified modules need not be physically located together but can include entirely different instructions stored in different locations that, when logically joined together, comprise the module and achieve the stated purpose for the module. In fact, a module of executable code can be a single instruction or many instructions and can be distributed across several different code segments, among different programs, and even across several memory devices. Similarly, operational data (e.g., a material classification library described herein) can be identified and illustrated in modules herein and can be embodied in any suitable form and organized within any suitable type of data structure. The operational data can be collected as a single data set or distributed across different locations (including across different storage devices). The data can provide electronic signals over a system or network.

[0218] These program instructions can be provided to one or more processors and / or controllers of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus (e.g., a controller) to create a machine such that the instructions (which execute via the processor (e.g., GPU 3401, CPU 3415) of the computer or other programmable data processing apparatus) cause circuitry or means to implement the functions / acts identified in one or more blocks of the flowcharts and / or block diagrams.

[0219] It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by a special-purpose hardware-based system (e.g., which can include one or more graphics processing units (e.g., GPU 3401)) that performs the specified functions or acts or a combination of special-purpose hardware and computer instructions. For example, a module can be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors (e.g., logic chips, transistors, controllers, etc.), or other discrete components. A module can also be implemented in a programmable hardware device (e.g., a field programmable gate array, programmable array logic, or programmable logic device, etc.).

[0220] Computer program code (i.e., instructions) for carrying out operations for aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​(e.g., Java, Smalltalk, Python, or C++), traditional procedural programming languages ​​(e.g., the "C" programming language or similar programming languages), programming languages ​​such as MATLAB® or LabVIEW, or any of the machine learning software disclosed herein. The program code can run entirely on the user's computer system, partially on the user's computer system, as a standalone software package, partially on the user's computer system (e.g., a computer system utilized for classification) and partially on a remote computer system (e.g., a computer system utilized to train a machine learning system), or entirely on a remote computer system or server. In the latter scenario, the remote computer system can be connected to the user's computer system through any type of network (including a local area network ("LAN") or a wide area network ("WAN")), or the connection can be to an external computer system (e.g., through the Internet using an Internet Service Provider). As examples of the foregoing, various aspects of the present disclosure may be configured to execute on one or more aspects of computer system 107, automation control system 108, vision system 110, and sensor system 120.

[0221] These program instructions can also be stored in a computer-readable storage medium that can instruct a computer system, other programmable data processing apparatus, controller, or other device to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture that includes instructions that implement the functions / acts identified in one or more blocks of the flowcharts and / or block diagrams.

[0222] The program instructions may also be loaded onto a computer, other programmable data processing apparatus, controller, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device, creating a computer-implemented process, such that the instructions executing on the computer or other programmable apparatus provide a process for implementing the functions / acts identified in one or more blocks of the flowcharts and / or block diagrams.

[0223] One or more databases can be included within the host for storing and providing access to data for various implementations. Those skilled in the art will also recognize that, for security reasons, any database, system, or component of the present disclosure can include any combination of databases or components in a single location or multiple locations, and each database or system can include any of a variety of appropriate security features (e.g., firewalls, access codes, encryption, decryption, etc.). The databases can be any type of database (e.g., relational, hierarchical, and / or object-oriented, etc.). Common database products that can be used to implement a database include DB2 by IBM, any of the database products available from Oracle Corporation, Microsoft Access by Microsoft Corporation, or any other database product. The databases can be organized in any suitable manner, including data tables or lookup tables.

[0224] Embodiments of the present disclosure offer a paradigm shift from "binary" sorting, thereby reducing costs. This innovation is not immediately noticeable, but it significantly reduces the overall cost of sorting. Existing sorters are designed to sort material in a binary manner, where an air nozzle at the end of the conveyor ejects one class into one of two bins. If eight classes need to be separated, as in Zorba's case, the entire stream would need to be run across the binary sorter eight different times, which would take eight times longer than trying to remove one single object in the stream. Embodiments of the present disclosure allow multiple classes to be sorted in one pass, which in this case would reduce the overall sorting time by a factor of eight.

[0225] An aspect of the present disclosure provides a method for sorting scrap pieces from a stream of conveyed material, the method including: performing one or more vision checks on each scrap piece in the stream of conveyed material, each of the one or more vision checks including classifying each scrap piece in response to processing a visual image captured from each scrap piece through an AI system; performing one or more sensor system classifications on each scrap piece in the stream of conveyed material; and sorting the scrap pieces from the stream of conveyed material into one or more classification groups in response to a combination of the one or more vision checks and the one or more sensor system classifications. The sorting step may further include sorting one or more of the cast aluminum alloys from the conveyed stream of material into one or more first classification groups based on a first combination of one or more vision checks and one or more sensor system classifications, and sorting one or more of the wrought aluminum alloys from the conveyed stream of material into one or more second classification groups based on a second combination of one or more vision checks and one or more sensor system classifications, optionally wherein the sorting of one or more cast aluminum alloys from the conveyed stream of material is performed before the sorting of one or more wrought aluminum alloys from the conveyed stream of material. The sorting step may include the steps of: a) sorting copper scrap pieces from the conveyed stream of material based on a combination of one or more vision checks and one or more sensor system classifications, the combination comprising: a first vision check to determine if the scrap pieces are comprised of copper;a) a first of one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a first predetermined value, wherein the scrap pieces are sorted from the stream of conveyed material to be classified as copper scrap pieces when the first vision check determines that the scrap pieces are comprised of copper and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than the first predetermined value, optionally further comprising the step of sorting printed circuit board scrap pieces from the stream of conveyed material based on a second vision check, wherein the step of sorting the printed circuit board scrap pieces is performed before the step of sorting the copper scrap pieces; or b) a first of one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a first predetermined value, wherein the scrap pieces are sorted from the stream of conveyed material to be classified as copper scrap pieces when the first vision check determines that the scrap pieces are comprised of copper and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than the first predetermined value, optionally further comprising the step of sorting printed circuit board scrap pieces from the stream of conveyed material based on a second vision check, wherein the step of sorting the printed circuit board scrap pieces is performed before the step of sorting the copper scrap pieces. and sorting the brass scrap pieces from the conveyed material stream, the combination including a first vision check to determine whether the scrap pieces are comprised of brass and a first of one or more sensor system classifications to determine whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a first predetermined value, the scrap pieces being sorted to be classified as brass scrap pieces from the conveyed material stream when the first vision check determines that the scrap pieces are comprised of brass and the first of the one or more sensor system classifications determines that the ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is less than the first predetermined value; optionally, the scrap pieces being sorted to be classified as brass scrap pieces when the vision check determines that the scrap pieces are comprised of brass and the first of the one or more sensor system classifications determines that the ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is less than the first predetermined value.The method can further include sorting a scrap piece from the conveyed stream of material to be classified as a red brass scrap piece when a first of the one or more sensor system classifications determines and a second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than a second predetermined value, and optionally sorting a scrap piece from the conveyed stream of material to be classified as a yellow brass scrap piece when the vision check determines that the scrap piece is composed of brass and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value and a second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the second predetermined value. The sorting step includes: a) sorting the Ni-plated scrap pieces from the conveyed material stream based on a combination of sensor system classifications, the combination including: (1) a first one of the one or more sensor system classifications for determining whether a measured amount of chromium in the scrap piece is greater than a first predetermined value; and (2) a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap piece is greater than a second predetermined value; wherein the scrap pieces are classified as Ni-plated when the first one of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value and the second one of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is greater than the second predetermined value.and (b) sorting the scrap pieces from the conveyed stream of material to be classified as Ni-plated scrap pieces, optionally wherein the step of sorting the Ni-plated scrap pieces from the conveyed stream of material based on a combination of sensor system classifications is performed before the step of sorting the wrought or cast aluminum scrap pieces from the conveyed stream of material; or (c) sorting the stainless steel scrap pieces from the conveyed stream of material based on a combination of sensor system classifications, the combination including: (1) a first one of one or more sensor system classifications for determining whether a measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second one of one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is greater than a second predetermined value, wherein the scrap pieces are sorted based on the measured amount of chromium in the scrap pieces being greater than the first predetermined value. and wherein the scrap pieces are sorted from the conveyed material stream to be classified as stainless steel scrap pieces when a first of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap pieces is less than a second predetermined value, and a second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap pieces is less than a second predetermined value, optionally the step of sorting the stainless steel scrap pieces from the conveyed material stream based on the combination of the sensor system classifications is performed before the step of sorting the wrought or cast aluminum scrap pieces from the conveyed material stream, and optionally the combination further includes a third of the one or more sensor system classifications for determining whether the measured amount of manganese in the scrap pieces is greater than a third predetermined value, and the scrap pieces are sorted from the conveyed material stream to be classified as stainless steel scrap pieces when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap pieces is greater than a first predetermined value,a scrap piece is sorted from the conveyed material stream to be classified as a 201 and / or 202 stainless steel scrap piece when a second one of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than a second predetermined value and a third one of the one or more sensor system classifications determines that the measured amount of manganese in the scrap piece is greater than a third predetermined value, and optionally the scrap piece is sorted from the conveyed material stream to be classified as a 201 and / or 202 stainless steel scrap piece when a second one of the one or more sensor system classifications determines that the ratio of the measured amount of chromium in the scrap piece is greater than a first predetermined value; The method can further include sorting the scrap pieces from the conveyed material stream to be classified as 301, 302, 304, and / or 316 stainless steel scrap pieces when a first of the one or more sensor system classifications determines that a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is less than a second predetermined value, a second of the one or more sensor system classifications determines that a measured amount of manganese in the scrap pieces is less than a third predetermined value. The sorting step further includes sorting the 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material based on a combination of a sensor system classification to determine whether the measured amounts of copper and zinc in the scrap pieces are greater than predetermined values ​​and a vision check to determine whether the scrap pieces are cast aluminum alloy, wherein the sorting step includes: when the sensor system classification determines that the measured amounts of copper and zinc in the scrap pieces are less than predetermined values ​​and the vision check determines that the scrap pieces are cast aluminum alloy;The method may further include sorting scrap pieces from the conveyed stream of material to be classified as 356 or 360 cast aluminum alloy, optionally wherein the step of sorting the 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material is performed before the step of sorting the wrought aluminum alloy scrap pieces from the conveyed stream of material. The sorting step may include: a) combining a first of the one or more sensor system classifications to determine if the measured amounts of copper and zinc in the scrap pieces are greater than a first predetermined value, a vision check to determine if the scrap pieces are cast aluminum alloy, and a second of the one or more sensor system classifications to determine if the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than a second predetermined value; and (3) a second one of the one or more sensor system classifications determines that a ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than a second predetermined value. Optionally, the step of sorting the die-cast zinc scrap pieces from the conveyed stream of material further includes sorting the scrap pieces from the conveyed stream of material to be classified as die-cast zinc scrap pieces when: (1) a first one of the one or more sensor system classifications determines that a measured amount of copper and zinc in the scrap piece is greater than a first predetermined value; and (2) the first vision check determines that the scrap piece is comprised of a cast aluminum alloy; and (3) a second one of the one or more sensor system classifications determines that a ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than a second predetermined value. Optionally, the step of sorting the die-cast zinc scrap pieces from the conveyed stream of material further includes sorting the die-cast zinc scrap pieces from the conveyed stream of material to be classified as die-cast zinc scrap pieces. optionally, the step of sorting die cast zinc scrap pieces from the conveyed stream of material is performed before the step of sorting, and optionally the step of sorting is performed before the step of sorting 7xxx series aluminum alloy scrap pieces from the conveyed stream of material, and optionally the step of sorting further includes the step of sorting scrap pieces from the conveyed stream of material to be classified as 38x or 319 cast aluminum alloy scrap pieces when: (1) a first one of the one or more sensor system classifications determines that the combined measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value; and (2) the first vision check determines that the scrap pieces are comprised of cast aluminum alloy; and (3) a second one of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than a second predetermined value; orb) sorting 38x or 319 cast aluminum alloy scrap pieces from the conveyed stream of material based on a combination of a first of one or more sensor system classifications for determining whether a measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value, a vision check for determining whether the scrap pieces are cast aluminum alloy, and a second of one or more sensor system classifications for determining whether a ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than a second predetermined value, wherein the sorting step includes: (1) determining whether a total of the copper and zinc in the scrap pieces is greater than a second predetermined value; The method can further include the steps of: (1) determining, via a first vision check, that the scrap piece is comprised of a cast aluminum alloy; and (2) sorting the scrap piece from the conveyed stream of material to be classified as a 38x or 319 cast aluminum alloy scrap piece when a first of the one or more sensor system classifications determines that the measured amount is greater than a first predetermined value; and (3) determining, via a first vision check, that the scrap piece is comprised of a cast aluminum alloy; and (4) sorting the scrap piece from the conveyed stream of material to be classified as a 38x or 319 cast aluminum alloy scrap piece when a second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than a second predetermined value. The step of sorting one or more of the wrought aluminum alloys from the conveyed stream of material into one or more first classification groups comprises the steps of: a) sorting scrap pieces from the conveyed stream of material to be classified as 2xxx series wrought aluminum alloys when the vision check determines that the scrap pieces are comprised of wrought aluminum alloys and the sensor system classification determines that the measured amount of copper in the scrap pieces is greater than a predetermined value; or b) sorting scrap pieces from the conveyed stream of material to be classified as 2xxx series wrought aluminum alloys when the vision check determines that the scrap pieces are comprised of wrought aluminum alloys and the sensor system classification determines that the measured amount of zinc in the scrap pieces is greater than a predetermined value.The method may further include either a step of: c) sorting scrap pieces from the conveyed stream of material to be classified as a 7xxx series wrought aluminum alloy when the sensor system classification determines; or b) sorting scrap pieces from the conveyed stream of material to be classified as a 3xxx series wrought aluminum alloy when: (1) the vision check determines that the scrap pieces are composed of a wrought aluminum alloy; and (2) when a first of the one or more sensor system classifications determines: (i) the measured amount of manganese in the scrap pieces is less than a first predetermined value; and (ii) the measured amount of iron in the scrap pieces is less than a second predetermined value. Each of the one or more vision checks may be performed by a single vision system implementing one or more AI models in the AI ​​system, and each of the one or more sensor system classifications may be performed by a single sensor system implementing one or more algorithms for analyzing spectral data collected from the scrap pieces, optionally an XRF system implementing one or more algorithms for analyzing XRF spectral data collected from the scrap pieces. The conveyed material stream may be a conveyed stream of Zorba material. The method may further include sorting one or more junk materials from the conveyed material stream based on the one or more vision checks, the sorting of the junk materials being performed before the sorting of the one or more wrought aluminum alloys and the sorting of the one or more cast aluminum alloys. The one or more sensor system classifications may be performed by a spectroscopy system, optionally an X-ray fluorescence system.

[0226] An aspect of the present disclosure is a method for sorting scrap pieces from a conveyed stream of material, the method comprising: performing a vision check on each scrap piece in the conveyed stream of material, each vision check including classifying each scrap piece in response to processing a visual image captured from each scrap piece through an AI system, the vision checks being performed by a single vision system with a different AI model implemented in the AI ​​system for each vision check; and performing a sensor system classification on each scrap piece in the conveyed stream of material, each sensor system classification being performed by a different algorithm analyzing spectral data collected from each scrap piece by the single sensor system; and a) classifying a plurality of different non-ferrous metal scrap pieces based on one or more combinations of the one or more vision checks and the one or more sensor system classifications. a) sorting the plurality of different cast aluminum alloy scrap pieces into separately sorted classification groups based on one or more combinations of one or more vision checks and one or more sensor system classifications; and c) sorting the plurality of different wrought aluminum alloy scrap pieces into separately sorted classification groups based on one or more combinations of one or more vision checks and one or more sensor system classifications. optionally, step a) is performed before either or both of steps b) and c), and optionally, steps a), b), and c) are performed in that order; optionally, the plurality of different non-ferrous metal scrap pieces are selected from the group consisting of copper, red brass, yellow brass, Ni-plated, stainless steel, die-cast zinc, and lead; optionally, the plurality of different non-ferrous metal scrap pieces are selected from the group consisting of 356, 360, 319, and 38x cast aluminum alloys; optionally, the plurality of different wrought aluminum alloy scrap pieces areand wherein the scrap pieces are selected from the group consisting of 2xxx, 3xxx, 5xxx, 6xxx, and 7xxx series wrought aluminum alloys; optionally, the sensor system classifications are XRF sensor system classifications, each of the XRF sensor system classifications being implemented by a different algorithm that analyzes XRF spectral data collected from each scrap piece by a single XRF system; and optionally, the conveyed stream of material is a conveyed stream of Zorba material.

[0227] An aspect of the present disclosure provides a system for sorting scrap pieces, the system including: a conveyor system configured to convey a stream of material; a vision system configured to perform one or more vision checks on each scrap piece in the conveyed stream of material, each of the one or more vision checks including classifying the respective scrap piece in response to processing a visual image captured from the respective scrap piece through an AI system; a sensor system configured to perform one or more sensor system classifications on each scrap piece in the conveyed stream of material; and a sorting device configured to sort the scrap pieces from the conveyed stream of material into one or more classification groups in response to a combination of the one or more vision checks and the one or more sensor system classifications. The sorting device may be configured to sort one or more of the cast aluminum alloys from the conveyed stream of material into one or more first classification groups in response to instructions received from a first combination of one or more vision checks and one or more sensor system classifications, and to sort one or more of the wrought aluminum alloys from the conveyed stream of material into one or more second classification groups in response to instructions received from a second combination of one or more vision checks and one or more sensor system classifications, optionally wherein the step of sorting the one or more cast aluminum alloys from the conveyed stream of material is performed before the step of sorting the one or more wrought aluminum alloys from the conveyed stream of material. The sorting device is configured to: a) sort copper scrap pieces from the conveyed stream of material in response to instructions received from a combination of one or more vision checks and one or more sensor system classifications,a) a sorting device including a first vision check to determine whether the scrap pieces are comprised of copper and a first of one or more sensor system classifications to determine whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a first predetermined value, wherein the scrap pieces are sorted from the conveyed stream of material to be classified as copper scrap pieces when the first vision check determines that the scrap pieces are comprised of copper and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than the first predetermined value; optionally, the sorting device further includes a sorting device configured to sort printed circuit board scrap pieces from the conveyed stream of material in response to instructions received from the second vision check, wherein the step of sorting the printed circuit board scrap pieces is performed before the step of sorting the copper scrap pieces; or a sorting device configured to sort brass scrap pieces from a conveyed stream of material in response to instructions received from a combination of a plurality of vision checks and one or more sensor system classifications, the combination including a first vision check to determine whether the scrap pieces are comprised of brass and a first of the one or more sensor system classifications to determine whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a first predetermined value, wherein the scrap pieces are sorted to be classified as brass scrap pieces from the conveyed stream of material when the first vision check determines that the scrap pieces are comprised of brass and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is less than the first predetermined value;The method can further include sorting a scrap piece from the conveyed stream of material to be classified as a red brass scrap piece when the vision check determines that a first of the one or more sensor system classifications determines that a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is less than a first predetermined value and a second of the one or more sensor system classifications determines that a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a second predetermined value; and optionally, sorting a scrap piece from the conveyed stream of material to be classified as a yellow brass scrap piece when the vision check determines that the scrap piece is composed of brass, the first of the one or more sensor system classifications determines that a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is less than the first predetermined value and a second of the one or more sensor system classifications determines that a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is less than a second predetermined value. a sorting device configured to sort Ni-plated scrap pieces from a conveyed stream of material in response to instructions received from a combination of sensor system classifications, the combination including: (1) a first one of the one or more sensor system classifications for determining whether a measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is greater than a second predetermined value; the scrap pieces are sorted by the first one of the one or more sensor system classifications, the first one of the one or more sensor system classifications determining that a measured amount of chromium in the scrap pieces is greater than the first predetermined value;a) sorting the scrap pieces to be classified as Ni-plated scrap pieces from the stream of conveyed material when a second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap pieces is greater than a second predetermined value, and optionally, the step of sorting the Ni-plated scrap pieces from the stream of conveyed material in response to instructions received from a combination of sensor system classifications is performed before the step of sorting the wrought or cast aluminum scrap pieces from the stream of conveyed material; or b) a sorting device configured to sort stainless steel scrap pieces from the stream of conveyed material in response to instructions received from a combination of sensor system classifications, the combination comprising: (1) a first of the one or more sensor system classifications for determining whether the measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second of the one or more sensor system classifications for determining whether the ratio of the measured amount of chromium in the scrap pieces is greater than a second predetermined value. a sorting apparatus including a second of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium is greater than a second predetermined value, wherein a scrap piece is sorted to be classified as a stainless steel scrap piece from the conveyed material stream when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value and the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value, optionally wherein the step of sorting the stainless steel scrap piece from the conveyed material stream in response to instructions received from a combination of sensor system classifications is performed before the step of sorting wrought or cast aluminum scrap pieces from the conveyed material stream, optionally wherein the combinationThe scrap piece further includes a third of the one or more sensor system classifications for determining whether a measured amount of manganese in the scrap piece is greater than a third predetermined value, wherein the scrap piece is configured to remove 201 and / or 202 stainless steel from the conveyed material stream when the first of the one or more sensor system classifications determines that a measured amount of chromium in the scrap piece is greater than a first predetermined value, the second of the one or more sensor system classifications determines that a ratio of a measured amount of nickel to a measured amount of chromium in the scrap piece is less than a second predetermined value, and the third of the one or more sensor system classifications determines that a measured amount of manganese in the scrap piece is greater than a third predetermined value. and optionally, the scrap pieces are sorted from the conveyed material stream to be classified as 301, 302, 304, and / or 316 stainless steel scrap pieces when a first of the one or more sensor system classifications determines that a measured amount of chromium in the scrap pieces is greater than a first predetermined value, a second of the one or more sensor system classifications determines that a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is less than a second predetermined value, and a third of the one or more sensor system classifications determines that a measured amount of manganese in the scrap pieces is less than a third predetermined value. The sorting device is further configured to sort 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material in response to instructions received from a combination of the sensor system classification to determine whether a measured amount of copper and zinc in the scrap pieces is greater than a predetermined value and the vision check to determine whether the scrap pieces are cast aluminum alloy, and the sorting step comprises:The method can further include sorting the scrap pieces from the conveyed stream of material to be classified as 356 or 360 cast aluminum alloy when the sensor system classification determines that the measured amounts of copper and zinc in the scrap pieces are less than predetermined values ​​and the vision check determines that the scrap pieces are cast aluminum alloy, optionally wherein the step of sorting the 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material is performed before the step of sorting the wrought aluminum alloy scrap pieces from the conveyed stream of material. The sorting device includes: a) measuring the copper and zinc in the scrap pieces; 1. A sorting device configured to sort die cast zinc scrap pieces from a conveyed stream of material in response to instructions received from a combination of a first of one or more sensor system classifications for determining whether a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a first predetermined value, a vision check for determining whether the scrap piece is a cast aluminum alloy, and a second of one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a second predetermined value, wherein the sorting step includes: (1) determining, by the first of the one or more sensor system classifications, that a measured amount of copper and zinc in the scrap piece is greater than a first predetermined value; and (2) determining, by the first vision check, that the scrap piece is comprised of a cast aluminum alloy; and (3) determining, by the second vision check, that a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a second predetermined value. the sorting device further comprising: sorting the scrap pieces from the conveyed stream of material to be classified as die cast zinc scrap pieces when a second of the one or more sensor system classifications determines that the measured amount of copper and zinc in the scrap pieces is less than a predetermined value; optionally, the step of sorting the die cast zinc scrap pieces from the conveyed stream of material is performed before the step of sorting wrought aluminum alloy scrap pieces from the conveyed stream of material; optionally, the step of sorting the die cast zinc scrap pieces from the conveyed stream of material is performed before the step of sorting 7xxx series aluminum alloy scrap pieces from the conveyed stream of material; optionally, the sorting device is configured to: (1) determine that a first of the one or more sensor system classifications determines that a measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value; and (2) determine that the scrap pieces are composed of cast aluminum alloy; and(3) further configured to sort scrap pieces from the conveyed stream of material to be classified as 38x or 319 cast aluminum alloy scrap pieces when a second one of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than a second predetermined value; or b) to instructions received from a combination of a first one of the one or more sensor system classifications to determine whether the measured amounts of copper and zinc in the scrap pieces are greater than a first predetermined value, a vision check to determine whether the scrap pieces are cast aluminum alloy, and a second one of the one or more sensor system classifications to determine whether the ratio of the measured amount of copper to the measured amount of zinc in the scrap pieces is greater than a second predetermined value. In response, the sorting device may further include either: a sorting device configured to separate 38x or 319 cast aluminum alloy scrap pieces from the conveyed stream of material, the scrap pieces being classified as 38x or 319 cast aluminum alloy scrap pieces when: (1) a first of the one or more sensor system classifications determines that the combined measured amount of copper and zinc in the scrap piece is greater than a first predetermined value; (2) a first vision check determines that the scrap piece is comprised of a cast aluminum alloy; and (3) a second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than a second predetermined value. The sorting device may further include: a) a vision check determines that the scrap piece is comprised of a wrought aluminum alloy and the sensor system classification determines that the measured amount of copper in the scrap piece is greater than a predetermined value;The method may further include any of: a) a sorting device configured to sort scrap pieces from a conveyed stream of material to be classified as a 2xxx series wrought aluminum alloy; or b) a sorting device configured to sort scrap pieces from a conveyed stream of material to be classified as a 7xxx series wrought aluminum alloy when the vision check determines that the scrap pieces comprise a wrought aluminum alloy and the sensor system classification determines that the measured amount of zinc in the scrap pieces is greater than a predetermined value; or c) a sorting device configured to sort scrap pieces from a conveyed stream of material to be classified as a 3xxx series wrought aluminum alloy when: (1) the vision check determines that the scrap pieces comprise a wrought aluminum alloy; and (2) a first of the one or more sensor system classifications determines that: (i) the measured amount of manganese in the scrap pieces is less than a first predetermined value, and (ii) the measured amount of iron in the scrap pieces is less than a second predetermined value. Each of the one or more vision checks may be performed by a single vision system implementing one or more AI models in the AI ​​system, and each of the one or more sensor system classifications may be performed by a single sensor system implementing one or more algorithms for analyzing spectral data collected from the scrap pieces, and optionally the single sensor system is an XRF system implementing one or more algorithms for analyzing XRF spectral data collected from the scrap pieces. The conveyed stream of material may be a conveyed stream of Zorba material. The sorting device may be further configured to sort one or more junk materials from the conveyed stream of material in response to instructions received from the one or more vision checks, wherein sorting the junk materials includes:The one or more sensor system classifications may be performed prior to the step of sorting the one or more wrought aluminum alloys and the step of sorting the one or more cast aluminum alloys. Each of the one or more sensor system classifications may be performed by a spectroscopy system, and optionally, the spectroscopy system is an X-ray fluorescence system.

[0228] References are made herein to "configuring" a device or a device being "configured" to perform some function. This should be understood to include selecting predefined logic blocks and logically associating them to provide a specific logic function, which may include monitoring or control functions. It may also include programming computer software-based logic in a custom control device, wiring discrete hardware components, or a combination of any or all of the foregoing. Such a configured device is physically designed to perform one or more specified functions.

[0229] In the description herein, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, controllers, etc., to provide a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will recognize that the present disclosure can be practiced without one or more of the specific details or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations may not be shown or described in detail to avoid obscuring aspects of the present disclosure.

[0230] Throughout this specification, references to "one embodiment," "many embodiments," or similar language mean that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, throughout this specification, appearances of the phrases "in one embodiment," "in an embodiment," "embodiment," "particular embodiment," "various embodiments," and similar language may, but do not necessarily, all refer to the same embodiment. Moreover, the described features, structures, aspects, and / or characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Correspondingly, even if features may initially be claimed to operate in a particular combination, one or more features from a claimed combination may, in some cases, be deleted from that combination, and the claimed combination may be directed to subcombinations or variations of subcombinations.

[0231] Benefits, advantages, and solutions to problems have been described above with respect to specific embodiments. However, the benefits, advantages, solutions to problems, and any elements that may cause any benefit, advantage, or solution to occur or become more pronounced should not be construed as critical, necessary, or essential features or elements of any or all claims. Furthermore, no component described herein is required to practice the present disclosure unless expressly described as essential or critical.

[0232] Those skilled in the art, having read this disclosure, will recognize that changes and modifications can be made to the embodiments without departing from the scope of the disclosure. It should be recognized that the particular implementations shown and described herein may be for purposes of illustrating the disclosure and its best mode, and may not be intended to otherwise limit the scope of the disclosure in any way. Other variations may be within the scope of the appended claims.

[0233] While this specification contains many specific examples, these should not be construed as limitations on the scope of the disclosure or the scope of what may be claimed, but rather as descriptions of features unique to particular implementations of the disclosure. Headings herein may not be intended to limit the disclosure, the embodiments of the disclosure, or other matters disclosed under the heading.

[0234] As used herein, the term "or" may be intended to be inclusive, such that "A or B" includes A or B, and also includes both A and B. As used herein, the term "and / or" when used in the context of a list of entities refers to the entities being present either alone or in any combination. Thus, for example, the phrase "A, B, C, and / or D" includes A, B, C, and D individually, but also any and all combinations and subcombinations of A, B, C, and D.

[0235] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly dictates otherwise.

[0236] The corresponding structure, material, acts, and equivalents of all means or step-plus-function elements in the following claims may be intended to include any structure, material, or acts for performing a function in combination with other claimed elements as specifically claimed.

[0237] As used herein with respect to an identified characteristic or circumstance, "substantially" refers to a degree of deviation that is small enough so as not to measurably impair the identified characteristic or circumstance. The exact degree of acceptable deviation may, in some cases, depend on the specific context.

[0238] As used herein, a plurality of items, structural elements, compositional elements, and / or materials may be presented in common lists for convenience. However, these lists should be construed as though each member of the list were individually identified as a separate and unique member. Accordingly, the individual members of such lists should not be construed as de facto equivalents of any other members of the same list solely based on their presentation in a common group, unless indicated to the contrary.

[0239] Unless defined otherwise, all technical and scientific terms used herein (such as acronyms used for chemical elements in the periodic table) have the same meaning as commonly understood by one of ordinary skill in the art to which the presently disclosed subject matter belongs. Although any methods, devices, and materials similar or equivalent to those described herein can be used in the practice or testing of the presently disclosed subject matter, representative methods, devices, and materials are now described. [Explanation of symbols]

[0240] 100 Material Handling Systems 101 Material Pieces 102 Hopper 103 Conveyor System 104 Conveyor system motor 105 Position detector 106 Singulator 107 Computer Systems 108 Automation Control Systems 109 Still cameras, live action cameras 110 Vision System 111 Material Piece Tracking Device 112 Control System 120 Sensor System 121 Energy Emission Source 122 Power supply 123 Control System 124 detectors 125 Detector Electronics 126 Sorting Device 127 Sorting Device 128 Sorting Device 129 Sorting Device 136 Sorting Receptor 137 Sorting Receptor 138 Sorting Receptor 139 Sorting Receptor 140 Receptor 800 Material Handling System 801 Vision System, XRF System 802 Sorting Device 803 Sorting Device 804 Sorting Device 805 Conveyor System 806 Conveyor System 807 Conveyor System 808 Conveyor System 1600 Material Handling System 1601 Material Pieces 1602 Conveyor Belt 1603 Conveyor System 1604 Conveyor System 1605 Conveyor System 1606 Conveyor System 1607 Conveyor System 1608 Conveyor System 1610 XRF or AI system 1611 XRF or AI system 1612 XRF or AI system 1620 Sorting Device 1621 Sorting Device 1622 Sorting Device 1630 Receptor 1631 Reception Department 1632 Reception Department 1633 Reception Department 3400 Computer System 3401 GPU 3405 Local Bus 3412 User Interface Adapter 3413 Keyboard 3414 Mouse 3415 Processor, CPU 3416 Display Adapter 3420 Volatile Memory 3425 Communication Adapter 3430 I / O adapter 3431 hard disk drive 3432 tape drive 3435 Non-volatile Memory 3440 Display 3801a Materials, material pieces 3801b Material Piece 3801c Material Piece 3802a Ramp, chute 3802b deposited 3802c Deposited 3802d deposited 3803a Conveyor System 3803b Conveyor System 3803c Conveyor System 3803d Conveyor System 3810a Materials Classification System 3810b Materials Classification System 3810c Materials Classification System 3810d Materials Classification System 3826a Sorter 3826b Sorter 3826c Sorter 3826d Sorter 3836a Receptor 3836b Aluminum alloy (twitch) material piece, receiving part 3836c copper 3836d Receptor 3837c Copper Wire 3837d Receptor 3840 Receptor

Claims

1. 1. A method for sorting scrap pieces from a conveyed stream of material, comprising: performing one or more vision checks on each scrap piece in the conveyed stream of material, each of the one or more vision checks including classifying each scrap piece in response to processing a visual image captured from each scrap piece through an artificial intelligence ("AI") system; performing one or more sensor system classifications on each scrap piece in the conveyed stream of material; sorting scrap pieces from the conveyed stream of material into one or more classification groups in response to a combination of the one or more vision checks and the one or more sensor system classifications; A method comprising:

2. the conveyed stream of material comprises one or more wrought aluminum alloys and one or more cast aluminum alloys, and the step of sorting comprises: sorting one or more of the cast aluminum alloys from the conveyed stream of material into one or more first classification groups based on a first combination of the one or more vision checks and the one or more sensor system classifications; sorting one or more of the wrought aluminum alloys from the conveyed stream of material into one or more second classification groups based on a second combination of the one or more vision checks and the one or more sensor system classifications; further comprising 10. The method of claim 1, wherein optionally, the step of separating the one or more cast aluminum alloys from the conveyed stream of material is performed before the step of separating the one or more wrought aluminum alloys from the conveyed stream of material.

3. The step of sorting comprises: a) sorting copper scrap pieces from the conveyed stream of material based on a combination of the one or more vision checks and the one or more sensor system classifications, the combination including a first vision check to determine if the scrap piece is comprised of copper and a first of the one or more sensor system classifications to determine if a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a first predetermined value, wherein a scrap piece is sorted from the conveyed stream of material to be classified as a copper scrap piece when the first vision check determines that the scrap piece is comprised of copper and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than the first predetermined value; Optionally, further comprising the step of sorting printed circuit board scrap pieces from the conveyed stream of material based on a second vision check, wherein the step of sorting the printed circuit board scrap pieces is performed before the step of sorting the copper scrap pieces; or b) sorting brass scrap pieces from the conveyed stream of material based on a combination of the one or more vision checks and the one or more sensor system classifications, the combination including a first vision check to determine if the scrap piece is comprised of brass and a first of the one or more sensor system classifications to determine if a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a first predetermined value, wherein a scrap piece is sorted from the conveyed stream of material to be classified as a brass scrap piece when the first vision check determines that the scrap piece is comprised of brass and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value; Optionally, a scrap piece is sorted from the conveyed stream of material to be classified as a red brass scrap piece when the vision check determines that the scrap piece is composed of brass, the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value, and the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than a second predetermined value; Optionally, a scrap piece is sorted from the conveyed stream of material to be classified as a yellow brass scrap piece when the vision check determines that the scrap piece is composed of brass, the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value, and the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the second predetermined value. The method of claim 1, further comprising:

4. The step of sorting comprises: a) sorting Ni-plated scrap pieces from the conveyed material stream based on a combination of sensor system classifications, the combination including: (1) a first one of the one or more sensor system classifications for determining whether a measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is greater than a second predetermined value; a scrap piece is sorted from the conveyed material stream to be classified as a Ni-plated scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value and the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is greater than the second predetermined value; and optionally the step of sorting Ni-plated scrap pieces from the conveyed material stream based on the combination of sensor system classifications is performed before the step of sorting wrought or cast aluminum scrap pieces from the conveyed material stream; or b) sorting stainless steel scrap pieces from the conveyed material stream based on a combination of sensor system classifications, the combination including: (1) a first one of the one or more sensor system classifications for determining whether a measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is greater than a second predetermined value; a scrap piece is sorted from the conveyed stream of material to be classified as a stainless steel scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value and the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value; and optionally wherein the step of sorting stainless steel scrap pieces from the conveyed stream of material based on the combination of sensor system classifications is performed prior to the step of sorting wrought or cast aluminum scrap pieces from the conveyed stream of material, and optionally includes: the combination further includes a third of the one or more sensor system classifications for determining whether a measured amount of manganese in the scrap piece is greater than a third predetermined value; a scrap piece is sorted from the conveyed material stream to be classified as a 201 and / or 202 stainless steel scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value, the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value, and the third of the one or more sensor system classifications determines that the measured amount of manganese in the scrap piece is greater than the third predetermined value; and optionally a scrap piece is sorted from the conveyed material stream to be classified as a 301, 302, 304, and / or 316 stainless steel scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value, the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value, and the third of the one or more sensor system classifications determines that the measured amount of manganese in the scrap piece is less than the third predetermined value; The method of claim 1, further comprising:

5. the sorting step further comprises the steps of: sorting 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material based on a combination of a sensor system classification to determine whether a measured amount of copper and zinc in the scrap piece is greater than a predetermined value and a vision check to determine whether the scrap piece is a cast aluminum alloy; and the sorting step further comprises the steps of: sorting the scrap piece from the conveyed stream of material to be classified as 356 or 360 cast aluminum alloy when the sensor system classification determines that a measured amount of copper and zinc in the scrap piece is less than the predetermined value and the vision check determines that the scrap piece is a cast aluminum alloy; and optionally 10. The method of claim 1, wherein the step of separating 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material is performed before the step of separating wrought aluminum alloy scrap pieces from the conveyed stream of material.

6. the conveyed stream of material comprises one or more wrought aluminum alloys and one or more cast aluminum alloys, and the step of sorting comprises: a) sorting die cast zinc scrap pieces from the conveyed stream of material based on a combination of a first one of the one or more sensor system classifications for determining whether a measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value, a vision check for determining whether the scrap pieces are cast aluminum alloys, and a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a second predetermined value, wherein the sorting step comprises: (1) determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a second predetermined value; further comprising the step of: when (1) the first of the one or more sensor system classifications determines that the measured amount of total lead is greater than the first predetermined value, and (2) the first vision check determines that the scrap piece is composed of a cast aluminum alloy, and (3) the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the second predetermined value, sorting the scrap piece from the conveyed stream of material to be classified as a die-cast zinc scrap piece; Optionally, the step of separating die cast zinc scrap pieces from the conveyed stream of material is performed before the step of separating wrought aluminum alloy scrap pieces from the conveyed stream of material; Optionally, the step of separating die cast zinc scrap pieces from the conveyed stream of material is performed before the step of separating 7xxx series aluminum alloy scrap pieces from the conveyed stream of material; Optionally, the sorting step further includes sorting the scrap piece from the conveyed stream of material to be classified as a 38x or 319 cast aluminum alloy scrap piece when (1) the first of the one or more sensor system classifications determines that the combined measured amount of copper and zinc in the scrap piece is greater than the first predetermined value, and (2) the first vision check determines that the scrap piece is composed of a cast aluminum alloy, and (3) the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than the second predetermined value; or b) sorting 38x or 319 cast aluminum alloy scrap pieces from the conveyed stream of material based on a combination of a first one of the one or more sensor system classifications for determining whether a measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value, a vision check for determining whether the scrap pieces are cast aluminum alloy, and a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a second predetermined value, wherein the sorting step comprises: (1) determining whether a ratio of a measured amount of copper and zinc in the scrap pieces is greater than a second predetermined value; further comprising the step of: (1) sorting the scrap piece from the conveyed stream of material to be classified as a 38x or 319 cast aluminum alloy scrap piece when (1) the first of the one or more sensor system classifications determines that the measured amount of total lead is greater than the first predetermined value, and (2) the first vision check determines that the scrap piece is composed of a cast aluminum alloy, and (3) the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than the second predetermined value; The method of claim 1, further comprising:

7. said step of separating one or more of said wrought aluminum alloys from said conveyed stream of material into one or more first classification groups comprises: a) separating scrap pieces from the conveyed stream of material to be classified as 2xxx series wrought aluminum alloy when a vision check determines that the scrap pieces are composed of wrought aluminum alloy and a sensor system classification determines that the measured amount of copper in the scrap pieces is greater than a predetermined value; or b) separating scrap pieces from the conveyed stream of material to be classified as 7xxx series wrought aluminum alloy when a vision check determines that the scrap pieces are composed of wrought aluminum alloy and a sensor system classification determines that the measured amount of zinc in the scrap pieces is greater than a predetermined value; or c) separating scrap pieces from said conveyed stream of material to be classified as 3xxx series wrought aluminum alloy when: (1) A vision check determines that the scrap piece is composed of a wrought aluminum alloy; and (2) When a first of the one or more sensor system classifications determines that: (i) the measured amount of manganese in the scrap pieces is less than a first predetermined value; and (ii) the measured amount of iron in the scrap pieces is less than a second predetermined value; 3. The method of claim 2, further comprising:

8. 8. The method of any one of claims 1 to 7, wherein each of the one or more vision checks is performed by a single vision system that implements one or more AI models within the AI ​​system, and each of the one or more sensor system classifications is performed by a single sensor system that implements one or more algorithms for analyzing spectral data collected from the scrap piece, and optionally, the single sensor system is an X-ray fluorescence ("XRF") system that implements one or more algorithms for analyzing XRF spectral data collected from the scrap piece.

9. 8. The method of claim 1, wherein the conveyed stream of material is a conveyed stream of Zorba material.

10. 8. The method of claim 2, further comprising the step of sorting one or more junk materials from the conveyed stream of material based on one or more vision checks, wherein the step of sorting the junk materials is performed before the step of sorting the one or more wrought aluminum alloys and the step of sorting the one or more cast aluminum alloys.

11. each of the one or more sensor system classifications is implemented by a spectroscopic system; 8. The method of claim 1, wherein the spectroscopic system is an X-ray fluorescence system.

12. 1. A method for sorting scrap pieces from a conveyed stream of material, comprising: performing a vision check on each scrap piece in the conveyed stream of material, each of the vision checks including classifying each scrap piece responsive to processing a visual image captured from each scrap piece through an artificial intelligence ("AI") system, the vision checks being performed by a single vision system with a different AI model implemented within the AI ​​system for each of the vision checks; performing a sensor system classification on each scrap piece in the conveyed stream of material, each of the sensor system classifications being performed by a different algorithm analyzing spectral data collected from each scrap piece by a single sensor system; a) sorting a plurality of different non-ferrous metal scrap pieces into distinct sorted classification groups based on one or more combinations of one or more vision checks and one or more sensor system classifications; b) sorting the plurality of different cast aluminum alloy scrap pieces into distinctly sorted classification groups based on one or more combinations of one or more vision checks and one or more sensor system classifications; c) sorting the plurality of different wrought aluminum alloy scrap pieces into distinctly sorted classification groups based on one or more combinations of one or more vision checks and one or more sensor system classifications; and optionally Step a) is performed before either or both of steps b) and c), and optionally Steps a), b), and c) are performed in that order, and optionally: the plurality of different non-ferrous metal scrap pieces are selected from the group consisting of copper, red brass, yellow brass, Ni-plated, stainless steel, die-cast zinc, and lead; and optionally the plurality of different non-ferrous metal scrap pieces are selected from the group consisting of 356, 360, 319, and 38x cast aluminum alloys; and optionally the plurality of different wrought aluminum alloy scrap pieces are selected from the group consisting of 2xxx, 3xxx, 5xxx, 6xxx, and 7xxx series wrought aluminum alloys; and optionally the sensor system classifications are X-ray fluorescence ("XRF") sensor system classifications, each of the XRF sensor system classifications implemented by a different algorithm that analyzes XRF spectral data collected from each scrap piece by a single XRF system; and optionally, The method wherein the conveyed stream of material is a conveyed stream of Zorba material.

13. 1. A system for sorting scrap pieces, comprising: a conveyor system configured to convey a stream of material; a vision system configured to perform one or more vision checks on each scrap piece in the conveyed stream of material, each of the one or more vision checks including classifying each scrap piece in response to processing a visual image captured from each scrap piece through an artificial intelligence ("AI") system; and a sensor system configured to perform one or more sensor system classifications on each scrap piece in the conveyed stream of material; a sorting device configured to sort scrap pieces from the conveyed stream of material into one or more classification groups in response to a combination of the one or more vision checks and the one or more sensor system classifications; Including, the system.

14. the conveyed stream of material includes one or more wrought aluminum alloys and one or more cast aluminum alloys, and the sorting apparatus comprises: configured to sort one or more of the cast aluminum alloys from the conveyed stream of material into one or more first classification groups in response to instructions received from a first combination of the one or more vision checks and the one or more sensor system classifications; and configured to sort one or more of the wrought aluminum alloys from the conveyed stream of material into one or more second classification groups in response to instructions received from a second combination of the one or more vision checks and the one or more sensor system classifications; 14. The system of claim 13, wherein optionally, the step of separating the one or more cast aluminum alloys from the conveyed stream of material is performed before the step of separating the one or more wrought aluminum alloys from the conveyed stream of material.

15. The sorting device is a) the sorting device configured to sort copper scrap pieces from the conveyed stream of material in response to instructions received from a combination of the one or more vision checks and the one or more sensor system classifications, the combination including a first vision check to determine whether the scrap piece is comprised of copper and a first of the one or more sensor system classifications to determine whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a first predetermined value, wherein a scrap piece is sorted to be classified as a copper scrap piece from the conveyed stream of material when the first vision check determines that the scrap piece is comprised of copper and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than the first predetermined value; Optionally, the sorting device is configured to sort printed circuit board scrap pieces from the conveyed stream of material in response to instructions received from a second vision check, and the step of sorting the printed circuit board scrap pieces is performed before the step of sorting the copper scrap pieces; or b) the sorting device configured to sort brass scrap pieces from the conveyed stream of material in response to instructions received from a combination of the one or more vision checks and the one or more sensor system classifications, the combination including a first vision check to determine if the scrap piece is comprised of brass and a first of the one or more sensor system classifications to determine if a ratio of a measured amount of copper to a measured amount of zinc in the scrap piece is greater than a first predetermined value, wherein a scrap piece is sorted to be classified as a brass scrap piece from the conveyed stream of material when the first vision check determines that the scrap piece is comprised of brass and the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value; Optionally, a scrap piece is sorted from the conveyed stream of material to be classified as a red brass scrap piece when the vision check determines that the scrap piece is composed of brass, the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value, and the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than a second predetermined value; Optionally, a scrap piece is sorted from the conveyed stream of material to be classified as a yellow brass scrap piece when the vision check determines that the scrap piece is composed of brass, the first of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the first predetermined value, and the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the second predetermined value. The system of claim 13, further comprising:

16. The sorting device is a) the sorting device configured to sort Ni-plated scrap pieces from the conveyed material stream in response to instructions received from a combination of sensor system classifications, the combination including: (1) a first one of the one or more sensor system classifications for determining whether a measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is greater than a second predetermined value; a scrap piece is sorted from the conveyed material stream to be classified as a Ni-plated scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value and the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is greater than the second predetermined value; and optionally the step of sorting Ni-plated scrap pieces from the conveyed stream of material in response to instructions received from the combination of sensor system classifications is performed before the step of sorting wrought or cast aluminum scrap pieces from the conveyed stream of material; or b) the sorting device configured to separate stainless steel scrap pieces from the conveyed stream of material in response to instructions received from a combination of sensor system classifications, the combination including: (1) a first one of the one or more sensor system classifications for determining whether a measured amount of chromium in the scrap pieces is greater than a first predetermined value; and (2) a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of nickel to a measured amount of chromium in the scrap pieces is greater than a second predetermined value; a scrap piece is sorted from the conveyed stream of material to be classified as a stainless steel scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value and the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value; and optionally and optionally, the step of sorting stainless steel scrap pieces from the conveyed stream of material in response to instructions received from the combination of sensor system classifications is performed before the step of sorting wrought or cast aluminum scrap pieces from the conveyed stream of material. the combination further includes a third of the one or more sensor system classifications for determining whether a measured amount of manganese in the scrap piece is greater than a third predetermined value; a scrap piece is sorted from the conveyed material stream to be classified as a 201 and / or 202 stainless steel scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value, the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value, and the third of the one or more sensor system classifications determines that the measured amount of manganese in the scrap piece is greater than the third predetermined value; and optionally a scrap piece is sorted from the conveyed material stream to be classified as a 301, 302, 304, and / or 316 stainless steel scrap piece when the first of the one or more sensor system classifications determines that the measured amount of chromium in the scrap piece is greater than the first predetermined value, the second of the one or more sensor system classifications determines that the ratio of the measured amount of nickel to the measured amount of chromium in the scrap piece is less than the second predetermined value, and the third of the one or more sensor system classifications determines that the measured amount of manganese in the scrap piece is less than the third predetermined value; The system of claim 13, further comprising:

17. the sorting device is further configured to separate 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material in response to instructions received from a combination of a sensor system classification to determine whether a measured amount of copper and zinc in the scrap piece is greater than a predetermined value and a vision check to determine whether the scrap piece is a cast aluminum alloy, the sorting step further comprising the step of: separating the scrap piece from the conveyed stream of material to be classified as 356 or 360 cast aluminum alloy when the sensor system classification determines that a measured amount of copper and zinc in the scrap piece is less than the predetermined value and the vision check determines that the scrap piece is a cast aluminum alloy; and optionally 14. The system of claim 13, wherein the step of separating 356 and / or 360 cast aluminum alloy scrap pieces from the conveyed stream of material is performed before the step of separating wrought aluminum alloy scrap pieces from the conveyed stream of material.

18. the conveyed stream of material includes one or more wrought aluminum alloys and one or more cast aluminum alloys, and the sorting apparatus comprises: a) the sorting device configured to separate die-cast zinc scrap pieces from the conveyed stream of material in response to instructions received from a combination of a first one of the one or more sensor system classifications for determining whether a measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value, a vision check for determining whether the scrap pieces are cast aluminum alloy, and a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a second predetermined value, wherein the sorting step comprises: (1) detecting the scrap pieces; the sorting apparatus further comprising: when (1) the first of the one or more sensor system classifications determines that the measured amount of the sum of copper and zinc in the scrap piece is greater than the first predetermined value, and the first vision check determines that the scrap piece is comprised of a cast aluminum alloy, and (2) the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is less than the second predetermined value, Optionally, the step of separating die cast zinc scrap pieces from the conveyed stream of material is performed before the step of separating wrought aluminum alloy scrap pieces from the conveyed stream of material; Optionally, the step of separating die cast zinc scrap pieces from the conveyed stream of material is performed before the step of separating 7xxx series aluminum alloy scrap pieces from the conveyed stream of material; Optionally, the sorting device is further configured to sort the scrap piece from the conveyed stream of material to be classified as a 38x or 319 cast aluminum alloy scrap piece when (1) the first of the one or more sensor system classifications determines that the combined measured amount of copper and zinc in the scrap piece is greater than the first predetermined value, and (2) the first vision check determines that the scrap piece is comprised of a cast aluminum alloy, and (3) the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than the second predetermined value; or b) the sorting device configured to sort 38x or 319 cast aluminum alloy scrap pieces from the conveyed stream of material in response to instructions received from a combination of a first one of the one or more sensor system classifications for determining whether a measured amount of copper and zinc in the scrap pieces is greater than a first predetermined value, a vision check for determining whether the scrap pieces are cast aluminum alloy, and a second one of the one or more sensor system classifications for determining whether a ratio of a measured amount of copper to a measured amount of zinc in the scrap pieces is greater than a second predetermined value, wherein the scrap pieces are: (1) the sorting device sorting the scrap piece from the conveyed stream of material to be classified as a 38x or 319 cast aluminum alloy scrap piece when (1) the first of the one or more sensor system classifications determines that the combined measured amount of copper and zinc in the scrap piece is greater than the first predetermined value, and (2) the first vision check determines that the scrap piece is comprised of a cast aluminum alloy, and (3) the second of the one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the scrap piece is greater than the second predetermined value; The system of claim 13, further comprising:

19. The sorting device is a) the sorting device is configured to separate scrap pieces from the conveyed stream of material to be classified as 2xxx series wrought aluminum alloy when a vision check determines that the scrap pieces are composed of wrought aluminum alloy and a sensor system classification determines that a measured amount of copper in the scrap pieces is greater than a predetermined value; or b) the sorting device is configured to separate scrap pieces from the conveyed stream of material to be classified as 7xxx series wrought aluminum alloy when a vision check determines that the scrap pieces are composed of wrought aluminum alloy and a sensor system classification determines that the measured amount of zinc in the scrap pieces is greater than a predetermined value; or c) the sorting device configured to sort scrap pieces from the conveyed stream of material to be classified as 3xxx series wrought aluminum alloy when: (1) A vision check determines that the scrap piece is composed of a wrought aluminum alloy; and (2) When a first of the one or more sensor system classifications determines that: (i) the measured amount of manganese in the scrap pieces is less than a first predetermined value; and (ii) the measured amount of iron in the scrap pieces is less than a second predetermined value; The system of claim 14, further comprising:

20. 20. The system of any one of claims 13 to 19, wherein each of the one or more vision checks is performed by a single vision system implementing one or more AI models within the AI ​​system, and each of the one or more sensor system classifications is performed by a single sensor system implementing one or more algorithms for analyzing spectral data collected from the scrap piece, and optionally, the single sensor system is an X-ray fluorescence ("XRF") system implementing one or more algorithms for analyzing XRF spectral data collected from the scrap piece.

21. 20. The system of claim 13, wherein the conveyed stream of material is a conveyed stream of Zorba material.

22. 20. The system of claim 14, wherein the sorting device is further configured to sort one or more junk materials from the conveyed stream of material in response to instructions received from one or more vision checks, and wherein the step of sorting the junk materials is performed before the step of sorting the one or more wrought aluminum alloys and the step of sorting the one or more cast aluminum alloys.

23. each of the one or more sensor system classifications is implemented by a spectroscopic system; 20. The system of claim 13, wherein the spectroscopic system is an X-ray fluorescence system.

24. 20. A computer program product comprising instructions for causing a system according to any one of claims 13 to 19 to perform a method according to any one of claims 1 to 7.

25. 25. A computer readable storage medium having stored thereon the computer program product of claim 24.

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