Correction techniques for material classification

Abstract

When X-ray fluorescence ("XRF") spectroscopy is used to classify materials transported on a moving conveyor belt, the X-ray beam may only partially illuminate the material piece, resulting in an inaccurate capture of the XRF spectrum needed to classify the material piece. This can result in improper (mis)classification and consequent sorting of the material piece (e.g., aluminum alloy). In a material handling system, the area of ​​intersection between the X-ray beam spot from the X-ray fluorescence system and the material piece is measured and used to correspondingly correct the measured XRF spectrum associated with each material piece. The material pieces can then be sorted according to the corrected XRF spectrum.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to US Provisional Patent Application No. 63 / 478,823 and US Provisional Patent Application No. 63 / 418,242, both of which are incorporated herein by reference.

[0002] FIELD OF THE DISCLOSURE The present disclosure relates generally to material handling, and more particularly to sorting and / or separating materials. [Background technology]

[0003] This section is intended to introduce various aspects of the art that may be related to example embodiments of the present disclosure. This discussion is believed to help provide a framework to facilitate a better understanding of certain aspects of the present disclosure. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.

[0004] Recycling is the process of collecting materials that would otherwise be discarded as trash, treating them, and turning them into new products. Recycling benefits local communities and the environment because it reduces the amount of waste sent to landfills and incinerators, conserves natural resources, increases economic security by utilizing domestic material sources, prevents pollution by reducing the need to collect new raw materials, and saves energy. After collection, recyclables are typically sent to materials recovery facilities where they are sorted, cleaned, and processed into materials that can be used in manufacturing.

[0005] Recycling aluminum (Al) scrap is a highly attractive proposition, as it can save up to 95% of the energy costs associated with its production compared to the labor-intensive extraction of more expensive primary aluminum. Primary aluminum is defined as aluminum derived from aluminum-rich ores, such as bauxite. Meanwhile, demand for aluminum has steadily increased in markets such as car manufacturing due to its light weight. As a result, the aluminum industry can gain certain economies by developing well-planned yet simple recycling programs or systems. The use of recycled materials represents a cheaper metal resource than primary sources of aluminum. As the amount of aluminum sold to the automotive industry (and other industries) increases, there will be an increasing need to use recycled aluminum to supplement the availability of primary aluminum.

[0006] Correspondingly, efficient separation of aluminum scrap metal by alloy family is particularly desirable because mixed aluminum scrap from the same alloy family is much more valuable than indiscriminately mixed alloys. For example, in the blending methods used to recycle aluminum, any quantity of scrap composed of similar or identical alloys and of consistent quality is more valuable than scrap composed of mixed aluminum alloys. In such aluminum alloys, aluminum always constitutes the majority of the material. However, components such as copper, magnesium, silicon, iron, chromium, zinc, manganese, and other alloying elements provide various properties to alloyed aluminum and provide a means of distinguishing one aluminum alloy from another. Each individual aluminum alloy is a mixture of alloys in which aluminum (Al) is the predominant metal. Various other alloys, including magnesium (Mg), copper (Cu), silicon (Si), zinc (Zn), and other metals, are used to create each distinct aluminum alloy. As a result, each individual aluminum alloy has its own unique chemical and mechanical properties (and ranges), such as tensile strength, yield strength, elongation, and other physical properties.

[0007] The American Aluminum Association is the organization that defines the allowable limits for the chemical composition of aluminum alloys. Chemical composition data for aluminum wrought alloys is published by the American Aluminum Association in "International Alloy Designations and Chemical Composition Limits for Wrought Aluminum and Wrought Aluminum Alloys," updated January 2015, which is incorporated herein by reference.

[0008] The International Alloy Designation System is the most widely accepted naming scheme for wrought alloys. Each alloy is given a four-digit number (xxxx), where the first digit (Xxxx) indicates the major alloying element, the second digit (xXxx), if different from "0," indicates the alloy variant, and the third and fourth digits (xxXX) are arbitrary digits used to identify specific alloys in the series. For example, in aluminum alloy 3105, the first digit "3" indicates that the aluminum alloy is manganese-based, the second digit "1" indicates the first modification of aluminum alloy 3005, and the third and fourth digits "05" identify the specific alloy in the 3000 series. Generally, the 1xxx series of wrought aluminum alloys consists essentially of pure aluminum with a minimum aluminum content of 99% by weight, the 2xxx series consists of wrought aluminum alloyed primarily with copper (Cu), the 3xxx series consists of wrought aluminum alloyed primarily with manganese (Mn), the 4xxx series consists of wrought aluminum alloyed primarily with silicon (Si), the 5xxx series consists of wrought aluminum alloyed primarily with magnesium (Mg), the 6xxx series consists of wrought aluminum alloyed primarily with magnesium and silicon, the 7xxx series consists of wrought aluminum alloyed primarily with zinc (Zn), and the 8xxx series consists of other categories. The American Aluminum Association also has a similar document for cast aluminum alloys.

[0009] The presence of mixed pieces of different alloys (i.e., heterogeneous mixtures) within a chunk of scrap limits the ability of the scrap mixture to be usefully recycled unless the different alloys (or at least alloys belonging to different compositional families, such as those designated by the American Aluminum Association) can be separated (e.g., sorted) prior to remelting. This is because when mixed scrap of multiple different alloy compositions or compositional families is remelted, the resulting molten mixture contains too high a proportion of the major alloys and elements (or different compositions) to meet the compositional limits required for a particular commercial alloy.

[0010] Additionally, recycling sheet metal scrap, including that generated during the manufacturing of automotive components from sheet aluminum, is increasingly desirable, as evidenced by the significant increase in body and frame parts made of aluminum instead of steel in the production and sale of the Ford F-150 pickup. Scrap recycling involves remelting scrap to provide a molten mass of metal, which can be cast and / or rolled into aluminum parts useful in the further production of such vehicles. However, automotive manufacturing scrap (as well as scrap metal from other sources, such as airplanes and commercial and household appliances) often contains a mixture of scrap pieces of forged and cast pieces and / or two or more aluminum alloys substantially different in composition. A specific example of mixed aluminum sheet manufacturing scrap generated in certain current automotive manufacturing processes is a mixture of pieces of one or more American Aluminum Association 5000 series alloys and one or more American Aluminum Association 6000 series alloys. Thus, those skilled in the art of aluminum alloys will appreciate the difficulty of separating aluminum alloys, particularly processed alloys such as cast, forged, extruded, rolled, and generally wrought alloys, into reusable or recyclable processed products. Most of these alloys cannot be distinguished by visual inspection or other conventional scrap sorting techniques, such as density and / or eddy current techniques. Thus, for example, separating 2000, 3000, 5000, 6000, and 7000 series alloys is a difficult task, and furthermore, the ability to separate between aluminum alloys within the same American Aluminum Association series has not been achieved in the prior art.

[0011] As a result, the aluminum industry can gain certain economies by developing a well-planned yet simple recycling program or system. The use of recycled materials results in a cheaper metal resource than primary sources of aluminum. As the amount of aluminum sold to the automotive industry (and other industries) increases, there will be an increasing need to use recycled aluminum to supplement the availability of primary aluminum. [Prior art documents] [Non-patent literature]

[0012] [Non-Patent Document 1] American Aluminum Association, "International Alloy Designations and Chemical Composition Limits for Wrought Aluminum and Wrought Aluminum Alloys," updated January 2015 [Brief explanation of the drawings]

[0013] [Figure 1] 1 shows a schematic diagram of a material handling system configured in accordance with an embodiment of the present disclosure. [Figure 2] 1 illustrates an exemplary representation of a control set of pieces of material used during a training phase in an artificial intelligence (“AI”) system. [Figure 3] FIG. 1 shows a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 4] FIG. 1 shows a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 5] 1 illustrates a block diagram of a data processing system configured in accordance with an embodiment of the present disclosure. [Figure 6] 1 illustrates an exemplary X-ray fluorescence ("XRF") system. [Figure 7] An example of misalignment between the X-ray beam spot and the material piece is shown. [Figure 8] 1 shows an example of a singulator. [Figure 9] 1 shows an example of misalignment between an X-ray beam spot and a thin strip of material. [Figure 10] 1 shows a depiction of an exemplary thin strip piece of material. [Figure 11A] 1 shows an exemplary XRF spectrum demonstrating how erroneous XRF readings or measurements can result from improper alignment or intersection of the X-ray beam spot with a piece of material. [Figure 11B] 1 shows an exemplary XRF spectrum demonstrating how erroneous XRF readings or measurements can result from improper alignment or intersection of the X-ray beam spot with a piece of material. [Figure 11C] 1 shows an exemplary XRF spectrum demonstrating how erroneous XRF readings or measurements can result from improper alignment or intersection of the X-ray beam spot with a piece of material. [Figure 11D] 1 shows an exemplary XRF spectrum demonstrating how erroneous XRF readings or measurements can result from improper alignment or intersection of the X-ray beam spot with a piece of material. [Figure 11E] 1 shows an exemplary XRF spectrum demonstrating how erroneous XRF readings or measurements can result from improper alignment or intersection of the X-ray beam spot with a piece of material. [Figure 11F] 1 shows an exemplary XRF spectrum demonstrating how erroneous XRF readings or measurements can result from improper alignment or intersection of the X-ray beam spot with a piece of material. [Figure 12] FIG. 1 shows a flow chart diagram configured in accordance with an embodiment of the present disclosure. [Figure 13] 1 shows a non-limiting example of a measured XRF spectrum of a piece of material. [Figure 14] An example of partial irradiation of a piece of material by an X-ray beam spot is shown. [Figure 15] 1 shows an XRF spectrum of an exemplary piece of material corrected / modified according to an embodiment of the present disclosure. [Figure 16]FIG. 1 illustrates a flowchart diagram of a process configured to correct / correct an XRF spectrum of a piece of material, according to certain embodiments of the present disclosure. [Figure 17] A non-limiting example is shown in which the location of a piece of material (eg, on a moving conveyor belt) is determined by a laser line from a profilometer. [Figure 18] 1 shows a flow chart diagram configured in accordance with certain embodiments of the present disclosure. [Figure 19] 1 illustrates a flowchart diagram of a process for recycling end-of-life (“EOL”) objects, including but not limited to vehicles, aircraft, or appliances. [Figure 20] FIG. 1 shows a simplified schematic diagram of a laser camera-based profilometer configured in accordance with an embodiment of the present disclosure. [Figure 21] 21 shows a demonstration of the use of the laser camera-based profilometer of FIG. 20. [Figure 22A] 10 shows an alternative embodiment in which multiple lasers are utilized in combination with a camera. [Figure 22B] 10 shows an alternative embodiment in which multiple lasers of different colors are utilized in combination with a camera. [Figure 23] 10 shows an alternative embodiment in which lasers are utilized on both sides of the camera. [Figure 24] 1 shows a flow chart diagram configured in accordance with certain embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Various detailed embodiments of the present disclosure are disclosed herein. However, it should be understood that the disclosed embodiments are merely exemplary of the present disclosure, which may be embodied in various alternative forms. The figures are not necessarily to scale, and some features may be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details disclosed herein should not be construed as limiting, but merely as a representative basis for teaching those skilled in the art to employ various embodiments of the present disclosure.

[0015] As used herein, "material" refers to metals (ferrous and / or non-ferrous), metal alloys (e.g., aluminum alloys), Heavies, Zorba, Twitch, pieces of metal embedded in another, different material, plastics / polymers (including but not limited to any of the plastics / polymers disclosed herein, known in the industry, or newly created in the future), rubber, foams, glass (including but not limited to borosilicate or soda lime glass, and various colored glasses), ceramics, paper, cardboard, Teflon, PE, bundles of wire, insulated wire, rare earth elements, leaves, wood, plants, plant parts, textiles, biowaste, packaging, e-waste, batteries and accumulators. The term "recycling material" may include any item or object including, but not limited to, waste, scrap from end-of-life vehicles, mining, construction, and demolition waste, crop waste, forest residues, purpose-grown grasses, woody energy crops, microalgae, food waste, hazardous chemical and biomedical waste, construction debris, agricultural waste, biological items, non-biological items, objects with a particular carbon content, any other object that may be found in municipal solid waste, and any other object, item, or material disclosed herein, including 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.

[0016] In a more general sense, a "material" may include any item or object composed of a chemical element, a compound or mixture of chemical elements, or a compound or mixture of compounds or mixtures of chemical elements, with the compound or mixture ranging in complexity from simple to complex (all of which may be referred to herein as materials having a particular "chemical composition" (also referred to herein as a particular "material composition"). "Chemical element" means a chemical element in the Periodic Table of Chemical Elements, including chemical elements that may be discovered after the filing date of this application. Within this disclosure, the terms "scrap," "scrap piece," "material," "material piece," and "material scrap piece" may be used interchangeably. As used herein, a material piece or scrap piece referred to as having a metal alloy composition is a metal alloy having a particular chemical composition that distinguishes it from other metal alloys. As used herein, a "contaminant" is any material or material piece component that should be excluded from a group of sorted materials.

[0017] As used herein, the term "predetermined" refers to something that is established or determined in advance, such as by a user of an embodiment of the present disclosure.

[0018] As used herein, "known" means that the characteristics have been previously established and are therefore already known. As used herein, "spectral imaging" refers to imaging using one or more bands across the electromagnetic spectrum. While a typical camera captures images composed of light across three wavelength bands in the visible spectrum (e.g., red, green, and blue (RGB)), spectral imaging can encompass a wide variety of techniques, including and beyond the typical visible spectrum. For example, spectral imaging may use infrared, visible, ultraviolet, and / or X-ray spectra, or some combination of the above. Spectral data, or spectral image data, is a digital data representation of a spectral image. Spectral imaging may involve simultaneous acquisition of spectral data within visible and non-visible bands, illumination from outside the visible range, or the use of optical filters to capture specific spectral ranges. It is also possible to capture hundreds of wavelength bands per pixel in a spectral image.

[0019] As used herein, the term "image data packet" refers to a packet of digital data relating to a captured spectral image of an individual piece of material.

[0020] As used herein, the term "sorting" and any derivatives thereof refers to the physical separation of particular pieces of material (e.g., specifically classified pieces of material) from other pieces of material.

[0021] As used herein, the terms “identify” and “classify,” “identification” and “classification,” and any derivatives thereof, may be used interchangeably. As used herein, to “classify” a piece of material is to assign or determine (i.e., identify) the type or class of material to which the piece of material belongs. For example, according to certain embodiments of the present disclosure, a sensor system (described further herein) may be configured to capture and analyze any type of information to classify materials and distinguish such classified materials from other materials, which classification may be utilized in a sorting system to selectively sort pieces of material according to a set of one or more physical and / or chemical properties (which may be, for example, user-defined), including, but not limited to, color, texture, hue, shape, brightness, weight, density, chemical composition, size, uniformity, manufacturing type, chemical signature, predetermined fraction, radioactive signature, transmittance to light, sound, or other signals, and response to stimuli such as a wide variety of fields, including emitted and / or reflected electromagnetic radiation (“EM”) from the piece of material.

[0022] The types or classes (i.e., classifications) of material pieces may be user-definable (e.g., predetermined) and are not limited to any known classification of materials. The granularity of the types or classes may range from very coarse to very fine. For example, the types or classes may include plastics, ceramics, glasses, metals, and other materials, where the granularity of such types or classes is relatively coarse; different metals and metal alloys, such as zinc, copper, brass, chrome-plated, and aluminum, where the granularity of such types or classes is finer; or specific types intermediate between metal alloys, where the granularity of such types or classes is relatively fine. Thus, the types or classes may be configured to distinguish between materials of significantly different chemical compositions, such as plastics and metal alloys, or between materials of nearly identical chemical compositions, such as different types of metal alloys. It should be understood that the systems and methods discussed herein may be applied to accurately identify / classify pieces of material whose chemical composition is completely unknown before classification.

[0023] FIG. 19 illustrates a flowchart diagram of a process or series of processes 1900 for recycling end-of-life (“EOL”) objects, including, but not limited to, vehicles, aircraft, or appliances. Various steps and / or stages of process 1900 may be performed independently of one another by different parties. In process block 1901, objects are shredded, for example, by a commercially available shredder to generate EOL scrap. Often, such objects are EOL vehicles, aircraft, and / or appliances. A typical next step 1902 is the removal of ferrous materials from the scrap, such as with a magnet. For shredded objects containing one or more metals or metal alloys, such as vehicles, aircraft, and / or appliances, the remaining non-ferrous scrap is often referred to as Zorba 1903. Another commonly utilized process 1904 is the use of separation / sorting techniques to remove various specific materials from Zorba 1903, such as heavies and fluff (e.g., foam, fabric, wood, etc.). The remaining scrap is then often referred to as Twitch 1905, which may include various aluminum alloys. It is then often desirable to sort Twitch 1905 according to one or more different processes 1906, which may produce sorted alloys 1907. Embodiments of the present disclosure may be implemented within one or more of processes 1902, 1904, 1906.

[0024] The systems and methods described herein, according to certain embodiments of the present disclosure, receive a heterogeneous mixture of multiple pieces of material (e.g., EOL scrap, Zorba, Heavies, or Twitch), where at least one piece of material within the heterogeneous mixture is composed of 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 the other pieces of material, and / or at least one piece of material within the heterogeneous mixture is of a different class or type of material from the other pieces of material within the mixture, and the systems and methods are configured to identify / classify / separate / sort the one piece of material into a group separate from such other pieces of material. Embodiments of the present disclosure may be utilized to classify any type or class of material, as defined herein. In contrast, a homogeneous set or group of materials are all within the same identifiable class or type of material.

[0025] Although all embodiments of the present disclosure may be utilized to sort / separate any type of material as defined herein, embodiments of the present disclosure are described below for sorting / separating pieces of metal alloy scrap (also referred to as "metal alloy scrap pieces"), including aluminum alloy scrap pieces.

[0026] X-ray fluorescence ("XRF") spectroscopy provides a method for identifying elements and their relative amounts present in different materials through the use of characteristic X-rays that are emitted (fluoresce) under excitation by an X-ray beam, which can then be used to classify each material. The energy of the emitted X-rays depends on the atomic number of the fluorescing element. An energy-resolving detector is then used to detect the different energy levels at which the X-rays fluoresce and generate an X-ray fluorescence signal from the detected X-rays. This X-ray fluorescence signal may then be used to construct an energy spectrum (also called an "XRF spectrum") of the detected X-rays, and the information may identify one or more elements in the material from which the X-ray fluorescence was generated. X-ray fluorescence is isotopically emitted by the irradiated element, and the detected radiation depends on the solid angle subtended by the detector and any absorption of this radiation before it reaches the detector. The lower the energy of the X-rays, the shorter the distance they must travel before being absorbed by air. Thus, when detecting X-rays, the amount of X-rays detected depends on the amount of X-rays emitted, the energy level of the emitted X-rays, the amount of emitted X-rays absorbed in the medium (e.g., air and / or a non-vacuum environment, or a vacuum environment), the angle between the detected X-rays and the detector, and the distance between the detector and the irradiated material.

[0027] The emitted X-ray beam causes each piece of material to fluoresce at different energy levels depending on the elements contained in the piece. The fluorescent X-rays are detected and the pieces can then be classified based on the fluorescent X-rays. The pieces can then be sorted according to this classification.

[0028] In embodiments of the present disclosure, the detected x-ray fluorescence from the piece of material is utilized to identify some or all of the elements present in the piece of material, including the amounts or relative amounts of such elements. The embodiments of the present disclosure then utilize the identification of such elements to identify the type of material (e.g., a particular aluminum alloy) associated with the detected x-ray fluorescence. Furthermore, the embodiments of the present disclosure utilize the identification of elements in the piece of material to classify the piece of material according to predetermined criteria. For example, according to embodiments of the present disclosure, the detected x-ray fluorescence from an aluminum alloy material (e.g., aluminum alloy scrap piece) may be utilized to assign an aluminum alloy classification to the piece of material (including according to an aluminum alloy classification designated by the American Aluminum Association).

[0029] Embodiments of the present disclosure are described herein as sorting material pieces into separate groups by physically depositing (e.g., discharging) the material pieces into separate containers or bins according to user-defined groupings (e.g., classifications of the material pieces). By way of example, in embodiments of the present disclosure, material pieces are sorted into separate containers to separate material pieces comprised of a particular material composition or compositions from other material pieces comprised of a different material composition. Additionally, embodiments of the present disclosure may be configured to sort aluminum alloy scrap pieces into separate containers such that substantially all of the aluminum alloy scrap pieces having a material composition that falls within one of the aluminum alloy systems published by the American Aluminum Association are sorted into a single container (e.g., the container may correspond to one or more specific aluminum alloy systems (e.g., 1xxx, 2xxx, 3xxx, 4xxx, 5xxx, 6xxx, 7xxx, 8xxx)).

[0030] Additionally, as described herein, embodiments of the present disclosure may be configured to separate aluminum alloy scrap pieces into separate containers according to their alloy composition classifications, even if such alloy compositions fall into the same American Aluminum Association system. As a result, a sorting system configured according to embodiments of the present disclosure may separate and separate aluminum alloy scrap pieces having compositions that would all fall into a single aluminum alloy system (e.g., the 5xxx system or the 6xxx system) into separate containers according to their aluminum alloy composition. For example, embodiments of the present disclosure may separate and separate aluminum alloy scrap pieces classified as aluminum alloy 5086 from aluminum alloy 5022, or 6xx3 aluminum alloys from 6xx2 aluminum alloys.

[0031] FIG. 1 illustrates an example of a material handling system 100 configured in accordance with various embodiments of the present disclosure. A conveyor system 103 may be implemented to transport individual pieces of material 101 through the material handling system 100 so that each of the individual pieces of material 101 may be tracked, sorted, segregated, and / or separated into predetermined desired groups (e.g., classifications). Such a conveyor system 103 may be implemented using one or more conveyor belts on which the pieces of material 101 typically travel at a predetermined constant speed. However, certain embodiments of the present disclosure may be implemented using other types of conveyor systems, including systems in which the pieces of material free-fall through one or more of the various components of the material handling system 100 (or any other type of vertical sorter or other conveyor system disclosed herein). Hereinafter, where applicable, the conveyor system 103 may also be referred to as a conveyor belt 103. In one or more embodiments, some or all of the conveying, capturing, stimulating, detecting, classifying, differentiating, and sorting operations or functions may be performed automatically, i.e., without human intervention. For example, in material handling system 100, one or more cameras, one or more vision systems, one or more stimulus sources, one or more emission detectors, one or more classification modules, sorting devices, and / or other system components may be configured to perform these and other operations automatically.

[0032] Additionally, while the simplified diagram of FIG. 1 depicts a single stream of material pieces 101 on the conveyor belt 103, embodiments of the present disclosure may be implemented such that multiple such streams of material pieces pass parallel to one another through various components of the material handling system 100 (see, e.g., FIG. 8). According to certain embodiments of the present disclosure, any suitable feeder mechanism (e.g., another conveyor system, a bowl feeder, or a hopper 102) may be utilized to feed the material pieces 101 onto the conveyor system 103, which then transports the material pieces 101 through various components within the material handling system 100. According to certain embodiments of the present disclosure, a tumbler and / or vibrator may be utilized to separate individual material pieces from a collection (e.g., a physical pile) of material pieces. According to certain embodiments of the present disclosure, the material pieces may be positioned into one or more singulated (i.e., single-file) streams, which may be performed by an active singulator or a passive singulator 106. An example of a passive singulator is further described with respect to FIG. 8 and in US Pat. No. 10,207,296.

[0033] Thus, certain embodiments of the present disclosure can simultaneously track, sort, differentiate, and / or separate such advancing streams of material pieces. Alternatively, the conveyor system (e.g., conveyor belt 103) may simply randomly transport a collection of material pieces deposited on conveyor belt 103. Thus, according to certain embodiments of the present disclosure, individualization of the material pieces 101 is not required to track, sort, differentiate, and / or separate the material pieces.

[0034] In certain embodiments of the present disclosure, the conveyor system 103 is operated to advance at a predetermined speed by a conveyor system motor 104. This predetermined speed may be programmable and / or adjustable by an operator in any known manner. In certain embodiments of the present disclosure, control of the conveyor system motor 104 and / or position detector 105 may be performed by an automated control system 108. Such an automated control system 108 may be operated under the control of a computer system 107, and / or functionality for performing the automated control may be implemented in software within the computer system 107. When the conveyor system 103 is a conveyor belt, it may be a conventional endless belt conveyor employing a conventional drive motor 104 suitable for moving the conveyor belt 103 at the predetermined speed.

[0035] A position detector 105 (e.g., a conventional encoder) may be operatively coupled to the conveyor belt 103 and the automation control system 108 to provide information corresponding to the movement (e.g., speed) of the conveyor belt 103. Thus, as described further herein, through utilization of control over the conveyor belt drive motor 104 and / or the automation control system 108 (and alternatively including the position detector 105), as each piece of material 101 traveling on the conveyor belt 103 is identified, each piece of material 101 can be tracked in location and time (relative to the various components of the material handling system 100) such that various components of the material handling system 100 can be activated / deactivated as each piece of material 101 passes nearby. As a result, the automation control system 108 can track the location of each piece of material 101 as it travels along the conveyor belt 103.

[0036] 1 , certain embodiments of the present disclosure may utilize a vision or optical recognition system 110 as a means of tracking each piece of material 101 as it progresses on the conveyor system 103, and the vision or optical recognition system 110 may utilize one or more still or live action cameras 109 to record the position (i.e., location and timing) of each piece of material 101 on the moving conveyor system 103. The vision system 110 may additionally or alternatively be configured to perform a particular type of identification (e.g., classification) of all or some of the pieces of material 101, as described further herein. For example, such a vision system 110 may be utilized to capture or obtain information regarding each of the pieces of material 101. For example, the vision system 110, as described herein, may be configured to capture or gather any type of information from the pieces of material (e.g., using an artificial intelligence (“AI”) system as described further herein), which information may be utilized within the material handling system 100 to classify and / or selectively sort the pieces of material 101 according to a set of one or more characteristics (e.g., physical and / or chemical and / or radioactive, etc.). According to certain embodiments of the present disclosure, the vision system 110 may be configured to capture visual images of each of the pieces of material 101 (including one-dimensional, two-dimensional, three-dimensional, or holographic imaging), for example, by using optical sensors such as those 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 may represent images captured within optical wavelengths of light (i.e., wavelengths of light observable by a typical human eye). However, alternative embodiments of the present disclosure may utilize sensor systems configured to capture images of materials composed of wavelengths of light outside the visual wavelengths of the human eye.

[0037] According to certain embodiments of the present disclosure, the vision system 110 may 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 may be implemented in LabVIEW.

[0038] According to certain embodiments of the present disclosure, the material handling system 100 may be implemented with one or more sensor systems 120 that may be utilized alone or in combination with the vision system 110 to classify / identify / differentiate the pieces of material 101. Sensor system 120 may be configured with any type of sensor technology that utilizes emitted or reflected electromagnetic radiation (e.g., infrared ("IR"), Fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("VNIR"), near infrared ("NIR"), short wavelength infrared ("SWIR"), long wavelength infrared ("LWIR"), mid-wavelength infrared ("MWIR" or "MIR"), X-ray transmission ("XRT"), gamma ray, ultraviolet ("UV"), X-ray fluorescence ("XRF"), laser-induced breakdown spectroscopy ("LIBS"), Raman spectroscopy, anti-Stokes Raman spectroscopy, gamma ray spectroscopy, hyperspectral spectroscopy (e.g., any range beyond visible wavelengths), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy, including one-dimensional, two-dimensional, or three-dimensional imaging by any of the foregoing), or with any other type of sensor technology, including, but not limited to, chemical or radioactive sensor technology.

[0039] The sensor system 120 may include, for example, an energy-emitting source 121 that may be powered by a power source 122 to stimulate a response from each of the pieces of material 101. In certain embodiments of the present disclosure, the sensor system 120 may emit an appropriate detection signal toward each piece of material 101 as it passes near the emission source 121. One or more detectors 124 may be positioned and configured to sense / detect one or more characteristics from the piece of material 101 in a manner appropriate for the type of sensor technology employed. The one or more detectors 124 and associated detector electronics 125 capture these received sensed characteristics and perform signal processing on the one or more detectors 124 and associated detector electronics 125 to generate digitized information (e.g., spectral data) representative of the sensed characteristics, which may then be analyzed according to certain embodiments of the present disclosure and used to classify each of the pieces of material 101. According to certain embodiments of the present disclosure, such a sensor system may be an XRF system as further described herein. An exemplary XRF system implementation (eg, for use as sensor system 120 herein) is further described in US Pat. No. 10,207,296.

[0040] 1 is shown with a combination of vision system 110 and one or more sensor systems 120, it should be noted that embodiments of the present disclosure may be implemented with any combination of sensor systems utilizing any of the sensor technologies disclosed herein or any other sensor technologies currently available or developed in the future. In certain embodiments of the present disclosure, a combination of both vision system 110 and one or more sensor systems 120 may be used to classify the pieces of material 101. Furthermore, embodiments of the present disclosure may include any combination of one or more sensor systems and / or vision systems, and the output of such sensor / vision systems may be processed within an AI system (as further disclosed herein) to classify / identify / distinguish materials from a heterogeneous mixture of materials, which may then be sorted from one another.

[0041] According to certain embodiments of the present disclosure, vision system 110 may be configured to capture additional information about each piece of material, including information that a separate sensor system alone cannot collect. For example, vision system 110 may be configured to capture information about the color, size, shape, and / or uniformity of the piece of material, which can aid in identifying / sorting the piece of material. Furthermore, vision system 110 may be configured to identify undesirable pieces of material (e.g., containing contaminants) and can send a signal to reject the piece of material before it reaches the sensor system.

[0042] According to certain embodiments of the present disclosure, the vision system 110 and / or sensor system may be configured to identify which pieces of material 101 are not of the type that should be sorted by the material handling system 100 (e.g., contain contaminants) and send a signal to reject such pieces of material. In such a configuration, the identified pieces of material 101 may be diverted / ejected using one of the mechanisms described below for physically diverting sorted pieces of material into individual containers.

[0043] In certain embodiments of the present disclosure, a material piece tracking device 111 (or a profilometer as further described herein with respect to FIGS. 16-18 ) and an associated control system 112 may be utilized and configured to determine the size and / or shape of each of the material pieces 101 as they pass near the material piece tracking device 111, along with the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103. Exemplary operation of such a material piece tracking device 111 and control system 112 is further described in U.S. Pat. No. 10,207,296. Alternatively, as previously disclosed, a vision system 110 may be utilized to track the position (i.e., location and timing) of each of the material pieces 101 as they are transported by the conveyor system 103. Thus, certain embodiments of the present disclosure may be implemented without a material piece tracking device (e.g., material piece tracking device 111) for tracking the material pieces.

[0044] According to certain embodiments of the present disclosure, the material tracking device 111 may be implemented before the vision system 110 and / or the sensor system 120 (e.g., upstream of a conveyor system) so that when a piece of material 101 is detected by the material tracking system 111, the material tracking device 111 triggers the material handling system 100 when the vision system 110 and / or the sensor system 120 should capture characteristics of the piece of material. Additionally, the order in which the vision system 110 and the sensor system 120 are implemented within the material handling system 100 can be reversed.

[0045] The classification of the material pieces, which may be performed within the computer system 107 (e.g., utilizing one or more various algorithms in conjunction with information captured by the sensor system and / or vision system), may be utilized by the automated control system 108 to operate one of the N (N>1) sorting devices 126...129 of the sorting apparatus to sort (e.g., divert / discharge) the material pieces 101 into one or more N (N>1) sorting bins 136...139 (or onto another conveyor system that transports the material pieces to the bins) according to the determined classification. Four sorting devices 126...129 and four sorting bins 136...139 associated with the sorting devices are shown in FIG. 1 merely by way of non-limiting example.

[0046] The sorting apparatus may include any known mechanism for redirecting selected pieces of material 101 to a desired location, including, but not limited to, diverting the pieces of material 101 from a conveyor belt system into multiple sorting bins (or onto another conveyor system that transfers the pieces of material to a bin). For example, the sorting device may utilize air jets, each assigned to one or more of the classifications. When one or more of the air jets (e.g., 127) receives a signal from the automated control system 108, the air jet emits a stream of air that divertes / exhausts the pieces of material 101 from the conveyor system 103 into a sorting bin (e.g., 137) corresponding to that air jet (or onto another conveyor system).

[0047] 1 uses air jets to divert / eject the pieces of material, other mechanisms may be used to divert / eject the pieces of material, such as robotically removing the pieces of material from the conveyor belt, pushing the pieces of material from the conveyor belt (e.g., with a paintbrush-type plunger), providing openings (e.g., trap doors) in the conveyor system 103 through which the pieces of material can fall, or using air jets to separate the pieces of material into separate containers as they fall off the edge of the conveyor belt. A pusher device, as that term is used herein, may refer to any form of device that can be actuated to dynamically displace objects on or from a conveyor system / device, employing pneumatic, mechanical, or other means to do so, such as any suitable type of mechanical (e.g., ACME screw drive), pneumatic, or air jet pushing mechanism.

[0048] In addition to the N sorting bins 136...139 into which the pieces of material 101 are diverted / discharged, the material handling system 100 may also include a bin 140 that receives pieces of material 101 that are not diverted / discharged from the conveyor system 103 to any of the aforementioned sorting bins 136...139. For example, when the classification of the piece of material 101 has not been determined (or simply because the sorting device has not been able to properly divert / discharge the piece), the piece of material 101 may not be diverted / discharged from the conveyor system 103 to one of the N sorting bins 136...139. Thus, the bin 140 may function as a default bin into which unsorted or unsorted pieces of material are deposited. Alternatively, the bin 140 may be used to receive one or more classifications of pieces of material that are not intentionally assigned to any of the N sorting bins 136...139. These such pieces of material may then be further sorted according to other characteristics and / or by another sorting system.

[0049] Depending on the various desired classifications of the pieces of material, multiple classifications may be mapped to a single sorting device and associated sorting bin. In other words, there need not be a one-to-one correlation between classifications and sorting bins. For example, a user may desire to sort specific classifications of material into the same sorting bin. To achieve this sorting, the same sorting device may be activated to sort pieces of material 101 into the same sorting bin when they are classified as falling into predetermined classification groups. Such combinatorial sorting may be applied to generate any desired combination of sorted pieces of material. The classification mapping may be programmed by the user (e.g., using any of the sorting algorithms operated by the computer system 107, as described herein) to generate such desired combinations. Correspondingly, the classifications of the pieces of material may be user-definable and not limited to any particular known classifications of pieces of material.

[0050] With the material handling system 100 implementing an XRF system for the sensor system 120, signals representing the detected XRF spectrum may be converted into discrete energy histograms, such as by channel (i.e., element), as further described herein. Such a conversion process may be implemented within the control system 123 or computer system 107. In certain embodiments of the present disclosure, such a control system 123 or computer system 107 may include a commercially available spectral acquisition module, such as the commercially available Amptech MCA 5000 acquisition card, and software programmed to operate the card. Such a spectral acquisition module, or other software implemented within the material handling system 100, may be configured to implement multiple channels for dispersing the X-rays into such a discrete energy (XRF) spectrum (i.e., histogram) having multiple energy levels, whereby each energy level corresponds to an element that the material handling system 100 is configured to detect. The material handling system 100 may be configured to have enough channels corresponding to specific elements in the chemical periodic table that are important for distinguishing between different materials. The energy counts for each energy level may be stored in separate collection and storage registers. The computer system 107 then reads each collection register to determine the number of counts at each energy level during the collection interval and constructs an energy histogram. A sorting algorithm configured in accordance with certain embodiments of the present disclosure may then utilize this collected energy level histogram (also referred to herein as an XRF spectrum) 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.

[0051] As previously disclosed, according to alternative embodiments of the present disclosure, the vision system 110 may be configured with an AI system to capture or gather any type of information from the pieces of material, which information can be utilized within the material handling system 100 to classify and / or selectively sort the pieces of material 101 according to a set of one or more characteristics. The AI ​​system may be any known AI system (e.g., specific-purpose artificial intelligence ("ANI"), general-purpose artificial intelligence ("AGI"), and artificial superintelligence ("ASI")), machine learning systems including those implementing neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, reinforcement learning, etc.), supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, representation learning, sparse dictionary learning, anomaly detection, robotic learning, association rule learning, fuzzy logic, deep learning algorithms, deep structured learning hierarchical learning algorithms, support vector machines ("SVMs"). (e.g., linear SVM, non-linear SVM, SVM regression, etc.), decision tree learning (e.g., classification and regression trees (“CART”)), ensemble methods (e.g., ensemble learning, random forests, bagging and pasting, patch and subspace, boosting, stacking, etc.), dimensionality reduction (e.g., projection, manifold learning, principal component analysis, etc.), and / or deep machine learning algorithms such as those publicly available described on the deeplearning.net website (including hyperlinks to all software, publications, and available software referenced within this website), which is incorporated herein by reference.Non-limiting examples of publicly available machine learning software and libraries that may be utilized within embodiments of the present disclosure include Python, OpenCV, Inception, Theano, Torch, PyTorch, Pylearn2, Numpy, Blocks, TensorFlow, MXNet, Caffe, Lasagne, Keras, Chainer, Matlab Deep Learning, CNTK, MatConvNet (a MATLAB toolbox that implements convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for deep learning (by Rasmus Berg Palm)), BigDL, Cuda-Convnet (a fast C++ / CUDA implementation of convolutional (or more generally, feedforward) neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factor RBM and mcRBM, mPoT (Python code that uses CUDAMat and Gnumpy to train a model on natural images), ConvNet, Elektronn, OpenNN, NeuralDesigner, Theano Generalized Hebbian Learning, Apache Singa, Lightnet, and SimpleDNN.

[0052] According to certain embodiments of the present disclosure, certain types of machine learning may be performed in stages. For example, training is performed first, which may be performed offline, in that the material handling system 100 is not utilized to perform the actual sorting / separation of material pieces. The material handling system 100 may be utilized to train the machine learning system in that a homogenous set of material pieces (also referred to herein as a control sample) (i.e., having the same type or class of material or falling within the same predetermined fraction) is passed through the material handling system 100 (e.g., by the conveyor system 103), and all such material pieces may not be sorted but may be collected in a common container (e.g., the container 140). Alternatively, training may 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 material pieces. 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, feedforward, polynomial regression, learning curve, regularized learning model, and logistic regression. During this training phase, the algorithms within the machine learning system learn the relationships between materials and their features / characteristics (e.g., as captured by a vision system and / or sensor system) to create a knowledge base for subsequent classification of heterogeneous mixtures of material pieces received by the material handling system 100, which can then be sorted by desired classification. Such a knowledge base may include one or more libraries, each library containing parameters (e.g., neural network parameters) for the machine learning system to utilize in classifying material pieces. For example, one particular library may contain parameters configured by the training phase to recognize and classify a particular type or class of material, or one or more materials that fall into a predetermined fraction.According to certain embodiments of the present disclosure, such libraries may be input into a machine learning system, and a user of the material handling system 100 may then be able to adjust certain of the parameters (e.g., adjusting the threshold effectiveness of how well the machine learning system recognizes a particular piece of material from a heterogeneous mixture of materials) to adjust the operation of the material handling system 100.

[0053] Furthermore, the inclusion of certain materials within a piece of material results in identifiable physical characteristics (e.g., visually recognizable properties) within the material. As a result, when multiple pieces of material containing such specific compositions are put through the aforementioned training phase, a machine learning system can learn how to distinguish such pieces of material from other pieces of material. Consequently, a machine learning system (or any AI system) configured according to certain embodiments of the present disclosure can be configured to sort pieces of material according to their respective material / chemical compositions. It can be readily appreciated that embodiments of the present disclosure can be configured to utilize image data (e.g., visual images) of a piece of material as a proxy for a representation of one or more various physical and / or chemical attributes of the piece of material (e.g., ductility, malleability, brittleness, hardness, gloss, tensile strength, reactivity with various materials, etc.).

[0054] Referring to FIG. 2 , during a training phase, one or more pieces of material 201 of a particular type, classification, or fraction of material in a control sample may be delivered (e.g., by conveyor system 203) through a vision system and / or one or more sensor systems, whereby algorithms in the machine learning system detect, extract, and learn features representative of that type or class of material. For example, each piece of material 201 in the control sample may first be passed through such a training phase, whereby algorithms in the machine learning system "learn" (are trained) how to detect, recognize, and classify such pieces of material 201. When a vision system (e.g., vision system 110) is trained, it is trained to visually recognize (distinguish) pieces of material. This creates a library of parameters characteristic of such homogeneous class of pieces of material 201. The same process can be performed for images of any classification of pieces of material to create a library of parameters characteristic of such classification of pieces of material. For each type of material to be classified by the vision system, any number of example pieces of material of that classification may be passed through the vision system. Given the captured sensed information as input data, an algorithm within the machine learning system may use N classifiers, each of which may test one of N different material types. Note that the machine learning system may be "taught" (trained) to detect any type, class, or fraction of material, including any of the material types, classes, or fractions disclosed herein.

[0055] After the algorithms are established and the machine learning system has sufficiently learned (trained) the differences (e.g., visually recognizable differences) between material classifications (e.g., within a user-defined statistical confidence level), the library of different material classifications can then be implemented into a material sorting / separation system (e.g., material handling system 100) that is used to identify, distinguish, and / or classify material pieces from a heterogeneous mixture of material pieces, and then sort such classified material pieces when sorting is performed.

[0056] It should be understood that the present disclosure is not exclusively limited to AI techniques. Other common techniques for material classification / identification may also be used. For example, a sensor system may utilize optical spectroscopy techniques using a multispectral or hyperspectral camera to examine the spectral emissions of materials (i.e., spectral imaging) to provide a signal that may indicate the presence or absence of a material type, class, or fraction. A spectral image of a piece of material may be used in a template matching algorithm, and a database of spectral images may be compared to the acquired spectral image to find the presence or absence of a particular type of material from the database. Also, a histogram of the captured spectral image may be compared to a database of histograms. Similarly, a bag-of-words model may be used in conjunction with feature extraction techniques such as the Scale Invariant Feature Transform ("SIFT") to compare extracted features between the captured spectral image and the spectral images in the database.

[0057] One point to note here is that, according to certain embodiments of the present disclosure, the detected / captured features / characteristics (e.g., spectral image) of the piece of material may not necessarily be simply particularly identifiable or recognizable physical properties; they may be abstract formulations that can only be expressed mathematically, or not at all; nevertheless, the AI ​​system may be configured to analyze the spectral data to look for patterns that allow it to classify the control samples during the training phase. Furthermore, the AI ​​system may take subsections of the captured information (e.g., spectral image) of the piece of material and attempt to find correlations between predefined classifications.

[0058] According to certain embodiments of the present disclosure, instead of utilizing a training phase in which material pieces of a control (homogeneous) sample are passed to a vision system and / or sensor system, training of the AI ​​system may be performed utilizing labeling / annotation techniques (or any other supervised learning) whereby, as data / information for material pieces is captured by the vision / sensor system, a user inputs labels or annotations identifying each material piece, which are then used to create a library for use by the AI ​​system when classifying material pieces within a heterogeneous mixture of material pieces. In other words, a previously generated knowledge base of properties captured from one or more samples of a class of material may be achieved by any of the techniques disclosed herein, whereby such knowledge base is then utilized to automatically classify materials.

[0059] Thus, as disclosed herein, certain embodiments of the present disclosure provide for the identification / classification of one or more different materials to determine which pieces of material should be diverted from a conveyor system or device. According to certain embodiments, machine learning techniques may be utilized to train (i.e., configure) a neural network to identify one or more different classes or types of materials. Images or other types of sensed information may be captured from the material (e.g., traveling on a conveyor system), and based on such material identification / classification, the systems described herein can determine 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., placed in a collection bin or diverted onto another conveyor system).

[0060] According to certain embodiments of the present disclosure, any sensed property output by any of the sensor systems 120 disclosed herein may be input into an AI system to classify and / or sort materials. For example, in an AI system implementing supervised learning, the output of the sensor system 120 that uniquely characterizes a particular type or composition of material may be used to train the AI ​​system.

[0061] FIG. 3 is a flowchart diagram illustrating an exemplary embodiment of a process 3500 for sorting / separating material pieces utilizing a vision system and / or one or more sensor systems, according to certain embodiments of the present disclosure. Process 3500 may be performed to sort a heterogeneous mixture of material pieces into any combination of predetermined types, classes, and / or fractions. Process 3500 may be configured to operate in any of the embodiments of the present disclosure described herein, including material handling system 100 of FIG. 1. Note that depending on the particular sorting technique employed, not all of the process blocks may be implemented. The operations of process 3500 may be performed by hardware and / or software, including within a computer system (e.g., data processing system 3400 of FIG. 5) that controls a system (e.g., computer system 107, vision system 110, and / or sensor system 120 of FIG. 1). In process block 3501, material pieces may be deposited on a conveyor system. At process block 3502, the location of each piece of material on the conveyor system is detected to track each piece of material as it progresses through the material handling system 100. This may be performed by the vision system 110 (e.g., by distinguishing the piece of material from underlying conveyor system material while communicating with a conveyor system position detector (e.g., position detector 105)). Alternatively, the piece of material tracking device 111 can be used to track the piece. Or, any system capable of producing a light source (including, but not limited to, visible light, UV, and IR) and having a detector that can be used to identify the location of the piece. At process block 3503, sensed information / characteristics of the piece of material are captured / acquired as the piece of material progresses proximate to one or more of the vision system and / or sensor system.At process block 3504, a vision system such as that disclosed above (e.g., implemented in computer system 107) may perform preprocessing of the captured information, which may be used to detect (extract) information about each piece of material (e.g., from a background (e.g., a conveyor belt)—in other words, preprocessing may be used to identify differences between the piece of material and the background). Well-known image processing techniques such as dilation, thresholding, and contouring may be used to identify the piece of material as distinct from the background. At process block 3505, segmentation may be performed. For example, the captured information may include information about one or more pieces of material. Furthermore, a particular piece of material may be located on a seam of a conveyor belt when its image was captured. Therefore, in such cases, it may be desirable to separate the image of the individual piece of material from the background of the image. In an exemplary technique at process block 3505, the first step is to apply high contrast to the image, such that background pixels are reduced to substantially all black pixels and at least some of the pixels related to the piece of material are brightened to substantially all white pixels. The image pixels of the piece that are white are then expanded to cover the entire size of the piece. After this step, the location of the piece is a high-contrast image of all white pixels on a black background. A contouring algorithm can then be used to detect the boundary of the piece. The boundary information is saved, and the boundary location is then transferred to the original image. Segmentation is then performed on the original image in an area larger than the previously defined boundary. In this way, the piece is identified and separated from the background.

[0062] At optional process block 3506, the material pieces may be conveyed along a conveyor system near a material piece tracking device and / or sensor system to track each of the material pieces and / or determine the size and / or shape of the material pieces, which may be useful if an XRF system or some other spectroscopic sensor is also implemented in the sorting system. At process block 3507, post-processing may be performed. Post-processing may involve resizing the captured information / data to prepare it for use with a neural network. This may also include modifying certain properties in a way that will enhance the AI ​​system's ability to classify the material pieces (e.g., enhancing image contrast, changing the image background, or applying a filter). At process block 3509, the data may be resized. Under certain circumstances, data resizing may be desirable to meet the data input requirements of a particular AI system, such as a neural network. For example, a neural network may require an image size (e.g., 225 x 255 pixels or 299 x 299 pixels) that is much smaller than the size of an image captured by a typical digital camera. Additionally, the smaller the input data size, the less processing time is required to perform the classification, which ultimately increases the throughput of the material handling system 100, making it more valuable.

[0063] At process blocks 3510 and 3511, each piece of material is identified / classified based on the sensed / detected features. For example, process block 3510 may be configured using a neural network employing one or more algorithms that compare extracted features to features stored in a previously generated knowledge base (e.g., generated during a training phase) and assign the classification that most closely matches each piece of material based on such comparison. The algorithms may process the captured information / data hierarchically using automatically trained filters. The filter responses are then successfully combined at the next level of the algorithm until a probability is obtained in the final step. At process block 3511, these probabilities for each of the N classifications may be used to determine which of the N sorting bins the respective piece of material should be sorted into. For example, each of the N classifications may be assigned to one sorting bin, and the piece of material under consideration is sorted into the bin corresponding to the classification that returns the highest probability, higher than a predefined threshold. In embodiments of the present disclosure, such a predefined threshold may be preset by the user. A particular piece of material may be sorted into an outlier bin (eg, sort bin 140) if none of the probabilities are higher than a predetermined threshold.

[0064] Next, at process block 3512, a sorting device corresponding to the material's class or classes is activated (e.g., an instruction to sort is sent to the sorting device). Between the time the image of the material is captured and the time the sorting device is activated, the material has moved from the vicinity of the vision system and / or sensor system to a downstream location on the conveyor system (e.g., at the conveying speed of the conveyor system). In an embodiment of the present disclosure, the operation of the sorting device is timed so that when the material passes a sorting device mapped to the material's class, the sorting device is activated and the material is diverted / discharged from the conveyor system into its associated sorting bin. In an embodiment of the present disclosure, the operation of the sorting device may be timed by a respective position detector that detects when the material passes before the sorting device and sends a signal to enable the sorting device to be activated. At process block 3513, the sorting bin corresponding to the activated sorting device receives the diverted / discharged material.

[0065] 4 shows a flow chart diagram illustrating an exemplary embodiment of a process 400 configured in accordance with certain embodiments of the present disclosure. Process 400 may be configured to operate in any of the embodiments of the present disclosure described herein, including material handling system 100 of FIG.

[0066] The operations of process 400 may be performed by hardware and / or software, including within a computer system (e.g., data processing system 3400 of FIG. 5) that controls a system (e.g., computer system 107 of FIG. 1). At process block 401, pieces of material may be deposited on a conveyor system. Then, at optional process block 402, the pieces of material may be transported along the conveyor system near a piece of material tracking device and / or an optical imaging system (e.g., a profilometer or laser camera-based system described herein) to track each piece of material and / or determine the size and / or shape of the piece of material. At process block 403, as the pieces of material travel near the sensor system, they may be interrogated or stimulated with EM energy (waves) or some other type of stimulus appropriate for the particular type of sensor technology utilized by the sensor system (e.g., an XRF system described herein). At process block 404, physical properties of the pieces of material are sensed / detected and captured by the sensor system. At process block 405, at least some of the pieces of material are identified / classified as a material type based (at least in part) on the captured characteristics (eg, XRF spectra).

[0067] Next, at process block 406, if sorting of the material is to occur, a sorting device corresponding to the material class or classes is activated. Between the time the material is detected and the time the sorting device is activated, the material moves from the vicinity of the sensor system to a downstream location on the conveyor system at the conveying speed of the conveyor system. In certain embodiments of the present disclosure, operation of the sorting device is timed so that when the material passes a sorting device mapped to the material class, the sorting device is activated and the material is diverted / discharged from the conveyor system into its associated sorting bin. In certain embodiments of the present disclosure, operation of the sorting device may be timed by a respective position detector that detects when the material passes before the sorting device and sends a signal to enable operation of the sorting device. At process block 407, the sorting bin (or another conveyor system) corresponding to the activated sorting device receives the diverted / discharged material.

[0068] According to alternative embodiments of the present disclosure, process 400 may be configured to operate in conjunction with process 3500. For example, according to certain embodiments of the present disclosure, process blocks 403 and 404 may be incorporated into process 3500 (e.g., operating serially or in parallel with process blocks 3503-3510) to combine the efforts of vision system 110 implemented in conjunction with an AI system with a sensor system not implemented in conjunction with an AI system (e.g., sensor system 120) to sort and / or separate pieces of material.

[0069] As described herein, an XRF system performing XRF spectroscopy may be utilized as sensor system 120. When XRF spectroscopy is utilized to classify materials transported on a moving conveyor belt, the X-ray beam may only partially illuminate the material piece, resulting in an inaccurate XRF spectrum being captured that is needed to classify the material piece. This may result in improper (mis)classification and resulting sorting of the material piece (e.g., aluminum alloy).

[0070] 6 shows a simplified diagram of one example of an XRF system 120 comprising an X-ray tube 121 and a corresponding XRF detector 124. Such an XRF system may be any conventional XRF system known in the art. As the piece of material 101 is transported by the moving conveyor belt 103, the piece of material 101 is illuminated by an X-ray beam 601. The illuminated X-ray beam 601 typically has the form of a cone, which results in an X-ray beam spot 602 contacting (intersecting) the top surface of the piece of material 101. Depending on the exact height of the top surface of the piece of material relative to the surface of the conveyor belt and the shape of the various contours of such top surface, the X-ray beam spot 602 will have a corresponding diameter. For the operation of the XRF system, it is desirable that the X-ray beam spot 602 only illuminate at least a portion of the piece of material 101, and not illuminate any other piece of material or any portion of the conveyor belt 103, when the resulting fluorescent X-rays are being detected by the detector 124; this is necessary to generate an accurate XRF spectrum of the piece of material 101 in order to accurately classify the piece of material 101. The problem is that it may occur relatively frequently in the material handling system 100 that the X-ray beam spot 602 does not fall completely on (i.e., does not entirely intersect with) any portion of the piece of material 101 as it is transported through the XRF system 120.

[0071] 7, an example is shown where the piece of material 101 on the conveyor belt 103 is not properly aligned or positioned relative to the XRF system 120, resulting in the X-ray beam spot 602 illuminating only a portion of the piece of material 101. In other words, the X-ray beam spot 602 does not completely or fully intersect the transported piece of material 101. For example, deposition of the piece of material 101 on the conveyor belt 103 may result in the piece of material 101 being misaligned or only partially aligned with the X-ray beam spot 602 as the piece of material 101 is transported through the XRF system.

[0072] Referring to Figure 8, an exemplary singulator 106 is shown demonstrating how pieces of material 101 fed (deposited) on a conveyor system 103 are envisioned to be aligned with an XRF system so that the X-ray beam 601 properly irradiates each of the pieces of material 101. The example of Figure 8 schematically illustrates how one or more static alignment rods or bars (sometimes referred to as "fingers") 810...817 may be configured to align the individual pieces of material 101 with one or more individualized streams on a conveyor belt (or multiple conveyor belts) so that the individual pieces of material 101 travel directly beneath the X-ray emitter 121, thereby causing the X-ray beam spot 602 to land entirely on (intersect with) each piece of material. However, as will be understood by those skilled in the art, certain physical characteristics of the pieces of material (e.g., size or shape) may inhibit such proper alignment / positioning. 8 illustrates, without limitation, how pieces of material 101 may be separated into separate, individualized streams 802...805 (four individualized streams in this non-limiting example) of pieces of material 101 on conveyor belt 103. In embodiments of the present disclosure, a single conveyor belt may transport multiple such individualized streams, or multiple independently driven conveyor belts may be utilized, whereby each conveyor belt carries one or more of the separate, individualized streams (e.g., 802...805) of pieces of material 101.

[0073] Referring again to FIG. 7, if such a piece of material 101 is not positioned or fed (deposited) onto the conveyor belt 103 in proper alignment with the downstream XRF system 120, the X-ray beam spot 602 may not irradiate any part of the piece of material 101, or may only irradiate a portion of the piece of material 101, which may result in capturing an XRF spectrum that includes all or a portion of the underlying conveyor belt material (or adjacent piece of material), potentially resulting in incorrect classification (and, if implemented, sorting) of the piece of material 101.

[0074] 9 illustrates an example of another potential problem where the dimensions (e.g., cross-section) of a particular piece of material 101 are narrower than the effective diameter of the X-ray beam spot 602, such as in the case of a piece of material 101 having an elongated shape (also referred to herein as a thin strip or a piece of material having a thin strip shape). Again, in such a case, the captured XRF spectrum will include measurements of the X-ray fluorescence of elements in the underlying conveyor belt 103 over which the piece of material 101 is traveling.

[0075] As shown in FIG. 10, when such thin strips are deposited on the conveyor belt 103 for sorting and separation, they may end up being positioned on the conveyor belt 103 in a variety of orientations, many of which may result in the problem discussed above with respect to FIG. 9, whereby the X-ray beam spot 602 does not fall completely on (i.e., does not intersect entirely with) such thin strips, i.e., the intersection between the X-ray beam spot 602 and the piece of material 101 is less than 100% (and therefore also irradiates a portion of the conveyor belt underneath, resulting in XRF measurements by the XRF detector of elements within the conveyor belt).

[0076] As used herein, a piece of material having a thin strip shape or form is any piece of material having a cross-sectional dimension less than the effective diameter of the XRF beam spot 602 utilized in a system for sorting pieces of material utilizing X-ray fluorescence analysis (e.g., material handling system 100). For example, if the effective XRF beam spot that contacts (intersects) the piece of material in such a system has a diameter of 2 inches, then a thin strip piece of material would be any piece of material having a cross-sectional dimension less than 2 inches. Similarly, if the effective XRF beam spot in such a system has a diameter of 2 millimeters, then a thin strip piece of material would be any piece of material having a cross-sectional dimension less than 2 millimeters, and so on. As shown in FIGS. 9 and 10, such thin strips generally have a length that is substantially greater than their width (e.g., the length is three or more times the width dimension).

[0077] A non-limiting example of the aforementioned problem is the sorting and separation of aluminum alloys, as described above, particularly with respect to FIG. 9. When various wrought or sheet aluminum alloys are utilized in the manufacture of a particular end-use device (e.g., as body components for a vehicle (e.g., a Ford F-150)), aluminum alloy material not used in the stamping process (often referred to in the industry as "clips") composed of different wrought aluminum alloys may be mixed and chopped into a heterogeneous mixture of chopped aluminum alloy pieces, including those having elongated or thin strip shapes or forms. Furthermore, in the automotive industry, it is known that chopped pieces of clips having elongated shapes or forms (i.e., thin strips) are often composed of aluminum alloys having a relatively high copper content (e.g., 6xx3).

[0078] If such thin strip is known to have a relatively high copper content (e.g., >0.2%) (and thus belongs to the 6xx3 family of aluminum alloys), and there is an incomplete intersection (e.g., <100%) of the X-ray beam spot 602 with the thin strip, the XRF measurement of the copper content within the thin strip will be inaccurate, and may even be sufficiently low that the piece of thin strip material having a high copper content will be classified as a different aluminum alloy other than a 6xx3 aluminum alloy (e.g., an aluminum alloy having a lower copper content relative to a different aluminum alloy such as a 6xx2 or 5xx3 aluminum alloy).

[0079] 11A through 11F show example XRF spectra demonstrating how such erroneous XRF readings or measurements can result from improper alignment (i.e., intersection) of the XRF beam spot 602 with the piece of material 101 (e.g., a thin strip as shown in FIG. 9 or 10). FIG. 11A shows an example XRF spectrum captured when the X-ray beam spot 602 fully illuminates at least a portion of the piece of material 101 (i.e., there is 100% intersection between the X-ray beam spot 602 and at least a portion of the piece of material 101). FIG. 11B shows an example XRF spectrum captured when there is 85% intersection between the X-ray beam spot 602 and the piece of material 101. FIG. 11C shows an example XRF spectrum captured when there is 72% intersection between the X-ray beam spot 602 and the piece of material 101. FIG. 11D shows an example XRF spectrum captured when there is 62% intersection between the X-ray beam spot 602 and the piece of material 101. FIG. 11E shows an example XRF spectrum captured when there is 40% intersection of the X-ray beam spot 602 with the piece of material 101. FIG. 11F shows an example XRF spectrum captured when there is 12% intersection of the X-ray beam spot 602 with the piece of material 101. In each of these XRF spectra, three element peaks are highlighted to demonstrate how this issue can result in erroneous XRF readings or measurements. The peak labeled 1100 represents an XRF measurement (i.e., energy level count) of an element known to be contained in the piece of material (e.g., copper (Cu) in a particular aluminum alloy) and known not to be contained (or at least within a lower (e.g., substantially lower) amount than known) in the material from which the conveyor belt 103 is constructed. The peaks labeled 1101 and 1102 represent elements (e.g., titanium (Ti), zinc (Zn), or nickel (Ni)) that are known to be contained in the conveyor belt material 103 and that are also known to be absent (or at least in amounts lower (e.g., substantially lower) than known) in the piece of material being classified.As can be easily seen from a relative comparison of the exemplary spectra shown in Figures 11A to 11F, peak 1100 decreases and peaks 1101 and 1102 increase in proportion to the decrease in the intersection percentage between the X-ray beam spot 602 and the piece of material 101.

[0080] Embodiments of the present disclosure address the aforementioned problems by measuring the area of ​​intersection between the X-ray beam spot and the piece of material and correcting / modifying the measured XRF spectrum associated with each piece of material accordingly.

[0081] According to certain embodiments of the present disclosure, when a conveyor belt is utilized with known distinct elements that can be specifically recognized and accounted for (e.g., including knowing the amounts and relative percentages of these elements contained in the conveyor belt), an algorithm may be implemented to determine the intersection area between the X-ray beam spot and the piece of material.

[0082] 11A-11F, the conveyor belt 103 may be implemented within the material handling system 100 such that the conveyor belt 103 is comprised of one or more distinct elements known not to be contained in the pieces of material to be sorted and / or separated. For example, if the pieces of material are known to contain one or more particular specific elements (e.g., copper (Cu), manganese (Mn), chromium (Cr), and / or iron (Fe)), a conveyor belt may be implemented that does not contain these elements (or at least within a lower (e.g., substantially lower) amount range than known) and instead contains one or more other known distinct elements known not to be contained in the pieces of material (e.g., zinc (Zn), titanium (Ti), and / or nickel (Ni)). A sorting system is correspondingly implemented to correct / modify the XRF spectral measurements associated with each of the pieces of material according to the measurements of one or more of these particular elements known to be contained in the conveyor belt. As demonstrated in the exemplary XRF spectra shown in Figures 11A through 11F, with respect to the use of a conveyor belt specifically composed of elements including titanium and zinc, as the intersection of the X-ray beam spot 602 with the piece of material 101 decreases, the peaks associated with titanium 1101 and zinc 1102 increase, while the peak 1100 associated with measuring copper in the piece of material decreases proportionately.

[0083] Referring now to FIG. 12 , a system and process 1200 configured to correct / correct a measured XRF spectrum of a piece of material is shown, according to certain embodiments of the present disclosure. System and process 1200 may be implemented within system and process 400, as described further herein. In this non-limiting example, the conveyor belt comprises titanium and zinc, and the piece of material (which may comprise a thin strip) has a relatively high (e.g., >0.2%) copper content. In optional process block 1201, at some point before the sorting of the piece of material begins, if not already known or predetermined, the XRF spectrum of the conveyor belt is measured (e.g., calibrated with no piece of material present, i.e., 0% crossover between the piece of material and the conveyor belt) to determine the amount and / or relative amounts of elements in the conveyor belt. At the start of operation of material handling system 100, in process block 1201, the XRF spectrum of the piece of material is measured as the piece travels through the XRF system (e.g., XRF system 120 of FIG. 1 ). 13 shows a non-limiting example of a measured XRF spectrum of a piece of material with a measured peak for copper 1300, a measured peak for titanium 1301, and a measured peak for zinc 1302. In process block 1203, specific conveyor belt elements specific to the conveyor belt composition are then measured (e.g., energy level counts) from the measured XRF spectrum of the piece of material to determine the percentage of the conveyor belt measured by the X-ray beam of the XRF system.

[0084] 14, in this particular example, the X-ray beam spot 602 partially irradiated the piece of material 101. In process block 1204, the intersection of the X-ray beam spot 602 and the piece of material 101 is inferred from XRF measurements of one or more elements specific to the conveyor belt (in this example, titanium and zinc). In this particular example, it was determined (calculated) that the X-ray beam spot irradiated 72% of the piece of material and 28% of the conveyor belt. Such an inference can be made because, for example, the energy level counts of either or both of the titanium and zinc elements are 28% of the known amounts in the conveyor belt (i.e., the ratio of the measured amounts of either or both of these elements to their known amounts in the conveyor belt). In process block 1205, the measured XRF spectrum associated with the piece of material 101, as shown in FIG. 13, is corrected / corrected by subtracting measured XRF spectra of the belt's elements (which may be known (predetermined) or obtained at some previous time (e.g., see process block 1201)) and dividing by the intersection percentage (i.e., the measured quantities (energy level counts) associated with the various elements identified in the XRF spectrum are divided by the intersection percentage, which in this example would be divided by 0.72), resulting in a corrected / corrected XRF spectrum for the piece of material 101, as shown in FIG. 15, which shows how the copper peak 1300 has increased while the titanium and zinc peaks 1301, 1302, respectively, have decreased. The piece of material 101 may then be classified utilizing this corrected / corrected XRF spectrum according to process block 405 of the system and process 400 of FIG. 4. And, if sorting of the material pieces is performed, the material pieces 101 may be sorted according to process block 406 and process 400 of the system of FIG.

[0085] Thus, it can be readily appreciated that the system and process 1200 is configured to correct / correct the XRF measurements of the piece of material in the absence of 100% intersection of the illuminated X-ray beam spot 602 with the piece of material 101. In this particular example, the copper measured within the exemplary piece of material is correctly accounted for, which may result in such piece of material being classified / sorted as a high copper aluminum alloy rather than a low copper aluminum alloy.

[0086] 16, a system and process 1600 configured to correct / correct a measured XRF spectrum of a piece of material is shown, according to an alternative embodiment of the present disclosure. System and process 1600 may be implemented within system and process 400, as further described herein. In this non-limiting example, the conveyor belt comprises titanium and zinc, and the piece of material (which may comprise a thin strip) has a relatively high (e.g., >0.2%) copper content. As previously disclosed, such a high copper content aluminum alloy may be misclassified as a low copper aluminum alloy.

[0087] In process block 1601, the location of the piece of material 101 on the conveyor belt 103 is measured (determined) relative to the location (known / predetermined) of the illuminated XRF beam spot on the conveyor belt 103 as the piece of material 101 passes through an XRF system (e.g., XRF system 120 of FIG. 1). That is, the location of the piece of material along the width of the conveyor belt is determined to determine whether it will properly pass under the XRF system such that the X-ray beam spot intersects the entire piece of material. Measuring (determining) the location of the piece of material on the conveyor belt may be performed according to the principles of laser triangulation with a commercially available laser profilometer, which may be implemented in the material handling system 100 as device 111 (see FIG. 1). This is demonstrated by the non-limiting example shown in FIG. 17, in which the location of the piece of material 101 on the conveyor belt 103 is determined by a laser line 1701 from the profilometer as the piece of material 101 travels on the conveyor belt 103. Process block 1602 determines the intersection of the piece of material 101 with the XRF beam spot 602 to determine (calculate) the percentage of the conveyor belt that will be measured by the X-ray beam spot 602 of the XRF system.

[0088] Then, in process block 1603, the XRF spectrum measurement associated with the piece of material 101 (see, e.g., FIG. 13 ) is corrected / corrected by subtracting conveyor belt measurements of the belt's elements (which may be known (predetermined) or obtained at an earlier time period (see, e.g., process block 1201)) and dividing by the intersection percentage, resulting in a corrected / corrected XRF spectrum (see, e.g., FIG. 15 ) for the piece of material 101. The piece of material 101 may then be classified according to process block 405 and process 400 of the system of FIG. 4 . And, if sorting of the material is performed, the piece of material 101 may be sorted according to process block 406 and process 400 of the system of FIG. 4 .

[0089] Therefore, it can be easily understood that the system and process 1600 is configured to correct / correct the XRF measurements of the piece of material when there is not 100% intersection of the irradiated X-ray beam spot 602 with the piece of material 101.

[0090] Referring now to FIG. 18 , a system and process 1800 configured in accordance with certain embodiments of the present disclosure is shown. In process block 1801, the piece of material 101 is measured while on the moving conveyor belt 103 as it passes through an optical profilometer or other similar device. Such a profilometer may be any commercially available optical profilometer (which may be implemented in the material handling system 100 as device 111 (see FIG. 1 )). In process block 1802, the profilometer performs a three-dimensional (“3D”) reconstruction of the piece of material 101. In process block 1803, it is determined whether the piece of material 101 is a thin strip (or any other predetermined shape) based on the shape generated by the 3D reconstruction (i.e., process block 1803 performs classification / identification of such particular piece of material). Based on the determination / identification of the piece of material 101, the piece of material 101 can then be sorted in process block 1804. For example, a thin strip of aluminum alloy may be classified / sorted as a high copper content aluminum alloy.

[0091] System and process 1800 may be implemented independently within material handling system 100 or within system and process 400. For example, process blocks 1801-1803 may be performed in parallel with process block 405 in system and process 400 of Figure 4. And, if sorting of material pieces is performed, material pieces 101 may then be sorted according to process block 406. Thus, material pieces not classified / identified by process block 1803 may be classified / identified according to process blocks 403-405.

[0092] Alternatively, any of the commercially available profilometers mentioned above may be replaced with a combination of a camera and a two-dimensional laser scanner. FIG. 20 shows a simplified schematic diagram of such a laser camera-based system, which operates according to the principles of laser sectioning or laser triangulation. As the workpiece 101 travels beneath the camera 2001 on the conveyor belt 103, an image of the moving workpiece 101 is captured while it is illuminated by a laser line emanating at an angle θ from a laser 2002 positioned proximate to the camera 2001. As can be seen from the top view in FIG. 20, the height x of each portion of the workpiece 101 may be determined as it moves past the camera 2001 by positioning the line traced by the laser beam on the workpiece 101. Of course, basic geometry can be used to determine this height x. For example, if the laser beam is predetermined (positioned) to arrive at a 45° angle relative to the plane of the conveyor belt 103, the height x will be equal to the distance x from the center of the image captured by the camera 2001. An exemplary demonstration of this is shown in FIG. 21, where it is easy to see how the z-dimension of a piece of material can be determined by measuring the displacement between the displaced laser line and a reference line.

[0093] An alternative embodiment to that shown in Figure 20 is shown in Figure 22A, whereby multiple lasers emanating from laser 2202 are utilized in combination with camera 2201 to provide higher resolution at a given frame rate and belt speed. In this case, multiple laser beams can be used to reduce the frame rate.

[0094] FIG. 22B shows another alternative embodiment in which the camera 2203 is combined with multiple lasers 2204 having laser beams of different colors (e.g., two or more) emanating therefrom, further reducing the frame rate.

[0095] It should be noted that in these embodiments, different colored laser beams may be utilized in those cases where the piece of material is folded so that different portions of the piece of material can be properly identified and measured.

[0096] FIG. 23 shows another alternative embodiment whereby two different lasers 2302, 2303 are utilized on either side of the camera 2301 to overcome the problem that the piece of material 101 may be curved or folded, thereby preventing the camera 2301 from imaging one of the laser beams, or where there is a vertical wall on the piece of material 101, resulting in the laser light not being reflected from a portion of the piece of material 101.

[0097] Any structured light system (eg, a grid laser, a single laser with a mirror array, etc.) may be utilized in place of the laser systems described herein.

[0098] FIG. 24 illustrates a process 2400 configured in accordance with certain embodiments of the present disclosure. Process 2400 may be utilized in any of the previously described embodiments illustrated with respect to FIGS. 20, 21, 22A, 22B, and 23. At process block 2401, a camera will capture a raw image of the piece of material. At process block 2402, the laser is extracted by a machine vision algorithm. Then, at process block 2403, each section of the piece of material is calculated for its height based on its geometry. Then, at process block 2404, multiple sections of the piece of material are combined as the conveyor belt moves to achieve a three-dimensional reconstruction of the piece of material. Then, based on the determination / identification of the piece of material, the piece of material can be sorted at process block 2405.

[0099] Referring now to FIG. 5, a block diagram illustrating a data processing (“computer”) system 3400 is shown in which aspects of an embodiment of the present disclosure may be implemented. (The terms “computer,” “system,” “computer system,” and “data processing system” may be used interchangeably herein.) The computer system 107, the automation control system 108, aspects of the sensor system 120, and / or the vision system 110 may be configured similarly to the data processing system 3400. The data processing system 3400 may employ a local bus 3405 (e.g., a peripheral component interconnect (“PCI”) local bus architecture). Any suitable bus architecture may be utilized, such as accelerated graphics port (“AGP”) and industry standard architecture (“ISA”), among others. One or more processors 3415, volatile memory 3420, and non-volatile memory 3435 may be connected to the local bus 3405 (e.g., via a PCI bridge (not shown)). An integrated memory controller and cache memory may be coupled to the one or more processors 3415. The one or more processors 3415 may include one or more central processing units and / or one or more graphics processing units and / or one or more tensor processing units. Additional connections to the local bus 3405 may be made by direct component interconnects or via add-in boards. In the illustrated example, a communications (e.g., network (LAN)) adapter 3425, an I / O (e.g., small computer system interface ("SCSI") host bus) adapter 3430, and an expansion bus interface (not shown) may be connected to the local bus 3405 by direct component connections. An audio adapter (not shown), a graphics adapter (not shown), and a display adapter 3416 (coupled to a display 3440) may be connected to the local bus 3405 (e.g., by add-in boards inserted into expansion slots).

[0100] User interface adapter 3412 may provide connections for a keyboard 3413 and mouse 3414, a modem (not shown), and additional memory (not shown). I / O adapter 3430 may provide connections for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).

[0101] An operating system may run on one or more processors 3415 and be used to coordinate and provide control of various components within data processing system 3400. In Figure 5, the operating system may be a commercially available operating system. An object-oriented programming system (e.g., Java, Python, etc.) may work in conjunction with the operating system to provide calls to the operating system from programs or programs (e.g., Java, Python, etc.) running on data processing system 3400. Instructions for the operating system, object-oriented operating system, and programs may be located on a non-volatile memory 3435 storage device, such as hard disk drive 3431, and may be loaded into volatile memory 3420 for execution by processor 3415.

[0102] Those skilled in the art will appreciate that the hardware in FIG. 5 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash ROM (or equivalent non-volatile memory) or optical disk drives, may be used in addition to or in place of the hardware depicted in FIG. 5. Also, any of the processes of this disclosure may be applied to a multiprocessor computer system or performed by multiple such data processing systems 3400. For example, training of vision system 110 may be performed by a first data processing system 3400, while operating vision system 110 for sorting may be performed by a second data processing system 3400.

[0103] As another example, data processing system 3400 may be a stand-alone system configured to be bootable without relying on some type of network communication interface, regardless of whether data processing system 3400 includes such an interface. As a further example, data processing system 3400 may be an embedded controller configured with ROM and / or flash ROM to provide non-volatile memory for storing operating system files or user-generated data.

[0104] 5 and above-described examples are not meant to imply architectural limitations. Moreover, the computer program format of aspects of the present disclosure may reside on any computer-readable storage medium for use by a computer system (i.e., floppy disk, compact disc, hard disk, tape, ROM, RAM, etc.).

[0105] As described herein, embodiments of the present disclosure may be implemented to perform various described functions to identify, track, sort, differentiate, and / or separate pieces of material. Such functions may be implemented in hardware and / or software, such as in one or more data processing systems (e.g., data processing system 3400 of FIG. 5 ), such as aspects of the aforementioned computer system 107, vision system 110, sensor system 120, and / or automation control system 108. Nevertheless, the functions described herein should not be limited for implementation on any particular hardware / software platform.

[0106] As will be appreciated by those skilled in the art, aspects of the present disclosure may be embodied as a system, process, method, and / or program product. Accordingly, various aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, microcode, etc.), or an embodiment combining software and hardware aspects, which may be generally referred to herein as a "circuit," "circuitry," "module," or "system." Furthermore, aspects of the present disclosure may take the form of a program product embodied in one or more computer-readable storage medium(s) having computer-readable program code embodied therein. (However, any combination of one or more computer-readable medium(s) may be utilized. The computer-readable medium(s) may be a computer-readable signal medium or a computer-readable storage medium.)

[0107] A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, biological, atomic, or semiconductor system, apparatus, controller, or device, or any suitable combination thereof, and the computer-readable storage medium is not itself a transitory signal. More specific examples (a non-exhaustive list) of computer-readable storage media may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (“RAM”) (e.g., RAM 3420 of FIG. 5), a read-only memory (“ROM”) (e.g., ROM 3435 of FIG. 5), 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., hard drive 3431 of FIG. 5), or any suitable combination thereof. In the context of this specification, a computer-readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, controller, or device. Program code embodied on a computer-readable signal medium may be transmitted using any appropriate medium, including, but not limited to, wireless, wired, fiber optic cable, RF, etc., or any suitable combination thereof.

[0108] A computer-readable signal medium may include a propagated data signal in which computer-readable program code is embodied, for example in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including but not limited to, electromagnetic, optical, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, controller, or device other than a computer-readable storage medium.

[0109] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, processes, and program products according to various embodiments of the present disclosure. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, including one or more executable program instructions for implementing the specified logical function(s). It should also be noted that in some implementations, the functions noted in the blocks may occur in a different order than noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending on the functionality involved.

[0110] Modules implemented in software for execution by various types of processors (e.g., CPU 3415) may, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, or function. Nevertheless, the executable files of identified modules need not be physically located together but may comprise entirely different instructions stored in different locations that, when logically combined, comprise the module and achieve the module's stated purpose. Indeed, a module of executable code may be a single instruction or many instructions, and may even be distributed across several different code segments, among different programs, and across several memory devices. Similarly, operational data (e.g., material classification libraries and neural network parameters described herein) may be identified and depicted in modules herein and may be embodied in any suitable form and organized within any suitable type of data structure. Operational data may be collected as a single data set or distributed across different locations, including across different storage devices. Data may provide electronic signals over a system or network.

[0111] These program instructions may be provided to one or more processors and / or controllers of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus (e.g., a controller) to produce a machine such that the instructions, executed by the processor (e.g., CPU 3415) of the computer or other programmable data processing apparatus, create circuitry or means for implementing the function(s) / act(s) specified in the block or blocks of the flowcharts and / or block diagrams.

[0112] It should also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, may be implemented by a special-purpose hardware-based system (which may include, for example, one or more graphics processing units) that performs the specified functions or operations, or a combination of special-purpose hardware and computer instructions. For example, a module may be implemented as a hardware circuit comprising custom VLSI circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, controllers, or other discrete components, etc. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, or programmable logic devices.

[0113] In the description herein, flowcharted techniques may be described as a series of sequential actions. The order of actions, and the elements performing the actions, may be freely changed without departing from the scope of the teachings. Actions may be added, deleted, or modified in some manner. Similarly, actions may be reordered or looped. Furthermore, while a process, method, algorithm, or the like may be described in a sequential order, such a process, method, algorithm, or any combination thereof, may be operable to be performed in an alternative order. Furthermore, some actions within a process, method, or algorithm may be performed simultaneously, at least some of the time (e.g., actions performed in parallel), or may be performed in whole, in part, or in any combination thereof.

[0114] As used herein, reference is made to "configuring" a device or a device "configured" to perform some function. This should be understood to include selecting predefined logic blocks and logically associating them to provide a particular logical function, including monitoring or control functions. It may also include programming the computer software-based logic of the control device, wiring discrete hardware components, or a combination of any or all of the foregoing. A device so configured is physically designed to perform one or more specified functions.

[0115] Unless otherwise described herein, many details regarding specific materials, processing operations, and circuits are conventional and can be found in textbooks and other sources in the computing, electronics, and software arts.

[0116] Computer program code, i.e., instructions, for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, Smalltalk, Python, or C++; traditional procedural programming languages ​​such as the “C” programming language or similar programming languages; programming languages ​​such as MATLAB or LabVIEW; or any of the AI ​​software disclosed herein. The program code may run entirely on the user's computer system, partially on the user's computer system as a standalone software package, partially on the user's computer system (e.g., a computer system utilized for screening), partially on a remote computer system (e.g., a computer system utilized to train the AI ​​system), or entirely on a remote computer system or server. In the latter scenario, the remote computer system may be connected to the user's computer system via any type of network, including a local area network (“LAN”) or a wide area network (“WAN”), or the connection may be to an external computer system (e.g., via the Internet using an Internet Service Provider). As an example of the foregoing, various aspects of the present disclosure may be configured to execute on one or more of aspects of computer system 107, automation control system 108, vision system 110, and sensor system 120.

[0117] These program instructions may also be stored on a computer-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 on the computer-readable medium produce an article of manufacture that includes instructions that implement the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams.

[0118] The program instructions may also be loaded onto a computer, other programmable data processing apparatus, controller, or other device to cause the computer, other programmable apparatus, or other device to perform a series of operational steps such that the instructions, which execute on the computer or other programmable apparatus, provide a process for implementing the functions / acts specified in one or more blocks of the flowcharts and / or block diagrams, thereby creating a computer-implemented process.

[0119] Association of specific data (e.g., between a classified piece of material and its known chemical composition, such as in the aforementioned collection and storage register) may be achieved by any data association technique known and practiced in the art. For example, association may be achieved either manually or automatically. Automatic association techniques may include, for example, database lookup, database merge, GREP, AGREP, and / or SQL. The association step may be achieved, for example, by a database merge function using key fields in each of the manufacturer and retailer data tables. The key fields divide the database according to high-level classes of objects defined by the key fields. For example, a particular class may be designated as a key field in both a first data table and a second data table, and the two data tables may then be merged based on the class data in the key field. In these embodiments, the data corresponding to the key field in each of the merged data tables is preferably the same. However, data tables with similar but not identical data in the key field may also be merged using, for example, AGREP.

[0120] In the description herein, numerous specific details are provided, such as examples of programming, software modules, user selections, network transactions, database queries, database structures, hardware modules, hardware circuits, hardware chips, controllers, etc., to provide a thorough understanding of embodiments of the present disclosure. However, those skilled in the art will recognize that the present disclosure may be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations may not be shown or described in detail to avoid obscuring aspects of the present disclosure.

[0121] As used herein, "manufacturing type" refers to the type of manufacturing process by which a piece of material is produced, such as a forged metal part formed by a forging process, cast (including, but not limited to, expendable die casting, permanent die casting, and powder metallurgy), material removal process, etc.

[0122] As referred to herein, a "conveyor system" may be any known mechanical handling equipment that moves material from one location to another, including, but not limited to, aeromechanical conveyors, automotive conveyors, conveyor belts, belt-driven live roller conveyors, bucket conveyors, chain conveyors, chain-driven live roller conveyors, drag conveyors, dust-tight conveyors, electric rail car systems, flexible conveyors, gravity conveyors, gravity skate wheel conveyors, line shaft roller conveyors, electrically driven roller conveyors, overhead I-beam conveyors, overland conveyors, pharmaceutical conveyors, plastic belt conveyors, pneumatic conveyors, screw or auger conveyors, spiral conveyors, tubular gallery conveyors, vertical conveyors, vibratory conveyors, wire mesh conveyors, and transporting pieces of material in a fluid (including, but not limited to, very small particles suspended in the fluid) past a vision and / or sensor system.

[0123] Throughout this specification, reference to "one embodiment," "an embodiment," or similar language means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the present disclosure. Thus, throughout this specification, appearances of the phrases "in one embodiment," "in one embodiment," "an embodiment," "an embodiment," "particular embodiment," "various embodiments," and similar language may, but do not necessarily, all refer to the same embodiment. Furthermore, the described features, structures, aspects, and / or characteristics of the present disclosure may be combined in any suitable manner in one or more embodiments. Correspondingly, even if features may initially be claimed as acting in a particular combination, one or more features from a claimed combination may, in some cases, be deleted from the combination, and the claimed combination may be directed to a subcombination or a variation of the subcombination.

[0124] Benefits, advantages, and solutions to problems have been described above with respect to particular embodiments. However, the benefits, advantages, solutions to problems, and any elements that may cause or make more pronounced any benefit, advantage, or solution should not be construed as critical, necessary, or essential features or elements of any or all claims. Furthermore, no element described herein is required for the practice of the present disclosure unless expressly described as essential or critical.

[0125] Those skilled in the art who have read this disclosure will recognize that changes and modifications may be made to the embodiments without departing from the scope of the disclosure. It should be understood that the specific implementations shown and described herein may be illustrative of the disclosure and its best mode, and should not be intended to otherwise limit the scope of the disclosure in any way. Other variations may be within the scope of the following claims.

[0126] As used herein, the term "or" may be intended to be inclusive, such that "A or B" includes A or B, and also includes both A and B. As used herein, the term "and / or," when used in the context of a list of entities, refers to the entities being present singly or in combination. Thus, for example, the phrase "A, B, C, and / or D" includes A, B, C, and D individually, but also any and all combinations and subcombinations of A, B, C, and D.

[0127] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms "a," "an," and "the" may be intended to include the plural forms as well, unless the context clearly dictates otherwise.

[0128] The corresponding structure, material, operations, and equivalents of all means-plus-function or step-plus-function elements in the following claims may be intended to include any structure, material, or operation for performing the function in combination with other specifically claimed elements.

[0129] As used herein with respect to an identified property or condition, "substantially" refers to a degree of deviation that is small enough so as not to measurably impair the identified property or condition. The exact degree of acceptable deviation may in some cases depend on the particular context.

[0130] As used herein, a plurality of items, structural elements, compositional elements, and / or materials may be presented in common lists for convenience. However, these lists should be construed as though each member of the list were individually identified as a separate and unique member. Accordingly, the individual members of such lists should not be construed as de facto equivalents of any other members of the same list solely based on their presentation in a common group, absent indication to the contrary.

[0131] Unless otherwise defined, all technical and scientific terms used herein (such as acronyms used for chemical elements in the periodic table) have the same meaning as commonly understood by one of ordinary skill in the art to which the subject matter of this disclosure belongs.

[0132] Unless otherwise indicated, all numbers expressing quantities of ingredients, reaction conditions, and the like used in the specification and claims should be understood to be modified in all instances by the term "about." Thus, unless indicated to the contrary, the numerical parameters set forth in the specification and appended claims are approximations that may vary depending on the desired properties sought to be obtained by the subject matter of the present disclosure. As used herein, the term "about," when referring to a value or an amount of mass, weight, time, volume, concentration, or percentage, is meant to encompass variations from the specified amount, in some embodiments, ±20%, in some embodiments, ±10%, in some embodiments, ±5%, in some embodiments, ±1%, in some embodiments, ±0.5%, and in some embodiments, ±0.1%, where such variations are appropriate for carrying out the disclosed methods.

[0133] The term "coupled," as used herein, is not intended to be limited to a direct or mechanical coupling. Unless otherwise specified, terms such as "first" and "second" are used to arbitrarily distinguish between elements to which such terms refer. As such, these terms are not necessarily intended to indicate a temporal or other priority of such elements. [Explanation of symbols]

[0134] 100 material handling system, 101 material piece, 102 another conveyor system, bowl feeder, or hopper, 103 conveyor system, conveyor belt, 104 conventional drive motor, conveyor system motor, conveyor belt drive motor, 105 position detector, 106 active singulator or passive singulator, exemplary singulator, 107 computer system, 108 automation control system, 109 one or more still or live action cameras, 110 visual or optical recognition system, vision system, 111 material piece tracking device, material tracking device, material tracking system, 112 control system, 120 one or more sensor systems, XRF system, 121 emission source, X-ray tube, X-ray emitter, 122 power supply, 123 control system, 124 one or more detectors, XRF detector, 125 associated detector electronics, 126...129 sorting device, 136...139 Sorting container, 140 Sorting container, 201 Plurality of material pieces, 203 Conveyor system, 400 Process, system and process, 401 Process block, 402 Optional process block, 403 Process block, 404 Process block, 405 Process block, 406 Process block, 407 Process block, 601 X-ray beam, 602 X-ray beam spot, XRF beam spot, 802...805 Separate individualized stream, 810...817 Bar, 1100 Peak, 1101 Peak, titanium, 1102 Peak, zinc, 1200 System and process, 1201 Optional process block, 1203 Process block, 1204 Process block, 1205 Process block, 1300 Measured peak of copper, copper peak, 1301 Measured peak of titanium, titanium peak, 1302 Measured peak of zinc, zinc peak, 1600 system and process, 1601 process block, 1602 process block, 1603 process block, 1701 laser line, 1800 system and process, 1801 process block, 1802 process block, 1803 process block, 1804 process block, 1900 process or series of processes, 1901Process block, 1902 Step, process, 1903 Zorba, 1904 Process, 1905 Twitch, 1906 Process, 1907 Selected alloy, 2001 Camera, 2002 Laser, 2201 Camera, 2202 Laser, 2203 Camera, 2204 Multiple lasers, 2301 Camera, 2302 Laser, 2303 Laser, 2400 Process, 2401 Process block, 2402 Process block, 2403 Process block, 2404 Process block, 2405 Process block, 3400 Data processing ("computer") system, 3405 Local bus, 3412 User interface adapter, 3413 Keyboard, 3414 Mouse, 3415 CPU, one or more processors, 3416 Display adapter, 3420 Volatile memory, RAM, 3425 Network (LAN) adapter, 3430 I / O adapter, 3431 Hard disk drive, 3432 Tape drive, 3435 Non-volatile memory, ROM, 3440 Display, 3500 Process, 3501 Process block, 3502 Process block, 3503 Process block, 3503-3510 Process block, 3504 Process block, 3505 Process block, 3506 Optional process block, 3507 Process block, 3509 Process block, 3510 Process block, 3511 Process block, 3512 Process block, 3513 Process block, X Height, X Distance, θ Angle

Claims

1. transporting the pieces of material on a moving conveyor belt past an X-ray fluorescence ("XRF") system; irradiating the piece of material with an X-ray beam emitted by the XRF system; measuring an XRF spectrum of the piece of material resulting from the irradiation of the piece with the X-ray beam; determining an intersection area between an X-ray beam spot of the irradiated X-ray beam and the piece of material; modifying the measured XRF spectrum of the piece of material according to the determined intersection area; classifying the pieces of material according to the modified XRF spectra; A method comprising:

2. The method of claim 1 further comprising the step of separating the pieces of material from a mixture of pieces of material being transported on the moving conveyor belt.

3. determining the intersection area between the illuminated X-ray beam spot of the X-ray beam and the piece of material; determining a first location of the piece of material on the moving conveyor belt relative to a second location where the X-ray beam spot of the X-ray beam contacts the conveyor belt; calculating the intersection area as a function of the determined first and second locations; The method of claim 1 , comprising:

4. determining the intersection area between the illuminated X-ray beam spot of the X-ray beam and the piece of material; measuring the amount of a first specific element in the measured XRF spectrum and comparing said amount to a known amount of said first specific element contained in said conveyor belt; determining the intersection area from a ratio of the measured quantity to the known quantity; The method of claim 1 further comprising:

5. 5. The method of claim 4, wherein the known amount of the first particular element contained in the conveyor belt is determined from a measurement of an XRF spectrum of the conveyor belt.

6. 5. The method of claim 4, wherein the first specific element contained in the conveyor belt is known to be absent from the piece of material.

7. 7. The method of claim 6, wherein the step of correcting the measured XRF spectrum of the piece of material in response to the determined intersection area further comprises dividing the measured amount of a second particular element in the measured XRF spectrum by one minus the ratio of the measured amount to the known amount.

8. 7. The method of claim 6, wherein the step of correcting the measured XRF spectrum of the piece of material in response to the determined intersection area further comprises the step of subtracting the measured amount of the first particular element from the measured XRF spectrum.

9. modifying the measured XRF spectrum of the piece of material in response to the determined intersection area; subtracting the measured amount of the first particular element from the measured XRF spectrum; dividing the measured amount of the second specific element in the measured XRF spectrum by one minus the ratio of the measured amount to the known amount; The method of claim 6 further comprising:

10. 10. The method of claim 9, wherein the second specific element is known not to be contained in the conveyor belt.

11. The method of claim 10 , wherein the piece of material is composed of an aluminum alloy and the second specific element is copper.

12. The method of claim 11 , wherein the first specific element is tin or zinc.

13. 12. The method of claim 11 , wherein the piece of material has the shape of a thin strip having a cross-sectional dimension that is smaller than a diameter of the X-ray beam spot, and wherein the piece of material is classified as a 6xx3 aluminum alloy according to the modified XRF spectrum.

14. a conveyor system configured to convey the mixture of material pieces; an XRF system configured to (i) irradiate a piece of material with an X-ray beam emitted by the XRF system, and (ii) measure an XRF spectrum of the piece of material resulting from the irradiation of the piece of material by the X-ray beam; circuitry configured to determine an intersection area between an x-ray beam spot of the applied x-ray beam and the piece of material; circuitry configured to modify the measured XRF spectrum of the piece of material in response to the determined intersection area; circuitry configured to classify the piece of material according to the modified XRF spectrum; circuitry configured to separate the pieces of material from a mixture of the pieces of material conveyed on a moving conveyor belt; A material handling system comprising:

15. 15. The material handling system of claim 14, wherein the intersection area between the X-ray beam spot of the irradiated X-ray beam and the piece is determined from a ratio of a measured amount of a first specific element in the measured XRF spectrum to an amount of the first specific element known to be contained in the conveyor belt, and the first specific element is not contained in the piece.

16. 16. The material handling system of claim 15, wherein the known amount of the first particular element contained in the conveyor belt is determined from a separate measurement of an XRF spectrum of the conveyor belt.

17. the circuitry configured to modify the measured XRF spectrum of the piece of material in response to the determined intersection area; circuitry configured to subtract the measured amount of the first particular element from the measured XRF spectrum; circuitry configured to divide the measured amount of a second specific element in the measured XRF spectrum by one minus the ratio of the measured amount to the known amount; 16. The material handling system of claim 15, further comprising:

18. 18. The material handling system of claim 17, wherein the piece of material is composed of an aluminum alloy and the second specific element is copper.

19. 20. The material handling system of claim 18, wherein the first particular element is tin or zinc.

20. 20. The material handling system of claim 18, wherein the piece of material has the shape of a thin strip having a cross-sectional dimension that is smaller than a diameter of the X-ray beam spot, and wherein the piece of material is classified as a 6xx3 aluminum alloy according to the modified XRF spectrum.

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