EP4422807A1 - Séparation de métaux dans un parc à ferraille - Google Patents

Séparation de métaux dans un parc à ferraille

Info

Publication number
EP4422807A1
EP4422807A1 EP22888018.3A EP22888018A EP4422807A1 EP 4422807 A1 EP4422807 A1 EP 4422807A1 EP 22888018 A EP22888018 A EP 22888018A EP 4422807 A1 EP4422807 A1 EP 4422807A1
Authority
EP
European Patent Office
Prior art keywords
metal alloy
heap
pieces
piece
recited
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP22888018.3A
Other languages
German (de)
English (en)
Inventor
Manuel Gerardo Garcia, Jr.
Nalin Kumar
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sortera Technologies Inc
Original Assignee
Sortera Technologies Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sortera Technologies Inc filed Critical Sortera Technologies Inc
Publication of EP4422807A1 publication Critical patent/EP4422807A1/fr
Pending legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3422—Sorting according to other particular properties according to optical properties, e.g. colour using video scanning devices, e.g. TV-cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B07C5/361—Processing or control devices therefor, e.g. escort memory
    • B07C5/362—Separating or distributor mechanisms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0036—Sorting out metallic particles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0054—Sorting of waste or refuse
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0063—Using robots

Definitions

  • the present disclosure relates in general to the separation of materials, and in particular, to the classifying and/or sorting of materials gathered over a large area, such as in a metal scrap yard.
  • Recycling is the process of collecting and processing materials that would otherwise be thrown away as trash, and turning them into new products. Recycling has benefits for communities and for the environment, since it reduces the amount of waste sent to landfills and incinerators, conserves natural resources, increases economic security by tapping a domestic source of materials, prevents pollution by reducing the need to collect new raw materials, and saves energy.
  • Scrap metals are often shredded, and thus require sorting to facilitate reuse of the metals. By sorting the scrap metals, metal is reused that may otherwise go to a landfill. Additionally, use of sorted scrap metal leads to reduced pollution and emissions in comparison to refining virgin feedstock from ore. Scrap metals may be used in place of virgin feedstock by manufacturers if the quality of the sorted metal meets certain standards.
  • the scrap metals may include types of ferrous and nonferrous metals, heavy metals, high value metals such as nickel or titanium, cast or wrought metals, and other various alloys.
  • FIG. 1 illustrates a schematic of a material handling system, which may be utilized to train an artificial intelligence (“Al”) system in accordance with embodiments of the present disclosure.
  • Al artificial intelligence
  • FIG. 2 illustrates an exemplary representation of a control set of material pieces used during a training stage in an artificial intelligence system.
  • FIG. 3 illustrates a flowchart diagram configured in accordance with embodiments of the present disclosure.
  • FIG. 4 depicts heaps of metal scrap that has been collected within a scrap yard.
  • FIG. 5 illustrates a block diagram of a data processing system configured in accordance with embodiments of the present disclosure.
  • FIG. 6 schematically illustrates an apparatus configured in accordance with embodiments of the present disclosure for classifying/identifying and/or sorting/separating materials that have been collected into one or more heaps, such as metal scrap in a scrap yard.
  • Embodiments of the present disclosure utilize an artificial intelligence (“AI”)/vision system configured to identify/classify various metal alloys that have been collected in a scrap yard.
  • AI artificial intelligence
  • materials may include any item or object, including but not limited to, metals (ferrous and nonferrous), metal alloys, scrap metal alloy pieces, heavies, Zorba, Twitch, pieces of metal embedded in another different material, plastics (including, but not limited to, any of the plastics disclosed herein, known in the industry, or newly created in the future), rubber, foam, glass (including, but not limited to, borosilicate or soda lime glass, and various colored glass), ceramics, paper, cardboard, Teflon, PE, bundled wires, insulation covered wires, rare earth elements, leaves, wood, plants, parts of plants, textiles, bio-waste, packaging, electronic waste, batteries and accumulators, scrap from end-of-life vehicles, mining, construction, and demolition waste, crop wastes, forest residues, purpose-grown grasses, woody energy crops, microalgae, urban food waste, food waste, hazardous chemical and biomedical wastes, construction debris, farm wastes, biogenic items, non-biogenic
  • a “material” may include any item or object composed of a chemical element, a compound or mixture of one or more chemical elements, or a compound or mixture of a compound or mixture of chemical elements, wherein the complexity of a compound or mixture may range from being simple to complex (all of which may also be referred to herein as a material having a specific “chemical composition”).
  • “Chemical element” means a chemical element of the periodic table of chemical elements, including chemical elements that may be discovered after the filing date of this application.
  • the terms “scrap,” “scrap pieces,” “materials,” and “material pieces” may be used interchangeably.
  • a material piece or scrap piece referred to as having a metal alloy composition is a metal alloy having a specific chemical composition that distinguishes it from other metal alloys.
  • a “pile” of materials refers to a heap of things laid on or lying one on top of another.
  • a “heap” of things is usually untidy, and often has the shape of a hill or mound.
  • the term “chemical signature” refers to a unique pattern (e.g., fingerprint spectrum), as would be produced by one or more analytical instruments, indicating the presence of one or more specific elements or molecules (including polymers) in a sample.
  • the elements or molecules may be organic and/or inorganic.
  • Such analytical instruments include any of the sensor systems disclosed herein, and also disclosed in U.S. patent application serial no. 17/667,397, which is hereby incorporated by reference herein.
  • one or more such sensor systems may be configured to produce a chemical signature of a material piece.
  • a “polymer” is a substance or material composed of very large molecules, or macromolecules, composed of many repeating subunits.
  • a polymer may be a natural polymer found in nature or a synthetic polymer.
  • “Multilayer polymer films” are composed of two or more different polymer compositions. The layers are at least partially contiguous and preferably, but optionally, coextensive.
  • the terms “plastic,” “plastic piece,” and “piece of plastic material” (all of which may be used interchangeably) refer to any object that includes or is composed of a polymer composition of one or more polymers and/or multilayer polymer films.
  • a “fraction” refers to any specified combination of organic and/or inorganic elements or molecules, polymer types, plastic types, polymer compositions, chemical signatures of plastics, physical characteristics of the plastic piece (e.g., color, transparency, strength, melting point, density, shape, size, manufacturing type, uniformity, reaction to stimuli, etc.), etc., including any and all of the various classifications and types of plastics disclosed herein.
  • Non-limiting examples of fractions are one or more different types of plastic pieces that contain: LDPE plus a relatively high percentage of aluminum; LDPE and PP plus a relatively low percentage of iron; PP plus zinc; combinations of PE, PET, and HDPE; any type of red-colored LDPE plastic pieces; any combination of plastic pieces excluding PVC; blackcolored plastic pieces; combinations of #3— #7 type plastics that contain a specified combination of organic and inorganic molecules; combinations of one or more different types of multi-layer polymer films; combinations of specified plastics that do not contain a specified contaminant or additive; any types of plastics with a melting point greater than a specified threshold; any thermoset plastic of a plurality of specified types; specified plastics that do not contain chlorine; combinations of plastics having similar densities; combinations of plastics having similar polarities; plastic bottles without attached caps or vice versa.
  • predetermining refers to something that has been established or decided in advance, such as by a user of embodiments of the present disclosure.
  • spectral imaging is imaging that uses multiple bands across the electromagnetic spectrum. While a typical camera captures images composed of light across three wavelength bands in the visible spectrum, red, green, and blue (RGB), spectral imaging encompasses a wide variety of techniques that include and go beyond RGB. For example, spectral imaging may use the infrared, visible, ultraviolet, and/or x-ray spectrums, or some combination of the above.
  • Spectral data, or spectral image data is a digital data representation of a spectral image. Spectral imaging may include the acquisition of spectral data in visible and non-visible bands simultaneously, illumination from outside the visible range, or the use of optical filters to capture a specific spectral range. It is also possible to capture hundreds of wavelength bands for each pixel in a spectral image.
  • image data packet refers to a packet of digital data pertaining to a captured spectral image of an individual material piece.
  • the terms “identify” and “classify,” the terms “identification” and “classification,” and any derivatives of the foregoing, may be utilized interchangeably.
  • to “classify” a material piece is to determine (i.e., identify) a type or class of materials to which the material piece belongs.
  • a sensor system may be configured to collect and analyze any type of information for classifying materials and distinguishing such classified materials from other materials, which classifications can be utilized within a separation apparatus to selectively separate material pieces as a function of a set of one or more physical and/or chemical characteristics (e.g., which may be user-defined), including but not limited to, color, texture, hue, shape, brightness, weight, density, chemical composition, size, uniformity, manufacturing type, chemical signature, predetermined fraction, radioactive signature, transmissivity to light, sound, or other signals, and reaction to stimuli such as various fields, including emitted and/or reflected electromagnetic radiation (“EM”) of the material pieces.
  • physical and/or chemical characteristics e.g., which may be user-defined
  • the types or classes (i.e., classification) of materials may be user-definable (e.g., predetermined) and not limited to any known classification of materials.
  • the granularity of the types or classes may range from very coarse to very fine.
  • the types or classes may include plastics, ceramics, glasses, metals, and other materials, where the granularity of such types or classes is relatively coarse; different metals and metal alloys such as, for example, zinc, copper, brass, chrome plate, and aluminum, where the granularity of such types or classes is finer; or between specific types of metal alloys, where the granularity of such types or classes is relatively fine.
  • the types or classes may be configured to distinguish between materials of significantly different chemical compositions such as, for example, plastics and metal alloys, or to distinguish between materials of almost identical chemical compositions such as, for example, different types of metal alloys. It should be appreciated that the methods and systems discussed herein may be applied to accurately identify/classify material pieces for which the chemical composition is completely unknown before being classified.
  • manufacturing type refers to the type of manufacturing process by which the material piece was manufactured, such as a metal part having been formed by a wrought process, having been cast (including, but not limited to, expendable mold casting, permanent mold casting, and powder metallurgy), having been forged, a material removal process, etc.
  • a heterogeneous mixture of a plurality of material pieces contains at least one material piece having a chemical composition different from one or more other material pieces, and/or at least one material piece within this heterogeneous mixture is physically distinguishable from other material pieces, and/or at least one material piece within this heterogeneous mixture is of a class or type of material different from the other material pieces within the mixture, and the apparatuses and methods are configured to identify/classify/distinguish/separate this material piece into a group separate from such other material pieces.
  • a homogeneous set or group of materials all fall within the same identifiable class or type of material.
  • Embodiments of the present disclosure may be described herein as separating material pieces (e.g., different metal alloys) into such separate groups by physically separating the material pieces into separate heaps, piles, receptacles, or bins as a function of user-defined or predetermined groupings (e.g., material type classifications).
  • material pieces may be separated into separate heaps or receptacles in order to separate material pieces classified as belonging to a certain class or type of material that are distinguishable from other material pieces (for example, which are classified as belonging to a different class or type of material).
  • the materials to be separated may have irregular sizes and shapes.
  • such material may have been previously run through some sort of shredding mechanism that chops up the materials into such irregularly shaped and sized pieces (producing scrap pieces).
  • alloys that may be collected in groups, heaps, or piles, such as within a warehouse or in a scrap yard.
  • alloy steel and “steel alloy” refer to steels with other alloying elements added in addition to carbon. Common alloyants include manganese (Mn), nickel (Ni), chromium (Cr), molybdenum (Mo), vanadium (V), silicon (Si), and boron (B).
  • alloyants include aluminum (Al), cobalt (Co), copper (Cu), cerium (Ce), niobium (Nb), titanium (Ti), tungsten (W), tin (Sn), zinc (Zn), lead (Pb), and zirconium (Zr).
  • FIG. 4 depicts an exemplary scrap yard 400 containing N (N>1) heaps 401.. .403 of mixtures of different types of steel alloy scrap pieces in each heap.
  • N N>1 heaps 401.. .403 of mixtures of different types of steel alloy scrap pieces in each heap.
  • a non-limiting advantage of the present disclosure is that it provides for the separation of large metal pieces before they are shredded into much smaller pieces, which can enable the separation of metal alloys before such mixture of metal alloys are shredded into much smaller pieces, many of which may be discarded or shredded into such small dimensions that they are essentially lost and not effectively recycled.
  • Such large metal pieces can even be as large as engine blocks or large portions of engine blocks, steel beams, etc.
  • FIG. 6 there is illustrated a simplified diagram of an apparatus 600 configured in accordance with embodiments of the present disclosure for classifying/identifying and/or separating material pieces that have been collected into one or more heaps, such as metal scrap pieces in a scrap yard (e.g., see FIG. 4).
  • the material pieces will be various metal (e.g., steel) alloy pieces of different chemical compositions.
  • FIG. 6 depicts metal alloy pieces collected in a heap of these metal alloy pieces denoted by the metal alloy heap 605 in which the different metal alloy pieces may be mixed in a random manner within the metal alloy heap 605.
  • the metal alloy heap 605 may be located within a warehouse or in a scrap yard, such as depicted in FIG. 4.
  • a camera 610 may be mounted (e.g., on a pole or a wall of a building) within a vicinity of the metal alloy heap 605 in order to capture images and/or videos of the metal alloy pieces in the heap 605.
  • the camera 610 may be similar to the camera 109 described herein with respect to FIG. 1.
  • the image data collected by the camera 610 may be transmitted to the computer system 612 by any appropriate means, including by wired or wireless transmission.
  • the computer system 612 may include a vision system similar to the vision system 110 described herein with respect to FIG. 1.
  • the computer system 612 may be configured to process the image data in accordance with the system and process 300 described herein with respect to FIG.
  • This location and classification information may then be transmitted to a controller 614, which controls the actions of a separation device 620 to grab/collect and remove from the heap 605 each of the classified metal alloy pieces and then optionally separate them into different metal alloy heaps 601, 602, and 603.
  • the separation device 620 may include any appropriate robotic arm manipulator that can be controlled by the controller 614 to automatically select a particular metal alloy piece in the heap 605 that has been identified by the vision system as belonging to a metal alloy classification, physically grab or pick up by any appropriate means the particular metal alloy piece and remove it from the heap 605.
  • the separation device 600 may also be configured to deposit it into a predetermined location (e.g., one of the metal alloy heaps 601, 602, or 603. An example of a robotic arm is disclosed in K.
  • the apparatus 600 may be configured to remove a certain classification of metal alloys from the heap 605, or remove all but one or more classifications of metal alloy pieces from the heap 605.
  • the apparatus 600 may be configured to remove one or more certain classifications of metal alloy pieces from the heap 605 and place them in one or more other locations, such as one or more receptacles or bins, or one or more other heaps (e.g., one or more of heaps 601, 602, 603).
  • training of the vision system may be performed in accordance with various appropriate techniques as disclosed herein so that the vision system is capable of classifying/identifying the different steel alloys using image data from the captured images.
  • the camera 610 may be configured with one or more devices for capturing or acquiring images of the material pieces within the heap 605.
  • the devices may be configured to capture or acquire any desired range of wavelengths irradiated or reflected by the material pieces, including, but not limited to, visible, infrared (“IR”), ultraviolet (“UV”) light.
  • the camera 610 may be configured with one or more cameras (still and/or video, either of which may be configured to capture two-dimensional, three-dimensional, and/or holographical images) positioned in proximity (e.g., above) the heap 605 so that images of the material pieces are captured (e.g., as image data).
  • the camera 610 may include one or more laser lights for illuminating certain pieces and a camera for then capturing images of the illuminated pieces.
  • certain types of plastics can be illuminated with certain wavelengths of light to distinguish them from other types of plastics.
  • the information may then be sent to the computer system 612 to be processed (e.g., by a vision system and an Al system) in order to identify and/or classify each of the material pieces.
  • An Al system may implement any well-known Al system (e.g., Artificial Narrow Intelligence (“ANT’), Artificial General Intelligence (“AGI”), and Artificial Super Intelligence (“ASI”)), a machine learning system including one that implements a neural network (e.g., artificial neural network, deep neural network, convolutional neural network, recurrent neural network, autoencoders, reinforcement learning, etc.), a machine learning system implementing supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature learning, sparse dictionary learning, anomaly detection, robot learning, association rule learning, fuzzy logic, deep learning algorithms, deep structured learning hierarchical learning algorithms, support vector machine (“SVM”) (e.g., linear SVM, nonlinear SVM, SVM regression, etc.), decision tree learning (e.g., classification and regression tree (“CART”), ensemble methods (e.g., ensemble learning, Random Forests, Bagging and Pasting, Patches and Subspaces, Boosting, Stacking, etc.), dimensionality
  • Non-limiting examples of publicly available machine learning software and libraries that could be utilized within embodiments of the present disclosure include Python, OpenCV, Inception, Theano, Torch, PyTorch, Pylearn2, Numpy, Blocks, TensorFlow, MXNet, Caffe, Lasagne, Keras, Chainer, Matlab Deep Learning, CNTK, MatConvNet (a MATLAB toolbox implementing convolutional neural networks for computer vision applications), DeepLearnToolbox (a Matlab toolbox for Deep Learning (from Rasmus Berg Palm)), BigDL, Cuda-Convnet (a fast C++/CUDA implementation of convolutional (or more generally, feedforward) neural networks), Deep Belief Networks, RNNLM, RNNLIB-RNNLIB, matrbm, deeplearning4j, Eblearn.lsh, deepmat, MShadow, Matplotlib, SciPy, CXXNET, Nengo-Nengo, Eblearn, cudamat, Gnumpy, 3-way factore
  • certain types of machine learning may be performed in two stages. For example, first, training occurs, which may be performed offline in that the system 100 is not being utilized to perform actual classifying/sorting of material pieces.
  • the system 100 may be utilized to train the machine learning system in that homogenous sets (also referred to herein as control samples) of material pieces (i.e., having the same types or classes of materials, or falling within the same predetermined fraction) are passed through the system 100 (e.g., by a conveyor system 103), and may be collected in a common receptacle (e.g., receptacle 140).
  • the training may include using some other mechanism for collecting sensed information (characteristics) of control sets of material pieces.
  • algorithms within the machine learning system extract features from the captured information (e.g., using image processing techniques well known in the art).
  • training algorithms include, but are not limited to, linear regression, gradient descent, feed forward, polynomial regression, learning curves, regularized learning models, and logistic regression. It is during this training stage that the algorithms within the machine learning system learn the relationships between materials and their features/characteristics (e.g., as captured by the vision system and/or sensor system(s)), creating a knowledge base for later classification of a heterogeneous mixture of material pieces (e.g., by the apparatus 600), which may then be separated by desired classifications.
  • Such a knowledge base may include one or more libraries, wherein each library includes parameters (e.g., neural network parameters) for utilization by the machine learning system in classifying material pieces.
  • each library includes parameters (e.g., neural network parameters) for utilization by the machine learning system in classifying material pieces.
  • one particular library may include parameters configured by the training stage to recognize and classify a particular type or class of material, or one or more material that fall with a predetermined fraction.
  • such libraries may be inputted into the computer system 612 and then the user of the apparatus 600 may be able to adjust certain ones of the parameters in order to adjust an operation of the apparatus 600 (for example, adjusting the threshold effectiveness of how well the machine learning system recognizes a particular material piece within the heap 605).
  • a machine learning system configured in accordance with certain embodiments of the present disclosure may be configured to distinguish between material pieces as a function of their respective material/chemical compositions. For example, such a machine learning system may be configured so that material pieces containing a particular element can be classified/identified as a function of the percentage (e.g., weight or volume percentage) of that element contained within the material pieces.
  • examples of one or more material pieces 201 of a specific class or type of material may be delivered past the vision system (e.g., by a conveyor system 203) so that the one or more algorithms within the machine learning system detect, extract, and learn what characteristics or features represent such a type or class of material.
  • the material pieces 201 may be any of the “materials” disclosed herein (e.g., metal alloy pieces representing those that will reside within a heap 605).
  • each of the material pieces 201 may represent one or more particular types or classes of metal alloy, which are passed through such a training stage so that the one or more algorithms within the machine learning system “learn” (are trained) how to detect, recognize, and classify such material pieces.
  • a vision system e.g., the vision system 110
  • the same process can be performed with respect to images of any classification of material pieces creating a library of parameters particular to such classification of material pieces.
  • any number of exemplary material pieces of that classification of material may be passed by the vision system.
  • the algorithms within the machine learning system may use N classifiers, each of which test for one of N different material types.
  • the machine learning system may be “taught” (trained) to detect any type, class, or fraction of material, including any of the types, classes, or fractions of materials disclosed herein.
  • the libraries of parameters for the different materials may be then implemented into a material classifying and/or separation system (e.g., the apparatus 600) to be used for identifying and/or classifying material pieces from a mixture of material pieces (e.g., within the heap 605), and then possibly separating such classified material pieces as described with respect to FIG. 6.
  • a material classifying and/or separation system e.g., the apparatus 600
  • a vision system may utilize optical spectrometric techniques using multi- or hyper-spectral cameras for the camera 610 to provide a signal that may indicate the presence or absence of a type of material (e.g., containing one or more particular elements) by examining the spectral emissions of the material.
  • Photographs of a material piece may also be used in a template-matching algorithm, wherein a database of images is compared against an acquired image to find the presence or absence of certain types of materials from that database.
  • a histogram of the captured image may also be compared against a database of histograms.
  • a bag of words model may be used with a feature extraction technique, such as scale-invariant feature transform (“SIFT”), to compare extracted features between a captured image and those in a database.
  • SIFT scale-invariant feature transform
  • training of the machine learning system may be performed utilizing a labeling/annotation technique (or any other supervised learning technique) whereby as data/information of material pieces are captured by a vision system, a user inputs a label or annotation that identifies each material piece, which is then used to create the library for use by the machine learning system when classifying material pieces within a heterogenous mixture of material pieces (e.g., a heap 605).
  • a previously generated knowledge base of characteristics captured from one or more samples of a class of materials may be accomplished by any of the techniques disclosed herein, whereby such a knowledge base is then utilized to automatically classify materials.
  • certain embodiments of the present disclosure provide for the identification/classification of one or more different materials in order to separate the material pieces in the heap 605 from each other.
  • machine learning techniques may be utilized to train (i.e., configure) a neural network to identify a variety of one or more different classes or types of materials.
  • the collected/captured/detected/extracted features/characteristics of the material pieces may not be necessarily simply particularly identifiable physical characteristics; they can be abstract formulations that can only be expressed mathematically, or not mathematically at all; nevertheless, the machine learning system may be configured to parse all of the data to look for patterns that allow the control samples to be classified during the training stage. Furthermore, the machine learning system may take subsections of captured information of a material piece and attempt to find correlations between the pre-defined classifications.
  • an electronic machine vision apparatus is commonly employed in conjunction with an automatic machining, assembly and inspection apparatus, particularly of the robotics type.
  • Television cameras are commonly employed to observe the object being machined, assembled, read, viewed, or inspected, and the signal received and transmitted by the camera can be compared to a standard signal or database to determine if the imaged article is properly machined, finished, oriented, assembled, determined, etc.
  • a machine vision apparatus is widely used in inspection and flaw detection applications whereby inconsistencies and imperfections in both hard and soft goods can be rapidly ascertained and adjustments or rejections instantaneously effected.
  • a machine vision apparatus detects abnormalities by comparing the signal generated by the camera with a predetermined signal indicating proper dimensions, appearance, orientation, or the like. See International Published Patent Application WO 99/2248, which is hereby incorporated by reference herein. Nevertheless, machine vision systems do not perform any sort of further data processing (e.g., image processing) that would include further processing of the captured information through an algorithm. See definition of Machine Vision in Wikipedia, which is hereby incorporated by reference herein. Therefore, it can be readily appreciated that a machine vision apparatus or system does not further include any sort of algorithm, such as a machine learning algorithm. Instead, a machine vision system essentially compares images of parts to templates of images.
  • FIG. 3 illustrates a flowchart diagram depicting exemplary embodiments of a process 300 for classifying/identifying and then separating material pieces utilizing a vision system in accordance with certain embodiments of the present disclosure.
  • the process 300 may be configured to operate within any of the embodiments of the present disclosure described herein, including the system 100 of FIG. 1 and the apparatus 600 of FIG. 6.
  • the process 300 may be utilized in the system 100 in order to train a machine learning system as described herein.
  • the process blocks 302, 306, 312, and/or 313 may not be utilized.
  • the process 300 may be utilized in the apparatus 600 in order to remove/separ te material pieces from a heap 605 as described herein. In such an instance, the process block 306 may not be utilized.
  • Operation of the process 300 may be performed by hardware and/or software, including within a computer system (e.g., computer system 3400 of FIG. 5) controlling the system (e.g., the computer system 107, the vision system 110, and/or the vision system implemented within the computer system 612).
  • a computer system e.g., computer system 3400 of FIG. 5
  • the system e.g., the computer system 107, the vision system 110, and/or the vision system implemented within the computer system 612.
  • images of the material pieces are taken with a camera (e.g., the camera 109, the camera 610).
  • the location in the heap 605 of each material piece is detected by the vision system for identifying the location of each material piece to be classified/identified.
  • sensed information/characteristics of the material piece is captured/acquired from the images by the vision system.
  • the vision system may perform preprocessing of the captured information, which may be utilized to detect (extract) each of the material pieces (e.g., from the background (e.g., the other material pieces in the heap 605); in other words, the preprocessing may be utilized to identify the difference between the material piece and the background).
  • the preprocessing may be utilized to identify the difference between the material piece and the background.
  • Well- known image processing techniques such as dilation, thresholding, and contouring may be utilized to identify the material piece as being distinct from the background.
  • segmentation may be performed.
  • the captured information may include information pertaining to one or more material pieces.
  • a first step is to apply a high contrast of the image; in this fashion, background pixels are reduced to substantially all black pixels, and at least some of the pixels pertaining to the material piece are brightened to substantially all white pixels.
  • the image pixels of the material piece that are white are then dilated to cover the entire size of the material piece.
  • the location of the material piece is a high contrast image of all white pixels on a black background.
  • a contouring algorithm can be utilized to detect boundaries of the material piece. The boundary information is saved, and the boundary locations are then transferred to the original image.
  • the process block 305 may implement an image segmentation process, such as Mask R-CNN.
  • the size and/or shape of the material pieces may be determined.
  • post processing may be performed. Post processing may involve resizing the captured information/data to prepare it for use in the neural networks. This may also include modifying certain properties (e.g., enhancing image contrast, changing the image background, or applying filters) in a manner that will yield an enhancement to the capability of the machine learning system to classify the material pieces.
  • the data may be resized. Data resizing may be desired under certain circumstances to match the data input requirements for certain machine learning systems, such as neural networks.
  • neural networks may require much smaller image sizes (e.g., 225 x 255 pixels or 299 x 299 pixels) than the sizes of the images captured by typical digital cameras.
  • image sizes e.g., 225 x 255 pixels or 299 x 299 pixels
  • the smaller the input data size the less processing time is needed to perform the classification.
  • the process block 310 may be configured with a neural network employing one or more machine learning algorithms, which compare the extracted features with those stored in the knowledge base generated during the training stage, and assigns the classification with the highest match to each of the material pieces based on such a comparison.
  • the algorithms of the machine learning system may process the captured information/data in a hierarchical manner by using automatically trained filters. The filter responses are then successfully combined in the next levels of the algorithms until a probability is obtained in the final step.
  • these probabilities may be used for each of the N classifications (e.g., to decide into which of N separate heaps 601...603 the respective material pieces should be sorted).
  • each of the N classifications may be assigned to one of the heaps 601.. .603, and the material piece under consideration is removed from the heap 605 and placed into that heap that corresponds to the classification returning the highest probability larger than a predefined threshold.
  • predefined thresholds may be preset by the user.
  • a particular material piece may be sorted into an outlier heap if none of the probabilities is larger than the predetermined threshold.
  • the separation device 620 is activated via the controller 614 to pick up or grab the classified/identified material piece and remove it from the heap 605.
  • the separation device 620 may then place the classified/identified material piece into the appropriate one of the heaps 601.. .603.
  • the vision system may be adjusted to compensate for different environmental conditions by which images of the material pieces are obtained, such as different durations of time for which the material pieces have been in the scrap yard, amount of dirt on the material pieces, different weather conditions (e.g., snow, rain, sunlight), material pieces sprayed with and without water mixed with chemicals/detergents, and material pieces obtained from different sources.
  • different environmental conditions such as different durations of time for which the material pieces have been in the scrap yard, amount of dirt on the material pieces, different weather conditions (e.g., snow, rain, sunlight), material pieces sprayed with and without water mixed with chemicals/detergents, and material pieces obtained from different sources.
  • FIG. 1 illustrates an example of a material handling system 100, which may be configured in accordance with various embodiments of the present disclosure to train a machine learning system implemented within the computer 612 to classify/identify different types/classes of materials, such as different metal alloys.
  • a conveyor system 103 may be implemented to convey individual (i.e., physically separable) material pieces 101 through the system 100 so that each of the individual material pieces 101 can be classified into predetermined desired groups.
  • Such a conveyor system 103 may be implemented with one or more conveyor belts (e.g., the conveyor belts 102, 103) on which the material pieces 101 travel, typically at a predetermined constant speed.
  • certain embodiments of the present disclosure may be implemented with other types of conveyor systems.
  • the conveyor belt 103 may be a conventional endless belt conveyor employing a conventional drive motor 104 suitable to move the conveyor belt 103 at the predetermined speeds.
  • some sort of suitable feeder mechanism may be utilized to feed the material pieces 101 onto the conveyor belt 103, whereby the conveyor belt 103 conveys the material pieces 101 past various components within the system 100.
  • the conveyor belt 103 is operated to travel at a predetermined speed by a conveyor belt motor 104.
  • a belt speed detector 105 e.g., a conventional encoder
  • a tumbler and/or a vibrator may be utilized to separate the individual material pieces from a collection of material pieces, and then they may be positioned into one or more singulated (i.e., single file) streams.
  • the material pieces may be positioned into one or more singulated (i.e., single file) streams, which may be performed by an active or passive singulator 106.
  • An example of a passive singulator is further described in U.S. Patent No. 10,207,296.
  • the conveyor system e.g., the conveyor belt 103 may simply convey a collection of material pieces, which have been deposited onto the conveyor belt 103, in a random manner.
  • the vision system 110 may utilize one or more still or live action cameras 109 to collect or capture information about each of the material pieces 101.
  • the vision system 110 may be configured (e.g., with a machine learning system) to collect or capture any type of information that can be utilized within the system 100 to selectively classify the material pieces 101 as a function of a set of one or more (user-defined) physical characteristics, including, but not limited to, color, hue, size, shape, texture, overall physical appearance, uniformity, composition, and/or manufacturing type of the material pieces 101.
  • the vision system 110 captures images of each of the material pieces 101 (including one-dimensional, two- dimensional, three-dimensional, or holographic imaging), for example, by using an optical sensor as utilized in typical digital cameras and video equipment.
  • Such images captured by the optical sensor are then stored in a memory device as image data.
  • image data represents images captured within optical wavelengths of light (i.e., the wavelengths of light that are observable by a typical human eye).
  • alternative embodiments of the present disclosure may utilize sensors that are capable of capturing an image of a material made up of wavelengths of light outside of the visual wavelengths of the typical human eye. This captured image data is then utilized to classify the control sets of material pieces so that the machine learning system is trained (as described herein) for each different type of material to be separated by the apparatus 600.
  • FIG. 5 a block diagram illustrating a data processing (“computer”) system 3400 is depicted in which aspects of embodiments of the disclosure may be implemented.
  • the terms “computer,” “system,” “computer system,” and “data processing system” may be used interchangeably herein.
  • the computer system 107, the computer system 612, and/or the vision system 110 may be configured similarly as the computer system 3400.
  • the computer system 3400 may employ a local bus 3405 (e.g., a peripheral component interconnect (“PCI”) local bus architecture). Any suitable bus architecture may be utilized such as Accelerated Graphics Port (“AGP”) and Industry Standard Architecture (“ISA”), among others.
  • AGP Accelerated Graphics Port
  • ISA Industry Standard Architecture
  • 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 cache 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 and/or one or more tensor processing units. Additional connections to the local bus 3405 may be made through direct component interconnection or through add-in boards.
  • a communication (e.g., network (LAN)) adapter 3425, an I/O (e.g., small computer system interface (“SCSI”) host bus) adapter 3430, and expansion bus interface (not shown) may be connected to the local bus 3405 by direct component connection.
  • An audio adapter (not shown), a graphics adapter (not shown), and display adapter 3416 (coupled to a display 3440) may be connected to the local bus 3405 (e.g., by addin boards inserted into expansion slots).
  • the user interface adapter 3412 may provide a connection for a keyboard 3413 and a mouse 3414, modem/router (not shown), and additional memory (not shown).
  • the I/O adapter 3430 may provide a connection for a hard disk drive 3431, a tape drive 3432, and a CD-ROM drive (not shown).
  • One or more operating systems may be run on the one or more processors 3415 and used to coordinate and provide control of various components within the computer system 3400.
  • the operating system(s) may be a commercially available operating system.
  • An object-oriented programming system e.g., Java, Python, etc.
  • Java, Python, etc. may run in conjunction with the operating system and provide calls to the operating system from programs or programs (e.g., Java, Python, etc.) executing on the system 3400.
  • Instructions for the operating system, the object-oriented operating system, and programs may be located on non-volatile memory 3435 storage devices, such as a hard disk drive 3431, and may be loaded into volatile memory 3420 for execution by the processor 3415.
  • FIG. 5 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. 5.
  • 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 vision system 110 may be performed by a first computer system 3400, while operation of the computer system 612 for classifying may be performed by a second computer system 3400.
  • the 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 the computer system 3400 includes some type of network communication interface.
  • the computer system 3400 may be an embedded controller, which is configured with ROM and/or flash ROM providing non-volatile memory storing operating system files or user-generated data.
  • FIG. 5 The depicted example in FIG. 5 and above-described examples are not meant to imply architectural limitations. Further, a computer program form of aspects of the present disclosure may reside on any computer readable storage medium (i.e., floppy disk, compact disk, hard disk, tape, ROM, RAM, etc.) used by a computer system.
  • any computer readable storage medium i.e., floppy disk, compact disk, hard disk, tape, ROM, RAM, etc.
  • embodiments of the present disclosure may be implemented to perform the various functions described for identifying, locating, classifying, and/or separating material pieces.
  • Such functionalities may be implemented within hardware and/or software, such as within one or more data processing systems (e.g., the data processing system 3400 of FIG. 5), such as the previously noted computer system 107, the vision system 110, and/or the computer system 612. Nevertheless, the functionalities described herein are not to be limited for implementation into any particular hardware/software platform.
  • aspects of the present disclosure may be embodied as a system, process, method, program product and/or apparatus. Accordingly, various 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 embodiments combining software and hardware aspects, which may generally be referred to herein as a “circuit,” “circuitry,” “module,” or “system.” Furthermore, aspects of the present disclosure may take the form of a program product embodied in one or more computer readable storage medium(s) having computer readable program code embodied thereon. (However, any combination of one or more computer readable medium(s) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium.)
  • each block in the flowcharts or block diagrams may represent a module, segment, or portion of code, which includes one or more executable program instructions for implementing the specified logical function) s).
  • the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • Modules implemented in software for execution by various types of processors may, for instance, include one or more physical or logical blocks of computer instructions, which may, for instance, be organized as an object, procedure, or function. Nevertheless, the executables of an identified module need not be physically located together, but may include disparate instructions stored in different locations which, when joined logically together, include the module and achieve the stated purpose for the module. Indeed, a module of executable code 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.
  • operational data e.g., material classification libraries described herein
  • modules 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 electronic signals on a system or network.
  • 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) (e.g., CPU 3415) of the computer or other programmable data processing apparatus, create circuitry or means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • processors e.g., CPU 3415
  • each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations can be implemented by special purpose hardware-based systems (e.g., which may include one or more graphics processing units) that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
  • a module may be implemented as a hardware circuit including custom 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.
  • a flow-charted technique may be described in a series of sequential actions.
  • the sequence of the actions, and the element performing the actions may be freely changed without departing from the scope of the teachings.
  • Actions may be added, deleted, or altered in several ways.
  • the actions may be re-ordered or looped.
  • processes, methods, algorithms, or the like may be described in a sequential order, such processes, methods, algorithms, or any combination thereof may be operable to be performed in alternative orders.
  • some actions within a process, method, or algorithm may be performed simultaneously during at least a point in time (e.g., actions performed in parallel), and can also be performed in whole, in part, or any combination thereof.
  • Computer program code i.e., instructions, for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including 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, programming languages such as MATLAB or Lab VIEW, 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, as a stand-alone software package, partly on the user’s computer system (e.g., the computer system utilized for sorting) and partly on a remote computer system (e.g., the computer system utilized to train the machine learning system), or entirely on the remote computer system or server.
  • 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).
  • LAN local area network
  • WAN wide area network
  • various aspects of the present disclosure may be configured to execute on one or more of the computer system 107, the vision system 110, and the computer system 612.
  • a sensor device may be utilized to classify each of the material pieces in a heap 605.
  • a sensor device may be mounted somewhere on the separation device 600, such as the arm or the grabbing mechanism of a robotic manipulator.
  • a sensor device may be configured with any type of sensor technology, including sensors utilizing irradiated or reflected electromagnetic radiation (e.g., utilizing infrared (“IR”), Fourier Transform IR (“FTIR”), Forward-looking Infrared (“FLIR”), Very Near Infrared (“VNIR”), Near Infrared (“NIR”), Short Wavelength Infrared (“SWIR”), Long Wavelength Infrared (“LWIR”), Medium Wavelength Infrared (“MWIR” or “MIR”), X-Ray Transmission (“XRT”), Gamma Ray, Ultraviolet (“UV”), X-Ray Fluorescence (“XRF”), Laser Induced Breakdown Spectroscopy (“LIBS”), Raman Spectroscopy, Antistokes Raman Spectroscopy, Gamma Spectroscopy (which can be utilized to sense objects that are obscured by other objects, such as in a heap), Hyperspectral Spectroscopy (e.g., any range beyond visible wavelengths), Acoustic
  • the following sensor systems may also be used within certain embodiments of the present disclosure for determining the chemical signatures of plastic pieces and/or classifying plastic pieces.
  • the previously disclosed various forms of infrared spectroscopy may be utilized to obtain a chemical signature specific of each plastic piece that provides information about the base polymer of any plastic material, as well as other components present in the material (mineral fillers, copolymers, polymer blends, etc.).
  • Differential Scanning Calorimetry (“DSC”) is a thermal analysis technique that obtains the thermal transitions produced during the heating of the analyzed material specific for each material.
  • Thermogravimetric analysis (“TGA”) is another thermal analysis technique resulting in quantitative information about the composition of a plastic material regarding polymer percentages, other organic components, mineral fillers, carbon black, etc.
  • Capillary and rotational rheometry can determine the rheological properties of polymeric materials by measuring their creep and deformation resistance.
  • Optical and scanning electron microscopy (“SEM”) can provide information about the structure of the materials analyzed regarding the number and thickness of layers in multilayer materials (e.g., multilayer polymer films), dispersion size of pigment or filler particles in the polymeric matrix, coating defects, interphase morphology between components, etc.
  • Chromatography e.g., LC-PDA, LC-MS, LC-LS, GC-MS, GC- FID, HS-GC
  • plastic materials such as UV stabilizers, antioxidants, plasticizers, anti-slip agents, etc., as well as residual monomers, residual solvents from inks or adhesives, degradation substances, etc.
  • aspects of the present disclosure provide an apparatus that includes a camera configured to capture images of individual metal alloy pieces contained within a first heap of a heterogeneous mixture of metal alloy pieces comprising at least one metal alloy piece having a first metal alloy composition and at least one metal alloy piece having a second metal alloy composition; a data processing system implemented with an Al system configured to assign a first classification to a first metal alloy piece having the first metal alloy composition as a function of a processing of the captured image of the first metal alloy piece through the Al system; and a separation device configured to automatically grab and remove the first metal alloy piece from the first heap in response to the first classification.
  • the separation device may be configured to deposit the first metal alloy piece on a second heap of metal alloy pieces.
  • the second heap may consist of metal alloy pieces having the first metal alloy composition.
  • the Al system may be configured to assign a second classification to a second metal alloy piece having the second metal alloy composition as a function of a processing of the captured image of the second metal alloy piece through the Al system, wherein the separation device is configured to automatically grab and remove the second metal alloy piece from the first heap in response to the second classification, wherein the separation device is configured to deposit the second metal alloy piece on a third heap of metal alloy pieces.
  • the third heap may be a homogenous collection of metal alloy pieces having the second metal alloy composition.
  • the artificial intelligence system may be configured with a neural network employing one or more algorithms that compare features detected in the captured images with those stored in a knowledge base generated during a training stage, wherein during the training stage, the one or more algorithms learn relationships between one or more specified classes of metal alloys and their features extracted from captured image data that creates the knowledge base.
  • the camera may be configured to capture visual images of the individual metal alloy pieces.
  • the first metal alloy piece may be a steel alloy piece.
  • the separation device may include a robotic arm having a controller receiving instructions from the data processing system identifying a first location of the first metal alloy piece within the first heap, and identifying a second location of the second heap. The first and second heaps may be located within a metal scrap yard.
  • aspects of the present disclosure provide an apparatus that includes a sensor configured to capture one or more characteristics of each of a mixture of material pieces contained within a first heap of material pieces, wherein the mixture of material pieces comprises material pieces having different chemical compositions; a data processing system configured to assign a first classification to a first material piece having a first chemical composition as a function of a processing of the captured one or more characteristics of the first material piece; and a separation device configured to automatically grab and remove the first material piece from the first heap in response to the first classification.
  • the separation device may include a robotic arm having a controller receiving instructions from the data processing system identifying a first location of the first material piece within the first heap, and identifying a second location of the second heap, wherein the sensor is an x-ray fluorescence system mounted on an arm of the separation device.
  • the sensor may be a camera configured to capture visual images of the material pieces, and wherein the data processing system is implemented with an Al system configured to assign a first classification to a first material piece having a first chemical composition as a function of a processing of the captured visual image of the first material piece through the Al system.
  • the Al system may be configured to assign a second classification to a second material piece having a second chemical composition as a function of a processing of the captured visual image of the second material piece through the Al system, wherein the separation device is configured to automatically grab and remove the second material piece from the first heap in response to the second classification, wherein the first and second classifications are different from each other, wherein the separation device is configured to deposit the first material piece on a second heap of material pieces, wherein the separation device is configured to deposit the second material piece on a third heap of material pieces.
  • the second heap may be a homogenous collection of material pieces having the first chemical composition
  • the third heap is a homogenous collection of material pieces having the second chemical composition.
  • the material pieces may be scrap metal alloy pieces and the first material piece may be a first scrap metal alloy piece, wherein the first and second heaps are located within a metal scrap yard, wherein the separation device is configured to deposit the first scrap metal alloy piece on a second heap of scrap metal alloy pieces.
  • the separation device may include a robotic arm having a controller receiving instructions from the data processing system identifying a first location of the first scrap metal alloy piece within the first heap, and identifying a second location of the second heap.
  • the Al system may be configured with a neural network employing one or more algorithms that compare features detected in the captured images with those stored in a knowledge base generated during a training stage, wherein during the training stage, the one or more algorithms learn relationships between one or more specified classes of scrap metal alloys and their features extracted from captured image data that creates the knowledge base.
  • the term “or” may be intended to be inclusive, wherein “A or B” includes A or B and also includes both A and B.
  • the term “and/or” when used in the context of a listing of entities refers to the entities being present singly or in combination.
  • the phrase “A, B, C, and/or D” includes A, B, C, and D individually, but also includes any and all combinations and subcombinations of A, B, C, and D.
  • substantially refers to a degree of deviation that is sufficiently small so as to not measurably detract from the identified property or circumstance.
  • the exact degree of deviation allowable may in some cases depend on the specific context.
  • Coupled is not intended to be limited to a direct coupling or a mechanical coupling. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements.

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Abstract

L'invention concerne un appareil de classification de matériaux faisant intervenir un système de vision, qui peut mettre en œuvre un système d'intelligence artificielle afin d'identifier ou de classer chacun des matériaux, qui peut ensuite être séparé d'un tas dans un parc à ferraille en groupes séparés, tels que d'autres tas, sur la base d'une telle identification ou classification. Le système d'intelligence artificielle peut faire intervenir un réseau de neurones, et peut être préalablement entraîné pour reconnaître et classifier certains types de matériaux.
EP22888018.3A 2021-10-29 2022-10-24 Séparation de métaux dans un parc à ferraille Pending EP4422807A1 (fr)

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