CN116917055A

CN116917055A - Sorting based on chemical compositions

Info

Publication number: CN116917055A
Application number: CN202280016480.3A
Authority: CN
Inventors: N·库马; 小曼纽尔·G·加西亚
Original assignee: Sotera Technology Co ltd
Current assignee: Sotera Technology Co ltd
Priority date: 2021-09-28
Filing date: 2022-03-16
Publication date: 2023-10-20

Abstract

Systems and methods for sorting and sorting materials to produce a collection of materials composed of a particular chemical composition in a collection. The system may utilize a vision system and one or more sensor systems, which may implement a machine learning system to identify or classify each of the materials. Then, sorting is performed according to the classification.

Description

Sorting based on chemical compositions

The present application claims priority from U.S. provisional patent application Ser. No. 63/249069 and U.S. provisional patent application Ser. No. 63/285964. The present application is a partial continuation of U.S. patent application serial No. 17/667397, U.S. patent application serial No. 17/667397 claims to be a partial continuation of U.S. provisional patent application serial No. 63/146892 and U.S. provisional patent application serial No. 63/173301, and is a partial continuation of U.S. patent application serial No. 17/495291, U.S. patent application serial No. 17/495291 is a partial continuation of U.S. patent application serial No. 17/380928, U.S. patent application serial No. 17/380955 is a partial continuation of U.S. patent application serial No. 17/227245, U.S. patent application serial No. 17/227245 is a partial continuation of U.S. patent application serial No. 16/939011, U.S. patent application serial No. 16/939011 is a continuation of U.S. patent application serial No. 16/375675 (issued as U.S. patent application No. 10722922), U.S. patent application serial No. 16/375675 is a partial continuation of U.S. patent application serial No. 15/963755 (issued as U.S. patent application 10710119), and U.S. patent application serial No. 15/4921362 is hereby incorporated by reference to be a full priority, and U.S. patent application serial No. patent No. 15/21362 is hereby incorporated by reference. U.S. patent application Ser. No. 17/495291 is also a continuation-in-part application of U.S. patent application Ser. No. 17/491415 (issued as U.S. patent No. 11278937), U.S. patent application Ser. No. 17/491415 is a continuation-in-part application of U.S. patent application Ser. No. 16/852514 (issued as U.S. patent No. 11260426), U.S. patent application Ser. No. 16/852514 is a divisional application of U.S. patent application Ser. No. 16/358374 (issued as U.S. patent No. 10625304), and U.S. patent application Ser. No. 16/358374 is a continuation-in-part application of U.S. patent application Ser. No. 15/963755 (issued as U.S. patent No. 10710119), all of which are hereby incorporated by reference in their entirety.

Government licensing rights

The present disclosure is made with U.S. government support under DE-AR0000422 awarded by the U.S. department of energy. The united states government may have certain rights in this disclosure.

Technical Field

The present disclosure relates generally to sorting of materials, and in particular, to sorting of materials to obtain a particular composition of chemical elements within the sorted materials.

Background

Recycling refers to the process of collecting and disposing of materials that would otherwise be discarded as waste and converting them into new products. Since recycling reduces the amount of waste to landfill and incinerator, protects natural resources, improves economic safety by using domestic material sources, prevents pollution by reducing the need for collecting new raw materials, and saves energy, recycling has benefits to communities as well as to the environment. After collection, the recyclables are generally sent to a material recycling facility for sorting, cleaning, and processing into materials that can be used for manufacturing.

Drawings

Fig. 1 illustrates a schematic diagram of a sorting system configured in accordance with an embodiment of the present disclosure.

Fig. 2 illustrates a table listing the chemical compositions of common aluminum alloys.

Fig. 3 illustrates a table listing chemical compositions of exemplary aluminum alloys to be produced according to embodiments of the present disclosure.

Fig. 4 illustrates a flow chart configured in accordance with an embodiment of the present disclosure.

Fig. 5 illustrates a flow chart configured for determining a size of a piece of material according to an embodiment of the present disclosure.

Fig. 6 shows a visual image of an exemplary piece of material of cast aluminum.

FIG. 7 illustrates a visual image of an exemplary material piece of an aluminum extrusion.

Fig. 8 shows a visual image of an exemplary piece of wrought aluminum.

Fig. 9 illustrates a flow chart configured in accordance with an embodiment of the present disclosure.

Fig. 10 illustrates a flow chart configured in accordance with an embodiment of the present disclosure.

FIG. 11 illustrates a block diagram of a data processing system configured in accordance with an embodiment of the present disclosure.

Detailed Description

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

As used herein, "chemical element" means a chemical element in the periodic table of chemical elements, which includes chemical elements that may be found at the time of filing of the present application. As used herein, a "material" may include a solid composed of a compound or mixture of one or more chemical elements, where the complexity of the compound or mixture may vary from simple to complex (all of which may also be referred to herein as a material having a particular "chemical composition").

As used herein, "aggregate chemical composition (aggregate chemical composition)" means a composition of chemical elements and the relative weight percent (wt%) of these chemical elements within an individual, separate collection or group of material pieces. (note that weight percent (or percent by weight), also referred to as mass fraction, is the mass of a particular chemical element within a material or substance as a percentage of the total mass of the material or substance.) for example, if a collection of individual metal alloy pieces are melted together, the resulting "melt" will have a chemical composition that is equivalent to the bulk chemical composition. As referred to herein, "melt" refers to the percentage (e.g., weight percent) of the various chemical elements present within the melt as and upon the selected pieces of material melt together.

Classes of materials may include metals (ferrous and non-ferrous), metal alloys, plastics (including but not limited to PCB, HDPE, UHMWPE and various colored plastics), rubber, foam, glass (including but not limited to borosilicate or soda lime glass, and various colored glasses), ceramics, paper, cardboard, polytetrafluoroethylene, PE, bundled wires, insulated coated wires, rare earth elements, leaves, wood, plants, plant parts, textiles, biowaste, packaging, electronic waste, batteries, scrap pieces from scrapped vehicles, mining, construction and demolition waste, crop waste, forest residues, specialty planted grasses, woody energy crops, microalgae, municipal food waste, hazardous chemicals and biomedical waste, construction waste, farm waste, biological items, non-biological items, objects having a specific carbon content, any other object that may be found within municipal solid waste, and any other object, item or material disclosed herein, which may be distinguished from one another by any of the preceding classes of technologies including but not limited to any of the sensors of the one or more sensor systems disclosed herein. Within this disclosure, the terms "scrap," "scrap piece," "material piece," and "piece" may be used interchangeably. As used herein, a piece of material or scrap piece referred to as having a metal alloy composition is a metal alloy having a particular chemical composition that distinguishes the metal alloy from other metal alloys.

As is well known in the art, a "polymer" is a substance or material composed of very large molecules or macromolecules, which is composed of many repeating subunits. The polymer may be a natural polymer or a synthetic polymer found in nature.

"Multi-layer PolymerThe composite film "is composed of two or more different compositions and may have up to about 7.5 _– ⁸ ×10 ^-4 m thickness. The layers are at least partially continuous and preferably but optionally coextensive.

As used herein, the terms "plastic", "plastic part" and "plastic material part" (all of which may be used interchangeably) refer to a polymeric composition comprising or consisting of one or more polymers and/or multi-layer polymeric films.

As used herein, the term "chemical feature" refers to a unique pattern (e.g., fingerprint spectrum) that would be produced by one or more analytical instruments, which indicates the presence of one or more specific elements or molecules (including polymers) in a sample. The element or molecule may be organic and/or inorganic. Such analytical instrumentation includes any of the sensor systems disclosed herein. According to embodiments of the present disclosure, one or more sensor systems disclosed herein may be configured for producing chemical characteristics of a piece of material (e.g., a piece of plastic).

As used herein, "fraction" refers to any particular combination of: organic and/or inorganic elements or molecules, polymer types, plastic types, polymer compositions, chemical characteristics of the plastic, physical characteristics of the plastic part (e.g., color, transparency, strength, melting point, density, shape, size, type of manufacture, uniformity, response to stimuli, etc.), etc., including any and all of the various classifications and types of plastics disclosed herein. A non-limiting example of a score is one or more different types of plastic pieces comprising: LDPE plus a relatively high percentage of aluminum; LDPE and PP plus a relatively low percentage of iron; adding zinc into PP; a combination of PE, PET and HDPE; any type of red LDPE plastic; any combination of plastic parts other than PVC; a black plastic member; a combination of type #3- #7 plastics comprising a specific combination of organic and inorganic molecules; a combination of one or more different types of multilayer polymeric films; combinations of specific plastics that do not contain specific contaminants or additives; any type of plastic having a melting point greater than a particular threshold; any of a number of specific types of thermoset; specific plastics that do not contain chlorine; combinations of plastics with similar densities; combinations of plastics with similar polarity; plastic bottles without attached caps or attached caps without plastic bottles.

"catalytic pyrolysis" involves the degradation of a polymeric material by heating the polymeric material in the absence of oxygen and a catalyst.

The term "predetermined" refers to a predetermined or decided thing.

"spectral imaging" is imaging using multiple bands across the electromagnetic spectrum. While a common camera captures light across three bands in the visible spectrum (red, green, and blue ("RGB")), spectral imaging encompasses a variety of technologies including but exceeding RGB. For example, spectral imaging may use infrared, visible, ultraviolet, and/or x-ray spectra, or some combination of the above. The spectral data or spectral image data is a digital data representation of the spectral image. Spectral imaging may include simultaneous acquisition of spectral data in the visible and invisible bands, illumination from outside the visible range, or use of optical filters for capturing a particular spectral range. It is also possible to capture hundreds of bands for each pixel in the spectral image.

As used herein, the term "image data packet" refers to a digital data packet associated with a captured spectral image of each piece of material.

As used herein, the terms "classify … …," "identify … …," "select … …," and "identify … …," as well as the terms "classify," "identify," "select" and "identify," and any derivatives of the foregoing, may be utilized interchangeably. As used herein, "classifying" a piece of material is to determine (i.e., identify) the type or class of material to which the piece of material belongs (or at least should belong based on sensed characteristics of the piece of material). For example, according to some embodiments of the present invention, a sensor system (as further described herein) may be configured to collect and analyze any type of information for classifying materials, wherein classification may be utilized within a sorting system to selectively sort pieces of material according to one or more sensed sets of physical and/or chemical characteristics (which may be, for example, user-defined), including, but not limited to: color; texture; color tone; shape; brightness; a weight; a density; a composition; size of the material; uniformity; the type of manufacture; chemical characteristics; a predetermined score; a radioactive feature; transmittance of light, sound, or other signals; and responses to stimuli such as various fields including emitted and/or reflected electromagnetic radiation ("EM") of the piece of material. As used herein, "manufacturing type" refers to the type of manufacturing process by which a piece of material is manufactured, such as a cast (including but not limited to consumable mold casting, permanent mold casting, and powder metallurgy), forged metal part formed by a forging process; material removal processes, and the like.

The type or class (i.e., classification) of material may be user-definable and is not limited to any known classification of material. The granularity of a type or class may vary from very coarse to very fine. For example, the type or category may include: plastics, ceramics, glass, metals, and other materials, wherein the particle size of such types or classes is relatively coarse; different metals and metal alloys such as, for example, zinc, copper, brass, chrome plating and aluminum, wherein the particle size of such types or classes is finer; or between specific subclasses of metal alloys, wherein the granularity of such types or classes is relatively fine. Thus, the type or class may be configured for distinguishing between materials of substantially different compositions, such as, for example, plastics and metal alloys, or for distinguishing between materials of substantially similar or nearly identical chemical compositions, such as, for example, metal alloys of different subclasses. It should be appreciated that the methods and systems discussed herein may be applied to identify/sort pieces of material for which the chemical composition is completely unknown prior to the pieces being sorted.

As referred to herein, a "conveyor system" may be any known piece of mechanical handling equipment that moves material from one location to another, including, but not limited to: pneumatic mechanical conveyor, automatic conveyor, belt driven live roller conveyor, bucket conveyor, chain driven live roller conveyor, drag conveyor, dust-proof conveyor, electric rail vehicle system, flexible conveyor, gravity slide conveyor, spool roller conveyor, electric roller conveyor, overhead I-steel conveyor, land conveyor, drug conveyor, plastic belt conveyor, pneumatic conveyor, screw or auger conveyor, screw conveyor, piping lane conveyor, vertical conveyor, vibration conveyor, and wire mesh conveyor.

According to certain embodiments of the present disclosure, the systems and methods described herein receive a mixture of a plurality of pieces of material, wherein at least one piece of material within the mixture comprises a chemical composition (e.g., a metal alloy composition, a chemical feature) that is different from one or more other pieces of material and/or at least one piece of material in the mixture is manufactured in a different manner from one or more other materials and/or at least one piece of material in the mixture is distinguishable from the other pieces of material (e.g., a visually distinguishable characteristic or feature, a different chemical feature, etc.), and the systems and methods are configured to identify/sort the pieces of material accordingly. Embodiments of the present disclosure may be used to sort any type or class of material or score defined herein.

It should be noted that the pieces of material to be sorted may have irregular sizes and shapes (see, e.g., fig. 6-8). For example, the material (e.g., zorba and/or Twitch) may have been previously crushed by some type of crushing mechanism that cuts the material into such irregularly shaped and sized pieces (creating scrap pieces) that are then fed or placed onto a conveyor system.

Embodiments of the present disclosure may be described herein as sorting pieces of material into one or more individual groups or collections by physically placing (e.g., transferring or discharging) the pieces of material into such individual groups or collections according to a user-defined grouping or collection (e.g., a predetermined particular aggregate chemical composition, a particular material type classification or score). As an example, in certain embodiments of the present disclosure, pieces of material may be sorted into one or more separate containers in order to separate pieces of material composed of one or more particular chemical compositions from other pieces of material composed of different particular chemical compositions in order to produce a predetermined particular aggregate chemical composition within a collection or group of sorted pieces of material. In a non-limiting example, a twintch set including various aluminum alloys (e.g., various different wrought and/or cast aluminum alloys) may be sorted in accordance with embodiments of the present disclosure to produce aluminum alloys having a desired chemical composition (which may include aluminum alloys having a unique chemical composition that is different from the chemical composition of known aluminum alloys).

Fig. 1 illustrates an example of a system 100 configured in accordance with various embodiments of the invention. The conveyor system 103 may be implemented to convey one or more (organized or random) streams of individual pieces of material 101 through the system 100 such that each piece of material 101 in the individual pieces of material 101 may be tracked, sorted, and sorted into a predetermined desired group or collection (e.g., one or more predetermined specific aggregate chemical compositions). Such conveyor systems 103 may be implemented using one or more conveyors on which the pieces of material 101 travel at a generally predetermined constant speed. However, certain embodiments of the present disclosure may be implemented with other types of conveyor systems (as disclosed herein), including systems in which pieces of material freely fall past selected components of the system 100 (or any other type of vertical sorter), or vibrating conveyor systems. Hereinafter, where applicable, the conveyor system 103 may also be referred to as a conveyor belt 103. In one or more embodiments, some or all of the acts of conveying, tracking, stimulating, detecting, classifying, and sorting may be performed automatically (i.e., without human intervention). For example, in system 100, one or more stimulus sources, one or more emission detectors, classification modules, sorting devices, and/or other system components may be configured to automatically perform these and other operations.

Furthermore, while the simplified illustration in fig. 1 depicts a single stream of pieces of material 101 on a conveyor belt 103, wherein multiple such streams of pieces of material are parallel to each other through embodiments of the present disclosure of various components of the system 100 may be implemented. For example, as further described in U.S. patent No. 10207296, pieces of material may be distributed into two or more parallel separate streams or sets of parallel conveyors traveling on a single conveyor. According to certain embodiments of the present disclosure, the incorporation or use of a separator is not required. Instead, a conveyor system (e.g., conveyor system 103) may simply convey a large number of pieces of material that have been placed onto conveyor system 103 in a random fashion (or placed onto conveyor system 103 in a large number, such as by a vibrating mechanism, and then caused to separate). As such, certain embodiments of the present disclosure are capable of simultaneously tracking, sorting, and/or sorting a plurality of such conveyed pieces of material.

According to certain embodiments of the present disclosure, some suitable feeding mechanism (e.g., another conveyor system or hopper 102) may be utilized to feed the pieces of material 101 onto the conveyor system 103, whereby the conveyor system 103 conveys the pieces of material 101 through various components within the system 100. An optional drum/vibrator/separator 106 may be used to separate each material piece from the combined bulk of material pieces after the material pieces 101 are received by the conveyor system 103. In certain embodiments of the present disclosure, the conveyor system 103 is operated by the conveyor system motor 104 to travel at a predetermined speed. The predetermined speed may be programmable and/or adjustable by an operator in any known manner. The monitoring of the predetermined speed of the conveyor system 103 may alternatively be performed with the position detector 105. Within certain embodiments of the present disclosure, control of the conveyor system motor 104 and/or the position detector 105 may be performed by the automated control system 108. Such an automation control system 108 may be operated under control of the computer system 107 and/or the functions for performing the automation control may be implemented in software within the computer system 107.

Thus, as will be further described herein, by utilizing control of the conveyor belt drive motor 104 and/or the automated control system 108 (and, alternatively, the position detector 105), as each of the pieces of material 101 traveling on the conveyor belt 103 is identified, they may be tracked by position and time (relative to the various components of the system 100) such that the various components of the system 100 may be activated/deactivated as each piece of material 101 passes in proximity to the various components of the system 100. As a result, the automated control system 108 is able to track the position of each of the pieces 101 as each of the pieces 101 travels along the conveyor belt 103.

According to certain embodiments of the present disclosure, after the pieces 101 are received by the conveyor belt 103, rollers and/or vibrators may be used to separate each piece from a large number (e.g., a physical stack) of pieces. According to alternative embodiments of the present disclosure, the pieces of material may be positioned into one or more separate (i.e., single column) streams, which may be performed by an active or passive separator 106. Examples of passive separators are further described in U.S. patent No. 10207296. As previously discussed, the incorporation or use of a separator is not required. Instead, the conveyor system (e.g., conveyor belt 103) may simply convey a collection of pieces of material that may have been placed onto conveyor belt 103 in a random manner.

Referring again to fig. 1, certain embodiments of the present disclosure may utilize a visual or optical recognition system 110 and/or a material tracking and measuring device 111 for tracking each of the pieces of material 101 as the pieces of material 101 travel on the conveyor belt 103. The vision system 110 may utilize one or more stationary or real-time motion cameras 109 to record the position (i.e., position and timing) of each of the pieces 101 on the moving conveyor belt 103.

The vision system 110 may be further or alternatively configured to perform certain types of identification (e.g., classification) of all or a portion of the piece of material 101, as will be further described herein. For example, such vision systems 110 may be used to capture or gather information about each of the pieces of material 101. For example, the vision system 110 may be configured (e.g., using a machine learning system) to capture or collect any type of information from the pieces of material that may be utilized within the system 100 to classify the pieces of material 101 and/or selectively sort according to one or more sets of characteristics (e.g., physical and/or chemical and/or radioactivity, etc.), as described herein. According to certain embodiments of the present disclosure, the vision system 110 may capture visual images (including one-dimensional, two-dimensional, three-dimensional, or holographic imaging) of each of the pieces of material 101, for example, by using optical sensors utilized in typical digital cameras and video equipment. Such visual images captured by the optical sensor are then stored as image data in a memory device (e.g., formatted as image data packets). Such image data may represent images captured within the optical wavelength of light (i.e., the wavelength of light that is observable by a typical human eye), according to certain embodiments of the present disclosure. However, alternative embodiments of the present disclosure may utilize a sensor system configured to capture an image of a material composed of wavelengths of light other than the human eye's visual wavelength. All such images may also be referred to herein as spectral images.

According to certain embodiments of the present disclosure, the system 100 may be implemented using one or more sensor systems 120, which one or more sensor systems 120 may be utilized alone or in combination with the vision system 110 to classify/identify the piece of material 101. The sensor system 120 may be configured with any type of sensor technology, the sensor system 120 including a sensor system that utilizes irradiated or reflected electromagnetic radiation (e.g., utilizing infrared ("IR"), fourier transform IR ("FTIR"), forward-looking infrared ("FLIR"), very near infrared ("NIR"), short wave infrared ("SWIR"), long wave infrared ("LWIR"), medium wave infrared ("MWIR" or "MIR"), X-ray transmission ("XRT"), gamma rays, ultraviolet ("UV"), X-ray fluorescence ("XRF"), laser induced breakdown spectroscopy ("LIBS"), raman spectroscopy, anti-stokes raman spectroscopy, gamma spectroscopy, hyperspectral spectroscopy (e.g., any range beyond visible wavelengths), acoustic spectroscopy, NMR spectroscopy, microwave spectroscopy, terahertz spectroscopy), one-dimensional, two-dimensional, three-dimensional or holographic imaging including those having any of the foregoing, or by any other type of sensor technology including, but not limited to, chemistry or radioactivity. An implementation of an exemplary XRF system (e.g., for use as sensor system 120 herein) is further described in U.S. patent No. 10207296.

It should be noted that although fig. 1 is illustrated with a combination of vision system 110 and one or more sensor systems 120, embodiments of the present disclosure may be implemented using any combination of sensor systems that utilize any of the sensor technologies disclosed herein or any other sensor technology currently available or developed in the future. Although fig. 1 is illustrated as including one or more sensor systems 120, implementations of such sensor system(s) are optional within certain embodiments of the present disclosure. Within certain embodiments of the present disclosure, a combination of both the vision system 110 and the one or more sensor systems 120 may be used to categorize the piece of material 101. Within certain embodiments of the present disclosure, any combination of one or more of the different sensor technologies disclosed herein may be used to classify the piece of material 101 without utilizing the vision system 110. Further, embodiments of the present disclosure may include any combination of one or more sensor systems and/or vision systems, wherein the outputs of such sensor and/or vision systems are processed within a machine learning system (as further disclosed herein) to classify/identify materials from a mixture of materials, which may then be sorted from one another. The sensor system(s) 120 may be omitted from the system 100 (or simply disabled) if the sorting system (e.g., system 100) is configured to operate with only such vision system(s) 110.

According to certain embodiments of the present disclosure, and as further described herein with respect to fig. 4, the vision system 110 and/or sensor system(s) may be configured to identify which of the pieces of material 101 are not sorted by the system 100 for inclusion within a collection to produce a type of a particular collection chemical composition (e.g., a piece of material containing a particular contaminant or chemical element), and to send a signal to not transfer such piece of material along with other sorted pieces of material.

In certain embodiments of the present disclosure, the material tracking and measuring device 111 and accompanying control system 112 may be utilized and configured to measure the size and/or shape of each of the material pieces 101 as the material pieces 101 pass within the vicinity of the material tracking and measuring device 111, along with the position (i.e., location and timing) of each of the material pieces 101 on the moving conveyor system 103, which may be utilized by the system 100 to determine an approximate mass of each of the material pieces. Alternatively, the vision system 110 may be used to track the position (i.e., position and timing) of each of the pieces of material 101 as the pieces of material 101 are transported by the conveyor system 103.

Non-limiting exemplary operation of such a material tracking and measurement device 111 and control system 112 is described herein with reference to fig. 5. Such a material tracking and measuring device 111 may be implemented with a known laser system that continuously measures the distance that the laser light travels before being reflected back to the detector of the laser system. Thus, as each of the pieces of material 101 passes within the vicinity of the device 111, it outputs a signal indicative of such distance measurement to the control system 112. Thus, such a signal may essentially represent an intermittent series of pulses, whereby during those moments when the piece of material is not in the vicinity of the device 111, a baseline of the signal is generated as a result of distance measurements between the distance device 111 and the conveyor belt 103, whereas each pulse provides a measurement of the distance between the distance measuring device 111 and the piece of material 101 passing on the conveyor belt 103. Since the material 101 may have an irregular shape, such pulse signals may sometimes have an irregular height. However, each pulse signal generated by the device 111 may provide the height of a portion of each of the pieces of material 101 as the pieces of material 101 pass over the conveyor belt 103. The length of each of such pulses also provides a measure of the length of each of the pieces of material 101, measured along a line substantially parallel to the direction of travel of the conveyor belt 103. It is this length measurement (and, alternatively, the height measurement) (corresponding to the timestamp of process block 506 of fig. 5) that may be utilized within embodiments of the present disclosure to determine or at least roughly estimate the mass of each piece of material 101, which may then be used to assist in classifying the piece of material, as further described herein.

Referring next to fig. 5, a flow chart of an exemplary system and process 500 for determining an approximate size, shape, and/or mass of each piece of material is illustrated. Such systems and processes 500 may be implemented within any of the visual/optical recognition systems and/or material tracking and measurement devices described herein (such as the material tracking and measurement device 111 and control system 112 illustrated in fig. 1). In process block 501, a material tracking and measurement device may be initialized at n=0, where n represents a condition where a first piece of material to be conveyed along the conveyor system has not been measured. As previously described, such material tracking and measuring devices may establish a baseline signal that represents the distance between the material tracking and measuring device and the conveyor belt and the absence of any carried objects (i.e., pieces of material) on the conveyor belt. In process block 502, the material tracking and measurement device generates a continuous, or substantially continuous, distance measurement. Process block 503 represents a decision within the material tracking and measurement device to determine whether the detected distance has changed from a predetermined threshold amount. Recall that once the system 100 has been initiated, at some point in time the piece of material 101 will travel along the conveyor system sufficiently close to the material tracking and measuring device to be detected by the employed mechanism measuring distance. In embodiments of the present disclosure, this may occur as the traveling piece of material 101 passes within the laser line used to measure distance. Once an object, such as piece of material 101, begins to be detected by the material tracking and measuring device (e.g., laser), the distance measured by the material tracking and measuring device will change from its baseline value. The material tracking and measuring device may be predetermined to detect the presence of a material piece 101 passing within its vicinity only when the height of any portion of the material piece 101 is greater than a predetermined threshold distance value. Fig. 5 shows an example in which such a threshold is 0.15 (e.g., representing 0.15 mm), but embodiments of the present disclosure should not be limited to any particular value.

As long as the threshold distance value has not been reached, the system and process 500 will continue (i.e., repeat process blocks 502-503) to measure the current distance. Once a measured height greater than the threshold has been detected, the process will proceed to process block 504 to record that a piece of material 101 passing within the vicinity of the material tracking and measurement device has been detected on the conveyor system. Thereafter, in process block 505, the variable n may be incremented to indicate to the system 100 that another piece of material 101 has been detected on the conveyor system. This variable n may be used to assist in tracking each of the pieces of material 101. In process block 506, a time stamp is recorded for the detected piece of material 101, which may be utilized by the system 100 to track the specific location and timing of the detected piece of material 101 as the detected piece of material 101 travels on the conveyor system, while also representing the length of the detected piece of material 101. In optional process block 507, the recorded time stamp may then be used to determine when to activate (start) and deactivate (stop) the acquisition of the sensor-initiated measurement signal (e.g., the X-ray fluorescence spectrum from the piece of material 101) associated with the time stamp. The start and stop times of the time stamps may correspond to the aforementioned pulse signals generated by the material tracking and measuring device. In process block 508, the timestamp along with the recorded height of the pieces of material 101 may be recorded in a table utilized by the system 100 to track each of the pieces of material 101 and its resulting classifications.

Thereafter, in optional process block 509, a signal may then be sent to the sensor system indicating a period of time in which to activate/deactivate sensor-initiated measurement signal acquisition from the piece of material 101, which may include a start time and a stop time corresponding to the length of the piece of material 101 as determined by the material tracking and measurement device. Embodiments of the present disclosure are able to accomplish such tasks because the time stamp received from the material tracking and measuring device and the known predetermined speed of the conveyor system indicate when the leading edge of the piece of material 101 will pass the irradiation source and when the trailing edge of the piece of material 101 will thereafter pass the irradiation source.

The system and process 500 for distance measurement of each of the pieces of material 101 traveling along the conveyor system may then be repeated for each passing piece of material 101.

Within certain embodiments of the present disclosure implementing one or more sensor systems 120, one or more sensor systems 120 may be configured to assist the vision system 110 in identifying the chemical composition, relative chemical composition, and/or type of manufacture of each of the pieces of material 101 as the pieces of material 101 pass within the vicinity of the one or more sensor systems 120. The one or more sensor systems 120 may include an energy emitting source 121, which energy emitting source 121 may be powered, for example, by a power source 122 to stimulate a response from each of the pieces 101.

According to certain embodiments of the present disclosure implementing an XRF system as sensor system 120, source 121 may comprise an inline X-ray fluorescence ("IL-XRF") tube, such as further described in U.S. Pat. No. 10207296. Such IL-XRF tubes may include separate X-ray sources, each dedicated to one or more (e.g., separate) streams of conveyed material. In such cases, one or more detectors 124 may be implemented as XRF detectors for detecting fluorescent X-rays from material pieces 101 within each of the separate streams.

In certain embodiments of the present disclosure, the sensor system 120 may emit an appropriate sensing signal toward the pieces of material 101 as each piece of material 101 passes within the vicinity of the emission source 121. The 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 form suitable for the type of sensor technology utilized. One or more detectors 124 and associated detector electronics 125 capture these received sensed characteristics to perform signal processing thereon and generate digitized information (e.g., spectral data) representative of the sensed characteristics, which are then analyzed in accordance with certain embodiments of the present disclosure so that they can be used (alone or in combination with vision system 110) to classify each of the pieces of material 101. This sorting may be performed within the computer system 107 and then may be utilized by the automated control system 108 to activate one of the N (N.gtoreq.1) sorting devices 126 … … 129 of the sorting apparatus for sorting (e.g., transferring/discharging) the pieces of material 101 into one or more N (N.gtoreq.1) sorting containers 136 … … 139 according to the determined sorting. Four sorting apparatuses 126 … … 129 and four sorting containers 136 … … 139 associated with the sorting apparatuses are illustrated in fig. 1 by way of non-limiting example only.

The sorting apparatus may include any known sorting mechanism for redirecting selected pieces of material 101 toward a desired location, including but not limited to transferring pieces of material 101 from a conveyor system into a plurality of sorting containers. For example, the sorting device may utilize air injectors, wherein each of the air injectors is assigned to one or more of the classifications. When one of the air injectors (e.g., 127) receives a signal from the automation control system 108, that air injector emits an air flow that causes the pieces of material 101 to be diverted/discharged from the conveyor system 103 to a sorting bin (e.g., 137) corresponding to that air injector.

Other mechanisms may be used to transfer/eject the pieces of material, such as robotically removing the pieces from the conveyor belt, pushing the pieces of material from the conveyor belt (e.g., using a paint brush type plunger), thereby creating an opening (e.g., a trapdoor) in the conveyor system 103 from which the pieces of material may fall, or using an air jet to transfer the pieces of material into separate containers as they fall from the edge of the conveyor belt. As the term is used herein, a pusher device may refer to any form of device that may be activated to dynamically transfer objects on or from a conveyor system/device, employing a pneumatic, mechanical, or other means such as any suitable type of mechanical pushing mechanism (e.g., ACME screw drive), pneumatic pushing mechanism, or air ejector pushing mechanism. Some embodiments may include multiple pusher devices located at different locations and/or having different transfer path orientations along the path of the conveyor system. In various implementations, the sorting systems described herein may determine which pusher device (if any) to activate depending on the classification of the pieces of material performed by the machine learning system. Further, determining which pusher device to activate may be based on the presence and/or characteristics of other objects detected, which may also be in the transfer path of the pusher device concurrently with the target article (e.g., the classified material piece). Further, even for facilities in which separation along the conveyor system is imperfect, the disclosed sorting system can identify when the plurality of objects are not well separated and dynamically select a pusher device from the plurality of pusher devices that should be activated based on which pusher device provides the best transfer path for potentially separating objects within the immediate vicinity. In some embodiments, the object identified as the target object may represent material that should be transferred away from the conveyor system. In other embodiments, the object identified as the target object represents material that should be allowed to remain on the conveyor system such that non-target material is transferred instead.

In addition to the N sorting containers 136 … … 139 into which the pieces 101 are transferred/discharged, the system 100 may also include a container 140 that receives pieces 101 that are not transferred/discharged from the conveyor system 103 into any of the aforementioned sorting containers 136 … … 139. For example, when the sorting of the pieces of material 101 is not determined (or simply because the sorting equipment fails to adequately transfer/discharge the pieces), when the pieces of material 101 contain contaminants detected by the vision system 110 and/or the sensor system 120, or because no particular aggregate chemical composition is required for the pieces of material 101, the pieces of material 101 may not be transferred/discharged from the conveyor system 103 into one of the N sorting containers 136 … … 139. Alternatively, the container 140 may be used to receive one or more sorted pieces of material that are not intentionally assigned to any one of the N sorting containers 136, … …, 139. These pieces of material may then be further sorted according to other characteristics and/or by another sorting system.

Multiple classifications may be mapped to a single sorting apparatus and associated sorting container depending on the particular requirements of a particular aggregate chemical composition being predetermined. In other words, there is not necessarily a one-to-one correlation between classifications and containers. For example, a user may desire to sort certain classified materials into the same container in order to obtain a particular aggregate chemical composition. To achieve such sorting, when pieces of material 101 are classified as meeting one or more requirements for achieving a particular aggregate chemical composition, the same sorting apparatus may be activated to sort these into the same sorting container. Such combination sorting may be applied to produce any desired combination of sorted pieces of material (e.g., one or more specific aggregate chemical compositions). The mapping of classifications may be programmed by a user (e.g., using a sorting algorithm operated by computer system 107 (see, e.g., fig. 4)) to produce such desired combinations. Additionally, the classification of the pieces of material is user definable and is not limited to any particular known classification of pieces of material.

Within certain embodiments of the present disclosure, the conveyor system 103 may be divided into multiple belts (such as, for example, two belts) configured in series, with a first belt conveying pieces of material through the vision system 110 and/or implemented sensor system(s) 120 and a second belt conveying certain sorted pieces of material through the implemented sensor system 120 for subsequent sorting. Further, such a second conveyor belt may be at a lower height than the first conveyor belt, such that the pieces of material fall from the first belt onto the second belt.

Within certain embodiments of the present disclosure implementing sensor system 120, emission source 121 may be located above the detection zone (i.e., above conveyor system 103); however, certain embodiments of the present disclosure may position the emission source 121 and/or the detector 124 in other locations that still produce acceptable sensed/detected physical characteristics.

It should be appreciated that while the systems and methods described herein are described primarily with respect to classifying solid pieces of material, the present disclosure is not so limited. The systems and methods described herein may be applied to categorize materials having any of a range of physical states including, but not limited to, liquid, molten, gaseous, or powdered solid, another state, and any suitable combination thereof.

Regardless of the type(s) of sensed characteristics/information of the captured pieces of material, the information may then be sent to a computer system (e.g., computer system 107) for processing by a machine learning system to identify and/or classify each of the pieces of material. Such machine learning systems may implement any known machine learning system, including machine learning systems that implement: neural networks (e.g., artificial neural networks, deep neural networks, convolutional neural networks, recurrent neural networks, auto encoders, reinforcement learning, etc.), supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, robotics learning, association rule learning, fuzzy logic, artificial intelligence ("AI"), deep learning algorithms, deep structure learning hierarchical learning algorithms, support vector machines ("SVM") (e.g., linear SVM, nonlinear SVM, SVM regression, etc.), decision tree learning (e.g., classification and regression trees ("CART"), integrated methods (e.g., integrated learning, random forest, bagging (Bagging), and Pasting (pressing), patches and subspaces, boosting (Stacking), etc.), dimension reduction (e.g., projection, manifold learning, principal component analysis, etc.), and/or deep machine learning algorithms (such as those described and publicly available in the deep learning website, including all software, object and available software cited in the text website) that may be implemented by the web site and the machine learning method including the use of the machine learning example of the disclosure; python, openCV, inception, theano library, torch library, pyTorch library, pylearn2 library, numpy library, blocks library, tensorFlow library, MXNet library, caffe library, lasagne library, keras library, chainer library, matlab deep learning, CNTK, matConvNet (MATLAB toolbox implementing convolutional neural networks for computer vision applications), MATLAB, deep learntoolbox (MATLAB toolbox for deep learning (from Rasmus Berg Palm)), bigDL (large DL), cuda-Convnet (convolutional (or more generally, feed-forward) neural network fast c++/Cuda implementation), deep belief network, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning j (deep learning4 j), ebean.1sh, deep mat library, MShadow library, matplob library, sciPy library, CXXNET, nengo-Nengo library, eblearn, CUDAMat, gnumpy, three-way factors RBM and mcRBM, mPoT (Python code using cudamatand Gnumpy training natural image models), convNet, elektronn, openNN library, neurosensier, thuno generalized henb learning, apac Singa, lightner and simple DNN.

According to certain embodiments of the present disclosure, machine learning may be performed in two phases. For example, first, training occurs, which may be performed offline, as the system 100 is not utilized to perform actual sorting/sorting of the pieces of material. The system 100 may be used to train a machine learning system in that a homogeneous set (also referred to herein as a control sample) of pieces of material (i.e., of the same type or class of material, or falling within the same predetermined fraction) is conveyed through the system 100 (e.g., by the conveyor system 103); and all such pieces of material may not be sorted, but may be collected in a common container (e.g., container 140). Alternatively, training may be performed at another location remote from the system 100, including using some other mechanism for collecting sensed information (characteristics) of the control set of pieces of material. During this training phase, algorithms within the machine learning system extract features from the captured information (e.g., using image processing techniques well known in the art). Non-limiting examples of training algorithms include, but are not limited to: linear regression, gradient descent, feed forward, polynomial regression, learning curve, canonical learning model, and logistic regression. It is during this training phase that algorithms within the machine learning system learn the relationship between the material (e.g., captured by the vision system and/or sensor system (s)) and its features/characteristics, creating a knowledge base for later classification of the mixture of pieces of material received by the system 100. Such knowledge bases may include one or more libraries, where each library includes parameters (e.g., neural network parameters) for use by the machine learning system in classifying the piece of material. For example, a particular library may include parameters configured by the training phase for identifying and classifying a particular type or class of material, or one or more materials that fall within a predetermined score. According to certain embodiments of the present disclosure, such libraries may be input into a machine learning system, and then a user of the system 100 may be able to adjust certain of the parameters in order to adjust the operation of the system 100 (e.g., adjust how well the machine learning system identifies a threshold effectiveness of a particular piece of material from a mixture of materials).

Additionally, inclusion of certain materials (e.g., chemical elements or compounds), or combinations of certain chemical elements or compounds, in a piece of material (e.g., a metal alloy) may create identifiable physical features (e.g., visually discernable characteristics) in the material. Thus, as multiple pieces of material containing such specific compositions are passed through the foregoing training phase, the machine learning system may learn how to distinguish such pieces of material from other pieces of material. Thus, a machine learning system configured in accordance with certain embodiments of the present disclosure may be configured for sorting between pieces of material according to their respective chemical compositions. For example, such machine learning systems may be configured such that different aluminum alloys may be sorted according to the percentage of a given alloy material contained within the aluminum alloy.

For example, FIG. 6 illustrates a captured or acquired image of an exemplary piece of material of cast aluminum alloy that may be used during the foregoing training phase. FIG. 7 illustrates captured or acquired images of an exemplary piece of extruded aluminum alloy material that may be used during the foregoing training phase. Fig. 8 illustrates captured or acquired images of an exemplary piece of material of aluminum wrought alloy that may be used during the foregoing training phase. During the training phase, multiple pieces of material of a particular (homogeneous) material class (type) as a control sample may be delivered (e.g., by a conveyor system) through a vision system and/or one or more sensor systems such that algorithms within the machine learning system detect, extract, and learn what features (e.g., visually discernable characteristics) represent such type or class of material. In other words, an image of a cast aluminum alloy material piece such as that shown in fig. 6 may be passed through such a training phase so that an algorithm within the machine learning system "learns" (is trained) how to detect, identify, and classify material pieces composed of the cast aluminum alloy. In the case of training a vision system (e.g., vision system 110), it is trained to visually distinguish between pieces of material. This creates a library of parameters specific to the cast aluminum alloy material piece. Then, the same processing may be performed with respect to an image of the extruded aluminum alloy material piece such as that shown in fig. 7, thereby creating a parameter library specific to the extruded aluminum alloy material piece. Also, the same processing may be performed for an image of a piece of wrought aluminum alloy material such as that shown in fig. 8, creating a library of parameters specific to the piece of wrought aluminum alloy material. As can be seen from the exemplary image of the cast aluminum alloy shown in fig. 6, such cast aluminum alloy materials have visually discernable features such as sharp, defined angles. As can be seen from the exemplary image of extruded aluminum alloy shown in fig. 7, such extruded aluminum alloy materials have visually discernable features such as rounded corners and hammer texture. As can be seen from the exemplary image of the aluminum wrought alloy shown in fig. 8, such aluminum wrought alloy materials have visually discernable features such as folding of the material and smoother texture than cast and extruded material.

Embodiments of the present disclosure are not limited to the materials illustrated in fig. 6-8. For each type of material to be classified by the vision system, any number of exemplary pieces of material of that type may be delivered by the vision system. Given the captured sensed information as input data, algorithms within the machine learning system may use N classifiers, each of which tests for one of N different material types, classes, or scores. Note that the machine learning system may be "taught" (trained) to detect any type, class, or fraction of materials, including any type, class, or fraction of materials found within Municipal Solid Waste (MSW), or any other material in which a chemical composition of materials produces a visually discernable characteristic.

After the parameters within the algorithm have been established and the machine learning system has sufficiently learned (trained) the differences (e.g., visually discernable differences) in material classifications (e.g., within a user-defined statistical confidence level), a library for different material classifications is then implemented into a material classification and/or sorting system (e.g., system 100) for identifying and/or sorting material pieces from a mixture of material pieces, and such sorted material pieces are then sorted if sorting is to be performed (e.g., to produce a particular aggregate chemical composition).

As found in the relevant literature, techniques for constructing, optimizing, and utilizing machine learning systems are known to those of ordinary skill in the art. Examples of such documents include publications: "ImageNet Classification with Deep Convolutional Networks (ImageNet Classification using deep convolutional networks)", "25 th International conference treatise on neuro-information handling systems", 2012, 12 months 3-6 days, taihao lake, nevada; and LeCun et al, "Gradient-Based Learning Applied to Document Recognition (applied to Gradient-based learning of document recognition)", institute of Electrical and Electronics Engineers (IEEE), month 11 in 1998, both of which are hereby incorporated by reference in their entirety.

In an exemplary technique, data captured by the sensors and/or vision system about a particular piece of material may be processed into an array of data values within a data processing system (e.g., the data processing system 3400 of fig. 11 (configured with a machine learning system)). For example, the data may be spectral data captured by a digital camera or other type of sensor system with respect to a particular piece of material and processed into an array of data values (e.g., image data packets). Each data value may be represented by a single number, or by a series of numbers representing the value. These values may be multiplied by a neuron weight parameter (e.g., using a neural network), and may have added bias. This can be fed into the neuron nonlinearity. The resulting number of outputs from the neurons may be processed as the original value, with the outputs multiplied by subsequent neuron weight values, optionally with the addition of bias, and again fed into the neuron nonlinearity. Each such iteration of the process is referred to as a "layer" of the neural network. The final output of the final layer may be interpreted as the probability that material is present or absent in the captured data relating to the piece of material. Examples of such processes are described in detail in both the previously mentioned "ImageNet Classification with Deep Convolutional Networks (ImageNet classification using deep convolutional networks)" and "Gradient-Based Learning Applied to Document Recognition (Gradient-based learning applied to document recognition)" references.

According to certain embodiments of the present disclosure in which the neural network is implemented as a final layer ("classification layer"), a final set of outputs of neurons are trained to represent the likelihood that a piece of material is associated with the captured data. During operation, if the likelihood that the piece of material is associated with the captured data exceeds a user-specified threshold, it is determined that the piece of material is indeed associated with the captured data. These techniques may be extended to determine not only the presence of a material type associated with a particular captured data, but also whether a sub-region of the particular captured data belongs to one type of material or another type of material. This process is known as segmentation and there are techniques in the literature that use neural networks (such as what is known as "fully-convoluted" neural networks), or networks that additionally include convolved portions if not fully-convoluted (i.e., partially-convoluted). This allows the location and size of the material to be determined.

It should be understood that the present disclosure is not limited exclusively to machine learning techniques. Other common techniques for material classification/identification may also be used. For example, the sensor system may provide a signal that may indicate the presence or absence of a certain type, class, or fraction of material by examining the spectral emissions (i.e., spectral imaging) of the material using optical spectrometry techniques using a multispectral or hyperspectral camera. The spectral images of the piece of material may also be used in a template matching algorithm, wherein a database of spectral images is compared to the acquired spectral images to find the presence or absence of certain types of material from the database. The histogram of the captured spectral image may also be compared to a histogram database. Similarly, the bag of words model may be used with feature extraction techniques such as scale invariant feature transform ("SIFT") to compare extracted features between captured spectral images and spectral images in a database.

Thus, as disclosed herein, certain embodiments of the present disclosure provide for the identification/classification of one or more different types, categories, or fractions of materials to determine (e.g., according to one or more predetermined specific aggregate chemical compositions) which pieces of material should be transferred (i.e., sorted) from a conveyor system in a defined group. According to some embodiments, machine learning techniques are utilized to train (i.e., configure) a neural network to identify a variety of one or more different types, categories, or scores of materials. A spectral image or other type of sensed information of the material (e.g., traveling on the conveyor system) is captured, and based on the identity/classification of such material, the systems described herein can decide which material piece should be allowed to remain on the conveyor system, and which material piece should be transferred/removed from the conveyor system (e.g., either into a collection container or onto another conveyor system).

According to certain embodiments of the present disclosure, a machine learning system (e.g., system 100) for an existing device may be dynamically reconfigured to identify/classify a new type, category, or fraction of material by replacing a current set of neural network parameters with a new set of neural network parameters.

It is to be mentioned herein that the collected/captured/detected/features/characteristics (e.g., spectral images) of the piece of material are not necessarily simple particularly identifiable or discernable physical characteristics, according to certain embodiments of the present disclosure; they may be abstract formulas that can only be expressed mathematically, or not at all; however, the machine learning system may be configured to parse the spectral data for patterns that allow classification of the control samples during the training phase. Further, the machine learning system may acquire sub-portions of the captured information (e.g., spectral images) of the piece of material and attempt to find correlations between the predefined classifications.

According to certain embodiments of the present disclosure, instead of a training phase in which control samples of material pieces are communicated by a vision system and/or sensor system(s), training of a machine learning system may be performed using a marking/annotation technique whereby a user enters a marking or annotation identifying each material piece as data/information of the material piece is captured by the vision/sensor system and then used to create a library for use by the machine learning system when classifying material pieces within a mixture of material pieces.

According to certain embodiments of the present disclosure, any sensed characteristic output by any of the sensor systems 120 disclosed herein may be input into a machine learning system for sorting and/or sorting materials. For example, in a machine learning system implementing supervised learning, the sensor system 120 output that uniquely characterizes a particular type or composition of material (e.g., a particular metal alloy) may be used to train the machine learning system.

Fig. 9 illustrates a flow chart depicting an exemplary embodiment of a process 3500 for sorting/sorting pieces of material using vision system 110 and/or one or more sensor systems 120, in accordance with certain embodiments of the present disclosure. Process 3500 may be performed to classify the mixture of pieces of material into any combination of predetermined types, categories, and/or scores, including generating a predetermined specific aggregate chemical composition. Process 3500 may be configured for operation within any of the embodiments of the present disclosure described herein, including system 100 of fig. 1. As will be further described, process 3500 may be utilized within the system and process 400 of fig. 4. The operations of process 3500 may be performed by hardware and/or software included within a computer system (e.g., computer system 3400 of fig. 11) of a control system (e.g., computer system 107, vision system 110, and/or sensor system(s) 120 of fig. 1).

In process block 3501, a piece of material 101 may be placed onto a conveyor system 103. In process block 3502, the position of each piece of material 101 on the conveyor system 103 is detected for tracking each piece of material 101 as each piece of material 101 travels through the system 100. This may be performed by vision system 110 (e.g., by distinguishing piece of material 101 from the underlying conveyor system material when communicating with a conveyor system position detector (e.g., position detector 105). Alternatively, the piece of material tracking device 111 may be used to track the piece of material 101. Alternatively, a light source (including but not limited to visible light, UV, and IR) may be created and have any system that can be used to track the corresponding detectors of the piece of material 101. In process block 3503, the sensed information/characteristics of the piece of material 101 are captured/collected when the piece of material 101 has traveled into proximity with one or more of the vision system 110 and/or the sensor system(s) 120. In process block 3504, a vision system, such as previously disclosed (e.g., implemented within computer system 107), may perform preprocessing on the captured information, which may be used to detect (extract) information (e.g., from the background (e.g., conveyor belt 103)) for each of the pieces of material 101, in other words, to identify differences between the pieces of material 101 and the background. Well-known image processing techniques such as dilation, thresholding and contouring may be used to identify the piece of material 101 as distinct from the background. In process block 3505, segmentation may be performed. For example, the captured information may include information related to one or more pieces of material 101. Additionally, when an image of a particular piece of material 101 is captured, that particular piece of material 101 may be located on the seam of the conveyor belt 103. Thus, in such instances, it may be desirable to isolate the image of each piece of material 101 from the background of the image. In an exemplary technique for process block 3505, the first step is to apply a high contrast of the image; in this way, the background pixels are reduced to substantially all black pixels, and at least some of the pixels associated with the piece of material 101 are brightened to substantially all white pixels. The white image pixels of piece of material 101 are then expanded to cover the entire size of piece of material 101. After this step, the position of the piece of material 101 is a high contrast image of all white pixels on a black background. The boundary of the piece of material 101 may then be detected using a contouring algorithm. The boundary information is saved and then the boundary position is transferred to the original image. Then, segmentation is performed on the original image over an area larger than the earlier defined boundary. In this way, the piece of material 101 is identified and separated from the background.

In optional process block 3506, the piece of material 101 may be conveyed along the conveyor system 103 within the vicinity of the material tracking and measuring device 111 and/or the sensor system 120 to determine the size and/or shape of the piece of material 101. Such material tracking and measuring devices 111 may be configured to measure one or more dimensions of each material piece so that the system may calculate (determine) an approximate mass of each material piece. In process block 3507, post-processing may be performed. Post-processing may involve resizing the captured information/data to prepare it for use in a machine learning system. This may also include modifying certain properties in some way (e.g., enhancing image contrast, changing image background, or applying a filter), which will result in an enhancement of the ability of the machine learning system to classify the piece of material 101. In process block 3509, the size of the data may be adjusted. In some cases, it may be desirable to resize the data to match the data input requirements for some machine learning systems (such as neural networks). For example, a neural network may require an image data size (e.g., 225x 255 pixels or 299x 299 pixels) that is much smaller than the size of an image captured by a typical digital camera. In addition, the smaller the input data size, the less processing time is required to perform classification. Thus, smaller data sizes may increase the throughput of the system 100 and increase its value.

In process blocks 3510 and 3511, each piece of material 101 is identified/categorized based on the sensed/detected characteristics. For example, process block 3510 may be configured with a neural network employing one or more machine learning algorithms that compare extracted features to features stored in a knowledge base that was previously generated (e.g., generated during a training phase), and assign a classification with the highest match to each of the pieces of material 101 based on such comparison. The algorithms of the machine learning system can process the captured information/data in a hierarchical manner by using automatically trained filters. The filter responses are then successfully combined in the next stage algorithm until probabilities are obtained in the final step. In process block 3511, these probabilities may be used for each of the N classifications to determine into which of the N sorting containers the respective piece of material 101 should be sorted. Each of the N classifications may be associated with N different predetermined specific aggregate chemical compositions. For example, each of the N classifications may be assigned to one sorting container, and the piece of material 101 under consideration is sorted into the container corresponding to the highest probability of returning greater than a predefined threshold. Within embodiments of the present disclosure, such predefined thresholds may be preset by a user. If none of the probabilities is greater than a predetermined threshold, the particular piece of material 101 may be sorted into an abnormal container (e.g., sorting container 140).

Next, in process block 3512, sorting devices 126 … … 129 corresponding to one or more classifications of the pieces of material 101 are activated. Between the time that the image of the piece of material 101 is captured and the time that the sorting device 126 … … 129 is activated, the piece of material 101 has moved (e.g., at the transfer rate of the conveyor system) from near the vision system 110 and/or the sensor system(s) 120 to a position downstream of the conveyor system 103. In an embodiment of the present disclosure, activation of sorting device 126 … … 129 is timed such that as a piece of material 101 passes sorting device 126 … … 129 mapped to sorting of the piece of material, sorting device 126 … … 129 is activated and piece of material 101 is transferred/discharged from conveyor system 103 into its associated sorting container 136 … … 139. Within embodiments of the present disclosure, activation of sorting apparatus 126 … … 129 may be timed by a corresponding position detector that detects when piece of material 101 passes before sorting apparatus 126 … … 129 and sends a signal to enable activation of sorting apparatus 126 … … 129. In process block 3513, sorting containers 136 … … 139 corresponding to activated sorting apparatus 126 … … 129 receive diverted/discharged material pieces 101.

Fig. 10 illustrates a flow chart depicting an exemplary embodiment of a process 1000 of sorting/sorting a piece of material 101 according to certain embodiments of the present disclosure. Process 1000 may be configured for operation within any of the embodiments of the present disclosure described herein, including system 100 of fig. 1. As will be further described, process 1000 may be utilized within the system and process 400 of fig. 4.

Process 1000 may be configured to operate in conjunction with process 3500. For example, according to certain embodiments of the present disclosure, process blocks 1003 and 1004 may be incorporated into process 3500 (e.g., operate in series or in parallel with process blocks 3503-3510) to combine the operation of vision system 110 implemented in conjunction with a machine learning system with a sensor system (e.g., sensor system 120) not implemented in conjunction with a machine learning system to sort and/or sort pieces of material 101, which are included in accordance with the system and method 400 of fig. 4.

The operations of process 1000 may be performed by hardware and/or software included within a computer system (e.g., computer system 3400 of FIG. 11) that controls aspects of system 100 (e.g., computer system 107 of FIG. 1). In process block 1001, a piece of material 101 may be placed onto a conveyor system 103. Next, in optional process block 1002, the pieces of material 101 may be conveyed along a conveyor system 103 within the vicinity of the material tracking and measuring device 111 and/or the optical imaging system to track each piece of material and/or to determine the size and/or shape of the piece of material 101. Such material tracking and measuring devices 111 may be configured to measure one or more dimensions of each material piece so that the system may calculate (determine) an approximate mass of each material piece. In process block 1003, when the piece of material 101 has traveled near the sensor system 120, the piece of material 101 may be interrogated or stimulated with EM energy (waves) or some other type of stimulus suitable for the particular type of sensor technology utilized by the sensor system 120. In process block 1004, a physical characteristic of the piece of material 101 is sensed/detected and captured by the sensor system 102. In process block 1005, for at least some of the pieces of material 101, the type of material is identified/classified based (at least in part) on the captured characteristics, which may be combined (e.g., when performed in conjunction with process 3500) with the classification by the machine learning system in conjunction with vision system 110.

Next, if sorting of the pieces of material 101 is to be performed, in process block 1006, the sorting device 126 … … 129 corresponding to one or more classifications of the pieces of material 101 is activated. Between the time the piece of material is sensed and the time the sorting device 126 … … 129 is activated, the piece of material 101 has moved from the vicinity of the sensor system 120 to a position downstream of the conveyor system 103 at the conveying rate of the conveyor system. In certain embodiments of the present disclosure, activation of the sorting device 126 … … 129 is timed such that as the piece of material 101 passes through the sorting device 126 … … 129 mapped to sorting of the piece of material 101, the sorting device 126 … … 129 is activated and the piece of material 101 is transferred/discharged from the conveyor system 103 into its associated sorting container 136 … … 139. Within certain embodiments of the present disclosure, activation of sorting apparatus 126 … … 129 may be timed by a corresponding position detector that detects when piece of material 101 passes before sorting apparatus 126 … … 129 and sends a signal to enable activation of sorting apparatus 126 … … 129. In process block 1007, sorting containers 136 … … 139 corresponding to activated sorting apparatus 126 … … 129 receive transferred/ejected pieces of material 101.

According to various embodiments of the present disclosure, different types or classes of materials may be classified by different types of sensors, each for use with a machine learning system, and combined to classify pieces of material in a waste material or waste stream.

According to various embodiments of the present disclosure, data (e.g., spectral data) from two or more sensors may be combined using a single or multiple machine learning systems to perform classification of pieces of material.

According to various embodiments of the present disclosure, multiple sensor systems may be mounted on a single conveyor system, with each sensor system utilizing a different machine learning system. According to various embodiments of the present disclosure, multiple sensor systems may be mounted on different conveyor systems, with each sensor system utilizing a different machine learning system.

According to embodiments of the present disclosure, the system 100 may be configured (e.g., according to the system and method 400 of fig. 4) for outputting a collection of sorted materials having a particular chemical composition in the aggregate (i.e., a predetermined particular aggregate chemical composition). In other words, if a collection of such sorted materials is or at least theoretically can be combined into a single object or mass (e.g., melted together or mixed into a solution), such single object or mass will have a particular chemical composition. Further, embodiments of the present disclosure may be configured to output a collection of materials having a particular chemical composition that is not present within any individual piece of material fed into the system 100.

A non-limiting example would be producing an aluminum alloy having a chemical composition according to a predetermined (e.g., designed by a user of system 100) combination of specific weight percentages (wt.%) of aluminum, silicon, magnesium, iron, manganese, copper, and zinc. The aluminum alloy scrap pieces that may be fed into the system 100 may be the aluminum alloy scrap pieces listed in the table of fig. 2. Also, it may be desirable to produce aluminum alloys having chemical compositions substantially equivalent to those listed in the table of fig. 3 from the sorting of such usable aluminum alloy scrap pieces. However, even though system 100 may be configured to distinguish between each of the aluminum alloys listed in the table of fig. 2 (i.e., by categorizing each of aluminum alloy pieces 101 according to either or both of processes 1000 and 3500), none of these aluminum alloys have a chemical composition equivalent to the chemical composition listed in the table of fig. 3. Thus, sorting scrap pieces composed of any one of the aluminum alloys listed in the table of fig. 2 does not result in an aggregate of aluminum alloy scrap pieces having a chemical composition in the aggregate that is equivalent to the chemical composition listed in the table of fig. 3.

However, embodiments of the present disclosure may be configured for producing collections of aluminum alloy scrap pieces having a collection chemistry identical, or at least substantially identical, to the chemistry listed in the table of fig. 3. This is accomplished by utilizing one or more of the vision system 110 and/or the sensor system(s) 120 to sort, select, and sort for outputting a combination of the plurality of aluminum alloy scrap pieces of fig. 2 at a ratio that produces a bulk chemical composition (also referred to herein as a predetermined specific bulk chemical composition).

Since the individual aluminum alloy scrap pieces may have different sizes, and thus different masses, the material tracking and measuring device 111 may be used to estimate the mass of each aluminum alloy scrap piece. For example, the size of each of the scrap pieces measured by the material tracking and measuring device 111 may be utilized by the system 100 to determine (calculate) the mass, or at least approximate the mass, of each scrap piece. Because the system 100 has been configured to identify and classify each scrap piece as belonging to one of the plurality of aluminum alloys listed in the table of fig. 2, and because the particular chemical composition of each of the different aluminum alloys is known, the system 100 can use this information, along with the determined size of each scrap piece, to determine (calculate) the mass, or at least approximate the mass, of each of the different chemical elements contained within each aluminum alloy scrap piece.

To produce a collection of aluminum alloy scrap pieces having a collection chemical composition, the system 100 is configured for then sorting and selecting for sorting those aluminum alloy scrap pieces fed into the system 100 to obtain a collection chemical composition of a combined quality of the sorted aluminum alloy scrap pieces when the aluminum alloy scrap pieces are combined. In other words, if a collection of such aluminum alloy scrap pieces, which are sorted and output by the system 100, are melted together (which may be at some point), the resulting melt will have a collection chemistry, or at least substantially approach a collection chemistry that is within a desired accuracy threshold.

Thus, the system 100 may be configured to calculate, on an operational basis, the contribution to the individual mass of each of the chemical elements within the aggregate chemical composition as each aluminum alloy scrap piece is added to the sorted aggregate, such that the system 100 may then determine whether the next sorted aluminum alloy scrap piece should be added to the aggregate (i.e., sorted from the mixture of aluminum alloy scrap pieces).

FIG. 4 illustrates a block flow diagram of a system and process 400 for producing a collection of pieces of material having a predetermined particular aggregate chemical composition, configured in accordance with an embodiment of the present disclosure. The system and process 400 may be implemented as a computer program (or other type of algorithm) executed within the system 100 (e.g., by the computer system 107). The system and process 400 may be performed in conjunction with aspects of the system and process 3500 of fig. 9 and/or aspects of the system and process 1000 of fig. 10.

In process block 401, the system 100 receives or is input with a predetermined particular aggregate chemical composition that is desired to be generated at the output of one of the sorting devices 126 … … 129 within the system 100. In process block 402, as each of the pieces of material 101 is conveyed past the material tracking and measuring device 111, the material tracking and measuring device 111 will determine the size and/or shape of each of the pieces of material 101, as described herein. In process block 403, classifications are assigned to each of the pieces of material 101 by one or more of the vision system 110 and/or the sensor systems 120 in the manner described herein (e.g., see fig. 9 and 10). In process block 404, the system 100 will determine the chemical composition of each of the sorted pieces 101. This may be determined directly using one or more of the sensor systems 120 (such as an XRF or LIBS system) 120 that are capable of measuring and determining the weight percentages of various chemical elements within a particular piece of material. Alternatively, the chemical composition of each material piece 101 in the categorized material pieces 101 may be determined indirectly, such as inferred as a result of the categorization of the material pieces 101. For example, if various different categories or types of pieces of material 101 fed into the system 100 are known (e.g., as previously described with respect to fig. 2), then the particular chemical composition for each category or type of piece of material 101 may be input into the system 100 (e.g., and stored in a database), and then when a particular piece of material 101 is classified (e.g., by one or more of the vision system 110 and/or the sensor system 120), its particular chemical composition will match (be somehow associated with) its determined classification. Additionally, in process block 404, the mass of each of the pieces of material 101 may be approximately calculated based on the previously determined size and/or shape, and thus the approximate mass of each chemical element in the piece of material may be determined. This is achievable because the relative mass of the chemical elements of the various known types or classes of material pieces will be known and can be pre-entered into the system 100 in a similar manner to known chemical compositions.

In process block 405, the system 100 will sort each of the pieces 101 based on the determined chemical composition and mass to obtain a predetermined specific aggregate chemical composition. For example, the system 100 may be configured for sorting (e.g., transferring) each of the pieces of material 101 into a predetermined container (e.g., container 136) by a predetermined sorting apparatus (e.g., sorting apparatus 126). The remainder of the pieces 101 may be collected into a container 140, or the system 100 may be configured for sorting certain of the pieces 101 into another container (e.g., container 137) to obtain a second (e.g., different) predetermined particular aggregate chemical composition. Alternatively, the system 100 may be configured for sorting the remaining material pieces 101 based on any other type(s) of desired classification, such as sorting the remaining material pieces 101 into two different classifications (e.g., forged, extruded, and/or cast aluminum). In process block 406, the sorted pieces of material 101 used to obtain a particular aggregate chemical composition are collected into a predetermined container (e.g., container 136).

Process blocks 402-406 may be repeated as needed to obtain a particular aggregate chemical composition, to obtain a particular aggregate chemical composition within a specified accuracy threshold, or to obtain a particular aggregate chemical composition for a desired (predetermined) collected mass of material (which may be determined by calculating the amount of material transferred into a container). For example, as each piece of material is sorted, the system may continually determine (i.e., update) the aggregate chemical composition of the subsequently collected pieces of material, and then will continue sorting until the updated aggregate chemical composition is within a predetermined threshold level of the particular aggregate chemical composition. As each piece of material is sorted, the system will determine whether to transfer that piece of material to add to the collection, such as whether the piece of material will increase or decrease the total weight percentage of a particular chemical element within the piece of material that has been sorted and collected. Additionally, the system may be configured to not transfer certain pieces of material into the collection, as such pieces of material contain contaminants (e.g., wrought aluminum alloy pieces containing ferrous materials such as bolts) that are not desired to be included within the predetermined specific chemical composition. Alternatively, other systems may be implemented to remove pieces of material containing specific contaminants.

The material tracking and measuring device 111 may be a known one-or two-dimensional line scanner. If it is a one-dimensional line scanner, it will measure the length of each piece of material in the direction of travel. Such length measurements may be used to approximate the mass of each of the pieces if it can be assumed that the length and width of most of the pieces are approximately equal. If a two-dimensional line scanner is utilized, it can measure both the length and width of each piece of material for determining mass.

Alternatively, one or more cameras may be utilized in a known manner to image each piece of material and determine the approximate size of each piece of material. Such camera(s) may be positioned near the conveyor belt before the sorting device, or may be positioned downstream of the sorting device such that only sorted pieces of material are imaged to determine their approximate quality.

Such an implementation for determining the mass of each piece may be omitted if it can be assumed that enough pieces of material all have about the same size and mass.

Alternatively, the container collecting the transferred pieces of material may be positioned on a weight scale that continuously weighs the collected pieces of material, thereby providing an approximate weight and resulting mass of each piece of material as it is sorted and collected within the container. These qualities may then be used in the systems and processes 400 described herein.

According to certain embodiments of the present disclosure, at least a portion of the plurality of systems 100 may be linked together serially in order to perform a plurality of iterations or sorting layers. For example, when two or more systems 100 are linked in such a manner, the conveyor system may be implemented with a single conveyor belt or multiple conveyor belts to convey the pieces of material through a first vision system (and, according to certain embodiments, a sensor system) configured for sorting the pieces of material in the first collection of materials into the first one or more container collections (e.g., sorting containers 136 … … 139) by a sorter (e.g., first automated control system 108 and associated one or more sorting devices 126 … … 129), and then conveying the pieces of material through a second vision system (and, according to certain embodiments, another sensor system) configured for sorting the pieces of material in the second collection of materials into the second one or more sorting container collections by a second sorter. For further discussion of such multi-stage sorting see U.S. published patent application No. 2022/0016675, which is incorporated herein by reference.

Such continuous system 100 may comprise any number of such systems linked together in such a manner. According to certain embodiments of the present disclosure, each successive vision system or sensor system may be configured to sort out materials that are different from the materials of the previous vision system(s) or sensor system(s), with the end result producing a collection of pieces of material having a predetermined specific aggregate chemical composition.

Referring now to FIG. 11, a block diagram is depicted that illustrates a data processing ("computer") system 3400 in which aspects of embodiments of the present disclosure may be implemented. (the terms "computer," "system," "computer system," and "data processing system" may be used interchangeably herein.) computer system 107, automation control system 108, aspects of sensor system(s) 120, and/or vision system 110 may be configured similarly to computer system 3400. The computer system 3400 may employ a local bus 3405. Any suitable bus architecture may be utilized, such as a peripheral component interconnect ("PCI") local bus architecture, an accelerated graphics port ("AGP") architecture, or an industry standard architecture ("ISA"), among others. One or more processors 3415, volatile memory 3420, and non-volatile memory 3435 may be connected to the local bus 3405 (e.g., through a PCI bridge (not shown)). An integrated memory controller and buffer memory may be coupled to the one or more processors 3415. The one or more processors 3415 may include one or more central processor units and/or one or more graphics processor units 3401 and/or one or more tensor processing units. Additional connections to the local bus 3405 may be made through direct component interconnection or through a card. In the depicted example, communications (e.g., network (LAN)) adapter 3425, I/O (e.g., small computer system interface ("SCSI") host bus) adapter 3430, and expansion bus interface (not shown) may be connected to local bus 3405 by direct component connection. An audio adapter (not shown), a graphics adapter (not shown), and a display adapter 3416 (coupled to the display 3440) may be connected to the local bus 3405 (e.g., through a card plugged into an expansion slot).

The user interface adapter 3412 may provide a connection for a keyboard 3413 and a mouse 3414, a modem (not shown), and additional memory (not shown). The I/O adapter 3430 may provide connections for hard disk drive 3431, solid state drive 3432, and CD-ROM drive (not shown).

An operating system may run on the one or more processors 3415 and is used to coordinate and provide control of various components within the computer system 3400. In fig. 11, the operating system may be a commercially available operating system. An object oriented programming system (e.g., java, python, etc.) may run in conjunction with the operating system and provide calls to the operating system from one or more programs (e.g., java, python, etc.) executing on system 3400. Instructions for the operating system, the object-oriented operating system, and programs may be located on non-volatile memory 3435 storage devices, such as hard disk drive 3431 or solid state drive 3432, and may be loaded into volatile memory 3420 for execution by processor 3415.

Those of ordinary skill in the art will appreciate that the hardware in FIG. 11 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash ROM (or equivalent nonvolatile memory) or optical disk drives and the like, may be used in addition to or in place of the hardware depicted in FIG. 11. Further, any of the processes of the present disclosure may be applied to a multiprocessor computer system, or performed by a plurality of such systems 3400. For example, training of the machine learning system 100 may be performed by a first computer system 3400, while operations of the system 100 for sorting may be performed by a second computer system 3400.

As another example, computer system 3400 may be a stand-alone system configured to be bootable without relying on some type of network communication interface, whether or not computer system 3400 comprises some type of network communication interface. As a further example, the computer system 3400 may be an embedded controller configured with ROM and/or flash ROM that provides non-volatile memory that stores operating system files or user-generated data.

The depicted example in FIG. 11 and above-described examples are not meant to imply architectural limitations. Further, the computer program forms of aspects of the disclosure may reside on any computer readable storage medium (i.e., floppy disk, compact disk, hard disk, magnetic tape, ROM, RAM, etc.) used by a computer system.

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

As will be appreciated by one of skill in the art, aspects of the present disclosure may be embodied as systems, processes, methods, and/or computer program products. Thus, aspects of the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "circuit," circuitry, "" module "or" system. Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable storage media, the computer program product having computer-readable program code embodied on the one or more computer-readable storage media. ( However, any combination of one or more computer readable media may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. )

The 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 of the foregoing, where the computer readable storage medium itself is not a transitory signal. More specific examples (a non-exhaustive list) of the computer-readable storage medium could include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a solid state memory, a Random Access Memory (RAM) (e.g., RAM 3420 of fig. 11), a read-only memory (ROM) (e.g., RAM 3435 of fig. 11), 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. 11), or any suitable combination of the foregoing. In the context of this document, 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, wireline, optical fiber cable, radio frequency, etc., or any suitable combination of the foregoing.

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

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, processes and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises 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 block may occur out of the order noted in the figures. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.

In the description herein, the techniques of the flowcharts may be described in terms of a series of sequential actions. The order of the acts and the manner in which the acts are performed may be varied freely without departing from the scope of the present teachings. Actions may be added, deleted, or altered in several ways. Similarly, actions may be reordered or looped. Further, although processes, methods, algorithms, etc. may be described in a sequential order, such processes, methods, algorithms, or any combination thereof, may be operable to be executed in alternate orders. Further, some acts within a process, method, or algorithm may be performed concurrently (e.g., acts are performed in parallel) during at least one point in time, and may also be performed in whole, in part, or any combination thereof.

Modules implemented in software for execution by various types of processors (e.g., GPU 3401, CPU 3415) may, for example, comprise physical or logical blocks of one or more computer instructions, which may, for example, be organized as objects, procedures, or functions. However, the executables of an identified module need not be physically located together, but may comprise disparate instructions stored in different locations which, when joined logically together, comprise the module and achieve the stated purpose for the module. Indeed, a module of executable code may be a single instruction, or many instructions, and may even be distributed over several different code segments, among different programs, and across several memory devices. Similarly, operational data (e.g., the materials taxonomy library described herein) may be identified and illustrated herein within modules, and may be embodied in any suitable form and organized within any suitable type of data structure. The operational data may be collected as a single data set, or may be distributed over different locations including over different storage devices. The data may provide an electronic signal on a system or network.

These program instructions may be provided to one or more processors and/or controller(s) of a general purpose computer, special purpose computer, or other programmable data processing apparatus (e.g., controller) to produce a machine, such that the instructions, which execute via the processor(s) of the computer or other programmable data processing apparatus (e.g., GPU 3401, CPU 3415), create means or circuitry for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. In a particular embodiment, the computer program instructions may be configured to send a sorting instruction to the sorting device to direct sorting of certain ones of the pieces of material from the plurality of pieces of material to produce a collection of pieces of material having a predetermined particular aggregate chemical composition.

It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, which can include, for example, one or more graphics processing units (e.g., GPU 3401), or combinations of special purpose hardware and computer instructions. For example, a module may be implemented as a hardware circuit comprising custom Very Large Scale Integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, controllers, or other discrete components. A module may also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like.

Computer program code (i.e., instructions) for carrying out operations of aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, python, C ++ or the like, conventional procedural programming languages, such as the "C" programming language or similar programming languages, or any of the machine learning software disclosed herein. The program code may execute entirely on the user's computer system, partly on the user's computer system (e.g., the computer system used for sorting) and partly on a remote computer system (e.g., the computer system used for training the sensor system), or entirely on the remote computer system or server as a stand-alone software package. In the latter scenario, the remote computer system may be connected to the user's computer system through any type of network, including a local area network ("LAN") or a wide area network ("WAN"), or the connection may be made to an external computer system (for example, through the Internet using an Internet service provider).

These program instructions may also be stored in a machine-readable storage medium that can direct a computer system, other programmable data processing apparatus, controller, or other device to function in a particular manner, such that the instructions stored in the machine-readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.

One or more databases can be included in the host for storing data and providing access to data for various implementations. Those skilled in the art will also appreciate that any database, system, or component of the present disclosure may include any combination of databases or components in a single location or multiple locations for security reasons, wherein each database or system may include any of a variety of suitable security features (such as firewalls, access codes, encryption, decryption, compression, and the like). The database may be any type of database, such as a relational database, a hierarchical database, an object-oriented database, and the like. Common database products that may be used to implement a database include IBM's DB2, any of the database products available from Oracle corporation, microsoft Access from Microsoft corporation, or any other database product. The database may be organized in any suitable manner, including as a data table or a look-up table.

The correlation of certain data (e.g., between a classified material piece and its known chemical composition, or between a classified material piece and its calculated approximate mass) may be accomplished by any data correlation technique known and practiced in the art. For example, the association may be achieved either manually or automatically. Automatic association techniques may include, for example, database searches, database merging, GREP, AGREP, SQL, and the like. The association step may be implemented by a database merge function, for example, using key fields in each of the manufacturer and retailer data tables. The key field partitions the database according to the high-level class of objects defined by the key field. For example, a certain category may be specified as a key field in both the first data table and the second data table, and then the two data tables may be merged based on category data in the key field. In these embodiments, the data corresponding to the key fields in each of the merged data tables is preferably the same. However, for example, data tables with similar but not identical data in the key fields may also be consolidated by using AGREP.

Aspects of the present disclosure provide a method comprising: determining an approximate mass of each of the plurality of pieces of material, wherein at least one of the plurality of pieces of material has a material classification that is different from a material classification of the other pieces of material; classifying each of the plurality of pieces of material as belonging to one of a plurality of different classifications of material; and sorting some of the pieces of material from the plurality of pieces of material according to the determined approximate mass and classification of each of the plurality of pieces of material, wherein the sorting produces a collection of pieces of material having a predetermined specific aggregate chemical composition. Sorting may include transferring some of the pieces of material into a container. Sorting may include continuously determining aggregate chemical composition of the transferred pieces of material. Sorting may include transferring a next piece of material into the container so as to increase the weight percent of a particular chemical element in the aggregate chemical composition of the transferred piece of material. Sorting may include not transferring the next piece of material into the container so as to reduce the weight percent of the particular chemical element in the aggregate chemical composition of the transferred piece of material. Sorting may include not transferring the next piece of material into the container because the next piece of material contains undesirable contaminants within the predetermined particular aggregate chemical composition. Sorting is continued until the aggregate chemical composition of the predetermined minimum number of transferred pieces of material equals the predetermined threshold level of the particular aggregate chemical composition. The collection of pieces having a predetermined specific aggregate chemical composition may comprise at least one piece having a material classification that is different from the material classifications of the other pieces in the collection. The plurality of pieces of material may include pieces of material having different metal alloy compositions. The predetermined specific aggregate chemical composition may be different from the chemical composition of each of the plurality of pieces of material. The predetermined specific aggregate chemical composition may be different from the aggregate chemical composition of all of the plurality of material pieces. The collection of pieces of material may include pieces of material having different classifications of materials. The collection of pieces of material may include at least one of the pieces of material having a material classification that is different from the material classifications of the other pieces of material. The plurality of pieces may comprise aluminum wrought alloy pieces and cast aluminum alloy pieces, wherein the collection of material pieces may comprise at least one aluminum wrought alloy piece and at least one cast aluminum alloy piece, and wherein the predetermined specific aggregate chemical composition is different from the chemical composition of the aluminum wrought alloy pieces, and wherein the predetermined specific aggregate chemical composition is different from the chemical composition of the cast aluminum alloy pieces. The classifying may include processing, by the machine learning system, image data captured from each of the plurality of pieces of material.

Aspects of the present disclosure provide a system comprising: a sensor configured to capture one or more characteristics of each of a mixture of pieces of material, wherein the mixture of pieces of material may include pieces of material having different material classifications; a data processing system configured to classify each of the mixture of pieces of material as belonging to one of a plurality of different material classifications; and sorting equipment configured to sort certain of the pieces of material from the mixture of pieces of material according to a classification of each of the pieces of material in the mixture of pieces of material, wherein the sorting produces a collection of pieces of material having a predetermined specific aggregate chemical composition. The sensor may be a camera, wherein the one or more captured characteristics are captured by the camera, the camera configured to capture an image of each of the mixture of the pieces of material as the mixture of the pieces of material is conveyed past the camera, wherein the camera is configured to capture a visual image of each of the mixture of materials to produce image data, and wherein the characteristics are visually observed characteristics. The data processing system may include a machine learning system implementing a neural network configured to classify each of the mixture of material pieces as belonging to one of a plurality of different material classifications based on the captured visually observed characteristics. The system may further comprise means configured for determining an approximate mass of each of the plurality of pieces of material, wherein sorting is performed according to the determined approximate mass and classification of each piece of material. The apparatus may include a line scanner configured to measure an approximate size of each piece of material.

Aspects of the present disclosure provide a computer program product stored on a computer readable storage medium that, when executed by a data processing system, performs a process comprising: determining an approximate mass of each of the plurality of pieces of material, wherein at least one of the plurality of pieces of material has a material classification that is different from a material classification of the other pieces of material; classifying each of the plurality of pieces of material as belonging to one of a plurality of different classifications of material; and directing sorting of certain of the pieces of material from the plurality of pieces of material to produce a collection of pieces of material having a predetermined particular aggregate chemical composition, wherein sorting is performed according to the determined approximate mass and classification of each of the plurality of pieces of material, wherein the collection of pieces of material includes pieces of different material classifications. The classifying may include processing, by the machine learning system, image data captured from each of the plurality of pieces of material. The predetermined specific aggregate chemical composition may be different from the chemical composition of each of the plurality of pieces of material.

Reference herein is made to a "configuration" device or a "device configured to" perform certain functions. It should be appreciated that this may include selecting predefined logic blocks and logically associating them so that they provide specific logic functions, including monitoring or control functions. It may also include programming computer software-based logic of the control device, wiring discrete hardware components, or a combination of any or all of the foregoing.

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 disclosure. One skilled in the relevant art will recognize, however, that the disclosure may be practiced without one or more of the specific details, or with other methods, components, materials, and so forth. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.

Those skilled in the art will appreciate that various settings and parameters of the components of the system 100, including neural network parameters, may be customized, optimized, and reconfigured over time based on the type of materials being classified and sorted, the desired classification and sorting results, the type of equipment used, the empirical results of previous classifications, available data, and other factors.

Reference throughout this specification to "one embodiment" or "an embodiment" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrases "in one embodiment," "in an embodiment," "embodiments," "certain embodiments," "various embodiments," and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment. Furthermore, the described features, structures, aspects, and/or characteristics of the disclosure may be combined in any suitable manner in one or more embodiments. Accordingly, even though features initially claimed are functional in certain combinations, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Benefits, other advantages, and solutions to problems have been described herein with regard to specific embodiments. The benefits, advantages, solutions to problems, and any element(s) that may cause any benefit, advantage, or solution to occur or become more pronounced, however, are not to be construed as a critical, required, or essential feature or element of any or all the claims. Further, unless explicitly described as being necessary or critical, the components described herein are not required for the practice of the present disclosure.

Although this description contains many specific details, these should not be construed as limitations on the scope of the disclosure or of what may be claimed, but rather as descriptions of features specific to particular implementations of the disclosure. Headings herein are not intended to limit the disclosure, embodiments of the disclosure, or other content disclosed under the headings.

Herein, the term "or" may be intended to be included, wherein "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 entities that exist alone or in combination. Thus, for example, the phrase "A, B, C and/or D" includes A, B, C and D alone, but also includes any and all combinations and subcombinations of A, B, C and D.

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" are intended to include the plural forms as well, unless the context clearly indicates otherwise. "

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below may be intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed.

As used herein, terms such as "controller," "processor," "memory," "neural network," "interface," "sorter device," "sorting equipment," "pushing mechanism," "pusher equipment," "imaging sensor," "cartridge," "container," "system," and "circuitry" each refer to a non-generic equipment element that would be recognized and understood by one of skill in the art, and are not used herein as random number words or random number terms for the purpose of reference to 35u.s.c.112 (f).

As used herein, "substantially" with respect to an identified property or condition refers to a degree of deviation that is sufficiently small so as not to visually deviate from the identified property or condition. In some cases, the exact degree of allowable deviation may depend on the particular context.

As used herein, a plurality of items, structural elements, constituent elements, exemplary scores, and/or materials may be presented in a common list for convenience. However, these lists should be understood as though each member of the list is individually identified as a separate and unique member. Thus, any individual member of such list should not be construed as a de facto equivalent of any other member of the same list solely based on their presentation in a common group without indications to the contrary.

Unless defined otherwise, all technical and scientific terms used herein, such as abbreviations for chemical elements in the periodic table, have the same meaning as commonly understood by one of ordinary skill in the art to which the presently disclosed subject matter pertains. All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety unless a particular paragraph is cited. In case of conflict, the present specification, including definitions, will control. In addition, the materials, methods, and examples are illustrative only and not intended to be limiting.

To the extent not described herein, many details of the processing acts and circuits are conventional with respect to the specific materials and may be found in textbooks and other sources within the computing, electronic and software arts.

Unless otherwise indicated, all numbers expressing quantities of compositions, reaction conditions, and so forth used in the specification and claims are to be understood as being modified in all instances by the term "about". Accordingly, unless indicated to the contrary, the numerical parameters set forth in this specification and attached claims are approximations that may vary depending upon the desired properties sought to be obtained by the presently disclosed subject matter. As used herein, the term "about" when referring to a value or amount of mass, weight, time, volume, concentration, or percentage is intended to encompass variations of ± 20%, in some embodiments ± 10%, in some embodiments ± 5%, in some embodiments ± 1%, in some embodiments ± 0.5%, and in some embodiments ± 0.1% compared to the specified amount, as such variations are suitable for performing the disclosed methods. As used herein, the term "similar" may refer to values that are within a particular offset or percentage (e.g., 1%, 2%, 5%, 10%, etc.) of each other.

Claims

1. A method, the method comprising:

determining an approximate mass of each of a plurality of pieces of material, wherein at least one of the plurality of pieces of material has a material classification that is different from a material classification of other pieces of material;

classifying each of the plurality of pieces of material as belonging to one of a plurality of different classifications of material; and

sorting certain ones of the pieces of material from the plurality of pieces of material according to the determined approximate mass and classification of each of the plurality of pieces of material, wherein the sorting produces a collection of pieces of material having a predetermined specific aggregate chemical composition.

2. The method of claim 1, wherein the sorting comprises transferring some of the pieces of material into a container.

3. The method of claim 2, wherein the sorting comprises continuously determining aggregate chemical composition of the transferred pieces of material.

4. A method according to claim 3, wherein the sorting comprises transferring a next piece of material into the container so as to increase the weight percentage of a particular chemical element in the aggregate chemical composition of the transferred piece of material.

5. A method according to claim 3, wherein the sorting comprises not transferring a next piece of material into the container so as to reduce the weight percentage of a particular chemical element in the aggregate chemical composition of the transferred piece of material.

6. A method according to claim 3, wherein said sorting comprises not transferring a next piece of material into said container because said next piece of material contains contaminants within said predetermined specific aggregate chemical composition that are not desired.

7. A method according to claim 3, wherein the sorting is continued until a predetermined minimum number of transferred pieces of material have a bulk chemical composition equal to a threshold level of the predetermined specific bulk chemical composition.

8. The method of claim 1, wherein the collection of pieces of material having a predetermined specific aggregate chemical composition comprises at least one piece of material having a material classification that is different from the material classifications of the other pieces of material in the collection.

9. The method of claim 1, wherein the plurality of pieces of material comprise pieces of material having different metal alloy compositions.

10. The method of claim 1, wherein the predetermined specific aggregate chemical composition is different from the chemical composition of each of the plurality of pieces of material.

11. The method of claim 10, wherein the predetermined specific aggregate chemical composition is different from the aggregate chemical composition of all of the plurality of material pieces.

12. The method of claim 1, wherein the collection of pieces of material comprises pieces of material having different metal classifications.

13. The method of claim 12, wherein the collection of pieces of material includes at least one of the pieces of material having a metal classification that is different from the metal classifications of the other pieces of material.

14. The method of claim 1, wherein the plurality of pieces comprises aluminum wrought alloy pieces and aluminum cast alloy pieces, and wherein the collection of material pieces comprises at least one aluminum wrought alloy piece and at least one aluminum cast alloy piece, and wherein the predetermined specific aggregate chemical composition is different from the chemical composition of the aluminum wrought alloy pieces, and wherein the predetermined specific aggregate chemical composition is different from the chemical composition of the aluminum cast alloy pieces.

15. The method of claim 1, wherein the classifying includes processing, by a machine learning system, image data captured from each of the plurality of pieces of material.

16. A system, the system comprising:

a sensor configured to capture one or more characteristics of each of a mixture of pieces of material, wherein the mixture of pieces of material includes pieces of material having different material classifications;

a data processing system configured for classifying each material piece in the mixture of material pieces as belonging to one of a plurality of different material classifications; and

sorting apparatus configured to sort certain of the pieces of material from the mixture of pieces of material according to a classification of each of the pieces of material, wherein the sorting produces a collection of pieces of material having a predetermined specific aggregate chemical composition.

17. The system of claim 16, wherein the sensor is a camera, and wherein one or more captured characteristics are captured by the camera, the camera configured to capture an image of each of the mixture of pieces of material as the mixture of pieces of material is conveyed past the camera, wherein the camera is configured to capture a visual image of each of the mixture of materials to produce image data, and wherein the characteristics are visually observed characteristics.

18. The system of claim 17, wherein the data processing system comprises a machine learning system implementing a neural network, the machine learning system configured to classify each material piece in the mixture of material pieces as belonging to one of a plurality of different material classifications based on the captured visually observed characteristics.

19. The system of claim 16, further comprising means configured to determine an approximate mass of each of the plurality of pieces of material, wherein the sorting is performed according to the determined approximate mass and classification of each piece of material.

20. The system of claim 19, wherein the device comprises a line scanner configured to measure an approximate size of each piece of material.

21. A computer program product stored on a computer readable storage medium, which when executed by a data processing system performs a process comprising:

directing sorting of certain ones of the pieces of material from the plurality of pieces of material to produce a collection of pieces of material having a predetermined specific aggregate chemical composition, wherein the sorting is performed according to the determined approximate mass and classification of each of the plurality of pieces of material, wherein the collection of pieces of material includes pieces of different material classifications.

22. The computer program product of claim 21, wherein the classifying includes processing, by a machine learning system, image data captured from each of the plurality of pieces of material.

23. The computer program product of claim 21, wherein the predetermined specific aggregate chemical composition is different from a chemical composition of each of the plurality of pieces of material.