Sorting of aluminum alloys

Abstract

A material processing system utilizes a combination of a spectral sensor, such as x-ray fluorescence, and a visual system implementing an artificial intelligence system to sort mixed materials in order to identify or classify each of the materials, and then sort each of the materials into separate groups based on such identification or classification. The system enables sorting between materials common within the Zorba, such as forging, casting and extruding aluminum alloys.

Description

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

[0002] Government License Rights

[0003] This disclosure was made with U.S. Government support under DE-AR0000422 awarded by the U.S. Department of Energy. The U.S. Government can have certain rights in the disclosure. TECHNICAL FIELD

[0004] The present disclosure relates generally to the classification and sorting of mixtures of materials, and in particular to the classification and sorting of aluminum alloys from mixtures of Zorba material. BACKGROUND

[0005] This section is intended to introduce various aspects of the art, which can be associated with examples of the present disclosure. This discussion is intended to provide a framework to facilitate a better understanding of particular aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not as admissions of prior art to the claim.

[0006] Recycling is beneficial to communities and the environment because it reduces the amount of trash sent to landfills and incinerators, conserves natural resources, improves economic security by utilizing domestic sources of materials, prevents pollution by reducing the need to collect new raw materials, and conserves energy.

[0007] Recycling of aluminum (Al) scrap is a very attractive proposition because up to 95% of the energy costs associated with manufacturing can be saved compared to the laborious extraction of costlier primary aluminum. Primary aluminum, or virgin aluminum, is defined as aluminum derived from aluminum-rich ores, such as bauxite. At the same time, the demand for aluminum in markets such as automotive manufacturing is steadily growing due to its lightweight properties. Therefore, the aluminum industry can reap certain economic benefits by developing a careful and simple recycling program or system. The use of recycled materials would be a cheaper source of metal than a primary source of aluminum. As the amount of aluminum sold to the automotive industry (and other industries) increases, it will become increasingly necessary to supplement the availability of primary aluminum with recycled aluminum.

[0008] Therefore, it is especially desirable to efficiently separate aluminum scrap metal into alloy families because mixed aluminum scrap of the same alloy family is much more valuable than a haphazard mix of alloys. For example, in a mixed approach to recycled aluminum, any amount of scrap composed of similar or identical alloys and of consistent quality is more valuable than scrap composed of mixed aluminum alloys. Within such aluminum alloys, aluminum will always be the majority of the material. However, constituents such as copper, magnesium, silicon, iron, chromium, zinc, manganese, and other alloying elements provide a range of properties to the alloy aluminum and provide a means to distinguish one aluminum alloy from another.

[0009] The Aluminum Association is the organization that defines allowable limits for the chemical composition of aluminum alloys. Data for aluminum wrought alloy chemical compositions are published by the Aluminum Association in "International Alloy Designations and Chemical Composition Limits for Aluminum Alloys" (updated January 2015) and incorporated herein by reference. The International Alloy Designation System is the most widely accepted designation scheme for wrought alloys. Each alloy is given a four-digit number, where the first digit indicates the major alloying element, the second digit, if different from zero, indicates a modification of the alloy, and the third and fourth digits identify the specific alloy in the series. For example, in alloy 3105, the number 3 indicates that the alloy is in the manganese series, 1 indicates a first modification of the alloy 3005, and 05 identifies it in the 3000 series. Generally, according to the Aluminum Association, the 1xxx series of wrought aluminum alloys consist essentially of pure aluminum with a minimum aluminum content of 99% by weight; the 2xxx series are wrought aluminums alloyed primarily with copper (Cu); the 3xxx series are wrought aluminums alloyed primarily with manganese (Mn); the 4xxx series are wrought aluminums alloyed with silicon (Si); the 5xxx series are wrought aluminums alloyed primarily with magnesium (Mg); the 6xxx series are wrought aluminums alloyed primarily with magnesium and silicon; the 7xxx series are wrought aluminums alloyed primarily with zinc (Zn); and the 8xxx series is a miscellaneous category

[0010] The Aluminum Association also has a similar documentation for cast aluminum alloy designations. The 1xx series of cast aluminum alloys consist essentially of pure aluminum with a minimum aluminum content of 99% by weight; the 2xx series are cast aluminums alloyed primarily with copper; the 3xx series are cast aluminums alloyed primarily with silicon plus copper and / or magnesium; the 4xx series are cast aluminums alloyed primarily with silicon; the 5xx series are cast aluminums alloyed primarily with magnesium; the 6xx series is an unused series; the 7xx series are cast aluminums alloyed primarily with zinc; the 8xx series are cast aluminums alloyed primarily with tin; and the 9xx series are cast aluminums alloyed with other elements. Examples of cast alloys used for automotive parts include 38x (e.g., 380, 383, 384), 356, 360, and 319.

[0011] Generally, wrought aluminum alloys have a higher magnesium concentration than cast aluminum alloys, and cast aluminum alloys have a higher silicon concentration than wrought aluminum alloys.

[0012] Further, the presence of mixed scrap of different alloys in the waste body limits the ability of the waste to be effectively recycled unless the different alloys (or, at least, alloys belonging to different composition families (such as the alloys designated by the Aluminum Association)) can be separated prior to re-melting. This is because, when mixed waste of multiple different alloy compositions or composition families is re-melted, the resulting melt mixture contains too high a proportion of the major alloying elements (or different compositions) to satisfy the composition limits required for any particular commercial alloy.

[0013] The automotive industry is a vital sector of the U.S. economy in terms of revenue and employment. Over the past fifty years, the Detroit automotive industry has suffered many setbacks due to low-cost imports. The recent emergence of fuel-efficient light and electric vehicles provides an opportunity for Detroit to regain global leadership in automotive manufacturing, particularly given the massive investments by Ford and General Motors to capture significant market share. As a consequence, the materials used to manufacture automobiles are shifting from heavy steel to lightweight aluminum for vehicle bodies and battery trays, and copper for electric motors. For example, the Tesla Model 3 electric vehicle (EV) contains approximately 660 pounds of aluminum, compared to approximately 250 pounds of aluminum in a typical internal combustion engine (“ICE”) car. There is now a consensus that there will be a 2 billion pound shortfall of aluminum for manufacturing electric vehicles by 2025. Currently, Russia and China are the world’s two largest suppliers of primary aluminum. There is a similar situation for copper for electric motors, to the extent that copper prices have risen dramatically. It is therefore necessary to develop a domestic, reliable, and sustainable supply chain for these vital materials.

[0014] Zorba is mixed nonferrous scrap, typically containing 90-95% metal content, that is a byproduct of current steel manufacturing operations. Currently, in addition to white goods (e.g., washing machines, dryers, refrigerators, and other appliances) and construction scrap (e.g., aluminum siding, windows, and door frames), there are approximately 300 automobile shredders in the United States that shred 12-15 million end-of-life (“EOL”) vehicles per year. The shredders typically operate continuously to produce steel (ferrous) scrap (e.g., which is then used to produce rebar for building structures, bridges, and factories). After shredding, the steel scrap is removed by large electromagnets and sent to steel mills. The byproduct of the shredder is the remaining mixed nonferrous scrap, which contains all materials other than the magnetically removed steel. The next step traditionally involves using an eddy current sorter to separate low-value non-metals (typically half the weight of the car that is sent to landfills) and recover the mixed metal scrap (i.e., Zorba). However, such eddy current sorters are inefficient and very expensive to operate. As a result, a large amount of Zorba remains unsorted. Currently, over 10 billion pounds of Zorba are produced in the United States each year, and over 40% of it is shipped overseas for manual sorting.

[0015] Additionally, as evidenced by the production and sale of the Ford F-150 pickup truck, which has a body and frame made of aluminum rather than steel, it is additionally desirable to recycle metal scrap (e.g., wrought aluminum of certain alloy compositions) including scrap generated in the manufacture of automotive components from sheet aluminum (commonly referred to in the industry as "Clips"). The recycling of scrap involves re-melting the scrap to provide a molten metal body that can be cast and / or rolled into useful aluminum parts for further production of such vehicles. However, automotive manufacturing scrap (as well as metal scrap from other sources such as aircraft, as well as commercial and household appliances) often includes a mixture of scrap pieces of wrought pieces and cast pieces and / or two or more aluminum alloys that are substantially different from one another in composition. Thus, one skilled in the art of aluminum alloys will appreciate the difficulty of separating aluminum alloys, especially alloys that have been processed (such as cast, wrought, extruded, rolled, and generally wrought alloys), into reusable or recyclable processed products.

[0016] Currently, the only existing technology that separates cast from wrought in a cost effective manner is x-ray transmission ("XRT") technology. Cast is heavier than wrought due to a higher concentration of silicon, and thus cast alloys are denser than wrought alloys. X-ray transmission technology is able to measure the denser cast aluminum alloys and then sort that cast aluminum alloy from the wrought alloys. However, this method is not perfect. For example, cast alloys 319 and 380 / 383 have a relatively high concentration of zinc (e.g., about 3%) that makes these cast alloys have a higher respective density. However, cast alloy 360 has a lower relative concentration of zinc (e.g., about 0.5%) and thus 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. Thus, the x-ray transmission technology is not able to correctly classify all cast alloys because of their respective density differences. As a result, this cast alloy ends up being sorted with the wrought aluminum alloys, which will result in a relative silicon excess in the molten mixture. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 FIG. illustrates a schematic diagram of a material processing system configured in accordance with various embodiments of the present disclosure.

[0018] Figure 2 FIG. illustrates a flow diagram configured in accordance with certain embodiments of the present disclosure.

[0019] Figure 3 FIG. illustrates a flow diagram configured in accordance with certain embodiments of the present disclosure.

[0020] Figure 4 Various sorting that can be performed on scrap is schematically illustrated.

[0021] Figure 5 Exemplary techniques that can be used to sort and sort Zebra materials are schematically illustrated.

[0022] Figure 6 Exemplary techniques that can be used to sort and sort Twitch materials are schematically illustrated.

[0023] Figure 7A Figure 7B and Figure 7C flowcharts configured according to various embodiments of the present disclosure are illustrated.

[0024] Figure 8 systems and processes for sorting of materials according to certain embodiments of the present disclosure are illustrated.

[0025] Figure 9A and Figure 9B systems and processes for sorting of materials according to certain embodiments of the present disclosure are illustrated.

[0026] Figure 10 links of a continuous material processing system according to certain embodiments of the present disclosure are illustrated.

[0027] Figure 11 block diagrams of data processing systems configured according to various embodiments of the present disclosure are illustrated.

[0028] Figure 12A and Figure 12B flowcharts configured according to various embodiments of the present disclosure are illustrated. DETAILED DESCRIPTION

[0029] Various detailed embodiments of the present disclosure are disclosed herein. It should be understood, however, that the disclosed embodiments are merely exemplary of the present disclosure, which can be embodied in various forms and alternative ways. The drawings are not necessarily to scale; some features can be exaggerated or minimized for purpose of clarity. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as being representative of the embodiments taught by the present disclosure.

[0030] There is a desire to recover high value materials from Zorba with high robustness, throughput, efficiency, accuracy, and precision. As will be further described herein, after low value steel is removed 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.

[0031] ​Sorting / recovering various high-value materials from Zorba is more complex; and requires both spectral sensors and vision-based (implementing artificial intelligence systems) sensors. Embodiments of the present disclosure accomplish such tasks by essentially measuring the chemical composition of each type of material found in Zorba. Signals from vision systems and sensor systems (e.g., spectral systems such as XRF, LIBS, etc.) are combined for each different material to create a“fingerprint” representing both image data and composition data within the material, which is then used to sort between various materials within Zorba.

[0032] 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 objects, Zorba, Zebra, Twitch, metal pieces embedded in another, different material, plastics / polymers (including, but not limited to, any of those disclosed herein, known in the industry, or newly created in the future), rubber, foam, printed circuit boards (“PCBs”), glass (including, but not limited to, borosilicate or soda-lime glass, as well as various colored glasses), ceramics, paper, cardboard, polytetrafluoroethylene, polyethylene (PE), bundled wire, insulated covered wire, rare earth elements, leaves, wood, plants, plant parts, textiles, biological waste, packaging, electronic waste, batteries and accumulators, scrap from end-of-life (“EOL”) products (e.g., vehicles, aircraft, and / or appliances), mining, construction, and demolition waste, crop waste, forest residue, grass grown specifically, woody energy crops, microalgae, food waste, hazardous chemical and biomedical waste, construction debris, farm waste, biogenic items, non-biogenic items, objects having a specific carbon content, any other objects that can be found within municipal solid waste, and any other objects, items, or materials disclosed herein including further types or classes of any of the foregoing that can be distinguished from one another by one or more sensor systems including, but not limited to, any of the sensor technologies disclosed herein.

[0033] In a more general sense, “material” can include any article 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, where the complexity of the compounds or mixtures can vary 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”). By “chemical element” is meant a chemical element from the periodic table of chemical elements, which includes chemical elements that can be discovered after the filing date of this application. Within 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 that is 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 component of a material or material piece that is to be excluded from a sorted set of materials.

[0034] As used herein, the term “predetermined” refers to something that has been previously established or decided, such as by a user of an embodiment of the present disclosure.

[0035] As used herein, the term “chemical signature” refers to a unique pattern (e.g., a fingerprint spectrum) that would be produced by one or more analytical instruments that indicates the presence of one or more particular 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 also disclosed in U.S. Patent Application No. 2022 / 0161298, which is hereby incorporated by reference herein. According to embodiments of the present disclosure, one or more such sensor systems can be configured to produce a chemical signature of a material piece.

[0036] The term “Zorba” is a generic term for shredded non-ferrous metals, including but not limited to non-ferrous metals from EOL products (e.g., vehicles, aircraft, appliances) or waste electrical and electronic equipment (“WEEE”), as defined within the Non-Ferrous Scrap Guidelines promulgated by the Institute of Scrap Recycling Industries, Inc. (“ISRI”). ISRI has developed specifications for Zorba; each scrap piece in Zorba can be composed of a combination of non-ferrous metals: aluminum, copper, lead, magnesium, stainless steel, nickel, tin, and zinc in elemental or alloy (solid) form. In addition, the term “Twitch” shall mean shredded aluminum scrap. Twitch is traditionally produced by media separation techniques, such as a floatation process, whereby aluminum scrap floats to the top as the heavier metal scrap sinks (e.g., in some processes, sand can be mixed in to change the density of the water into which the scrap is submerged). The term “Zebra” shall mean high-density non-ferrous metals that are typically produced by this method.

[0037] As is well known in the industry, a "polymer" is a substance or material composed of very large molecules or macromolecules, which are composed of many repeating subunits. A polymer can be a natural polymer found in nature or a synthetic polymer. A "multilayer polymer film" is composed of two or more different compositions and can have a thickness of up to about 7.5 –8 x 10 -4 m. The layers are at least partially continuous and preferably, but optionally, coextensive. As used herein, the terms "plastic," "plastic piece," and "plastic material piece" (all of which can be used interchangeably) refer to any object that includes or is composed of one or more polymers and / or multilayer polymer films.

[0038] As used herein, a "fraction" refers to any particular combination of organic and / or inorganic elements or molecules, polymer types, plastic types, polymer compositions, chemical characteristics of plastics, physical properties of plastic pieces (e.g., color, transparency, strength, melting point, density, shape, size, type of manufacture, uniformity, reaction to stimuli, etc.), and the like, including any and all of the various classifications and types of plastics disclosed herein. Non-limiting examples of fractions are one or more different types of plastic pieces that include: LDPE plus a relatively high percentage of aluminum; LDPE and PP plus a relatively low percentage of iron; PP plus zinc; a combination of PE, PET, and HDPE; any type of red LDPE plastic piece; any combination of plastic pieces other than PVC; a black plastic piece; a combination of #3 - #7 type plastics, including a combination of specific organic and inorganic molecules; a combination of one or more different types of multilayer polymer films; a combination of specific plastics that do not include specific contaminants or additives; any type of plastic having a melting point greater than a specific threshold; a plurality of specific types of any thermoset plastic; a specific plastic that does not include chlorine; a combination of plastics having similar densities; a combination of plastics having similar polarities; a plastic bottle that does not have an attached cap or a plastic bottle that does not have an attached cap.

[0039] As used herein, the term "image data" refers to a grouping of digital data related to a captured visual image of an individual material piece.

[0040] As used herein, the term "sort," and any derivatives thereof, refers to the physical separation of certain material pieces (e.g., specifically classified material pieces) from other material pieces.

[0041] As used herein, the terms "identifying" and "classifying" and "identify" and "classify" and any derivatives of the foregoing can be used interchangeably. As used herein, "classifying" a piece of material is to assign or determine (i.e., identify) a type or class of material to which the piece of material belongs. For example, in accordance with certain embodiments of the present application, 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 for classifying materials and distinguishing such classified materials from other materials, which classification can be utilized within a material handling system as a function of a set of one or more physical and / or chemical properties (e.g., the set of one or more physical and / or chemical properties 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 score, radioactive signature, transmissivity to light, sound, or other signals, and response to stimuli such as various fields, including emitted and / or reflected electromagnetic radiation ("EM") of the piece of material.

[0042] The type or class of the piece of material (i.e., the classification) can be user- definable (e.g., predetermined) and is not limited to any known classification(s) of materials. The granularity of the type or class can vary from very coarse to very fine. For example, the type or class can include plastics, ceramics, glass, metals, foams, wood, and other materials, where the granularity of these types or classes is relatively coarse; different metals and metal alloys, such as, for example, zinc, copper, brass, lead, chrome plate, nickel plate, stainless steel, and aluminum, where the granularity of these types or classes is finer; or among specific types of aluminum alloys, where the granularity of such types or classes is relatively fine. As such, the type or class can be configured to distinguish between materials of significantly different chemical composition, such as, for example, plastics and metal alloys, or to distinguish between materials of nearly identical chemical composition, such as, for example, different types of aluminum alloys. It should be appreciated that the methods and systems discussed herein can be applied to accurately identify / classify a piece of material whose chemical composition is completely unknown prior to the piece of material being classified.

[0043] As used herein, "manufacturing type" refers to the type of manufacturing process by which the piece of material was manufactured, such as a metal part that has been formed by a forging process, cast (including, but not limited to, expendable pattern casting, permanent mold casting, and powder metallurgy), rolled, wrought; a material removal process, etc.

[0044] 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: pneumatic mechanical conveyors, automated conveyors, conveyer belts, belt driven live roller conveyors, bucket conveyors, chain conveyors, chain driven live roller conveyors, drag conveyors, dust tight conveyors, electric rail vehicle systems, flexible conveyors, gravity conveyors, gravity skate wheel conveyors, spool roller conveyors, motorized roller conveyors, overhead I-beam conveyors, overland conveyors, pharmaceutical conveyors, plastic belt conveyors, pneumatic conveyors, screw or auger conveyors, spiral conveyors, tube gallery conveyors, vertical conveyors, vibrating conveyors, wire mesh conveyors, and conveying pieces of material within a fluid past vision systems and / or sensor systems, including but not limited to very small particles suspended in a fluid.

[0045] According to certain embodiments of the present disclosure, the systems and methods described herein receive a heterogeneous mixture of multiple pieces of material (e.g., EOL scrap, Zorba, Heavies, Zebra, and / or Twitch), where at least one piece of material within the heterogeneous mixture includes a different chemical composition than one or more other pieces of material, and / or at least one piece of material within the heterogeneous mixture is physically distinguishable from other pieces of material, and / or at least one piece of material within the heterogeneous mixture has a different class or type of material than the class or type of material of other pieces of material in the mixture, and the systems and methods are configured to identify / classify / distinguish / sort that one piece of material into a group separate from such other pieces of material. Embodiments of the present disclosure can be used to sort any type or class of material as defined herein. In contrast, a homogeneous set or group of materials falls within the same identifiable class or type of material.

[0046] Certain embodiments of the present disclosure will be described herein as sorting pieces of material (e.g., physically depositing (e.g., discharging or transferring) pieces of material into separate containers or bins, or onto another conveyor system) into such separate groups or sets according to user-defined or predetermined groupings or sets (e.g., piece of material classifications). As an example, within certain embodiments of the present disclosure, pieces of material are sorted into separate containers in order to separate pieces of material composed of one or more particular material compositions from other pieces of material composed of different material compositions.

[0047] It should be noted that the material to be sorted can have irregular sizes and shapes. For example, such material (e.g., Zorba, Zebra, and / or Twitch) can have previously been shredded through some shredding mechanism that cuts the material into such irregularly shaped and sized pieces (creating scrap pieces), which are then fed or transferred onto the conveyor system.

[0048] Certain embodiments of the present disclosure can be configured for sorting aluminum alloy material pieces into separate containers such that substantially all of the aluminum alloy material pieces having a material composition falling within one of the aluminum alloy series published by the Aluminum Association are sorted into a single container (e.g., the container can correspond to one or more particular aluminum alloy series (e.g., lxxx, 2xxx, 3xxx, 4xxx, 5xxx, 6xxx, 7xxx, 8xxx, lxx, 2xx, 3xx, 4xx, 5xx, 6xx, 7xx, 8xx, 9xx)). Moreover, as will be described herein, certain embodiments of the present disclosure can be configured for sorting metal alloys into separate containers according to a classification of the metal alloy composition of the metal alloys, even if such metal alloy compositions fall within the same alloy series (e.g., as defined by the Aluminum Association). Thus, a material processing system configured in accordance with certain embodiments of the present disclosure can classify and sort aluminum alloy material pieces having compositions that classify them all into a single aluminum alloy series (e.g., a 3xx series or a 5xx series) into separate containers according to the aluminum alloy composition. For example, certain embodiments of the present disclosure can classify and sort aluminum alloy material pieces classified as cast aluminum alloy 360 separately from aluminum alloy material pieces classified as cast aluminum alloy 380 (or other similar cast aluminum alloys, such as 383) into separate containers.

[0049] Figure 1FIG. illustrates a non-limiting example of a material processing system 100 configured in accordance with various embodiments of the present disclosure. A conveyor system 103 can be implemented to convey individual pieces of material 101 through the material processing system 100 such that each of the individual pieces of material 101 can be tracked, sorted, differentiated, and / or sorted into predetermined desired groups (e.g., material classification). Such a conveyor system 103 can be implemented with one or more conveyor belts upon which the pieces of material 101 generally travel at a predetermined constant speed. However, certain embodiments of the present disclosure can be implemented with other types of conveyor systems including one or more in which the pieces of material free fall through various components of the material processing system 100 (or any other type of vertical sorter), or any of the other conveyor systems disclosed herein. Hereinafter, the conveyor system 103 can also be referred to as the conveyor belt 103 where applicable. In one or more embodiments, some or all of the acts or functions of conveying, capturing, stimulating, detecting, sorting, differentiating, and sorting can be performed automatically (i.e., without human intervention). For example, in the material processing system 100, one or more cameras, one or more vision systems, one or more sensor systems, one or more stimulation sources, one or more emission detectors, one or more sorting modules, sorting devices, one or more sorting apparatuses, and / or other system components can be configured to perform these and other operations automatically.

[0050] Furthermore, although Figure 1 The simplified illustration in FIG. depicts a single stream of pieces of material 101 on the conveyor belt 103, where multiple such streams of pieces of material pass through the various components of the material processing system 100 in parallel with one another can be implemented in accordance with embodiments of the present disclosure. In accordance with 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 pieces of material 101 onto the conveyor system 103, from which the conveyor system 103 conveys the pieces of material 101 through the various components within the material processing system 100. In accordance with certain embodiments of the present disclosure, a drum and / or a vibrator can be used to separate individual pieces of material from a collection (e.g., a physical pile) of pieces of material. In accordance with certain embodiments of the present disclosure, the pieces of material can be positioned into one or more separated (i.e., single column) streams, which can be performed by an active or passive separator 106. An example of a passive separator is further described in U.S. Patent No. 10,207,296.

[0051] Accordingly, certain embodiments of the present disclosure are capable of simultaneously tracking, sorting, differentiating, and / or singulating the flow of such pieces of material in transit. Alternatively, the conveyor system (e.g., conveyor belt 103) can simply convey a collection of pieces of material that have been deposited on the conveyor belt 103 in a random manner. Accordingly, in accordance with certain embodiments of the present disclosure, it is not required that the pieces of material 101 be separated in order to track, sort, differentiate, and / or singulate the pieces of material.

[0052] In certain embodiments of the present disclosure, the conveyor system 103 is operated by a conveyor system motor 104 to travel at a predetermined speed. The predetermined speed can 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 the position detector 105 can be performed by an automated control system 108. Such an automated control system 108 can be operated under the control of a computer system 107, and / or the functionality for performing automated control can be implemented in software within the computer system 107. If the conveyor system 103 is a conveyor belt, it can be a conventional endless belt conveyor employing a conventional drive motor 104 adapted to move the conveyor belt 103 at the predetermined speed.

[0053] The position detector 105 (e.g., a conventional encoder) can be operatively coupled to the conveyor belt 103 and the automated control system 108 to provide information corresponding to movement of the conveyor belt 103 (e.g., speed). Accordingly, as will be further described herein, by utilizing control of the conveyor belt drive motor 104 and / or the automated control system 108 (and, alternatively, including the position detector 105), each of the pieces of material 101 in transit on the conveyor belt 103 can be tracked by position and time (relative to the various components of the material handling system 100) as each of the pieces of material 101 is identified, such that the various components of the system 100 can be activated / deactivated as each piece of material 101 passes within the vicinity of the various components of the material handling system 100. As a result, the automated control system 108 is capable of tracking the position of each of the pieces of material 101 as each of the pieces of material 101 travels along the conveyor belt 103.

[0054] The vision system 110 can be configured to perform certain types of identification (e.g., classification) (also referred to herein as “vision inspection”) of all or a portion of the pieces of material 101, as will be further described herein. For example, such a vision system 110 can be used to capture or acquire information about each of the pieces of material 101. For example, the vision system 110 can be configured (e.g., with an artificial intelligence (“AI”) system as further described herein) to capture or collect any type of information from the pieces of material that can be utilized within the material handling system 100 to classify the pieces of material 101 according to a set of one or more properties (e.g., physical and / or chemical and / or radioactive, 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 pieces of material 101, for example, by using optical sensors utilized in typical digital cameras and video equipment. Such visual images captured by the optical sensors are then stored in a memory device as image data (e.g., formatted as image data packets). According to certain embodiments of the present disclosure, such image data can represent images captured within the optical wavelengths of light (i.e., wavelengths of light observable by a typical human eye). However, alternative embodiments of the present disclosure can utilize vision systems configured to capture images of materials composed of wavelengths of light outside of the human eye visual wavelengths.

[0055] According to alternative embodiments of the present disclosure, the vision system 110 can also be used as a means of tracking each of the pieces of material 101 as they travel on the conveyor system 103, which can utilize one or more still or live cameras 109 to record the position (i.e., location and timing) of each of the pieces of material 101 on the moving conveyor system 103.

[0056] According to alternative embodiments of the present disclosure, the vision system 110 can implement a machine vision system for analyzing and / or determining the shape or relative shape of each of the pieces of material 101, such as can be implemented within LabVIEW.

[0057] According to certain embodiments of the present disclosure, the material handling system 100 can utilize one or more sensor systems 120 that can be utilized individually or in combination with the vision system 110 to classify / identify / differentiate the pieces of material 101 (e.g., as described above with respect to FIG. 1), such as can be implemented within LabVIEW. Figures 7A-7C and Figures 12A-12BThe sensor system 120 can be configured with any type of sensor technology, including with radiated or reflected electromagnetic radiation (e.g., with infrared (“IR”), Fourier Transform IR (“FTIR”), Forward Looking IR (“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 (“LBS”), laser spark spectroscopy (“LSS”) laser induced optical emission spectroscopy (“LIOES”), Raman spectroscopy, coherent anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral (e.g., beyond any range of visible wavelengths), 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 with one-dimensional, two-dimensional, or three-dimensional imaging of any of the foregoing), or by any other type of sensor technology, including but not limited to chemical or radioactive, all of which are distinguished herein from the implementation of a vision system that utilizes AI technology (e.g., an AI model) to analyze visual images. An implementation of an example XRF spectroscopy system (e.g., for use as the sensor system 120 herein) is further described in U.S. Patent No. 10,207,296. XRF can be used within alternative embodiments of the present disclosure to identify inorganic materials within a plastic piece (e.g., for inclusion within a chemical signature).

[0058] As used herein, the terms “sensor system” and “sensor technology” refer to an implementation of any one of the sensor systems disclosed herein for classifying / identifying / distinguishing a piece of material (also referred to herein as “sensor system classification”), as distinguished from the use of a vision system that utilizes AI technology to classify / identify / distinguish a piece of material.

[0059] The following sensor systems can also be used in certain embodiments of the present disclosure to determine the chemical signature of a piece of plastic and / or to sort the piece of plastic. Various forms of infrared spectroscopy (e.g., IR, FTIR, FLIR, VNIR, NIR, SWIR, LWIR, MWIR, and / or MIR) previously disclosed can be used to obtain a chemical signature unique to each piece of plastic that provides information about the base polymer of any plastic material as well as other components present in the material (mineral fillers, copolymers, polymer blends, etc.). DSC is a thermal analysis technique that obtains a thermal transition produced during heating of the analyzed material that is specific to each material. TGA is another thermal analysis technique that produces quantitative information about the composition of a plastic material that relates to polymer percentage, other organic components, mineral fillers, carbon black, etc. Capillary and rotational rheometry can determine the rheological properties of a polymeric material by measuring its resistance to creep and deformation. Optical microscopy and SEM can provide information about the structure of the analyzed material that relates to the number and thickness of layers in a multi-layer material (e.g., a multi-layer polymeric film), the dispersion size of pigment or filler particles in a polymeric matrix, coating defects, interfacial morphology between components, etc. Chromatography can quantify trace components of a plastic material such as UV stabilizers, antioxidants, plasticizers, slip agents, etc., as well as residual monomers, residual solvents of inks or adhesives, degradation species, etc.

[0060] Although Figure 1 While illustrated as including one or more sensor systems 120, implementation of such sensor system(s) is optional within certain embodiments of the present disclosure. Within certain embodiments of the present disclosure, a combination of one or more vision systems and one or more sensor systems can be used to sort pieces of material 101. Within certain embodiments of the present disclosure, any combination of one or more of the different sensor technologies disclosed herein can be used to sort pieces of material 101 without utilizing vision systems 110.

[0061] 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 pieces of material 101 contain contaminants (e.g., steel or iron pieces containing copper; pieces of plastic containing particular contaminants, additives, or undesirable physical characteristics (e.g., attached container lids formed of a different type of plastic than the container)) and send a signal to divert / sort such pieces of material (e.g., from those pieces of material that do not contain contaminants). In such configurations, the identified pieces of material 101 can be diverted / sorted using one of the mechanisms described below for physically sorting pieces of material into separate containers.

[0062] In certain embodiments of the present disclosure, a piece tracking device 111 (or a commercially available profilometer) and an accompanying control system 112 may be utilized and configured to measure the size and / or shape of each of the pieces 101, as well as the position (i.e., location and timing) of each of the pieces 101 on the moving conveyor system 103, as the pieces 101 pass within a vicinity of the piece tracking device 111. Exemplary operation of such piece tracking devices 111 and control systems 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 Serial No. 18 / 491,692, which is incorporated herein by reference.

[0063] Alternatively, as disclosed herein, vision system 110 may be used to track the location (i.e., position and timing) of each of pieces 101 as they are transported by conveyor system 103. Thus, certain embodiments of the present disclosure may be implemented without a piece tracking device (e.g., piece tracking device 111) for tracking the pieces.

[0064] In certain embodiments of the present disclosure that implement one or more sensor systems 120, sensor system(s) 120 may be configured to identify the chemical composition, relative chemical composition (including, but not limited to, measuring the amount of a specific element within the material piece), and / or manufacturing type of each of the material pieces 101 as they pass within a vicinity of sensor system(s) 120. Sensor system(s) 120 may include an energy emission source 121, which may be powered by power source 122 to, for example, elicit a response from each of the material pieces 101. In certain embodiments of the present disclosure, sensor system 120 may emit an appropriate sensing signal toward each of the material pieces 101 as it passes within a vicinity of emission source 121. One or more detectors 124 may be positioned and configured to sense / detect one or more characteristics of the material piece 101 in a manner appropriate to the type of sensor technology being utilized. One or more detectors 124 and associated detector electronics 125 capture these received sensed characteristics to perform signal processing thereon and produce digitized information representative of the sensed characteristics (e.g., spectral data, such as XRF spectral data), which is then analyzed to classify each of the material pieces 101.

[0065] It should be noted that although Figure 1A combination of vision system 110 and one or more sensor system 120 is illustrated, but embodiments of the present disclosure can be implemented using 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.

[0066] Within certain embodiments of the present disclosure, the material piece tracking apparatus 111 and accompanying control system 112 can be utilized and configured to determine the size and / or shape of each of the material pieces 101 as well as the location (i.e., position and time) of each of the material pieces 101 on the moving conveyor system 103 as the material pieces 101 pass within the vicinity of the material piece tracking apparatus 111. Exemplary operation of such material piece tracking apparatus 111 and control system 112 are further described in U.S. Patent No. 10,207,296.

[0067] According to certain embodiments of the present disclosure, the material tracking apparatus 111 can be implemented prior to (e.g., upstream of) the vision system 110 and / or sensor system 120 such that when the material tracking system 111 detects a material piece 101, the material piece 101 triggers the material handling system 100 when the vision system 110 and / or sensor system 120 is to capture characteristics of the material piece. Further, the order in which the vision system(s) 110 and sensor system(s) 120 are implemented within the material handling system 100 can be interchanged.

[0068] Classification of the material piece can be performed within the computer system 107, which can 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 for sorting (e.g., diverting / ejecting) the material piece 101 according to the determined classification (e.g., into one or more of the N (N > 1) sorting receptacles 136...139, or onto one or more other conveyors). In Figure 1 Four sorting devices 126...129 and four sorting receptacles 136...139 associated with the sorting devices are illustrated in FIG. 1 merely as non-limiting examples.

[0069] The sorting device can include any known sorting mechanism for redirecting the selected pieces of material 101 toward a desired location, including but not limited to diverting the pieces of material 101 from the conveyor system into a plurality of sorting receptacles. For example, the sorting apparatus can utilize air jets, where each of the air jets is assigned to one or more of the categories. When one or more of the air jets (e.g., 127) receives a signal from the automated control system 108, the air jet(s) emits an air stream that causes the piece of material 101 to be diverted / ejected from the conveyor system 103 into a sorting receptacle (or onto another conveyor system) corresponding to the air jet (e.g., 137).

[0070] Although Figure 1 The example illustrated in FIG. 1 uses air jets to divert / eject the pieces of material, but other mechanisms can be used to divert / eject the pieces of material (such as a conveyor robot removing the pieces of material, pushing the pieces of material off of the conveyor (e.g., using a paintbrush-type plunger), creating an opening (e.g., a flap door) in the conveyor system 103 from which the pieces of material can fall, or using air jets to separate the pieces of material into separate receptacles as the pieces of material are thrown / fallen off the edge of the conveyor. As the term is used herein, a pusher apparatus can refer to any form of apparatus that can be activated to dynamically divert objects on or from a conveyor system / apparatus, doing so in pneumatic, mechanical, or other manners such as any appropriate type of mechanical push mechanism (e.g., an ACME screw drive), a pneumatic push mechanism, or an air jet push mechanism.

[0071] In addition to the N sorting receptacles 136...139 into which the pieces of material 101 are diverted / ejected, the material handling system 100 can also include a receptacle 140 that receives pieces of material 101 that are not diverted / ejected from the conveyor system 103 into any of the aforementioned N sorting receptacles 136...139. For example, a piece of material 101 can not be diverted / ejected from the conveyor system 103 into one of the N sorting receptacles 136...139 when the category of the piece of material 101 is not determined (or simply because the sorting apparatus fails to adequately divert / eject the piece). As such, the receptacle 140 can serve as a default receptacle into which unsorted or unsorted pieces of material are dumped. Alternatively, the receptacle 140 can be used to receive pieces of material that are intentionally not assigned to one or more categories of any of the N sorting receptacles 136...139. These such pieces of material can then be further sorted according to other characteristics and / or by another material handling system.

[0072] Depending on the various classifications of the desired material pieces, multiple classifications can be mapped to a single singulation device and / or associated singulation container. In other words, there need not be a one-to-one correlation between a classification and a singulation device and / or container. For example, a user can desire to singulate certain classifications of materials into the same singulation container. To accomplish this singulation, the same singulation device can be activated to singulate these material pieces 101 into the same singulation container (or onto another conveyor) when the material pieces 101 are classified as falling into a predetermined classification grouping. Such combined singulation can be applied to any desired combination of singulated material pieces. The mapping of classifications can be programmed by a user (e.g., using any of the singulation algorithms operated by the computer system 107 as described herein) to produce 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.

[0073] The systems and methods described herein can be applied to classifying and / or singulating individual material pieces having a variety of sizes. Although the systems and methods described herein are primarily described with respect to singulating individual material pieces in a singulated stream one at a time, the systems and methods described herein are not so limited. Such systems and methods can be used to simultaneously excite and / or detect emissions from multiple materials. For example, as opposed to a singulated stream of materials conveyed along one or more serially arranged conveyors, multiple singulated streams can be conveyed in parallel. Each stream can be on the same belt, or on different belts arranged in parallel. Further, the pieces can be randomly distributed across and along one or more conveyors. Accordingly, the systems and methods described herein can be used to simultaneously excite and / or detect emissions from multiple of these material pieces. In other words, multiple material pieces can be treated as a single piece, rather than each material piece being considered individually. Accordingly, multiple material pieces can be classified and singulated (e.g., diverted / evicted from the conveyor system) together.

[0074] The conveyor system 103 can include a recirculation loop (not shown) such that material pieces that are not classified are re-routed through the material handling system 100 for re-scanning and re-singulation into a category. Further, because the material handling system 100 is capable of specifically tracking each material piece 101 as it travels on the conveyor system 103, a certain singulation device (e.g., singulation device 129) can be implemented to direct / evict material pieces 101 that fail to be classified after a predetermined number of cycles through the material handling system 100 (or the material pieces 101 are collected in a container 140).

[0075] By implementing the material handling system 100 as an XRF system for the sensor system 120, the signals representing the detected / captured XRF spectra can be converted, such as on a per channel (i.e., element) basis, into a discrete energy histogram, as described further herein, which can be used to determine the amount of a particular element within a piece of material. Such a conversion process can be implemented within the control system 123 or computer system 107. Within certain embodiments of the present disclosure, such a control system 123 or computer system 107 can include a commercially available spectrum acquisition module, such as a commercially available Amptech MCA 5000 acquisition card and software programmed to operate the card. Such a spectrum acquisition module, or other software implemented within the material handling system 100, can be configured to implement a plurality of channels for dispersing the X-rays into a discrete energy spectrum (i.e., histogram) having a plurality of energy levels, whereby each energy level corresponds to an element that the material handling system 100 has been configured to detect. The material handling system 100 can be configured such that there are sufficient channels corresponding to certain elements within the periodic table of elements that are important for distinguishing between different materials. The energy counts for each energy level can be stored in separate collection storage registers. 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 greater 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 energy level histogram to classify at least certain of the pieces of material 101 (and / or assist the vision system 110 in classifying the pieces of material 101).

[0076] As previously described, certain embodiments of the present disclosure can implement one or more vision systems (e.g., vision system 110) configured (e.g., in combination with an AI system) for classifying and / or differentiating material pieces. Such AI systems can implement any known AI system (e.g., artificial narrow intelligence (“ANI”), artificial general intelligence (“AGI”), and artificial superintelligence (“ASI”)); machine learning systems including machine learning systems implementing neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, reinforcement learning, etc.); machine learning systems implementing supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, robotic learning, association rule learning, fuzzy logic, deep learning algorithms, deep structured learning hierarchical learning algorithms, decision tree learning (e.g., classification and regression trees (“CART”), ensemble methods (e.g., ensemble learning, random forests, Bagging and Pasting, patches and subspaces, Boosting, Stacking, etc.), dimensionality reduction (e.g., projections, manifold learning, principal component analysis, etc.), and / or deep machine learning algorithms such as those described and publicly available on the website deeplearning.net (including all software, publications, and hyperlinks to available software cited within this website), which is hereby incorporated by reference herein.Non-limiting examples of publicly available machine learning software and libraries that can be utilized within 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 (MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearn Toolbox (MATLAB toolbox for deep learning (from Rasmus Berg Palm)), BigDL, Cuda-Convnet (fast C++ / CUDA implementation of convolutional (or more generally, feed-forward) neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.1sh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, Three-way Factor RBM and mcRBM, mPoT (Python code for training natural image models using CUDAMat and Gnumpy), ConvNet, Elektronn, OpenNN, NeuralDesigner, Theano Generalized Hebbian Learning, Apache Singa, Lightnet, and SimpleDNN.

[0077] 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, as the material handling system 100 (or similar device) is not being used to perform actual sorting / classification of pieces of material. For example, the material handling system 100 (or similar device) can be used to train a machine learning system, where a control sample (e.g., a homogenous set) of pieces of material (i.e., having the same type or class of material, or falling within the same predetermined fraction) are passed through the material handling system 100 (e.g., by the conveyor system 103); and all such pieces of material can not be sorted, but rather can be collected in a common receptacle (e.g., receptacle 140). Alternatively, the training can be performed at another location remote from the material handling system 100, including using some other mechanism for collecting sensed information (characteristics) of the control set of pieces of material. During this training phase, algorithms within the machine learning system extract features from the captured information (e.g., using image processing techniques known in the art). Non-limiting examples of training algorithms include, but are not limited to: linear regression, gradient descent, feed forward, polynomial regression, learning curves, regularized learning models, and logistic regression. It is during this training phase that the algorithms within the machine learning system learn relationships between materials and their features / characteristics (e.g., captured by the vision system and / or sensor system(s)), thereby creating a knowledge base for later sorting of a heterogeneous mixture of pieces of material received by the material handling system 100, which can subsequently be sorted by desired classifications. Such a knowledge base can include one or more libraries, where each library includes parameters (e.g., neural network parameters) for the machine learning system to utilize in classifying pieces of material. For example, one particular library can include parameters configured by the training phase for identifying and classifying a particular type or class of material, or one or more materials falling within a predetermined fraction of materials. According to certain embodiments of the present disclosure, such a library can be input into the machine learning system, and subsequently a user of the material handling system 100 can be able to adjust certain of the parameters in order to adjust the operation of the material handling system 100 (e.g., adjust a threshold effectiveness of how well the machine learning system identifies (classifies) particular pieces of material from a heterogeneous mixture of materials).

[0078] Additionally, the inclusion of certain chemical elements in a piece of material can result in the material having identifiable physical characteristics (e.g., visually discernible properties). Thus, when multiple pieces of material containing such specific chemical compositions are passed through the aforementioned training phase, the machine learning system can learn how to distinguish such pieces of material from other pieces of material. Thus, a machine learning system (or any Al system) configured in accordance with certain embodiments of the present disclosure can be configured for sorting between pieces of material according to their respective material / chemical composition. It can be readily appreciated that embodiments of the present disclosure can be configured for utilizing image data (e.g., visual images) of pieces of material as a proxy for one or more various physical and / or chemical properties (e.g., ductility, malleability, brittleness, hardness, luster, tensile strength, reactivity with various materials, etc.) of the pieces of material.

[0079] For example, Twitch includes cast aluminum alloys and wrought aluminum alloys. These two alloys are the same color. The difference between these two alloys is their chemical composition. Cast aluminum alloys have a large concentration of silicon as an alloying element, while wrought aluminum alloys do not have a large concentration of silicon as an alloying element, and thus have a price premium. An Al system implemented within a vision system (e.g., vision system 110) can be configured for classifying these pieces at high speed with over 95% accuracy. This Al system is able to accurately classify these materials because the difference in silicon content causes the alloys to physically look different from one another. Due to the higher silicon concentration, cast aluminum alloys break apart after shredding and do not bend or fold. However, wrought aluminum alloys have much greater ductility due to not having a high silicon concentration. Wrought aluminum alloys bend and fold during the shredding process and thus have visual characteristics similar to a torn piece of fabric. These visual characteristics can be trained in the Al system and ultimately occur due to the chemical properties of the alloys.

[0080] During the training phase, a plurality of pieces of material of one or more specific types, classifications, or materials that are control samples can be delivered (e.g., by a conveyor system) through the vision system and / or one or more sensor systems, such that the algorithms within the machine learning system detect, extract, and learn what features represent such types or classes of materials. For example, each of the pieces of material in the control samples can first be passed through such a training phase, such that the algorithms within the machine learning system are “taught” (trained) how to detect, recognize, and classify such pieces of material - in the case of training a vision system (e.g., vision system 110), to visually discern (distinguish) the pieces of material. This creates a library of parameters specific to the homogenous class of such pieces of material. The same process can be performed for images of any classification of pieces of material, creating a library of parameters specific to such classification of pieces of material. Any number of exemplary pieces of material of a classification of pieces of material can be passed by the vision system for each type of material to be classified by the vision system. Given the captured sensed information as input data, the algorithms within the machine learning system can use N classifiers, each of which tests for one of N different material types. It should be noted that the machine learning system can be “taught” (trained) to detect any type, classification, or fraction of material, including any type, classification, or fraction of material disclosed herein.

[0081] After the algorithms have been established and the machine learning system has been sufficiently learned (trained) on the differences (e.g., visually discernable differences) in material classifications (e.g., within a user-defined statistical confidence level), the library for different material classifications is then implemented into the material classification / sorting system (e.g., material handling system 100) for use in identifying, distinguishing, and / or classifying pieces of material from a heterogeneous mixture of pieces of material, and subsequently sorting such classified pieces of material, if sorting is to be performed.

[0082] It is noted here that, in accordance with certain embodiments of the present disclosure, the detected / captured / detected features / characteristics (e.g., visual images) of pieces of material are not necessarily simple, particularly identifiable or discernable physical characteristics; they can be abstract formulas that can only be expressed mathematically, or not at all mathematically; however, the AI system can be configured to parse the spectral data to find patterns that allow for classification of control samples during the training phase. Further, the AI system can take a subset of the captured information of a piece of material and attempt to find correlations between predefined classifications.

[0083] According to certain embodiments of the present disclosure, instead of utilizing a training phase that passes control (homogeneous) samples of the pieces of material by the vision system, training of the AI system can be performed utilizing a labeling / annotation technique (or any other supervised learning technique), whereby as data / information of the pieces of material is captured by the vision system, a user inputs labels or annotations that identify each piece of material, which are then used to create a library for use by the AI system when classifying the pieces of material within the heterogeneous mixture of pieces of material. In other words, a library of previously generated characteristics captured from one or more samples in a material class can be done by any of the techniques disclosed herein, whereby such library is then used to automatically classify materials.

[0084] Accordingly, as disclosed herein, certain embodiments of the present disclosure provide for identification / classification of one or more different materials in order to determine which pieces of material should be diverted from a conveyor system or apparatus. According to certain embodiments, a neural network can be trained (i.e., configured) utilizing machine learning techniques to identify various one or more different classes or types of materials. Images or other types of sensed information of the materials (traveling on the conveyor system) can be captured, and based on the identification / classification of such materials, the systems described herein can decide which pieces of material should be allowed to remain on the conveyor system, and which pieces of material should be diverted / removed from the conveyor system (e.g., either into a collection container, or onto another conveyor system).

[0085] Figure 2 FIG. 1 illustrates a flowchart depicting an example embodiment of a process 200 for classifying / sorting pieces of material utilizing a vision system, according to certain embodiments of the present disclosure. The process 200 can be performed to classify a heterogeneous mixture of pieces of material into any combination of predetermined types, classes, and / or fractions. The process 200 can be configured to operate within any of the embodiments of the present disclosure described herein, including Figure 1 the material handling system 100 of FIG. 1, and with respect to the “vision inspection” described with respect to Figures 7A-7C and Figures 12A-12B The operations of the process 200 can be performed by hardware and / or software, including computer systems (e.g., the computer system 107 and / or the vision system 110) of a control system (e.g., the control system 105 of the material handling system 100 of FIG. 1), and with respect to the “vision inspection” described with respect to Figure 11In process block 201, pieces of material (e.g., on a conveyor system) are fed past the vision systems. In process block 202, the position of each piece of material on the conveyor system can be detected for tracking each piece of material as it travels through the material handling system 100. This can be performed by the vision systems 110 (e.g., by distinguishing the pieces of material from the underlying conveyor system material when in communication with a conveyor system position detector (e.g., position detector 105)). Alternatively, a piece tracking device 111 can be used to track the pieces. Or, any system that creates a light source (including but not limited to visible, UV, and IR) and has a detector can be used to locate the pieces of material. In process block 203, sensed information / characteristics of the pieces of material are captured / acquired when the pieces of material have traveled into the vicinity of one or more of the vision systems. In process block 204, the vision systems can perform pre-processing of the captured information, which can be used to (e.g., detect (extract) the information for each of the pieces of material from the background (e.g., the conveyor belt); in other words, the pre-processing can be used to identify the differences between the pieces of material and the background). Well-known image processing techniques such as dilation, thresholding, and contouring can be used to identify the pieces of material as distinct from the background. In process block 205, segmentation can be performed. For example, the captured information can include information relating to one or more pieces of material. Additionally, when an image of a particular piece of material is captured, that particular piece of material can be located on a seam of the conveyor belt. Thus, in such instances, it can be desirable to isolate the image of the individual piece of material from the background of the image. In an exemplary technique for process block 205, a first step is to apply a high contrast to the image; in this way, the background pixels are reduced to substantially all black pixels, and at least some of the pixels relating to the piece of material are lightened to substantially all white pixels. Then, the white image pixels of the piece of material are expanded to cover the entire size of the piece of material. After this step, the location of the piece of material is a high contrast image of all the white pixels on a black background. Then, a contouring algorithm can be utilized to detect the boundaries of the piece of material. The boundary information is saved, and then the boundary locations are transferred to the original image. Then, segmentation is performed on the original image on the area greater than the earlier defined boundaries. In this way, the piece of material is identified and separated from the background.

[0086] In optional process block 206, the pieces of material can be conveyed along the conveyor system within the vicinity of the material piece tracking device (or profiler) in order to determine the size and / or shape of the pieces of material, which can be helpful to classify certain materials due to their shape or size, or if an XRF system or some other spectroscopic sensor is also implemented within the material handling system. In process block 207, post-processing can be performed. The post-processing can involve resizing the captured information / data to make it ready for use in a neural network. This can also include modifying certain attributes in a way that will result in an enhancement of the ability of the AI system to classify the pieces of material in some way (e.g., enhancing image contrast, changing image background, or applying filters). In process block 209, the data can be resized. In some cases, it can be desirable to resize the data to match the data input requirements of certain AI systems, such as neural networks. For example, a neural network can require a much smaller image size (e.g., 225 x 255 pixels or 299 x 299 pixels) 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. As such, a smaller data size can ultimately increase the throughput of the material handling system 100 and increase its value.

[0087] In process blocks 210 and 211, an identification / classification is performed for each piece of material based on the sensed / detected features. For example, process block 210 can be configured to have a neural network that employs one or more algorithms that compare the extracted features to features stored in a knowledge base that was previously generated (e.g., during a training phase) and assign a classification with the highest match to each of the pieces of material based on such comparisons. The algorithms can process the captured information / data in a hierarchical manner by using automatically trained filters. The filter responses are then successfully combined in the next level of algorithms until probabilities are obtained in the last step. In process block 211, these probabilities can be used for each of the N classifications to decide how the corresponding piece of material should be classified. For example, each of the N classifications can be assigned to a sorting bin (or to another designated conveyor belt), and the piece of material under consideration is sorted into the bin (or another designated conveyor belt) corresponding to the classification that returns the highest probability that is greater than a predetermined threshold. Such a predefined threshold can be preset by a user within embodiments of the present disclosure. If none of the probabilities is greater than the predetermined threshold, the particular piece of material can be sorted into an exception bin (or onto another designated conveyor belt).

[0088] Next, in process block 212, a sorting device corresponding to one or more classifications of the pieces of material can be activated (e.g., instructions are sent to the sorting device to sort the pieces of material). As will be appreciated with respect to the present disclosure, the sorting device can be a bin, a conveyor belt, or some other device that is configured to sort the pieces of material in accordance with the one or more classifications. In some cases, the sorting device can be a bin that is configured to sort the pieces of material into a plurality of sub-bins, each of which corresponds to a different classification. In such cases, the sorting device can be configured to sort the pieces of material into the sub-bins in accordance with the one or more classifications. Figures 7A-7CAnd Figures 12A-12B As described in various aspects of the disclosure, such sortation instructions can be based on the classification of the vision system alone (also referred to herein as “vision check”) or from a combination of classification from the vision system and the sensor system (also referred to herein as “sensor system classification”). Between the time that the image of the piece of material is captured and the time that the sorting device is activated, the piece of material has moved from the vicinity of the vision system (e.g., at the rate of conveyance of the conveyor system) to a location downstream of the conveyor system. In embodiments of the disclosure, the activation of the sorting device is timed so that when the piece of material passes the sorting device that is mapped to the classification of the piece of material, the sorting device is activated and the piece of material is diverted / ejected (sorted) from the conveyor system into its associated sorting container (or onto another conveyor belt as the case can be). Within embodiments of the disclosure, the activation of the sorting device can be timed by a corresponding position detector that detects when the piece of material passes in front of the sorting device and sends a signal to enable the activation of the sorting device. In process block 213, the sorting container (or other conveyor belt) corresponding to the activated sorting device receives the sorted piece of material.

[0089] According to alternative embodiments of the disclosure, the AI system used to classify the pieces of material can be periodically or even continuously updated with newly acquired image data collected by the vision system as it classifies the pieces of material. In other words, as the vision system collects new image data, it is used to update one or more AI models within the AI system, such as if the classification accuracy increases.

[0090] Figure 3 FIG. 13 illustrates a flowchart depicting an example embodiment of a process 300 for classifying and / or sorting pieces of material with a sensor system 120, according to certain embodiments of the disclosure. The process 300 can be configured to operate within any of the embodiments of the disclosure described herein, including Figure 1 the material handling system 100 of FIG. 1 and aspects of the process 700 described with respect to Figures 7A-7C the process 700 described with respect to Figures 12A-12B the process 1200 described with respect to According to certain embodiments of the disclosure, the process 300 can be configured to operate in conjunction with the process 200. For example, process blocks 303-305 can be incorporated into the process 200 (e.g., operating in series or in parallel with process blocks 203-210) in order to combine the effects of one or more vision systems (e.g., the vision system 110) with one or more sensor systems (e.g., the sensor system 120).

[0091] The operations of the process 300 can be performed by hardware and / or software, including in a control system (e.g., the control system 130 of FIG. 1) that is configured to operate the sensor system 120. Figure 1a computer system of the sensor controller 123 and / or the computer system 107 (e.g., Figure 11 of the computer system 3400). In process block 301, pieces of material are fed along a conveyor system. Next, in optional process block 302, the pieces of material can be conveyed along the conveyor system within the vicinity of a material piece tracking device, a profilometer, and / or an optical imaging system, in order to track each piece of material and / or determine the size and / or shape of the pieces of material. In process block 303, as the pieces of material travel into the vicinity of the sensor system, the pieces of material can be interrogated or excited with EM energy (waves) or some other type of stimulus suitable for the particular type of sensor technology utilized by the sensor system. In process block 304, the physical properties of the pieces of material (e.g., captured XRF spectra) are sensed / detected and captured by the sensor system. In process block 305, the type of material is identified / classified based on the captured properties, which can be combined with classification by the AI system in conjunction with the vision system 110 (e.g., as described with respect to Figures 7A-7C and Figures 12A-12B as described with respect to various aspects of the present disclosure.

[0092] Next, if sorting of the pieces of material is to be performed, in process block 306, a sorting device corresponding to the classification of the piece of material is activated. Between the time the piece of material is sensed and the time the sorting device is activated, the piece of material has moved from the vicinity of the sensor system to a location downstream of the conveyor system at the rate of conveyance of the conveyor system. In certain embodiments of the present disclosure, the activation of the sorting device is timed so that when the piece of material passes the sorting device mapped to the classification of the piece of material, the sorting device is activated and the piece of material is diverted / ejected from the conveyor system into its associated sorting bin (or onto another conveyor belt). In certain embodiments of the present disclosure, the activation of the sorting device can be timed by a corresponding position detector that detects when the piece of material passes in front of the sorting device and sends a signal to enable activation of the sorting device. In process block 307, the sorting bin (or other conveyor belt) corresponding to the activated sorting device receives the diverted / ejected piece of material.

[0093] As can be readily appreciated, and as will be further disclosed with respect to Figures 7A-7C and Figures 12A-12B process blocks 303-305 (and, if desired, process block 302) within the material handling system 100 to achieve the various classifications / sortings described with respect to Figures 7A-7C and Figures 12A-12B .

[0094] Reference is made to Figure 4-Figure 6, shows a system and process configured according to certain embodiments of the present disclosure in which materials (e.g., scrap parts) can be sorted. Such materials can originate from shredded EOL products (e.g., vehicles, aircraft, and / or appliances). Reference Figure 4 , materials that may have been shredded into scrap can be sorted between ferrous and non-ferrous materials. For example, a magnet can be used to remove the ferrous material pieces. The remaining non-ferrous material can typically include non-ferrous metals (often referred to as Zorba) and other "junk" or "fluff" materials (e.g., cloth, leather, foam rubber, rubber, plastic, wood, PCBs, glass, coins, and any other non-metallic materials).

[0095] According to certain embodiments of the present disclosure, such as Figures 7A-7C and Figures 12A-12B As disclosed, the Zorba can then be separated (sorted / sorted) from the waste material (e.g., see process blocks 701-703). The Zorba 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., within a PCB), aluminum (cast, wrought, and / or extruded alloys, including but not limited to high-Z (high zinc concentration) cast aluminum alloys (e.g., cast aluminum alloy 319 and cast aluminum alloys 380 / 383) and / or low-Z (low zinc concentration) cast aluminum alloys (e.g., cast aluminum alloy 356 and cast aluminum alloy 360)).

[0096] According to certain embodiments of the present disclosure, Zorba can be sorted / sorted to separate heavy metals (also known as Zebra or "heavy stuff") from lighter metals (e.g., Twitch) (see, e.g., Figure 7A-7B 704 - 721).

[0097] Certain alternative embodiments of the present disclosure may be configured to sort "meatballs" and / or bladder cans from a stream of material. See, for example, U.S. Published Patent Application No. 2022 / 0371057, which is incorporated herein by reference.

[0098] According to certain embodiments of the present disclosure, Figure 5 The schematic diagram 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 enable Zebra to classify / sort and individually extract various metals (e.g., copper, zinc, brass, stainless steel, nickel plating, lead, etc.) from a conveyor stream of material pieces. Alternatively, any other sensor system (e.g., LIBS, XRT, etc.) in the disclosed sensor system 120 can be utilized in place of the XRF system.

[0099] According to certain embodiments of the present disclosure, Figure 6 It is schematically depicted how one or more various combinations of one or more vision systems (e.g., implementing an Al system) and / or one or more sensor systems (e.g., XRF systems) 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 sensor system (e.g., LIBS, XRT, etc.) of the disclosed sensor systems 120 can be utilized in place of the XRF systems.

[0100] Figures 7A-7C 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 unique combinations of classification by one or more vision systems (e.g., implementing an Al system) and classification by one or more sensor systems (e.g., XRF or LIBS systems) to classify and sort various materials (e.g., a stream of pieces of material conveyed by a conveyor system) is illustrated. The process 700, or any aspect or portion thereof, can be implemented within any material handling system that is suitably configured for utilizing the described classification and / or sorting functionality, operations, systems, devices, and apparatuses to perform one or more of the various described classification and sorting operations, including but not limited to those described with respect to Figure 1-Figure 3 The described classification and / or sorting functionality, operations, systems, devices, and apparatuses to perform one or more of the various described classification and sorting operations, including but not limited to those described with respect to Figure 1 、 Figure 8 、 Figure 9A-9B and Figure 10 The described material handling systems). Although certain process blocks are described as utilizing XRF systems, any one or more of such process blocks can be implemented with any one 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 within the process 700 can be optional or omitted in accordance with certain embodiments of the present disclosure.

[0101] In the flowchart diagram of Figures 7A-7C (and also with respect to certain aspects of Figures 12A-12B the following legend applies with respect to measurements by the XRF system(s) (resulting in a captured XRF spectrum for each piece of material):

[0102] Legend for variables

[0103] M-total = Total of raw counts from Ti, Cr, Mn, Fe, Ni, Cu, Zn, Sn, Pb measurements

[0104] N-total = Total counts after normalization with length and height (or mass) of the piece of material

[0105] XRF element percentages calculated for each piece of material:

[0106] TI = 100 x Titanium Raw Count / M-total

[0107] CR = 100 x Chromium Raw Count / M-total

[0108] MN = 100 x Manganese Raw Count / M-total

[0109] FE = 100 x Iron Raw Count / M-total

[0110] NI = 100 x Nickel Raw Count / M-total

[0111] CU = 100 x Copper Raw Count / M-total

[0112] ZN = 100 x Zinc Raw Count / M-total

[0113] SN = 100 x Tin Raw Count / M-total

[0114] PB = 100 x Lead Raw Count / M-total

[0115] Predetermined set points (values) for XRF elemental percentages for each material piece:

[0116] TIK, CRK, MNK, FEK, NIK, CUK, ZNK, SNK, and PBK represent XRF classification percentage constants for each element (can be any predetermined value set between 0.0 and 100.0)

[0117] Other predetermined set points (values):

[0118] AK = 0.4 x N-total for 319 standard cast alloy (a constant value for N-total calculated from the 319 standard for determining whether a cast or wrought category; 1-100,000)

[0119] BK = 0.1 x CU / ZN ratio for 319 standard (a ratio value calculated from the 319 standard for determining whether a 319 / 38x cast category or a die cast zinc category; 0.0-1.0 ratio)

[0120] CURB = ratio selected for distinguishing copper from brass - can be a value between 0.8 and 1.0 fraction (which can be set (predetermined) to a non-limiting exemplary default value of 0.9)

[0121] CUYB = ratio selected for distinguishing yellow brass from red brass - can be a value between 0.3 fraction and 0.8 fraction (which can be set (predetermined) to a non-limiting exemplary default value of 0.5)

[0122] MNSS = set (pre-determined) value used to classify the manganese content in the 2xx series stainless steels (which can be a value between 10 and 30)

[0123] Spectral data generated by the XRF system can be normalized with respect to the size of the piece. This step can be optional. This 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.

[0124] The process 700 is described as operating on a piece-by-piece basis; of course, when one material piece in a stream of conveyed material pieces has been operated on by a particular process block, then subsequent material pieces can be processed by that process block (e.g., when one material piece has been analyzed / classified by the vision system(s) and / or the XRF system(s), then subsequent material pieces can be analyzed / classified by the vision system(s) and / or the XRF system(s)). Note, Figures 7A-7C The flowchart illustrates a number of process blocks in which a "visual inspection" is performed; the other diamond-shaped process blocks represent analysis of the material pieces with an XRF system. Note that the "visual inspection" can be performed with any appropriate vision system, including but not limited to vision systems that implement appropriately configured AI technology. Each visual inspection classifies the material piece according to processing of a visual image captured from each material piece by an AI system. Further note that one or more different AI algorithms (models) can be implemented for each visual inspection, with a single vision system performing one or more of the "visual inspections" implemented within the process 700 on image data captured from a material piece. In a similar manner, one or more of the XRF sensor system classifications implemented within the process 700 can be performed on spectral data captured from a material piece with a single XRF system.

[0125] Although any number of vision systems and / or XRF systems can be implemented to perform operations within 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 within the material handling system (e.g., see Figure 8). For example, as each piece of material passes through a single implemented vision system, image data of the piece of material captured by the single vision system can be analyzed according to one or more of process blocks 701, 704, 718, and 721 (e.g., substantially simultaneously or in parallel). In other words, the algorithms associated with one or more of process blocks 701, 704, 718, and 721 can process the captured image data (e.g., by one or more AI models substantially simultaneously or in parallel with each other), the results of which are used for their respective classifications. 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., by one or more algorithms substantially simultaneously or in parallel).

[0126] Note further that each visual inspection described in process 700 represents an attempt to perform that classification on each piece of material within the stream of material by the specifically described algorithm; however, the visual inspection of some pieces of material may produce a "null" result, meaning that the specific visual inspection was unable to produce a classification (e.g., a visual inspection by process block 718 or process block 721 may produce a "null" output because the piece of material is neither a cast aluminum alloy nor a wrought aluminum alloy). Likewise, note that each classification using the XRF spectral data described in process 700 represents an attempt to perform that classification on each piece of material within the stream of material by the specifically described algorithm; however, the classification of some pieces of material may produce a "null" result, meaning that the specific 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 piece of material has an unmeasurable amount of zinc).

[0127] Note that according to Figure 3 The process 200 described in process blocks 203-211 is performed Figures 7A-7C Any of the visual inspections described in . Figures 7A-7C Any of the classifications performed by the XRF system(s) (or any other suitable sensor system) in Figure 4 303 - 305 of the process 300 as described.

[0128] As will be further described herein, certain aspects of the process 700 (i.e., classification and / or sorting as implemented within one or more process blocks) can need to be performed in one or more certain sequences in order to more efficiently and / or more accurately perform those aspects of the process 700. Note that while analysis / classification of the material pieces can be performed by a single vision system and a single XRF system (including substantially simultaneously or in parallel), sorting of the various material pieces can need to be performed in certain sequences (e.g., along a conveyor), as will be further described herein.

[0129] While the process 700 is described with respect to classification and sorting of a stream of conveyed material including Zorba, Zebra, and / or Twitch, aspects of the process 700 can be applicable to classification and / or sorting of other types of material pieces.

[0130] In process block 701, a visual inspection can be performed on the material piece to determine whether it should be classified as a certain predetermined specific material (e.g., “trash” material that accompanies Zorba). If the visual inspection classifies the material piece as the predetermined specific material, then in process block 702, the process 700 will send instructions to a designated sorting device to sort the material piece (e.g., from the stream of conveyed material pieces) in accordance with that classification (e.g., material piece consisting of or containing a PCB). Process block 703 represents that process blocks 700-702 can be performed for any other types of material pieces that a user desires to be separated from the stream of material pieces (e.g., other “trash” material), and can be classified using a vision system (e.g., a visual inspection using any of the vision-related techniques described herein, including but not limited to artificial intelligence (“AI”) techniques). This can be accomplished using a series of different vision systems, or a single vision system can be configured to perform classification of any multiple predetermined types of material pieces in a substantially simultaneous manner or in a parallel manner (e.g., image data captured by the 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 a multiple predetermined types of material pieces (e.g., a predetermined set of “trash” materials)).

[0131] Note that implementation of process blocks 701-703 in process 700 can be optional. If process blocks 701-process block 703 are implemented, it can be important to perform the sorting indicated by these process blocks prior to the sorting / classification indicated by one or more of the other process blocks of process 700 in order to improve the efficiency of the sorting / classification of those subsequent pieces of material. For example, it can be important to sort various materials (as classified in process blocks 701, 703) from the stream of material prior to performing the sorting of other metals and / or metal alloys as classified in one or more subsequent process blocks of process 700 (e.g., to remove “junk” material prior to sorting the remaining Zorba). For example, removing “junk” material prior to removing other materials from the stream of conveyed material can reduce the likelihood of such “junk” material contaminating subsequently sorted material. In a non-limiting example, PCBs often contain copper layers, whereby an XRF sensor can falsely classify such PCBs as copper scrap pieces. However, a vision system can be configured to distinguish green PCBs from red copper metal with a reasonably high degree of accuracy. Thus, it can be advantageous to use visual inspection to classify such PCBs so that they can be sorted from the stream of material, as classifying such PCBs with an XRF system can result in them being sorted as copper scrap pieces.

[0132] Process blocks 704-731 will now be described with respect to the classification and sorting of materials that can typically be found in Zorba (also referred to herein as “Zorba materials”). According to certain embodiments of the present disclosure, process blocks 704-process block 716 can be configured to classify / sort Zebra materials from Zorba.

[0133] In process block 704, a visual inspection can be performed on a piece of material to determine whether it should be classified as consisting essentially of or containing copper and / or brass. This can be achieved by a vision system (e.g., using any of the vision-related techniques described herein, including but not limited to AI techniques), as copper pieces and brass pieces often have unique colors and / or shapes (e.g., a vision system can be trained to classify certain tubular materials as consisting of or containing copper).

[0134] If the piece of material is classified by process block 704 as consisting of or containing copper and / or brass, process 700 can further distinguish between pieces of copper and pieces of brass using the piece of material's XRF sensor system classification in process block 705. Since 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 piece of material will be classified as a piece of copper when the ratio of the relative amounts of copper to zinc is greater than a predetermined value (e.g., when the ratio of the Cu / Zn value is greater than a predetermined value (CURB) that has been empirically determined to be between 0.8 and 1.0 according to certain non-limiting embodiments of the present disclosure). In other words, if the ratio of the calculated CU value to the calculated ZN value in the captured XRF spectrum of the piece of material is greater than the CURB value, the piece of material is classified as copper rather than brass. Thus, if CU / ZN > CURB, in process block 706, process 700 sends instructions to the designated sorting equipment to sort the piece of material as a piece of copper from the material stream.

[0135] However, if the piece of material is classified by process block 705 as a piece of brass, in process block 707, process 700 can perform a further determination of whether the piece of material consists of yellow brass or red brass based on the captured XRF spectrum of the piece of material. This can be performed by analyzing the ratio of the relative amounts of copper to zinc being greater than a second predetermined value (e.g., a CU / ZN value relative to a predetermined CUYB value that has been empirically determined to be between 0.3 and 0.8 according to certain non-limiting embodiments of the present disclosure). If the piece of material is classified as consisting of or containing red brass, in process block 708, process 700 sends instructions to the designated sorting equipment to sort the piece of material as a piece of red brass material. If the piece of material is classified as consisting of or containing yellow brass, in process block 790, process 700 sends instructions to the designated sorting equipment to sort the piece of material as a piece of yellow brass material.

[0136] Through the combination of process blocks 705-process block 708, process block 790, it can be readily seen that if a piece of material contains a certain amount of zinc relative to copper, the piece of material can be classified as consisting of brass. Red brass typically consists of about 85% copper and about 15% zinc, while yellow brass typically consists of about 60-70% copper and about 30-40% zinc.

[0137] It can further be readily appreciated that (1) the combination of the visual inspection of process block 704 and the XRF sensor system classification of process block 706 can be used to sort copper scrap pieces from the material stream, (2) the combination of the visual inspection of process block 704 and the XRF sensor system classification of process block 706 can be used to sort copper scrap pieces and brass scrap pieces from the material stream, (3) the combination of the visual inspection of process block 704 and the XRF sensor system classification of process block 707 can be used to sort brass scrap pieces from the material stream, (4) the combination of the visual inspection of process block 704 and the XRF sensor system classification of process blocks 706 and 707 can be used to separately sort red brass scrap pieces and yellow brass scrap pieces from the material stream, and (5) the combination of the visual inspection of process block 704 and the XRF sensor system classification of process blocks 706 and 707 can be used to sort copper scrap pieces and red / yellow brass scrap pieces from the material stream.

[0138] Note that process blocks 705-process block 708, process block 790 can optionally be implemented within process 700 (e.g., when it is known that the material pieces to be sorted do not contain any copper and / or brass). Moreover, if it is known that the material pieces to be sorted do not contain any one or more of copper, red brass, or yellow brass, then one or more of process blocks 705-708, 790 can optionally be omitted (or appropriately modified in a manner consistent with embodiments of the present disclosure described herein). Moreover, note that process 700 can be configured (e.g., process block 704 is modified accordingly) such that non-copper / non-brass materials that have a copper or brass color (or can be shaped to resemble copper tubing) are not classified / sorted as copper / brass pieces because they would not be classified as a result of the performance of process blocks 705-708, 790, which utilize XRF sensor system classification to accomplish the classification of copper / brass pieces for sorting from Zorba.

[0139] Further note that it can be important to perform the sorting of the material from the material stream indicated by process blocks 706, 708, and / or 790 prior to the sorting indicated by one or more of process blocks 723-724 because these process blocks also depend on the classification of the material pieces according to the CU / ZN ratio (process block 722) such that such material pieces would not contaminate the sorted material resulting from one or both of process blocks 723, 724. It can also be important to perform the sorting indicated by process blocks 706, 708, and / or 790 prior to either or both of the sorting indicated by process blocks 726 and / or process block 728 for similar reasons (e.g., because copper-containing material pieces can contaminate the 2xxx pieces sorted by process block 727 and because zinc-containing material pieces can contaminate the 7xxx pieces sorted by process block 728).

[0140] According to certain embodiments of the present disclosure, various combinations of process blocks 709-714 can be implemented to sort and sort nickel (Ni) and / or stainless steel ("SS") materials from a stream of material pieces. Process 700 can be configured to do so because these materials have a relatively higher concentration of nickel than aluminum alloys to be sorted later. Note that process block 709 is implemented as a sort utilizing a sensor system (e.g., XRF) rather than visual inspection because nickel plated and / or SS material pieces can visually appear similar to certain types of aluminum materials (cast and / or wrought). Moreover, because certain shredded pieces of such nickel plated or stainless steel materials can visually appear similar to certain cast and / or wrought aluminum pieces, it can be important to sort these material pieces prior to sorting aluminum alloys so that such nickel plated and / or stainless steel materials do not contaminate the sort / sorting of one or more various aluminum alloys.

[0141] In process block 709, a determination is made as to whether the captured XRF spectrum of the material piece indicates that the material piece contains an amount of chromium (Cr) that is greater than a predetermined threshold. This can be performed when it is known that aluminum alloys to be sorted by process 700 have a chromium concentration that is less than a known or predetermined value (e.g., a predetermined CRK value). Thus, based on the known or predetermined CRK value, material pieces can be sorted from the stream of material when the value CR is greater than the value CRK. According to certain embodiments of the present disclosure, and depending on the materials to be sorted downstream (sorted later), material sorted as having a predetermined amount (i.e., concentration) of chromium can be sorted from the stream of material pieces. Note that this can be implemented with respect to any elements in certain material pieces to be sorted.

[0142] If the result of process block 709 is affirmative, process 700 can further sort with an XRF sensor system of the material piece by process block 710 to further distinguish between nickel plated materials and SS materials. 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 by the calculation of NI / CR > 1, because typical XRF systems are only effective for x-ray fluorescence that measures a relatively small depth into the surface of the material piece, and because 2xx and 3xx stainless steels contain much less nickel than chromium, the XRF system will read a much greater content in a nickel plating on the surface of the material piece.

[0143] If the material piece is sorted as nickel plated, in process block 711, instructions will be sent to the designated sorting equipment to sort the material piece accordingly. If not, process 700 can proceed to process block 712 to determine whether 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 for sorting of manganese (Mn) content in 2xx series stainless steels.

[0144] It should be noted that the determining factor of whether a material piece is composed of a 2xx or 3xx series stainless steel is the amount of manganese. Thus, if MN > MNSS, the material piece is classified as a 2xx series stainless steel (e.g., 201, 202); otherwise, the material piece is classified as a 3xx series stainless steel (e.g., 301, 302, 304, 316). In process block 713, if the material piece is classified as a 2xx series stainless steel, instructions will be sent by process 700 to the designated sorting equipment to sort the material piece accordingly. In process block 714, if the material piece is classified as a 3xx series stainless steel, instructions will be sent by process 700 to the designated sorting equipment to sort the material piece accordingly.

[0145] It can be further readily appreciated that (1) the combination of XRF sensor system classifications of process blocks 709 and 710 can be used to sort nickel-plated pieces from the material stream, (2) the combination of XRF sensor system classifications of process blocks 709 and 710 can be used to sort SS pieces from the material stream, (3) the combination of XRF sensor system classifications of process blocks 709 and 710 can be used to separately sort nickel-plated pieces and SS pieces from the material stream, (4) the combination of XRF sensor system classifications of process blocks 709, 710, and 712 can be used to sort SS pieces from the material stream, (5) the combination of XRF sensor system classifications of process blocks 709, 710, and 712 can be used to sort SS pieces from the material stream into separate groups of SS 201 / 202 and SS 301, SS 302, SS 304, SS 316, and (6) the combination of XRF sensor system classifications of process blocks 709, 710, and 712 can be used to separately sort nickel-plated pieces and SS pieces from the material stream, where the SS pieces are further separated into SS 201 / 202 and SS 301, SS 302, SS 304, SS 316 groups. Additionally, all of the foregoing combinations can further include (1) the visual inspection of process block 704 and / or (2) the visual inspection(s) of process blocks 701 / 703.

[0146] In process block 715, a determination is made as to whether the captured XRF spectrum of the piece of material indicates that the piece of material contains an amount of lead (Pb) that is greater than a predetermined threshold. This can be performed when it is known that the pieces of material to be sorted by process 700 have a lead concentration that is less than a known or predetermined value (e.g., a predetermined PBK value). Thus, based on the known or predetermined PBK value, a piece of material can be sorted when the value PB is greater than the value CRK. In process block 716, process 700 will send instructions to the designated sorting device to sort the piece of material according to the lead classification (e.g., from the stream of pieces of material being conveyed). Note that the sorting produced by process blocks 704, 709, and 715 can be performed in a different order relative to one another according to certain embodiments of the present disclosure (e.g., if it is known that doing so will improve the accuracy and / or efficiency of the sorting of one or more of the materials). The implementation of process blocks 715-716 is optional according to embodiments of the present disclosure.

[0147] The stream of materials to be classified / sorted can also include other materials that can be classified based on containing unique (particular) chemical compositions or signatures (e.g., containing particular elements, metals, and / or alloying elements). In such cases, the sorting of these materials can be performed prior to sorting materials composed of more complex chemical compositions or signatures, where such unique chemical compositions or signatures can contaminate the classification of materials composed of more complex chemical compositions or signatures (and thus reduce the accuracy or efficiency of the sorting of such materials). Thus, it is important that these materials with such unique chemical compositions or signatures be sorted so that they are not inadvertently sorted into containers for materials composed of more complex chemical compositions or signatures (without contaminating). For example, process 700 can be configured to sort any materials known to be within Zebra prior to sorting Twitch materials (e.g., if it is known that doing so in this order will improve the accuracy and / or efficiency of the sorting of one or more certain Twitch materials). Alternatively, process 700 can be configured to sort any materials known to be within Twitch prior to classifying and sorting certain Zebra materials according to certain embodiments of the present disclosure (e.g., if it is known that doing so in this order will improve the accuracy and / or efficiency of the sorting of one or more certain Zebra materials).

[0148] According to certain embodiments of the present disclosure, it can be advantageous or desirable to perform sorting based on process blocks 717-731 after one or more of the previously described process blocks, as XRF technology is known to be very poor at distinguishing certain aluminum alloys (e.g., various Zebra materials (e.g., the presence of any one or more of the aforementioned copper, brass, nickel-plated, and SS materials can negatively or adversely affect XRF measurements within process blocks 717-process blocks 731). For example, die cast zinc pieces look very similar to certain aluminum alloys, and thus cannot be visually distinguished from one another based on visual inspection alone. However, XRF systems can easily distinguish die cast zinc from aluminum. Thus, it can be advantageous to sort and segregate die cast zinc (see process block 724) prior to 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 within a stream can have a relatively large zinc peak in the captured XRF spectrum, causing such material pieces to be falsely classified as 7xxx series aluminum alloys. Thus, it can be advantageous or desirable to perform sorting based on process blocks 721-724 prior to performing sorting based on process blocks 727-728.

[0149] Note that a captured XRF spectrum of a wrought aluminum piece (also referred to as sheet aluminum) will have a relatively lower total number of captured raw counts than a similarly sized cast aluminum piece, as cast aluminum contains a relatively larger amount of copper and zinc, resulting in a relatively larger total number of captured raw counts. An exception to the above is 356 and 360 series cast aluminum alloys, which have a relatively low concentration of copper and zinc. Thus, process blocks 717-720 are configured to identify (classify) and segregate 356 and / or 360 series cast aluminum pieces, which have a relatively low amount of copper and zinc similar to wrought aluminum. Otherwise, 356 and / or 360 series cast aluminum pieces can be falsely classified and thus segregated as wrought aluminum (e.g., in process block 721). Thus, it can be advantageous to perform sorting based on process blocks 717-719 prior to sorting based on process block 721.

[0150] In process block 717, a determination is made as to whether the total captured XRF counts for the piece of material (N-total) after normalization with the length and height (or mass) of the piece is greater than a predetermined constant value (AK). According to certain embodiments of the present disclosure, for 319 standard cast aluminum alloys (e.g., as determined by the Aluminum Association), this constant value can be set to 0.4 x N-total. According to certain embodiments of the present disclosure, process block 717 can determine whether the measured amount of copper and zinc in the scrap is greater than a predetermined value. For classifications that do not meet the criteria set forth by the classification of 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 the predetermined value), then process 700 can utilize the visual inspection of process block 718 to determine whether the piece of material is comprised of wrought or cast aluminum. If the piece of material is not classified as a wrought aluminum piece, then in process block 719, it is classified as a 356 cast aluminum piece and / or a 360 cast aluminum piece and instructions are sent to the designated sorting equipment to sort the piece of material accordingly.

[0151] Thus, according to embodiments of the present disclosure, if process block 717 determines that N-total < AK and the visual inspection classifies the piece of material as a cast aluminum piece, process 700 can be configured to sort the piece of material as a piece of material classified as a 356 / 360 cast aluminum material by the instructions sent by process block 719; otherwise, the piece of material remains on the main conveyor for classification / sorting by subsequent process blocks 721-731. Thus, it can be readily appreciated that process 700 can be configured such that the piece of material is sorted as a piece of material classified as a 356 / 360 cast aluminum material by the combination of process block 717 and process block 719. Additionally, the aforementioned combination can further include (1) the visual inspection of process block 704 and / or (2) the visual inspection(s) of process blocks 701 / 703, and / or (3) the XRF classification of process block 709.

[0152] According to alternative embodiments of the present disclosure, if the piece of material is classified as a wrought aluminum piece by process block 718, then in process block 720, instructions can be sent by process 700 to the designated sorting equipment to sort the piece of material accordingly.

[0153] According to further alternative embodiments of the present disclosure, such pieces of material classified as wrought aluminum pieces can instead be redirected in process block 720 in some suitable manner and / or returned to the main conveyor for classification / sorting by subsequent process blocks in process 700. This can be performed by depositing these pieces of material back onto the beginning of the material handling system and thereafter disabling process block 717 or by a return or circular conveyor that returns these pieces of material to a position downstream from the sorting locations on the conveyor associated with process block 719 and process block 720.

[0154] According to alternative embodiments of the present disclosure, the AK value can be predetermined (including on an empirical basis) to sort certain wrought aluminum alloys in process block 717 for classification, but not all wrought aluminum alloys. In other words, the AK value can be adjusted as needed to achieve a desired classification / sorting result. According to certain embodiments of the present disclosure, the AK value can be set such that a predetermined percentage of wrought aluminum pieces are passed by process block 717 to process block 721.

[0155] Note that according to alternative embodiments of the present disclosure, further classification and sorting (e.g., utilizing XRF or LIBS systems) can be performed to classify / sort between 356 cast aluminum alloys and 360 cast aluminum alloys (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-process block 1218 (e.g., process block 719 modified with or replaced by process blocks 1216-1218) as described with respect to Figures 12A-12B

[0156] Since 319 cast aluminum alloys and 38x cast aluminum alloys have material compositions (copper concentration of about 3-4%; zinc concentration <1%) represented by relatively large copper peaks as compared to zinc captured in the XRF spectrum, process blocks 721-724 can be implemented in process 700 to separate these alloys from die cast zinc metal pieces (or all other cast aluminum alloys remaining in the stream of material pieces). Note that according to alternative embodiments of the present disclosure, the material handling system can be configured such that all cast aluminum pieces that are not classified as 319 / 38x and / or die cast zinc metal pieces are collected in a catch-all container (e.g., in response to classification by process block 722, the process sends instructions to the sorting equipment to sort such cast aluminum pieces apart from 319 / 38x and / or die cast zinc metal pieces).

[0157] In process block 721, the visual inspection is configured to perform classification between cast aluminum alloys and wrought aluminum alloys. For those material pieces classified as cast aluminum, process 700 can be configured to perform further classification of each such material piece by process block 722, whereby a determination is made as to whether the 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 as to 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 x CU / ZN.

[0158] ​If so, the piece of material is classified as a 319 / 38x cast aluminum piece, and in process block 723, process 700 sends instructions to the designated sorting equipment to sort the piece of material accordingly. Otherwise, the piece of material is classified as a die cast zinc piece, and in process block 724, process 700 sends instructions to the designated sorting equipment to sort the piece of material accordingly. Thus, it can be readily appreciated that (1) the combination of the visual inspection 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 visual inspection 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 visual inspection 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 further include (1) any one or more of the visual inspections of process blocks 701 / 703 and 704, and / or (2) any one or more of the XRF classifications by process blocks 709, 715, and 717.

[0159] According to alternative embodiments of the present disclosure, the process can divert pieces of material classified as cast aluminum alloy pieces in process block 721 onto another conveyor belt (or any appropriate conveyor system; or to a different portion of the conveyor belt), thereby performing the classification / sorting between 319 / 38x cast alloy pieces and die cast zinc pieces.

[0160] Note that, according to alternative embodiments of the present disclosure, further classification and sorting (e.g., utilizing XRF or LIBS systems) can be performed to classify / sort between 319 cast aluminum alloy and 38x cast aluminum alloy (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-process block 1224 as described with respect to Figures 12A-12B

[0161] According to alternative embodiments of the present disclosure, the visual inspection implemented within process block 721 can be configured for classification between cast aluminum alloy, wrought aluminum alloy, and extruded aluminum alloy, such as described within U.S. Patent No. 11,471,916, which is incorporated by reference herein.

[0162] According to alternative embodiments of the present disclosure, optional process block 740 can be implemented in process 700 to perform a negative classification / sorting of those pieces of material to remove any cast aluminum alloy pieces classified as wrought aluminum by process block 721 from the material stream. Such classification can be implemented utilizing an appropriately configured visual inspection (or any of the sensor technologies described herein). ​

[0163] According to embodiments of the present disclosure, the visual inspection by process block 721 is classified as a material piece of a wrought aluminum alloy piece can be classified / sorted according to various combinations of one or more of process blocks 725-731. According to embodiments of the present disclosure, the 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 Figure 7A-7B process block 706. Note that according to certain embodiments of the present disclosure, any one or more of these classifications / sortings can be optional or omitted.

[0164] In process block 725, a determination is made using the captured XRF spectrum of the material piece 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 classified by process block 725 as belonging to a 2xxx series of wrought aluminum alloys; and, in process block 726, instructions are sent by process 700 to the designated sorting equipment to sort the material piece accordingly. Note that the 2xxx series of aluminum alloys are known to contain more of a particular amount of copper than other series of wrought aluminum alloys. Thus, the (predetermined) CUK value can be set so as to effectively (as predetermined by a user) sort these 2xxx series aluminum alloys from other material pieces in the material stream. It can be readily appreciated that it can be beneficial to perform the operations of or implement process block 726 after sorting any other material having a relatively high copper content (e.g., process blocks 706, 708, 790, and / or 723).

[0165] In process block 727, a determination is made using the captured XRF spectrum of the material piece whether the percentage amount of zinc (ZN value) in the material piece is greater than a predetermined value (ZNK). If so, the material piece is classified by process block 727 as belonging to a 7xxx series of wrought aluminum alloys; and, in process block 728, instructions are sent by process 700 to the designated sorting equipment to sort the material piece accordingly. Note that the 7xxx series of aluminum alloys are known to contain more of a particular amount of zinc than other series of wrought aluminum alloys. Thus, the (predetermined) ZNK value can be set so as to effectively (as predetermined by a user) sort these 7xxx series aluminum alloys from other material pieces in the material stream.

[0166] In process block 729, a determination is made using the captured XRF spectrum of the material piece whether the percentage amount of manganese (MN value) in the material piece is greater than a predetermined value (MNK) and whether the percentage amount of iron (FE value) in the material piece is less than a predetermined value (FEK). If so, the material piece is classified by process block 729 as belonging to a 3xxx series of wrought aluminum alloys; and, in process block 730, instructions are sent by process 700 to the designated sorting equipment to sort the material piece accordingly.

[0167] Process block 731 can represent any material pieces that are not classified / sorted as 3xxx series wrought aluminum alloys being classified as 5xxx / 6xxx series wrought aluminum alloys. This can be accomplished by simply allowing all such remaining material pieces in the material stream to be collected in a sorting container. According to alternative embodiments of the present disclosure, further classification / sorting can be performed between 5xxx and 6xxx series aluminum alloys. As is well 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, note that the concentrations of these aluminum alloy constituents are relatively very low, and thus, it can be important to configure process 700 such that the classification / sorting in process block 731 is subsequent to other classification / sorting associated with the larger peaks measured within the captured XRF spectrum, which can obscure the ability of process 700 to classify / sort 5xxx / 6xxx wrought aluminum pieces.

[0168] According to alternative embodiments of the present disclosure, process block 731 can be configured to implement a principal component analysis (“PCA”) algorithm (or any other appropriate configured algorithm) of the captured XRF spectrum in order to classify / sort between 5xxx and 6xxx series aluminum alloys.

[0169] According to certain embodiments of the present disclosure, process blocks 726, 728, 730, and / or 731 can be performed in any order; however, the effectiveness of one or more such classifications / sortings can be affected thereby. Moreover, these classifications / sortings can be interchanged / reordered in order to achieve particular effectiveness / efficiency of one or more of these classifications / sortings.

[0170] Additionally, according to alternative embodiments of the present disclosure, further classification / sorting can be performed after any one or more of sortings 726, 728, 730, or 731 to further separate these into more refined classifications of alloys (e.g., within that particular wrought aluminum series) by utilizing any appropriate sensor technology (e.g., XRF, LIBS, etc.), such as described in U.S. Patent No. 11,278,937 and U.S. Published Patent Application Nos. 2021 / 0346916 and 2021 / 0229133, which are incorporated herein by reference.

[0171] According to certain embodiments of the present disclosure, process 700 can be configured such that material pieces classified as cast aluminum alloys are sorted from the stream of material pieces, and then any material pieces that are not classified by any one of process blocks 725, 727, and 729 as belonging to a designated wrought aluminum alloy are collected in a designated container (e.g., see Figure 1container 140). According to certain embodiments of the present disclosure, the process 700 can be configured such that both classified cast aluminum alloys and classified wrought aluminum alloys 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 alloys and process blocks 725-731 are performed on the sorted wrought aluminum alloys. According to certain embodiments of the present disclosure, the process 700 can be configured such that both classified 3xxx series wrought aluminum alloys and classified 5xxx / 6xxx series wrought aluminum alloys are sorted from the stream of material pieces after process block 729, leaving any unclassified material pieces to be collected in a container (see, e.g., container 140). Figure 1

[0172] It can further be readily appreciated that (1) the combination of the visual inspection of process block 721 and the XRF sensor system classification of process block 725 can be used to sort 2xxx series wrought aluminum alloy pieces from a material stream, (2) the combination of the visual inspection of process block 721 and the XRF sensor system classification of process block 727 can be used to sort 7xxx series wrought aluminum alloy pieces from a material stream, (3) the combination of the visual inspection of process block 721 and the XRF sensor system classification of process block 729 can be used to sort 3xxx series wrought aluminum alloy pieces from a material stream, and (4) the combination of the visual inspection of process block 721 and the XRF sensor system classification of process block 729 can be used to sort 5xxx / 6xxx series wrought aluminum alloy pieces from a material stream. Additionally, any of the foregoing combinations can further include (1) any one or more of process blocks 701 / 703 and the visual inspection of process block 704, and / or (2) any one or more of the XRF classifications by process blocks 709, 715, and 717.

[0173] Figures 12A-12B FIG. illustrates a flowchart of a process 1200 configured in accordance with one or more alternative embodiments of the present disclosure in which a material handling system utilizes unique combinations of classification by one or more vision systems (e.g., implementing an Al system) and classification by one or more sensor systems (e.g., a spectroscopic sensor system such as an XRF or LIBS system) to classify and sort various materials (e.g., a stream of material pieces conveyed by a conveyor system). The process 1200, or any aspect or portion thereof, can be implemented within any material handling system that is suitably configured to perform one or more of the various described classification and / or sorting operations utilizing the classification and / or sorting functions, operations, systems, devices, and apparatuses described with respect to Figure 1-Figure 3 Figure 1 、 Figure 8 、 Figure 9A-9B and Figure 10 ​​The described material handling system). Although certain process blocks are described as utilizing XRF systems, any one or more of such process blocks can be implemented with any one of the sensor systems (e.g., LIBS, XRT, etc.) in the spectral sensor system as described herein. It should be noted that one or more of the various process blocks within process 1200 can be optional or omitted in accordance with certain embodiments of the present disclosure.

[0174] Process 1200 is described as operating on a piece-by-piece basis; of course, when one piece of material within a stream of conveyed pieces of material has been operated on by a particular process block, then subsequent pieces of material can be processed by that process block (e.g., when one piece of material has been analyzed / classified by the vision system(s) and / or the XRF system(s), then subsequent pieces of material can be analyzed / classified by the vision system(s) and / or the XRF system(s)). Note, Figures 12A-12B The flowchart shows a number of process blocks in which a“visual inspection” is performed; the other diamond-shaped process blocks represent analysis of pieces of material by XRF systems (also referred to as XRF sensor system classification). Note that the“visual inspection” can be performed with any appropriate vision system, including but not limited to vision systems that implement appropriately configured AI techniques. Each visual inspection classifies a piece of material according to processing visual images captured from each piece of material by the AI system.

[0175] Although any number of vision systems and / or XRF systems can be implemented to perform operations within 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 within the material handling system (e.g., see Figure 8). Thus, one or more of the “visual inspections” implemented within process 1200 can be performed on image data captured from the pieces of material utilizing a single vision system implementing one or more different AI algorithms (models) for each visual inspection. In a similar manner, one or more of the XRF sensor system classifications implemented within process 1200 can be performed on spectral data captured from the pieces of material utilizing a single XRF system. For example, when each piece of material passes through a single implemented vision system, the image data captured by the single vision system can be analyzed according to one or more of process blocks 1201, 1205, 1211, and 1213 (e.g., substantially simultaneously or in parallel). In other words, the algorithms associated with one or more of process blocks 1201, 1205, 1211, and 1213 can process the captured image data (e.g., by one or more AI models substantially simultaneously or in parallel with each other), the results of which are used for their respective classifications. Likewise, the 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).

[0176] Note that any of the visual inspections described in process blocks 203-process block 211 of process 200 with respect to Figure 3 may be performed according to process blocks 1201-1213 of process 1200. Figures 12A-12B Any of the classifications performed by the XRF system(s) (or any other suitable sensor system) in Figures 12A-12B may be performed according to process blocks 303-305 of process 300 with respect to Figure 4 .

[0177] As will be further described herein, certain aspects of process 1200 (i.e., the classifications and / or sorting as implemented within one or more process blocks) can need to be performed in one or more certain sequences in order to more efficiently and / or accurately perform those aspects of process 1200. Note that although the pieces of material can be analyzed / classified by a single vision system and a single XRF system (including substantially simultaneously or in parallel), it can be advantageous to sort the various pieces of material in certain sequences (along the conveyor), as will be further described herein.

[0178] Although process 1200 is described with respect to the classification and sorting of a stream of conveyed material including Zorba, Zebra, and / or Twitch, aspects of process 1200 can be applicable to the classification and / or sorting of other types of pieces of material.

[0179] In process block 1201, a visual inspection can be performed on the material piece to determine whether it should be classified as certain predetermined specific materials (e.g., "trash" or "fluff" materials associated with Zorba). If the visual inspection classifies the material piece as the predetermined specific material, then in process block 1202, process 1200 will send instructions to the designated sorting equipment to sort the material piece according to that classification (e.g., material pieces consisting of or containing PCBs) (e.g., from the conveyed stream of material pieces). Process block 1203 indicates that process blocks 1200-1202 can be performed for any other type of material piece that the user desires to separate from the stream of material pieces (e.g., other "trash" or "fluff" materials), and can be classified using a vision system (e.g., visual inspection using any of the vision-related techniques described herein, including but not limited to AI techniques). This can be accomplished utilizing a series of different vision systems, or a single vision system can be configured to perform classification for any multiple predetermined types of material pieces in a substantially simultaneous or parallel manner (e.g., image data captured by the 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 "trash" or "fluff" materials)).

[0180] Note 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 classification / sorting indicated by one or more of the other process blocks of process 1200, so as to improve the efficiency of the classification / sorting of those subsequent material pieces. For example, before performing the sorting of other metals and / or metal alloys as classified in one or more subsequent process blocks of process 1200, it may be important to sort the various materials (as classified in process blocks 1201, 1203) from the stream of material (e.g., to remove "junk" or "fluff" materials before sorting / sorting the remaining Zorba). For example, removing "junk" or "fluff" materials before removing other materials from the stream of conveyed material can reduce the possibility of such "junk" or "fluff" materials contaminating subsequently sorted materials. In a non-limiting example, PCBs typically contain copper layers, whereby an XRF sensor may mistakenly classify such PCBs as copper scrap pieces. However, a vision system can be configured to distinguish between green PCBs and red copper metal with considerable accuracy. Therefore, it may be advantageous to use visual inspection to sort such PCBs so that they can be separated from the material stream, as sorting such PCBs with an XRF system may result in them being sorted as copper scrap pieces.

[0181] The process blocks 1204-1225 will now be described with respect to the classification and sorting of materials that can typically be found in Zorba, also referred to herein as“Zorba materials”.

[0182] In process block 1204, the data generated by the XRF system can be normalized with respect to the size of the piece. This step can be optional. This 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.

[0183] According to embodiments as described with respect to Figures 12A-12B Process 1200 can be configured for classifying / sorting wrought and / or extruded aluminum alloys prior to classifying / sorting certain cast aluminum alloys, according to embodiments as described with respect to

[0184] In process block 1205, a visual inspection is performed to classify wrought aluminum alloy pieces within the stream of material. Material pieces classified as wrought aluminum alloy pieces by process block 1205 can be further classified and sorted according to various combinations of one or more of process blocks 1206-1210, according to embodiments of the present disclosure. Note that any one or more of these classifications / sortings can be optional or omitted, according to certain embodiments of the present disclosure.

[0185] In process block 1206, a determination is made as to whether the ratio of the measured amount of copper to the measured amount of zinc in the material piece is greater than a predetermined value (which has been empirically determined to be 10, according to certain non-limiting embodiments of the present disclosure) and whether the measured amount of copper in the material piece is greater than a predetermined value“A”, using the captured XRF spectrum of the material piece. According to certain embodiments of the present disclosure, the ratio of the CU / ZN values as described with respect to process 700 can be utilized. Further, according to certain embodiments of the present disclosure, the determination of CU>CUKas described with respect to process block 725 can be used for the determination of CU>A.

[0186] If both determination results in process block 1206 are affirmative, the piece of material is classified as belonging to a wrought aluminum alloy of the 2xxx series; and, in process block 1207, instructions are sent by process 1200 to the designated sorting equipment to sort the piece of material accordingly. Note that the 2xxx series of aluminum alloys are known to contain more of a certain amount of copper than other series of wrought aluminum alloys. Thus, an “A” value can be set (predetermined) so as to effectively (as predetermined by a user) sort these 2xxx series aluminum alloys from other pieces of material in the material stream.

[0187] In process block 1208, a determination is made as to whether the measured ratio of the amount of copper to the amount of zinc in the piece of material using the captured XRF spectrum of 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 the CU / ZN value as described with respect to process 700 can be utilized. Further, according to certain embodiments of the present disclosure, the determination of ZN>ZNKas described with respect to process block 727 can be used for the determination of ZN>B.

[0188] If both determination results in process block 1208 are affirmative, the piece of material is classified as belonging to a wrought aluminum alloy of the 7xxx series; and, in process block 1207, instructions are sent by process 1200 to the designated sorting equipment to sort the piece of material accordingly. Note that the 7xxx series of aluminum alloys are known to contain more of a certain amount of zinc than other series of wrought aluminum alloys. Thus, an “B” value can be set (predetermined) so as to effectively (as predetermined by a user) sort these 7xxx series aluminum alloys from other pieces of material in the material stream.

[0189] According to embodiments of the present disclosure, process 1200 can send instructions to the designated sorting equipment (process block 1210) to sort into a container those remaining pieces of material that are classified as wrought aluminum but are not sorted as 2xxx or 7xxx series wrought aluminum pieces (and, according to embodiments of the present disclosure, these pieces of material can be designated as 3xxx, 5xxx, and / or 6xxx series aluminum pieces). Thus, process block 1210 can represent that any pieces of material that are not classified / sorted as 2xxx or 7xxx series wrought aluminum alloys are classified as 3xxx / 5xxx / 6xxx series wrought aluminum alloys. According to alternative embodiments of the present disclosure, further classification / sorting can be performed among 3xxx, 5xxx, and / or 6xxx series aluminum alloys, such as described with respect to process blocks 729 731.

[0190] According to certain embodiments of the present disclosure, the sorting of process block 1209 can be performed prior to the sorting of process block 1207; however, the effectiveness of these sortings can be affected as a result. Moreover, these classifications / sortings can be interchanged / reordered so as to achieve a particular effectiveness / efficiency of one or more of these classifications / sortings.

[0191] Additionally, according to alternative embodiments of the present disclosure, any one or more of the sortings 1207, 1209, or 1210 can be further modified by further classifications / sortings to further separate these into more refined alloy classifications (e.g., within that particular wrought aluminum series) by utilizing classifications based on any suitable sensor technology (e.g., XRF, LIBS, etc.), such as described in U.S. Patent No. 11,278,937 and U.S. Published Patent Application Nos. 2021 / 0346916 and 2021 / 0229133, which are incorporated herein by reference.

[0192] According to certain embodiments of the present disclosure, the process 1200 can be configured such that material pieces classified as cast aluminum alloys are sorted from the stream of material pieces, and then any material pieces not classified by either of process blocks 1206 and 1208 as belonging to a designated wrought aluminum alloy are collected in a designated container (e.g., see Figure 1 container 140 of FIG. 1).

[0193] It can further be readily appreciated that (1) the combination of the visual inspection of process block 1205 and the XRF sensor system classification of process block 1206 can be used to sort 2xxx wrought aluminum alloy pieces from the material stream, (2) the combination of the visual inspection of process block 1205 and the XRF sensor system classification of process block 1206 can be used to sort 2xxx and / or 7xxx, 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from the material stream, (3) the combination of the visual inspection of process block 1205 and the XRF sensor system classifications of process blocks 1206 and 1208 can be used to sort 7xxx and 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from the material stream, (4) the combination of the visual inspection of process block 1205 and the XRF sensor system classifications of process blocks 1206 and 1208 can be used to sort 7xxx or 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from the material stream, and (5) the combination of the visual inspection of process block 1205 and the XRF sensor system classifications of process blocks 1206 and 1208 can be used to sort 3xxx, 5xxx, and / or 6xxx series wrought aluminum alloy pieces from the material stream. Additionally, all of the foregoing combinations can further include the visual inspection(s) of process block 701 / 703.

[0194] It can also be appreciated that it can be advantageous to configure the process 1200 such that the visual inspection for wrought aluminum alloys through process block 1205 is performed in conjunction with the sensor system classification through process block 1206, so as to sort 2xxx series aluminum alloys prior to sorting from certain cast aluminum alloys known to contain relatively high amounts of copper. It can further be appreciated that it can be advantageous to configure the process 1200 such that the visual inspection for wrought aluminum alloys through process block 1205 is performed in conjunction with the sensor system classification through process block 1208, so as to sort 7xxx series aluminum alloys prior to sorting from certain cast aluminum alloys known to contain relatively high amounts of zinc.

[0195] In process block 1211, a visual inspection can be performed to classify extruded aluminum alloy pieces within the stream of material (see, e.g., U.S. Patent No. 11,471,916, which is incorporated by reference herein). 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 to the designated sorting equipment (process block 1212) to sort those material pieces accordingly. Additionally, according to alternative embodiments of the present disclosure, further classification / sorting can be performed after the sorting 1212, to further separate these into more refined classifications of alloys (e.g., within those particular extruded aluminum series) by utilizing any appropriate sensor technology (e.g., XRF, LIBS, etc.).

[0196] In process block 1213, a visual inspection can be performed to classify cast aluminum alloy pieces within the stream of material. Process block 1214 represents that any material pieces that are not classified as cast aluminum and remain within the stream of material can be sorted from that stream as belonging to the “other” material classification (i.e., non-wrought aluminum, extruded aluminum, and cast aluminum). Additionally, process block 1213 can optionally be implemented, as all remaining material pieces that are not sorted are subsequently considered to be cast aluminum alloy pieces. Process block 1213 can also optionally be implemented by a presumption within process 1200, that all material pieces that are not classified as wrought aluminum alloys by process block 1205 and / or extruded aluminum alloys by process block 1211 are considered to be cast aluminum alloys thereafter within process 1200.

[0197] Note 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.

[0198] In process block 1215, a determination is made as to whether the total measured amount of copper and zinc in the piece of material is greater than a predetermined value "C". According to certain embodiments of the present disclosure, such a determination can be made using the CU value and the ZN value described with respect to process 700. Additionally, according to certain embodiments of the present disclosure, such a determination is made using the total captured XRF spectral counts for the piece of material relative to the value AK (after normalization with the length and height (or mass) of the piece) (such as disclosed with respect to process block 717). According to certain embodiments of the present disclosure, for 319 standard cast aluminum alloys (e.g., as determined by the Aluminum Association), this AK value can be set to 0.4 x N-total.

[0199] If the determination of process block 1215 is not affirmative, then in process block 1216, a determination is made as to whether the measured amount of iron in the piece of material 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 similarly described with respect to process block 729 can be used for the determination of FE > D. If FE > D, then the piece of material will be classified as a 360 cast aluminum piece, and in process block 1217, instructions can be sent by process 1200 to the designated sorting equipment to sort the piece of material accordingly. Otherwise, the piece of material will be classified as a 356 cast aluminum piece, and in process block 1218, instructions can be sent by process 1200 to the designated sorting equipment to sort the piece of material accordingly.

[0200] Thus, it can be readily appreciated that process 1200 can be configured such that the piece of material is sorted as a piece of 356 / 360 cast aluminum material classified by the combination of process block 1215 and process block 1216. Additionally, the aforementioned combination can further include any one or more of the visual inspections of process blocks 1201 / 1203, 1205, 1211, and / or 1213.

[0201] Since 319 cast aluminum alloys and 38x cast aluminum alloys have material compositions (copper concentration of about 3-4%; zinc concentration <1%) represented by a relatively large copper peak as compared to the zinc captured in the XRF spectrum, process blocks 1219-1224 can be implemented in process 1200 to separate these alloys from the die cast zinc metal pieces (or all other cast aluminum alloys remaining in the stream of pieces of material). Note that according to alternative embodiments of the present disclosure, the material handling system can be configured such that all cast aluminum pieces that are not classified as 319 / 38x and / or die cast zinc pieces are collected in an all-catch container (see process block 1225).

[0202] In process block 1219, a classification of each such material piece is performed whereby a determination is made as to whether the captured XRF spectrum of the material piece indicates that the material piece has a copper concentration that is relatively greater than a zinc concentration, as previously described. For example, a determination is made as to whether the ratio of the measured amount of copper to the measured amount of zinc in the material piece is greater than a predetermined value "E". According to certain embodiments of the present disclosure, the determination as to whether the ratio of the value CU / ZN is greater than the predetermined value BK can be utilized, as described with respect to process block 722.

[0203] If the ratio of the measured amount of copper to the measured amount of zinc in the material piece is not greater than the predetermined value "E", the material piece is classified as a die cast zinc piece, and in process block 1220, the process 1200 sends instructions to the designated sorting equipment to sort the material piece accordingly.

[0204] In process block 1221, a classification of each such material piece 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 material piece is greater than a predetermined value E and less than a predetermined value F. According to certain embodiments of the present disclosure, the determination as to whether the ratio of the value CU / ZN is greater than the predetermined value E and less than the predetermined value F can be utilized.

[0205] If so, the material piece is classified as a 38x cast aluminum piece, and in process block 1222, the process 1200 sends instructions to the designated sorting equipment to sort the material piece accordingly.

[0206] In process block 1223, a classification of each such material piece 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 material piece is greater than a predetermined value F and less than a predetermined value G. According to certain embodiments of the present disclosure, the determination as to whether the ratio of the value CU / ZN is greater than the predetermined value F and less than the predetermined value G can be utilized.

[0207] If so, the material piece is classified as a 319 cast aluminum piece, and in process block 1224, the process 1200 sends instructions to the designated sorting equipment to sort the material piece accordingly.

[0208] According to embodiments of the present disclosure, the process 1200 can be configured such that any other material pieces that are not sorted from the material stream are collected in a container, as indicated by process block 1225.

[0209] It can further be readily appreciated that (1) a combination of the XRF sensor system classifications of process blocks 1215 and 1219 can be used to sort die cast zinc pieces from the material stream, (2) a combination of the XRF sensor system classifications of process blocks 1219 and 1221 can be used to sort 38x cast aluminum alloy pieces from the material stream, (3) a combination of the XRF sensor system classifications of process blocks 1221 and 1223 can be used to sort 319 cast aluminum alloy pieces from the material stream, (4) a combination of the XRF sensor system classifications of process blocks 1219, 1221, and 1223 can be used to sort die cast zinc pieces, 38x cast aluminum alloy pieces, and 319 cast aluminum alloy pieces from the material stream, and (5) a combination of the XRF sensor system classifications of process blocks 1215, 1219, 1221, and 1223 can be used to sort 356 cast aluminum alloy pieces, 360 cast aluminum alloy pieces, 38x cast aluminum alloy pieces, and 319 cast aluminum alloy pieces, and die cast zinc pieces from the material stream individually. Additionally, the foregoing combinations can further include any one or more of the visual inspections of process blocks 1201 / 1203, 1205, 1211, and / or 1213.

[0210] Figure 8 FIG. illustrates a simplified schematic diagram of a non-limiting example of a material processing 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 equipment as described herein, including but not limited to one or more conveyor belts, for conveying a plurality of material pieces (not shown) past a vision system and XRF system 801 (or any other suitably configured sensor technology disclosed herein) and N (N > 1) sorting devices 802...804. The N sorting devices 802...804 can be configured for sorting particular classified material pieces into N corresponding receptacles or onto N other conveyor systems 805...807, respectively. In accordance with certain embodiments of the present disclosure, one or more of the N conveyor systems 805...807 can be configured for conveying respective sorted material pieces of one or more of the N conveyor systems 805...807 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 Figure 10

[0211] Accordingly, the material processing system 800 can be configured for implementing the methods described above with respect to Figures 7A-7C ​One or more aspects of the described process 700, whereby each classification 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 a combination of one or more of process blocks 704-708, 790, a combination of one or more of process blocks 709-714, a combination of one or more of process blocks 704, 709, 715, 717, and / or 721, or a combination of one or more of process blocks 725-731.

[0212] Similarly, the material handling system 800 can be configured to implement the aspects described with respect to Figures 12A-12B One or more aspects of the described process 1200, whereby each classification 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 a combination of one or more of process blocks 1205-1210, a combination of one or more of process blocks 1205-1218, a combination of one or more of process blocks 1205, 1211, 1213, 1215, 1219, 1221, and / or 1223, or a combination of one or more of process blocks 1219-1225.

[0213] According to non-limiting exemplary embodiments of the present disclosure, at least a portion of the components of the material handling system 100 can be linked together (e.g., serially or in parallel) in order to perform multiple iterations or tiers of classification / sorting. Such linking can be physical / mechanical linking of the system or by separate material handling systems (or the same material handling system performing the various classification / sorting processes separately to perform the classification / sorting processes in any desired manner. For example, when two or more systems 100 are linked in such a manner, the conveyor system can be implemented with a single conveyor belt or multiple conveyor belts to convey the pieces of material through a first vision system (and, according to certain embodiments, a sensor system) configured to classify / sort the pieces of material in a first set of a heterogeneous mixture of material pieces by a first set of sorting devices (e.g., the first automated control system 108 and associated one or more sorting devices 126...129) into a first set of one or more containers (e.g., the sorting containers 136...139), and then convey the pieces of material through a second vision system (and, according to certain embodiments, another sensor system) configured to classify / sort the pieces of material in a second set of a heterogeneous mixture of material by a second set of sorting devices into a second set of one or more sorting containers. Further discussion of such multi-level sorting is found in U.S. Published Patent Application No. 2022 / 0016675, which is incorporated herein by reference.

[0214] As further described herein, such a continuous material processing system 100 may include any number of such systems linked together in such a manner. According to certain embodiments of the present disclosure, each continuous material processing system may be configured to communicate with the previous system(s) (e.g., as described with respect to Figures 7A-7C and Figures 12A-12B The materials classified / sorted as described above are classified / sorted into different classified materials or material types.

[0215] Figure 9A-9B The figures illustrate configurations according to certain non-limiting exemplary embodiments of the present disclosure for use in processing a plurality of pieces of material (e.g., such as with respect to Figures 7A-7C The process 700 and Figures 12A-12B A simplified schematic diagram of a system and process 1600 for sorting / sorting (as described in various aspects of process 1200). Figure 9A An exemplary, non-limiting schematic diagram of a side view of such a system and process 1600 is shown, and Figure 9B The figure shows a top view.

[0216] A plurality of pieces of material 1601 may be conveyed or deposited into a hopper (e.g., via a conveyor belt 1602) to be picked up by an inclined conveyor system 1603. Note that for simplicity, the pieces of material 1601 are not shown in FIG. Figure 9B . A conveyor system 1603 conveys the material pieces 1601 through the AI ​​and / or XRF system to classify the material pieces for sorting. Alternatively, any other sensor system (e.g., LIBs, XRT, etc.) in the disclosed sensor system 120 may be utilized in place of the XRF system.

[0217] For example, various configurations of the system and process 1600 may be configured to implement a combination of one or more of process blocks 705-708, 790, a combination of one or more of process blocks 709-714, a combination of one or more of process blocks 704, 709, 715, 717, and / or 721, or a combination of one or more of process blocks 725-731.

[0218] Take the combination of process blocks 725-731 as an example. As a non-limiting example, an XRF or vision system implementing the Al system 1610 can be configured to classify which of the pieces of material 1601 are composed of a 2xxx series wrought aluminum alloy. The conveyor system 1603 can be configured to operate at a speed sufficient to "throw" pieces of material that are not classified as a 2xxx series wrought aluminum alloy onto a subsequent sloped conveyor system 1604. Pieces of material classified as being composed of a 2xxx series wrought aluminum alloy are ejected by the singulating device 1620 onto a lower positioned conveyor system 1606. For example, such a singulating device 1620 can be an air nozzle such as described herein that is actuated to eject pieces of material classified as a 2xxx series wrought aluminum alloy from the normal trajectory of the pieces of material "thrown" from the end of the conveyor system 1603 onto the conveyor system 1604. The pieces of material classified as a 2xxx series wrought aluminum alloy can be conveyed into a container 1630.

[0219] Pieces of material not classified as a 2xxx series wrought aluminum alloy can be conveyed past an XRF or Al system 1611, which can be configured to identify and classify those pieces of material composed of a 7xxx series wrought aluminum alloy. The conveyor system 1603 can be configured to operate at a speed sufficient to "throw" pieces of material that are not classified as a 7xxx series wrought aluminum alloy onto a subsequent sloped conveyor system 1605. Pieces of material classified as being composed of a 7xxx series wrought aluminum alloy are ejected by the singulating device 1621 onto a lower positioned conveyor system 1607. For example, such a singulating device 1621 can be an air nozzle such as described herein that is actuated to eject pieces of material classified as a 7xxx series wrought aluminum alloy from the normal trajectory of the pieces of material "thrown" from the end of the conveyor system 1604 onto the conveyor system 1605. The classified pieces of material can be conveyed into a container 1631.

[0220] Pieces of material not classified as a 7xxx series wrought aluminum alloy can be conveyed past an XRF or Al system 1612, which can be configured to identify and classify those pieces of material composed of a 3xxx series wrought aluminum alloy.

[0221] The conveyor system 1605 can be configured to operate at a speed sufficient to "throw" the pieces of material that are not classified as 3xxx series wrought aluminum alloys onto yet another conveyor system (not shown) or into a container 1633. The pieces of material that are classified as being of the 3xxx series wrought aluminum alloys can be ejected by the sorting device 1622 onto a lower positioned conveyor system 1622. For example, such a sorting device 1622 can be an air nozzle such as described herein that is actuated to eject the pieces of material that are classified as being of the 3xxx series wrought aluminum alloys from, for example, the normal trajectory of the pieces of material "thrown" from the end of the conveyor system 1605. These classified pieces of material can be conveyed into a container 1632. The remaining pieces of material thrown from the end of the conveyor belt 1605 can be considered to be classified as being of one or both of the 5xxx and 6xxx series wrought aluminum alloys.

[0222] Note that the system and process 1600 is not limited to one line of conveyor systems, but can be extended to multiple lines, each of the multiple lines ejecting classified pieces of material onto multiple conveyor systems (e.g., conveyor systems 1606...1608). Likewise, one or more of the conveyor systems 1606...1608 can be implemented with additional XRF or AI systems to further classify those pieces of material. For example, the pieces of material classified as being of the 5xxx and 6xxx series wrought aluminum alloys (and collected into the container 1633) can instead be conveyed (by a conveyor system not shown) past another XRF and / or AI system (or other sensor system 120) in order to classify and / or sort between those wrought aluminum alloys.

[0223] Thus, according to certain embodiments of the present disclosure, the classification / sorting system as described with respect to one or more of the process blocks 717-721 can first sort the cast aluminum pieces of material, and then can classify / sort the remaining pieces of material among the various remaining wrought aluminum alloys.

[0224] Similarly, the material handling system 1600 can be configured to implement one or more aspects of the process 1200 as described with respect to Figures 12A-12B the process 1200, whereby each classification is based on one or more classifications derived from the captured information from the vision system and / or the XRF system 801. For example, the various configurations of the system and process 800 can be configured to implement a combination of one or more of the process blocks 1205-1210, a combination of one or more of the process blocks 1205-1218, a combination of one or more of the process blocks 1205, 1211, 1213, 1215, 1219, 1221, and / or 1223, or a combination of one or more of the process blocks 1219-1225.

[0225] Referring to Figure 10 FIG. 1 illustrates a schematic diagram of a linked, non-limiting example of a continuous material processing system (physically or continuously executed by a plurality of appropriately configured material processing systems 100 (or the same material processing system appropriately configured for each continuous sortation), which can be implemented with one or more vision system implementations (e.g., with artificial intelligence (“AI”)) and / or one or more sensor systems 120 (such as, for example, to perform one or more various aspects as described with respect to Figures 7A-7C or Figures 12A-12B For simplicity, with respect to the following discussion of Figure 10 , such combinations of one or more vision systems and / or one or more sensor systems can simply be referred to as material sorting systems. In Figure 10 , the arrows schematically depict how various pieces of material are conveyed along such example material processing systems. In this non-limiting example, four separate material processing systems are illustrated, but any number of such material processing systems can be combined in any manner in order to separate and sort various different categories of material. Figure 10 The example in FIG. 1 describes various categories of material to be sorted (e.g., such as typically contained in Zorba, Zebra, and Twitch), but embodiments of the present disclosure are applicable to the sorting of any combination of a heterogeneous mixture of pieces of material.

[0226] In this particular example, a group of materials including a non-homogenous mixture of materials 3801a (e.g., Zorba and “trash” or “fluff” materials (e.g., aluminum, stainless steel, plastic, wood, rubber, brass, copper, PCBs, electronic scrap, copper wire, etc.)) is fed onto a first conveyor system 3803a (identified in FIG. 38 as Conveyor #1) from a ramp or chute 3802a (e.g., ramp or chute 102 for material from a hopper) in FIG. 38. Figure 10 Conveyor system 3803a conveys pieces of material 3801a past a material sorting system 3810a, which can be configured to sort the pieces of material (e.g., “trash” or “fluff” materials) from the remainder of the pieces of material (identified as Sort #1) with a sorter 3826a (e.g., see process blocks 701-703 of FIG. 37 or process blocks 1201-1203 of FIG. 38) that can utilize any of the sorting apparatuses described herein to deposit the pieces of material into one or more containers 3836a. Figure 7A Figure 12A

[0227] ​​The remaining heterogeneous mixture of material pieces 3801b (e.g., Zorba material) can then be conveyed along the same conveyor system, or deposited 3802b onto a conveyor system 3803b (identified as Conveyor #2 in Figure 10 The conveyor system 3803b conveys these material pieces 3801b through a material classification system 3810b, which can be configured to identify and separate Zebra pieces and Twitch pieces (identified as Sort #2) using a sorter 3826b, which can include one or more combinations of sorting / sorting (e.g., see process blocks 704, 709, and 715 of Figure 7A-7B

[0228] In this particular non-limiting example, copper and brass material pieces 3801c can then be deposited 3802c onto a conveyor system 3803c (identified as Conveyor #3 in Figure 10 to be sorted by a sorter 3826c (identified as Sort #3). This section of the material handling system can be configured to separate and sort material pieces made of copper and copper wire from brass (e.g., see process blocks 705-706), which can be deposited into one or more containers or conveyor systems for further sorting / sorting. According to certain embodiments of the present disclosure, each of the material pieces sorted as copper, copper wire, yellow brass, and red brass material pieces can be individually sorted and deposited into separate containers for copper 3836c and copper wire 3837c. The remaining heterogeneous mixture of material pieces (yellow brass and red brass) can then be deposited into a container 3840, or can be further processed by a sorting / sorting system (not shown) as previously described (e.g., see process blocks 707, 708, 790).

[0229] Embodiments of the present disclosure are not limited to linear continuity of such material handling systems, but can include combinations of branches of such material handling systems for further sorting and sorting of particular classes or multiple classes of materials. For example, Figure 10 FIGURE 13 illustrates how material pieces sorted as aluminum alloy (Twitch) material pieces 3836b that were sorted in Sort #2 can then be deposited 3802d onto a conveyor system 3803d (identified as Conveyor #4 in Figure 10 ​For example, sorter 3826b may physically sort such Twitch material pieces onto a conveyor system such as conveyor system 3803d, or container 3836b in which the Twitch material pieces have been deposited may be a ramp or chute for depositing the Twitch material pieces onto conveyor system 3803d, or the container containing the Twitch material pieces may simply be manipulated to deposit the Twitch material pieces onto conveyor system 3803d. Material sorting system 3810d may then be configured to sort these Twitch material pieces into cast aluminum alloys and wrought aluminum alloys (e.g., such as those described herein with respect to Figure 7B 721 ), or wrought aluminum alloys, extruded aluminum alloys, and cast aluminum alloys (e.g., such as those described herein with respect to Figure 12A 1 and 1213). In this classification #4, sorter 3826d can then be configured to separate the cast aluminum alloy from the wrought aluminum alloy based on the classification by material classification system 3810d, whereby the cast aluminum alloy can be deposited into container 3837d and the wrought aluminum alloy can be deposited into container 3836d or onto a conveyor system (not shown) for further classification / sorting.

[0230] Figure 10 Variations of the system may include further classifying / sorting the cast aluminum alloy into different predefined cast aluminum alloys using one or more sensor systems 120, including but not limited to XRF systems (e.g., such as Figure 7B Process blocks 722-724 and also regarding Figure 12B 1225 as described in process blocks 1215-1225). Figure 10 Another variation of the system may include further classifying / sorting the wrought aluminum alloy into different predefined wrought aluminum alloys using one or more sensor systems 120 including but not limited to XRF systems such as Figure 7C Process blocks 725-731 and also regarding Figure 12A 1206-1210).

[0231] As can be easily seen, Figure 10 The material handling system illustrated in FIG. 5 can be modified as needed to form any combination of sorting / sorting systems for sorting materials.

[0232] According to various embodiments of the present disclosure, different types or categories of materials may be classified by different types of sensors, each different type of sensor being used with an AI system and combined to classify pieces of material in a scrap or waste stream.

[0233] 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 pieces of material.

[0234] According to various embodiments of the present disclosure, multiple sensor systems can be mounted to a single conveyor system, with each sensor system utilizing a different AI system. According to various embodiments of the present disclosure, multiple sensor systems can be mounted to different conveyor systems, with each sensor system utilizing a different AI system.

[0235] According to various embodiments of the present disclosure, different types or classes of material can be classified by different types of sensors, each different type of sensor for use with an AI system and combined to classify pieces of material in a waste or scrap stream.

[0236] According to various embodiments of the present disclosure, data from two or more sensors (e.g., spectral data or XRF spectral data) can be combined using a single or multiple AI systems to perform classification of pieces of material.

[0237] Reference is now made to Figure 11, depicting a block diagram of a data processing ("computer") system 3400 that illustrates aspects of which can be implemented in embodiments of the present disclosure. (The terms "computer," "system," "computer system," and "data processing system" can be used interchangeably herein.) Aspects of the computer system 107, the automation control system 108, the sensor system(s) 120, and / or the vision system 110 can be configured similarly to the computer system 3400. The computer system 3400 can employ a local bus 3405 (e.g., a peripheral component interconnect ("PCI") local bus architecture). Any suitable bus architecture can be utilized, such as an accelerated graphics port ("AGP"), and industry standard architecture ("ISA"), etc. One or more processors 3415, volatile memory 3420, and non-volatile memory 3435 can be connected to the local bus 3405 (e.g., through a PCI bridge (not shown)). An integrated memory controller and cache memory can be coupled to the one or more processors 3415. The one or more processors 3415 can include one or more central processor units and / or 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 interconnect or through add-in boards. In the depicted example, a communication (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) can be connected to the local bus 3405 (e.g., through add-in boards that are inserted into expansion slots).

[0238] A user interface adapter 3412 can provide connections for a keyboard 3413 and mouse 3414, a modem (not shown), and additional memory (not shown). An I / O adapter 3430 can provide a connection for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).

[0239] An operating system can run on the one or more processors 3415 and is used to coordinate and provide control of various components within the computer system 3400. The operating system can be a commercially available operating system. A object-oriented programming system can run in conjunction with the operating system and provides calls to the operating system from programs executing on the system 3400 (e.g., Java, Python, etc.). Instructions for the operating system, the object-oriented operating system, and programs can be located in a non-volatile memory 3435 storage device such as a hard disk drive 3431, and can be loaded into the volatile memory 3420 for execution by the processors 3415.

[0240] Those of ordinary skill in the art will appreciate that Figure 11 The hardware in the exemplary embodiments of FIGS. 1-3 can vary depending on the implementation. As Figure 11 In addition to or in lieu of the hardware depicted in FIGS. 1-3, other hardware such as flash memory (or equivalent non-volatile memory) or optical disk drives can be used. Furthermore, any of the processes of the present disclosure can be applied to a multiprocessor computer system, or be performed by a plurality of such systems 3400. For example, training of the vision system 110 can be performed by a first computer system 3400, while operation of the vision system 110 for sorting can be performed by a second computer system 3400.

[0241] As another example, the computer system 3400 can be a standalone system configured to be bootable without relying on some type of network communication interface for operating system files to be installed. As a further example, the computer system 3400 can be a control system configured with ROM and / or flash ROM that provides non-volatile memory for storing operating systems files and / or user-generated data.

[0242] Figure 11 The examples depicted in FIGS. 1-3 and the above examples are not meant to imply architectural limitations. Further, the computer program form of the aspects of the present disclosure can reside on any computer readable storage medium, i.e., floppy diskette, compact diskette, hard disk, tape, ROM, RAM, etc., used by the computer system.

[0243] As described herein, embodiments of the present disclosure can be implemented to perform the various functions described for identifying, tracking, classifying, and / or sorting pieces of material. Such functions can be implemented within hardware and / or software, such as within one or more data processing systems (e.g., the vision system 110), as described herein. Figure 11implemented within a data processing system 3400) of one or more data processing systems such as the previously mentioned computer system 107, vision system 110, aspects of sensor system(s) 120, and / or automation control system 108. However, the functionality described herein is not limited to implementation in any particular hardware / software platform.

[0244] As will be appreciated by those skilled in the art, aspects of the present disclosure can be embodied as a system, process, method and / or program product. Accordingly, aspects of the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that can all generally be referred to herein as a "circuit," "circuitry," "module," or "system." Furthermore, aspects of the present disclosure can take the form of a program product embodied in one or more computer readable storage media (having one or more computer readable storage medium) having computer readable program code embodied thereon. (However, the computer readable medium can be any combination of one or more computer readable media.)

[0245] The computer readable storage medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, biological, atomic, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory ("RAM"), a read-only memory ("ROM") (e.g., the ROM 3435 of the data processing system 3400), an erasable programmable read-only memory ("EPROM" or Flash memory), an optical fiber, a portable compact disc read-only memory ("CD-ROM"), an optical storage device, a magnetic storage device (e.g., the hard disk drive 3431 of the data processing system 3400), 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, or device. Program code embodied on a computer readable signal medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program or program in the present context means any expression, in any language, code or notation, of a set of instructions intended to cause a system having an information processing capability to perform a particular function either directly or after either or both of the following: (a) conversion to another language, code or notation; and / or (b) reproduction in a different material form. Figure 11 Figure 11 Figure 11

[0246] ​​​A computer readable signal medium can include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium can be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport program for use by or in connection with an instruction execution system, apparatus, controller, or device.

[0247] The flow diagrams and the block diagrams in the drawings are meant as possible implementations of the systems, methods, processes, and program products according to embodiments of the disclosure. In this regard, each block in the flow diagrams or block diagrams can represent a module, segment, or portion of code, which comprises one or more executable program instructions implementing the specified logical functions. It should also be noted that in some implementations, the functions noted in the blocks can 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 can sometimes be executed in the reverse order, depending on the functionality involved.

[0248] A module of executable software in terms of the various types of processors (e.g., GPU 3401, CPU 3415) executing that software can include, for instance, one or more physical or logical blocks of computer instructions that may, for instance, be organized as an object, procedure, or function. Nevertheless, the executable file of an identified module need not be physically located together, but can include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose for the module. Indeed, a module of executable code can be a single instruction, or many instructions, and can even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data (e.g., a material classification library described herein) can be identified within modules and illustrated in this figure, 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 can be distributed over different locations including over different storage devices. The data can be electronically stored on a system or network.

[0249] These program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus (e.g., controller) to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus (e.g., GPU 3401, CPU 3415), create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0250] It will also be noted that each of the individual blocks and combinations of blocks in the block and / or flow diagrams can be implemented by a special purpose hardware-based system that performs the specified functions or actions, or a combination of special purpose hardware and computer instructions. For example, a module can be implemented as a hardware circuit comprising custom very large scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, controllers, or other discrete components. A module can also be implemented in a programmable hardware device such as a field programmable gate array, programmable array logic, programmable logic devices or the like.

[0251] 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 an object oriented programming language such as Java, Smalltalk, Python, C++, or the like, conventional procedural programming languages, such as the "C" programming language or similar programming languages, a programming language such as MATLAB or LabVIEW, or any of the machine learning software disclosed herein. The program code can execute entirely on the user's computer system, partly on the user's computer system (e.g., computer system for sorting), and partly on a remote computer system (e.g., computer system for training a machine learning system), or entirely on the 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 made 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 can be configured to execute on one or more of the following: computer system 107, automation control system 108, vision system 110, and aspects of sensor system(s) 120.

[0252] These program instructions can also be stored in a machine-readable storage medium that can direct 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 machine-readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0253] The program instructions can 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 to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0254] One or more databases can be included in the host for storing data and providing access to the data for various implementations. Those skilled in the art will also appreciate that any database, system or component of the present disclosure can include, among other things, any combination of databases or components in a single location or across multiple locations for security reasons. Each database or system can include any of various suitable security features, such as firewalls, access codes, encryption, decryption, compression, and the like. The databases can be any type of database, such as a relational database, a hierarchical database, an object-oriented database, and the like. Common database products that can be used to implement the databases include IBM's DB2, any of the database products available from the Oracle Corporation, Microsoft's Microsoft Access, or any other database product. The databases can be organized in any suitable manner, including as data tables or lookup tables.

[0255] Embodiments of the present disclosure provide a paradigm shift from "binary" sorting, thereby reducing costs. This innovation does not immediately draw attention; however, it greatly reduces the overall cost of sorting. Existing sorters are designed to sort material in a binary fashion, where air nozzles at the end of the conveyor expel one class into one of two bins. If eight classes need to be separated, as in the case of Zorba, the entire stream needs to run through a different eight times across the binary sorter, which takes eight times longer than trying to remove one single object in the stream. Embodiments of the present disclosure allow for sorting multiple classes in one pass, which in this case would reduce the total sorting time to one eighth of the original.

[0256] Aspects of the present disclosure provide a method for sorting pieces of material from a stream of conveyed material, including performing one or more visual inspections on each piece of material in the stream of conveyed material, wherein each of the one or more visual inspections includes classifying each piece of material as a function of processing a visual image captured from each piece of material by an AI system; performing one or more sensor system classifications on each piece of material within the stream of conveyed material; and sorting pieces of material from the stream of conveyed material into one or more classification groups as a function of a combination of the one or more visual inspections and the one or more sensor system classifications. The stream of conveyed material can include one or more wrought aluminum alloys and one or more cast aluminum alloys, wherein the sorting includes sorting one or more of the wrought aluminum alloys from the stream of conveyed material into one or more first classification groups based on a first combination of the one or more visual inspections and the one or more sensor system classifications; and sorting one or more of the cast aluminum alloys from the stream of conveyed material into one or more second classification groups based on a second combination of the one or more visual inspections and the one or more sensor system classifications, wherein the sorting of the one or more wrought aluminum alloys from the stream of conveyed material is performed prior to the sorting of the one or more cast aluminum alloys from the stream of conveyed material; optionally, wherein the first combination includes a visual inspection to determine whether a piece of material is composed of a wrought aluminum alloy and one or more sensor system classifications based on a measured amount of copper and a measured amount of zinc in the piece of material; optionally, wherein the second combination includes a visual inspection to determine whether a piece of material is composed of a cast aluminum alloy and one or more sensor system classifications based on a measured amount of copper and a measured amount of zinc in the piece of material; optionally, wherein the second combination further includes a visual inspection to determine whether a piece of material is composed of a wrought aluminum alloy, the visual inspection to determine whether a piece of material is composed of a wrought aluminum alloy being performed prior to the visual inspection to determine whether a piece of material is composed of a cast aluminum alloy.Sorting one or more of the wrought aluminum alloys from the stream of conveyed material into one or more first sort groups can include one of a), b), or c): a) sorting the stream of material pieces as 2xxx-series wrought aluminum alloys when the visual inspection determines that the material pieces consist of wrought aluminum alloy and the sensor system sort determines that (1) the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is greater than a first predetermined value and (2) the measured amount of copper in the material pieces is greater than a second predetermined value; or b) sorting the stream of material pieces as 7xxx-series wrought aluminum alloys when the visual inspection determines that the material pieces consist of wrought aluminum alloy and the sensor system sort determines that (1) the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is less than the first predetermined value and (2) the measured amount of zinc in the material pieces is greater than the second predetermined value; c) sorting the stream of material pieces as 3xxx and / or 5xxx and / or 6xxx-series wrought aluminum alloys when (1) the visual inspection determines that the material pieces consist of wrought aluminum alloy and (2) a first of the one or more sensor system sorts determines that (i) the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is not greater than the first predetermined value and (ii) the measured amount of copper in the material pieces is not greater than the second predetermined value and (3) a second of the one or more sensor system sorts determines that (i) the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is not less than a third predetermined value and (ii) the measured amount of zinc in the material pieces is not greater than a fourth predetermined value.Sorting one or more of the cast aluminum alloys from the conveyed stream of material into one or more second classification groups, the sorting comprising one of a), b), c), or d): a) sorting the material pieces from the stream of material pieces as being classified as 360 cast aluminum alloys when visual inspection determines that the material piece is not composed of a wrought aluminum alloy and a first sensor system classification of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is not greater than a first predetermined value and a second sensor system classification of the one or more sensor system classifications determines that the measured amount of iron in the material piece is greater than a second predetermined value; or b) sorting the material pieces from the stream of material pieces as being classified as 356 cast aluminum alloys when visual inspection determines that the material piece is not composed of a wrought aluminum alloy and a first sensor system classification of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is not greater than a first predetermined value and a second sensor system classification of the one or more sensor system classifications determines that the measured amount of iron in the material piece is not greater than a second predetermined value; or c) sorting the material pieces from the stream of material pieces as being classified as 356 cast aluminum alloys when visual inspection determines that the material piece is not composed of a wrought aluminum alloy and a first sensor system classification of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is not greater than a first predetermined value and a second sensor system classification of the one or more sensor system classifications determines that the measured amount of iron in the material piece is not greater than a second predetermined value; ) sorting material pieces from a stream of material pieces in such a manner that the material pieces are classified as 38x cast aluminum alloy when: (1) visual inspection determines that the material pieces are composed of cast aluminum alloy, and (2) a first sensor system classification in one or more sensor system classifications determines that the total measured amounts of copper and zinc in the material pieces are greater than a first predetermined value, and (3) a second sensor system classification in one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is greater than a second predetermined value and less than a third predetermined value; or d) sorting material pieces from a stream of material pieces in such a manner that the material pieces are classified as 319 cast aluminum alloy when: (1) visual inspection determines that the material pieces are composed of cast aluminum alloy, and (2) a first sensor system classification in one or more sensor system classifications determines that the total measured amounts of copper and zinc in the material pieces are greater than a first predetermined value, and (3) a second sensor system classification in one or more sensor system classifications determines that the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is greater than a second predetermined value and less than a third predetermined value. The method further includes sorting the material pieces from the stream of material pieces in such a manner that the material pieces are classified as die cast zinc parts when: (1) visual inspection determines that the material pieces are not composed of a wrought aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is greater than a first predetermined value, and (3) a second sensor system classification 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 material piece is less than a second predetermined value. The stream of conveyed material may include Zorba material.The method can further include sorting one or more scrap materials from the stream of conveyed materials based on one or more visual inspections, wherein the sorting of scrap materials is performed prior to the sorting of the one or more wrought aluminum alloys and the sorting of the one or more cast aluminum alloys from the stream of conveyed materials. The method further includes sorting extruded aluminum alloys into one or more third classification groups from the stream of conveyed materials based on a third combination of the one or more visual inspections, wherein the sorting of the one or more extruded aluminum alloys from the stream of conveyed materials is performed after the sorting of the one or more wrought aluminum alloys from the stream of conveyed materials and prior to the sorting of the one or more cast aluminum alloys from the stream of conveyed materials. Each of the one or more sensor system classifications is performed by a spectroscopy system; the spectroscopy system is an x-ray fluorescence system. Each of the one or more visual inspections is performed by a single vision system that implements one or more AI models within an AI system, and wherein each of the one or more sensor system classifications is performed by a single spectroscopy system that implements one or more algorithms for analyzing spectroscopy data collected from a piece of material.

[0257] Aspects of the present disclosure provide a method for sorting pieces of material from a stream of conveyed Zorba material, comprising: performing a visual inspection on each piece of material within the stream of conveyed Zorba material, wherein each of the visual inspections comprises classifying each piece of material from processing, by an AI system, of a visual image captured from each piece of material, wherein the visual inspections are performed by a single vision system that implements different AI models within the AI system for each of the visual inspections; performing a sensor system classification on each piece of material within the stream of conveyed Zorba material, wherein each of the sensor system classifications is performed by a different algorithm that analyzes spectroscopic data collected from each piece of material by a single spectroscopic system; sorting a plurality of different wrought aluminum alloy pieces of material from the stream of conveyed Zorba material into separately sorted classification groups based on a first combination of one or more of the visual inspections and one or more of the sensor system classifications; and sorting a plurality of different cast aluminum alloy pieces of material from the stream of conveyed Zorba material into separately sorted classification groups based on a second combination of one or more of the visual inspections and one or more of the sensor system classifications; optionally, wherein the sorting of the plurality of different wrought aluminum alloy pieces of material from the stream of conveyed Zorba material is performed prior to the sorting of the plurality of different cast aluminum alloy pieces of material from the stream of conveyed Zorba material; optionally, wherein the sensor system classification is based on a measured amount of copper and a measured amount of zinc in the piece of material; optionally, wherein the single spectroscopic system is an x-ray fluorescence system. The sorting of the plurality of different wrought aluminum alloy pieces of material from the stream of conveyed Zorba material into separately sorted classification groups can include one of a), b), or c): a) sorting the piece of material from the stream of conveyed Zorba material as a 2xxx series wrought aluminum alloy when the visual inspection determines that the piece of material consists of a wrought aluminum alloy and the first sensor system classification determines that (1) a ratio of the measured amount of copper to the measured amount of zinc in the piece of material is greater than a first predetermined value and (2) the measured amount of copper in the piece of material is greater than a second predetermined value; or b) sorting the piece of material from the stream of conveyed Zorba material as a 7xxx series wrought aluminum alloy when the visual inspection determines that the piece of material consists of a wrought aluminum alloy and the second sensor system classification determines that (1) a ratio of the measured amount of copper to the measured amount of zinc in the piece of material is less than a third predetermined value and (2) the measured amount of zinc in the piece of material is greater than a fourth predetermined value; or c) sorting the piece of material from the stream of conveyed Zorba material as a 3xxx and / or 5xxx and / or 6xxx series wrought aluminum alloy when the second sensor system classification determines that (1) a ratio of the measured amount of copper to the measured amount of zinc in the piece of material is not less than the third predetermined value and (2) the measured amount of zinc in the piece of material is not greater than the fourth predetermined value.Sorting the plurality of different cast aluminum alloy material pieces from the stream of conveyed Zorba material into individually sorted classification groups can include one of a), b), c), or d): a) sorting the material pieces from the stream of conveyed Zorba material as cast aluminum alloy pieces classified as 360 cast aluminum alloy when the visual inspection determines that the material pieces consist of cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material pieces is less than a first predetermined value, and the second system classification determines that the measured amount of iron in the material pieces is greater than a second predetermined value; or b) sorting the material pieces from the stream of conveyed Zorba material as cast aluminum alloy pieces classified as 356 cast aluminum alloy when the visual inspection determines that the material pieces consist of cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material pieces is less than a first predetermined value, and the second system classification determines that the measured amount of iron in the material pieces is less than a second predetermined value; or c) sorting the material pieces from the stream of conveyed Zorba material as cast aluminum alloy pieces classified as 38x cast aluminum alloy when the visual inspection determines that the material pieces consist of cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material pieces is greater than a first predetermined value, and the third sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is greater than a third predetermined value and less than a fourth predetermined value; or d) sorting the material pieces from the stream of conveyed Zorba material as cast aluminum alloy pieces classified as 319 cast aluminum alloy when the visual inspection determines that the material pieces consist of cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material pieces is greater than a first predetermined value, and the fourth sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is greater than a fourth predetermined value and less than a fifth predetermined value. The method can further include sorting the material pieces from the stream of conveyed Zorba material as cast aluminum alloy pieces classified as 319 cast aluminum alloy when the visual inspection determines that the material pieces consist of cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material pieces is greater than a first predetermined value, and the fifth sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is less than a third predetermined value.

[0258] Aspects of the present disclosure provide a system for sorting material pieces, the system comprising a conveyor system configured to convey a stream of material; a vision system configured to perform one or more visual inspections on each material piece within the conveyed stream of material, wherein each of the one or more visual inspections includes classifying each material piece based on visual images captured from each material piece processed by an AI system; a sensor system configured to perform one or more sensor system classifications on each material piece within the conveyed stream of material; and a sorting device configured to sort the material pieces from the conveyed stream of material into one or more classification groups in response to instructions received from a combination of the one or more visual inspections and the one or more sensor system classifications. The stream of conveyed material may include one or more wrought aluminum alloys and one or more cast aluminum alloys, wherein the sorting device is configured to: sort one or more of the wrought aluminum alloys from the stream of conveyed material into one or more first sorting groups in response to instructions received from a first combination of one or more visual inspections and one or more sensor system classifications; and sort one or more of the cast aluminum alloys from the stream of conveyed material into one or more second sorting groups in response to instructions received from a second combination of one or more visual inspections and one or more sensor system classifications, wherein the sorting of the one or more wrought aluminum alloys from the stream of conveyed material is performed before the sorting of the one or more cast aluminum alloys from the stream of conveyed material. Sorting of alloys; optionally, wherein the first combination includes visual inspection and one or more sensor system classifications, wherein the visual inspection is used to determine whether the material piece consists of a wrought aluminum alloy, and the one or more sensor system classifications are based on a measured amount of copper and a measured amount of zinc in the material piece; optionally, wherein the second combination includes visual inspection and one or more sensor system classifications, wherein the visual inspection is used to determine whether the material piece consists of a cast aluminum alloy, and the one or more sensor system classifications are based on a measured amount of copper and a measured amount of zinc in the material piece; optionally, wherein the second combination also includes a visual inspection, wherein the visual inspection is used to determine whether the material piece consists of a wrought aluminum alloy, and the visual inspection is performed before the visual inspection for determining whether the material piece consists of a cast aluminum alloy.The sorting device is configured to sort one or more of the wrought aluminum alloys from the stream of conveyed material into one or more first classification groups includes one of a), b), or c): a) the sorting device is configured to sort the pieces of material from the stream of pieces of material in a manner that the pieces of material are classified as 2xxx series wrought aluminum alloys in response to instructions received from a combination of the visual inspection and the sensor system classification: the visual inspection determines that the pieces of material consist of wrought aluminum alloy, the sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the pieces of material is greater than a first predetermined value and (2) the measured amount of copper in the pieces of material is greater than a second predetermined value; or b) the sorting device is configured to sort the pieces of material from the stream of pieces of material in a manner that the pieces of material are classified as 7xxx series wrought aluminum alloys in response to instructions received from a combination of the visual inspection and the sensor system classification: the visual inspection determines that the pieces of material consist of wrought aluminum alloy, the sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the pieces of material is less than a first predetermined value and (2) the measured amount of zinc in the pieces of material is greater than a second predetermined value; c) the sorting device is configured to sort the pieces of material from the stream of pieces of material in a manner that the pieces of material are classified as 3xxx and / or 5xxx and / or 6xxx series wrought aluminum alloys in response to instructions received from a combination of (1) the visual inspection determining that the pieces of material consist of wrought aluminum alloy and (2) a first sensor system classification of the one or more sensor system classifications determining that (i) a ratio of a measured amount of copper to a measured amount of zinc in the pieces of material is not greater than a first predetermined value and (ii) the measured amount of copper in the pieces of material is not greater than a second predetermined value and (3) a second sensor system classification of the one or more sensor system classifications determining that (i) a ratio of a measured amount of copper to a measured amount of zinc in the pieces of material is not less than a third predetermined value and (ii) the measured amount of zinc in the pieces of material is not greater than a fourth predetermined value.The sorting device is configured to sort one or more of the cast aluminum alloys from the stream of conveyed material into one or more second classification groups, the sorting device comprising one of a), b), c), or d): a) the sorting device is configured to sort the stream of material pieces from the stream of material pieces in a manner that the material pieces are classified as 360 cast aluminum alloy in response to instructions received from a combination of the first vision inspection, a first sensor system classification of the one or more sensor system classifications, and a second sensor system classification of the one or more sensor system classifications: the vision inspection determines that the material piece does not consist of wrought aluminum alloy, the first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is not greater than a first predetermined value, and the second sensor system classification of the one or more sensor system classifications determines that a measured amount of iron in the material piece is greater than a second predetermined value; or b) the sorting device is configured to sort the stream of material pieces from the stream of material pieces in a manner that the material pieces are classified as 356 cast aluminum alloy in response to instructions received from a combination of the first vision inspection, a first sensor system classification of the one or more sensor system classifications, and a second sensor system classification of the one or more sensor system classifications: the vision inspection determines that the material piece does not consist of wrought aluminum alloy, the first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is not greater than a first predetermined value, and the second sensor system classification of the one or more sensor system classifications determines that a measured amount of iron in the material piece is not greater than a second predetermined value; or c) the sorting device is configured to sort the stream of material pieces from the stream of material pieces in a manner that the material pieces are classified as 38x cast aluminum alloy in response to instructions received from a combination of: (1) the vision inspection determines that the material piece consists of cast aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is greater than a first predetermined value, and (3) a second sensor system classification 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 material piece is greater than a second predetermined value and less than a third predetermined value; or d) the sorting device is configured to sort the stream of material pieces from the stream of material pieces in a manner that the material pieces are classified as 319 cast aluminum alloy in response to instructions received from a combination of: (1) the vision inspection determines that the material piece consists of cast aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is greater than a first predetermined value, and (3) a third sensor system classification 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 material piece is greater than a third predetermined value and less than a fourth predetermined value.The system can further include a sorting device configured to sort the pieces of material from the stream of material pieces in a manner in which the pieces of material are classified as pieces of die cast zinc in response to instructions received from a combination of: (1) the first visual inspection determining that the pieces of material do not consist of wrought aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determining that the total measured amount of copper and zinc in the pieces of material is greater than a first predetermined value, and (3) a third sensor system classification of the one or more sensor system classifications determining that a ratio of the measured amount of copper to the measured amount of zinc in the pieces of material is less than a third predetermined value. The stream of conveyed material can include Zorba material. The system further includes a sorting device configured to sort one or more scrap materials from the stream of conveyed material in response to instructions received from a combination of the one or more visual inspections, wherein the sorting of scrap materials is performed prior to the sorting of one or more wrought aluminum alloys and the sorting of one or more cast aluminum alloys from the stream of conveyed material. The system can further include a sorting device configured to sort extruded aluminum alloys from the stream of conveyed material into one or more third classification groups in response to instructions received from a third combination of the one or more visual inspections, wherein the sorting of one or more extruded aluminum alloys from the stream of conveyed material is performed after the sorting of one or more wrought aluminum alloys from the stream of conveyed material and prior to the sorting of one or more cast aluminum alloys from the stream of conveyed material. Each of the one or more sensor system classifications is performed by a spectroscopy system; optionally, wherein the sensor system is an x-ray fluorescence system. Each of the one or more visual inspections can be performed by a single vision system that implements one or more AI models within an AI system, and wherein each of the one or more sensor system classifications is performed by a single spectroscopy system that implements one or more algorithms for analyzing spectroscopy data collected from the pieces of material; optionally, wherein the spectroscopy system is an x-ray fluorescence system.

[0259] Reference is made herein to a device being "configured" to perform some function or a device "being configured to" perform some function. It is understood that this can include selecting predefined logic blocks and associating them logically so that they provide the intended functionality, including monitoring or control functionality. It can also include programming a computer software based logic of a retrofit control device, wiring discrete hardware components, or a combination of any or all of the foregoing. Such configured devices are physically designed to perform the specified function or functions.

[0260] 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 circuitry, hardware chips, controllers, and the like, in order to provide a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the disclosure can be practiced without one or more of the specific details, or with other methods, components, materials, and the like. In other instances, well-known structures, materials, or operations are not shown or described in detail in order to avoid obscuring aspects of the disclosure.

[0261] Throughout this specification, the use of "an embodiment," "embodiments," or "one embodiment" or similar phrases means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosure. Thus, the appearance of the phrases "in one embodiment," "in embodiments," "an embodiment," "certain embodiments," "other embodiments," and similar phrases in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, aspects, and / or characteristics can be combined in any suitable manner in one or more embodiments. Accordingly, even though individual features, structures, aspects, and / or characteristics can be identified separately from other features, structures, aspects, and / or characteristics, one or more of such individual features, structures, aspects and / or characteristics can be combined with one or more other features, structures, aspects, and / or characteristics.

[0262] Benefits, advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any element(s) that facilitates or makes such benefits, advantages, or solutions possible, can not be to all embodiments. Further, the benefits, advantages, solutions to problems, and any one or more of the benefits, advantages or solutions can not be necessary or even advantageous in other embodiments. It should be understood that every maximum limitation on any of the claims hereof does inherently limit the other claims to the same extent. It should be further understood that a dependency or reference in a claim to a different claim, or to a paragraph of material in the specification or drawings does not, of itself, dedicate any of the claim's elements to the public under the anterior art doctrine.

[0263] Those skilled in the art who read the present disclosure will appreciate that changes and modifications can be made to the embodiments without departing from the scope of the present disclosure. It will be appreciated that the specific implementations shown and described herein can be illustrative, and that the scope of the disclosure and its best mode can not be considered limited by any means to the specific implementations. Other variations can fall within the scope of the claims.

[0264] While this specification contains many specifics, these should not be construed as limitations on the scope of the disclosure or of what can be claimed, but rather as descriptors of features specific to particular implementations. The headings herein are not intended to limit the scope of the disclosure, embodiments of the disclosure, or the claims under the headings, but are intended to aid ease of reference only.

[0265] Herein, the term "or" can be intended to mean an inclusive "or" rather than an exclusive "or" unless explicitly indicated otherwise. Such inclusive language manifests that a combination of elements is acceptable unless it is specifically stated otherwise. As used herein, the term "and / or" when used in a contextual listing of entities, refers to one or a combination of the entities existing individually or in combination. As such, for example, the phrase "A, B, C, and / or D" includes A, B, C, and D individually, but also includes any and all combinations and subcombinations of A, B, C, and D.

[0266] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0267] All corresponding structures, materials, acts, and equivalents thereof in the appended claims can be intended to include any structure, material, or act for performing the functions in combination with other claimed claim elements as specifically claimed.

[0268] As used herein, "substantially" with regard to a property or condition to be identified means a degree of deviation that is sufficiently small so as to not be visually discernible from the property or condition being identified. In some instances, the exact allowable degree of deviation can depend on the particular context.

[0269] As used herein, a plurality of items, structural elements, compositional elements, and / or materials can be presented in a common list for convenience. However, these lists should be construed as though each member of the list is individually identified as a separate and unique member. Thus, no individual member of such list should be construed as a de facto equivalent of any other member of the same list.

[0270] Unless otherwise defined, all technical and scientific terms used herein, such as abbreviations for chemical elements in the periodic table, have the same meaning as commonly understood by one of ordinary skill in the art to which this present 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 present subject matter, representative methods, devices, and materials are now described.

Claims

1. A method for sorting pieces of material from a stream of conveyed material, comprising: performing one or more visual inspections on each material piece within the stream of conveyed material, wherein each of the one or more visual inspections includes classifying each material piece based on processing visual images captured from each material piece by an artificial intelligence ("AI") system; performing one or more sensor system classifications on each piece of material within the stream of conveyed material; and Pieces of material are sorted from the stream of conveyed material into one or more sorting groups based on a combination of the one or more visual inspections and the one or more sensor system classifications.

2. The method according to claim 1, wherein The stream of conveyed material comprises one or more wrought aluminum alloys and one or more cast aluminum alloys, wherein the sorting comprises: sorting one or more of the wrought aluminum alloys into one or more first classification groups from the conveyed stream of material based on a first combination of the one or more visual inspections and the one or more sensor system classifications; and sorting one or more of the cast aluminum alloys into one or more second classification groups based on a second combination of the one or more visual inspections and the one or more sensor system classifications from the conveyed stream of material, wherein said sorting of one or more wrought aluminium alloys from the conveyed material stream is performed before said sorting of one or more cast aluminium alloys from the conveyed material stream. Optionally, wherein the first combination includes visual inspection and one or more sensor system classifications, the visual inspection being used to determine whether the material piece is composed of a wrought aluminum alloy, the one or more sensor system classifications being based on a measured amount of copper and a measured amount of zinc in the material piece; Optionally, wherein the second combination includes visual inspection and one or more sensor system classifications, the visual inspection being used to determine whether the material piece is composed of a cast aluminum alloy, the one or more sensor system classifications being based on a measured amount of copper and a measured amount of zinc in the material piece; Optionally, the second combination further comprises a visual inspection for determining whether the material piece is composed of a wrought aluminum alloy, wherein the visual inspection is performed before the visual inspection for determining whether the material piece is composed of a cast aluminum alloy.

3. The method according to claim 2, wherein: Sorting one or more of the wrought aluminum alloys from the stream of conveyed material into one or more first classification groups comprises one of a), b), or c): a) sorting the piece of material from a stream of the piece of material such that the piece of material is classified as a 2xxx series wrought aluminum alloy when visual inspection determines that the piece of material consists of a wrought aluminum alloy and a sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the piece of material is greater than a first predetermined value and (2) the measured amount of copper in the piece of material is greater than a second predetermined value; or b) sorting the piece of material from the stream of the piece of material in such a manner that the piece of material is classified as a 7xxx series wrought aluminum alloy when visual inspection determines that the piece of material consists of a wrought aluminum alloy and a sensor system classification determines that (1) the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is less than a first predetermined value and (2) the measured amount of zinc in the piece of material is greater than a second predetermined value; or c) sorting the pieces of material from the stream of the pieces of material in such a manner that the pieces of material are classified as 3xxx and / or 5xxx and / or 6xxx series wrought aluminum alloys if: (1) visual inspection confirms that the material piece is composed of a wrought aluminum alloy, and (2) A first sensor system classification of the one or more sensor system classifications determines: (i) a ratio of a measured amount of copper to a measured amount of zinc in the piece of material is no greater than a first predetermined value, and (ii) the measured amount of copper in the piece of material is no greater than a second predetermined value, and (3) A second sensor system classification of the one or more sensor system classifications determines: (i) said ratio of the measured amount of copper to the measured amount of zinc in said piece of material is not less than a third predetermined value, and (ii) the measured amount of zinc in the piece of material is no greater than a fourth predetermined value.

4. The method according to claim 2, wherein: Sorting one or more of the cast aluminum alloys and the wrought aluminum alloys from the conveyed stream of material into one or more second classification groups comprises one of a), b), c), or d): a) sorting the material piece from the stream of material pieces as being classified as 360 cast aluminum alloy when visual inspection determines that the material piece is not composed of a wrought aluminum alloy and a first sensor system classification of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is not greater than a first predetermined value and a second sensor system classification of the one or more sensor system classifications determines that the measured amount of iron in the material piece is greater than a second predetermined value; or b) sorting the piece of material from the stream of the piece of material in such a manner that the piece of material is classified as 356 cast aluminum alloy when visual inspection determines that the piece of material is not composed of a wrought aluminum alloy and a first sensor system classification of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the piece of material is not greater than a first predetermined value and a second sensor system classification of the one or more sensor system classifications determines that the measured amount of iron in the piece of material is not greater than a second predetermined value; or c) sorting the material pieces from the stream of the material pieces in such a manner that the material pieces are classified as 38x cast aluminum alloy if: (1) visual inspection confirms that the material piece is composed of a cast aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the piece of material is greater than a first predetermined value, and (3) a second sensor system classification 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 piece of material is greater than a second predetermined value and less than a third predetermined value; or d) sorting the material pieces from the stream of the material pieces in such a manner that the material pieces are classified as 319 cast aluminum alloy under the following circumstances: (1) visual inspection confirms that the material piece is composed of a cast aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the piece of material is greater than a first predetermined value, and (3) A second sensor system classification 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 piece of material is greater than a second predetermined value and less than a third predetermined value.

5. The method of claim 4 , further comprising sorting the material pieces from the stream of the material pieces in such a manner that the material pieces are classified as die-cast zinc parts when: (1) visual inspection confirms that the material piece does not consist of a wrought aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the piece of material is greater than a first predetermined value, and (3) A second sensor system classification 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 piece of material is less than a second predetermined value.

6. The method according to any one of claims 2 to 5, wherein: The stream of conveyed material includes Zorba material, and the method further includes sorting one or more waste materials from the stream of conveyed material based on one or more visual inspections, wherein the sorting of the waste materials is performed before the sorting of the one or more wrought aluminum alloys and the sorting of the one or more cast aluminum alloys.

7. The method of any one of claims 2-5, further comprising sorting the extruded aluminum alloy from the stream of conveyed material into one or more third classification groups based on a third combination of the one or more visual inspections, wherein Said sorting of said one or more extruded aluminium alloys from the conveyed material stream is performed after said sorting of said one or more wrought aluminium alloys from the conveyed material stream and before said sorting of said one or more cast aluminium alloys from the conveyed material stream.

8. The method according to any one of claims 2 to 5, wherein: Each of the one or more sensor system classifications is performed by a spectroscopic system; Optionally, the spectroscopic system is an x-ray fluorescence system.

9. The method according to any one of claims 2 to 5, wherein: each of the one or more visual inspections is performed by a single vision system that implements one or more AI models within the AI ​​system, and wherein each of the one or more sensor system classifications is performed by a single spectroscopic system that implements one or more algorithms for analyzing spectroscopic data collected from the piece of material; Optionally, the spectroscopic system is an x-ray fluorescence system.

10. A method for sorting pieces of material from a conveyed stream of Zorba material, comprising: performing a visual inspection on each material piece within the conveyed stream of Zorba material, wherein each of the visual inspections includes classifying each material piece based on processing a visual image captured from each material piece by an artificial intelligence ("AI") system, wherein the visual inspections are performed by a single vision system that implements a different AI model within the AI ​​system for each of the visual inspections; performing a sensor system classification on each material piece within the conveyed stream of Zorba material, wherein each of the sensor system classifications is performed by a different algorithm that analyzes spectral data collected from each material piece by a single spectral system; sorting a plurality of different pieces of wrought aluminum alloy material from a conveyed stream of Zorba material into individually sorted classification groups based on a first combination of one or more of the visual inspections and one or more of the sensor system classifications; and sorting a plurality of different pieces of cast aluminum alloy material from the conveyed stream of Zorba material into individually sorted classified groups based on a second combination of one or more of the visual inspections and one or more of the sensor system classifications; Optionally, wherein said sorting of said plurality of different pieces of wrought aluminium alloy material from said conveyed stream of Zorba material is performed before said sorting of said plurality of different pieces of cast aluminium alloy material from said conveyed stream of Zorba material; Optionally, wherein the sensor system classification is based on a measured amount of copper and a measured amount of zinc in the piece of material; Optionally, wherein the single spectroscopic system is an x-ray fluorescence system.

11. The method according to claim 10, wherein: Sorting the various different pieces of wrought aluminum alloy material from the conveyed stream of Zorba material into individually sorted classification groups includes one of a), b), or c): a) sorting the material piece from the conveyed stream of Zorba material such that the material piece is classified as a 2xxx series wrought aluminum alloy when visual inspection determines that the material piece consists of a wrought aluminum alloy and a first sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the material piece is greater than a first predetermined value and (2) the measured amount of copper in the material piece is greater than a second predetermined value; or b) sorting the piece of material from the conveyed stream of Zorba material such that the piece of material is classified as a 7xxx series wrought aluminum alloy when the visual inspection determines that the piece of material consists of a wrought aluminum alloy and a second sensor system classification determines that (1) the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is less than a third predetermined value and (2) the measured amount of zinc in the piece of material is greater than a fourth predetermined value; or c) sorting the material pieces from the conveyed stream of Zorba material in such a manner that the material pieces are classified as 3xxx and / or 5xxx and / or 6xxx series wrought aluminum alloys when the second sensor system classification determines that (1) the ratio of the measured amount of copper to the measured amount of zinc in the material piece is not less than the third predetermined value and (2) the measured amount of zinc in the material piece is not greater than the fourth predetermined value.

12. The method according to claim 10, wherein: Sorting the plurality of different cast aluminum alloy material pieces from the conveyed stream of Zorba material into individually sorted classification groups includes one of a), b), c) or d): a) sorting the material piece from the conveyed stream of Zorba material such that the material piece is classified as 360 cast aluminum alloy when visual inspection determines that the material piece consists of a cast aluminum alloy, a first sensor system classification determines that the total measured amount of copper and zinc in the material piece is less than a first predetermined value, and a second system classification determines that the measured amount of iron in the material piece is greater than a second predetermined value; or b) sorting the piece from the conveyed stream of Zorba material such that the piece is classified as 356 cast aluminum alloy when the visual inspection determines that the piece consists of a cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the piece is less than the first predetermined value, and the second system classification determines that the measured amount of iron in the piece is less than the second predetermined value; or c) sorting the piece of material from the conveyed stream of Zorba material such that the piece of material is classified as a 38x cast aluminum alloy when the visual inspection determines that the piece of material is composed of a cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the piece of material is greater than a first predetermined value, and the third sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the piece of material is greater than a third predetermined value and less than a fourth predetermined value; or d) sorting the material pieces from the conveyed stream of Zorba material in such a manner that they are classified as 319 cast aluminum alloy when the visual inspection determines that the material pieces are composed of a cast aluminum alloy, the first sensor system classification determines that the total measured amounts of copper and zinc in the material pieces are greater than the first predetermined value, and the fourth sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the material pieces is greater than the fourth predetermined value and less than a fifth predetermined value.

13. The method according to claim 12, further comprising: When the visual inspection determines that the material piece is composed of a cast aluminum alloy, the first sensor system classification determines that the total measured amount of copper and zinc in the material piece is greater than the first predetermined value, and the fifth sensor system classification determines that the ratio of the measured amount of copper to the measured amount of zinc in the material piece is less than the third predetermined value, the material piece is sorted from the stream of conveyed Zorba material in a manner that is classified as a die-cast zinc part.

14. A system for sorting material pieces, comprising: a conveyor system configured to convey a stream of material; a vision system configured to perform one or more visual inspections on each material piece within the stream of conveyed material, wherein each of the one or more visual inspections includes classifying each material piece based on processing visual images captured from each material piece by an artificial intelligence ("AI") system; a sensor system configured to perform one or more sensor system classifications on each piece of material within the stream of conveyed material; and A sorting device configured to sort pieces of material from the stream of conveyed material into one or more sorting groups in response to instructions received from a combination of the one or more visual inspections and the one or more sensor system sorting.

15. The system according to claim 14, wherein: The stream of conveyed material comprises one or more wrought aluminum alloys and one or more cast aluminum alloys, wherein the sorting device is configured to: sorting one or more of the wrought aluminum alloys from the conveyed stream of material into one or more first sorting groups in response to instructions received from a first combination of the one or more visual inspections and the one or more sensor system sorting; and sorting one or more of the cast aluminum alloys from the conveyed stream of material into one or more second sorting groups in response to instructions received from a second combination of the one or more visual inspections and the one or more sensor system sorting; Optionally, wherein said sorting of said one or more wrought aluminium alloys from the conveyed stream of material is performed before said sorting of said one or more cast aluminium alloys from the conveyed stream of material. Optionally, wherein the first combination includes visual inspection and one or more sensor system classification, wherein the visual inspection is used to determine whether the material piece is composed of a wrought aluminum alloy, and the one or more sensor system classification is based on a measured amount of copper and a measured amount of zinc in the material piece; Optionally, wherein the second combination includes visual inspection and one or more sensor system classifications, the visual inspection being used to determine whether the material piece is composed of a cast aluminum alloy, the one or more sensor system classifications being based on a measured amount of copper and a measured amount of zinc in the material piece; Optionally, the second combination further comprises a visual inspection for determining whether the material piece is composed of a wrought aluminum alloy.

16. The system according to claim 15, wherein: The sorting device is configured to sort one or more of the wrought aluminium alloys from the stream of conveyed material into one or more first classification groups comprising one of a), b) or c): a) the sorting device is configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as 2xxx series wrought aluminum alloys in response to instructions received from a combination of visual inspection that determines that the material pieces are composed of a wrought aluminum alloy and sensor system classification that determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the material pieces is greater than a first predetermined value and (2) the measured amount of copper in the material pieces is greater than a second predetermined value; or b) the sorting device is configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as 7xxx series wrought aluminum alloys in response to instructions received from a combination of visual inspection and sensor system classification: the visual inspection determines that the material pieces are composed of a wrought aluminum alloy, and the sensor system classification determines that (1) a ratio of a measured amount of copper to a measured amount of zinc in the material pieces is less than a first predetermined value and (2) the measured amount of zinc in the material pieces is greater than a second predetermined value; or c) the sorting device is configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as 3xxx and / or 5xxx and / or 6xxx series wrought aluminum alloys in response to instructions received from a combination of: (1) visual inspection confirms that the material piece is composed of a wrought aluminum alloy, and (2) A first sensor system classification of the one or more sensor system classifications determines: (i) a ratio of a measured amount of copper to a measured amount of zinc in the piece of material is no greater than a first predetermined value, and (ii) the measured amount of copper in the piece of material is no greater than a second predetermined value, and (3) A second sensor system classification of the one or more sensor system classifications determines: (i) said ratio of the measured amount of copper to the measured amount of zinc in said piece of material is not less than a third predetermined value, and (ii) the measured amount of zinc in the piece of material is no greater than a fourth predetermined value.

17. The system according to claim 15, wherein: The sorting device is configured to sort one or more of the cast aluminium alloys from the stream of conveyed material into one or more second classification groups comprising one of a), b), c) or d): a) the sorting device is configured to sort the material pieces from the stream of material pieces in response to instructions received from a combination of a first visual inspection, a first sensor system classification of the one or more sensor system classifications, and a second sensor system classification of the one or more sensor system classifications, in such a manner that the material pieces are classified as 360 cast aluminum alloy: the visual inspection determines that the material pieces are not composed of a wrought aluminum alloy, the first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the material piece is not greater than a first predetermined value, and the second sensor system classification of the one or more sensor system classifications determines that a measured amount of iron in the material piece is greater than a second predetermined value; or a) the sorting device is configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as 356 cast aluminum alloy in response to instructions received from a combination of a first visual inspection, the first of the one or more sensor system classifications, and the second of the one or more sensor system classifications: the visual inspection determines that the material pieces are not composed of a wrought aluminum alloy, the first of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the material piece is not greater than the first predetermined value, and the second of the one or more sensor system classifications determines that the measured amount of iron in the material piece is not greater than the second predetermined value; or c) the sorting device is configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as 38x cast aluminum alloy in response to instructions received from a combination of: (1) the visual inspection determines that the material piece is composed of a cast aluminum alloy, and (2) a first sensor system classification of the one or more sensor system classifications determines that a total measured amount of copper and zinc in the piece of material is greater than a first predetermined value, and (3) a second sensor system classification 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 piece of material is greater than a second predetermined value and less than a third predetermined value; or c) the sorting device is configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as 319 cast aluminum alloy in response to instructions received from a combination of: (1) the visual inspection determines that the material piece is composed of a cast aluminum alloy, and (2) the first sensor system classification of the one or more sensor system classifications determines that the total measured amount of copper and zinc in the piece of material is greater than the first predetermined value, and (3) A third sensor system classification 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 piece of material is greater than the third predetermined value and less than a fourth predetermined value.

18. The system of claim 17, further comprising the sorting device, the sorting device being configured to sort the material pieces from the stream of material pieces in such a manner that the material pieces are classified as die-cast zinc pieces in response to instructions received from a combination of: (1) the first visual inspection determines that the material piece is not composed of a wrought aluminum alloy, and (2) the first of the one or more sensor system categories determines that the total measured amount of copper and zinc in the piece of material is greater than a first predetermined value, and (3) A third sensor system classification 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 piece of material is less than a third predetermined value.

19. The system according to any one of claims 15 to 18, wherein: The stream of conveyed material includes Zorba material, and the system further includes the sorting device, which is configured to sort one or more waste materials from the stream of conveyed material in response to instructions received from a combination of one or more visual inspections, wherein the sorting of the waste materials is performed before the sorting from the one or more wrought aluminum alloys and the sorting from the one or more cast aluminum alloys.

20. The system of any one of claims 15-18, further comprising the sorting device configured to sort the extruded aluminum alloy from the stream of conveyed material into one or more third classification groups in response to instructions received from the third combination of the one or more visual inspections, wherein Said sorting of said one or more extruded aluminium alloys from the conveyed material stream is performed after said sorting of said one or more wrought aluminium alloys from the conveyed material stream and before said sorting of said one or more cast aluminium alloys from the conveyed material stream.

21. The system according to any one of claims 15 to 18, wherein: Each of the one or more sensor system classifications is performed by a spectroscopic system; Optionally, wherein the sensor system is an x-ray fluorescence system.

22. The system according to any one of claims 15 to 18, wherein: each of the one or more visual inspections is performed by a single vision system that implements one or more AI models within the AI ​​system, and wherein each of the one or more sensor system classifications is performed by a single spectroscopic system that implements one or more algorithms for analyzing spectroscopic data collected from the piece of material; Optionally, the spectroscopic system is an x-ray fluorescence system.

23. A computer program product comprising instructions for causing a system according to any one of claims 14 to 18 to perform a method according to any one of claims 1 to 5.

24. A computer-readable storage medium having stored thereon the computer program product according to claim 23.

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