Method for classifying and disposing of electronic component scraps

Abstract

To provide a classification method for an electronic component scrap that can properly determine mixed scraps including a plurality of different kinds of component types from electronic component scraps, and a processing method for an electronic component scrap.SOLUTION: A classification method for an electronic component scrap includes: a position shape identification step of identifying a position and a shape of each electronic component scrap from a plurality of electronic component scraps having different shapes and obtaining position shape-identification information including position information and shape information about each electronic component scrap; a feature analysis step of analyzing at least two features of each electronic component scrap to obtain feature analysis information; and a classification step of classifying each electronic component scrap for each predetermined component type, using the at least two features associated with one electronic component scrap having the same shape and at the same position as each electronic component scrap, on the basis of the position shape-identification information and the feature analysis information.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to a method for classifying scrap electronic components and a method for treating scrap electronic components, and more particularly to a method for classifying scrap electronic components and a method for treating scrap electronic components that can be used in a recycling process for used electronic and electrical equipment. [Background technology]

[0002] In recent years, from the perspective of resource conservation, recovery of valuable metals from discarded home appliances, PCs, mobile phones, and other electronic component scraps has become increasingly common, and efficient methods for recovering these metals have been studied and proposed.

[0003] For example, Japanese Patent Laid-Open Publication No. 9-78151 (Patent Document 1) describes a method for recycling valuable metals, which includes a step of charging scrap containing valuable metals, such as scrap electronic parts, into a flash smelting furnace for smelting copper ore and recovering the valuable metals in the matte remaining in the furnace.

[0004] Japanese Patent Application Laid-Open No. 2018-123380 (Patent Document 2) describes a method for recovering valuable metals from recycled raw materials containing aluminum, in which the recycled raw materials are charged into a melting furnace in a copper smelting process, the aluminum is oxidized to become a component of the molten slag layer and removed from the system, the valuable metals are dissolved in the metal layer and matte layer, and the dissolved valuable metals are recovered. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 9-78151 [Patent Document 2] JP 2018-123380 A Summary of the Invention [Problem to be solved by the invention]

[0006] In the processing of electronic waste, it is ideal to crush and separate the waste into multiple component types suitable for recovery, such as circuit boards, plastics, metal pieces, copper wire scraps, capacitors, IC chips, and other component types. Currently, in many sorting processes, the crushed materials are classified and processed on the assumption that they are in a state suitable for recovering valuable materials. For example, in the processing of electronic waste, one of the objectives may be to properly recover circuit boards that contain a relatively large amount of valuable metals.

[0007] However, currently, there exists mixed waste containing multiple types of scrap parts to be classified, such as scrap parts containing substrates attached to components such as capacitors and IC chips, and scrap parts containing substrates attached to plastics. In the case of such mixed waste, it is difficult to separate using the conventional sorting method. For example, in sorting using a metal sorter, a substrate containing a relatively large amount of plastic may be relatively sorted as plastic, depending on the detection sensitivity of the metal sorter, even if the substrate contains a large amount of precious metals to be recovered. In addition, a capacitor with a substrate attached thereto may be determined as a capacitor depending on the detection sensitivity of the sorter, and therefore not be recovered as a substrate, resulting in a decrease in the recovery efficiency of the substrate. As described above, the content ratio of valuable metals contained in electronic component scraps varies depending on the raw material, so that the conventional sorting method that relies on the detection sensitivity of the sorter is currently unable to efficiently separate mixed waste.

[0008] In consideration of the above problems, the present disclosure provides a method for classifying scrap electronic components and a method for disposing of scrap electronic components that are capable of appropriately determining mixed scrap containing multiple component types from among scrap electronic components. [Means for solving the problem]

[0009] In order to solve the above problems, the present inventors have conducted extensive research and have found that it is necessary to detect the characteristics of two or more types of scrap electronic components to be collected, since scrap electronic components are a mixture of two or more types of components such as plastics and metals, and there are complex circumstances in which some metals are recovered as valuable metals and others are not, etc. Then, the present inventors have found that it is effective to use an identification means to identify the position and shape of each scrap electronic component from among a plurality of scrap electronic components, analyze two or more characteristics of each scrap electronic component whose position and shape have been identified, and classify the scrap electronic components using the two or more characteristics associated with scrap electronic components of the same shape and the same position.

[0010] One aspect of an embodiment of the present invention completed based on the above findings is a method for sorting electronic component scraps, comprising: a position and shape identification step of identifying the position and shape of each electronic component scrap from among a plurality of electronic component scraps having different shapes, and obtaining position and shape identification information including position information and shape information of each electronic component scrap; a feature analysis step of analyzing at least two or more features of each electronic component scrap and obtaining feature analysis information; and a classification step of classifying each electronic component scrap into predetermined component types based on the position and shape identification information and the feature analysis information, using two or more features associated with one electronic component scrap of the same shape and in the same position.

[0011] In another aspect, an embodiment of the present invention is a method for processing scrap electronic components, including a position and shape identification step of identifying a position and shape of each scrap electronic component from a plurality of scrap electronic components having different shapes, and obtaining position and shape identification information including position information and shape information of each scrap electronic component; a feature analysis step of analyzing at least two or more characteristics of each scrap electronic component and obtaining feature analysis information associated with the position and shape identification information; a classification step of classifying each scrap electronic component into predetermined component types based on the position and shape identification information and the feature analysis information, using two or more characteristics associated with one scrap electronic component of the same shape and in the same position; and an extraction step of extracting scrap electronic components to be extracted from the plurality of scrap electronic components based on the classification result of the classification step and the position and shape identification information. Effect of the Invention

[0012] According to the present disclosure, it is possible to provide a method for classifying scrap electronic components and a method for disposing of scrap electronic components that are capable of appropriately identifying mixed scrap containing multiple component types from among scrap electronic components. [Brief description of the drawings]

[0013] [Figure 1] 1 is a schematic diagram illustrating an example of a sorting system according to an embodiment of the present invention. [Diagram 2] FIG. 2 is a block diagram showing an example of an image analysis means according to the embodiment of the present invention. [Diagram 3] FIG. 4 is a flow diagram showing an example of an image analysis process according to an embodiment of the present invention. [Figure 4] 1 is a photograph showing an example of area detection data. [Diagram 5] 1 is a photograph showing an example of multispectral imaging data. [Figure 6] 5 is a photograph showing an example of a case where an inspection area is assigned to data obtained by binarizing the area detection data of FIG. 4. [Figure 7] 11 is a table showing a comparison result of recognition accuracy when a conventional color camera and a camera equipped with multispectral illumination according to an embodiment of the present invention are used as an imaging means. [Figure 8] 1 is a plan view illustrating an example of a picking device according to an embodiment of the present invention. [Figure 9] 1 is a perspective view of a robot hand provided in a picking device according to an embodiment of the present invention. [Figure 10] 13 is a perspective view of a modified example of a robot hand provided in the picking device according to the embodiment of the present invention. FIG. [Figure 11] FIG. 2 is a flow chart showing an example of a method for processing electronic component scraps according to an embodiment of the present invention. [Figure 12] 1 is a photograph showing an example of single scrap of electronic components (aluminum scraps). [Figure 13] 1 is a photograph showing an example of single scrap (Fe scrap) of electronic components. [Figure 14]1 is a photograph showing an example of mixed scraps (Al scraps) of electronic component scraps. [Figure 15] 1 is a photograph showing an example of mixed scraps (Fe scraps) of electronic component scraps. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, the embodiments of the present invention will be described with reference to the drawings. Note that the embodiments shown below are merely examples of devices and methods for embodying the technical concept of the present invention, and the technical concept of the present invention is not limited to the structures and arrangements of the components described below.

[0015] (Sorting system) As shown in FIG. 1, a sorting system 100 according to an embodiment of the present invention includes a conveying unit 3 having a conveying surface 30 for conveying electronic component scraps 5, an image recognition unit 2 for performing image recognition on the electronic component scraps 5 conveyed onto the conveying surface 30, and a sorting unit 1 for conveying the electronic component scraps 5 as objects from a source to a destination using a picking robot 10.

[0016] The electronic waste 5 refers to the crushed electronic and electrical equipment such as discarded home appliances, PCs, and mobile phones, which are collected and then crushed to an appropriate size. The crushing to obtain the electronic waste 5 may be performed by the processor himself, or crushed electronic waste may be purchased from the market.

[0017] The crushing method is not limited to a specific device, and may be a shearing method or an impact method, but it is preferable to crush the components without damaging their shapes as much as possible. Therefore, devices belonging to the category of crushers intended for fine crushing are not included. Although not limited to the following, it is preferable to use, as the raw material, electronic component scraps 5 in this embodiment, typically scraps crushed to a grain size of 10 mm or more and 100 mm or less, more typically 15 mm or more and 50 mm or less.

[0018] As the electronic component scraps 5, electronic component scraps 5 that have been subjected to a sorting process using any one of a magnetic sorter, a color sorter, a metal sorter, an optical sorter including an infrared sensor, and a plastic sorter can be suitably used.

[0019] In particular, the electronic component scrap 5 includes component scraps (e.g., circuit boards, plastics, metal pieces, copper wire scraps, capacitors, IC chips, etc.) that are suitable for recovery in the smelting process described below and component scraps (heat sinks, cases, iron (Fe) scraps, aluminum (Al) scraps, stainless steel (SUS) scraps, synthetic resins, etc.) that are external raw materials, which exist in a single state (i.e., the ratio of main components to a single component scrap is 90% or more by weight), and mixed scraps in which multiple such component scraps are mixed together.

[0020] Examples of single piece scrap include, but are not limited to, aluminum scrap including a silver-white heat sink or housing as shown in FIG. 12(a), aluminum scrap including a black heat sink or housing as shown in FIG. 12(b), cylindrical black or blue capacitors as shown in FIG. 12(c), iron scrap including an iron core as shown in FIG. 13(a) and silver screws and springs as shown in FIG. 13(b).

[0021] The mixed scrap includes a heat sink with an IC as shown in Fig. 14(a), a capacitor with a substrate as shown in Fig. 14(b), aluminum scrap with copper wire as shown in Fig. 14(c), an iron core with a copper coil as shown in Fig. 15(a), iron scrap or iron core with a substrate as shown in Fig. 15(b), a substrate with lead wire as shown in Fig. 5(c), etc. The single scrap as shown in Fig. 12(a) to Fig. 13(b) can be properly sorted to some extent by properly adjusting the sorting accuracy of the sorting machine, but the mixed scrap as shown in Fig. 14(a) to Fig. 15(c) may be difficult to properly sort because it contains a mixture of smelting raw materials and external raw materials.

[0022] The electronic component scraps 5 are transported on the transport surface 30 along the transport direction from the transport source, and image analysis means 20 provided in the image recognition unit 2 performs image analysis processing. As shown in Fig. 2, the image analysis means 20 can include an imaging means 21 that captures an image of the electronic component scraps 5 within an imaging area set on the transport surface 30, a control means 200 that controls various operations of the imaging means 21, a storage device 210 that stores information necessary for the operation of the control means 200, and an input means 120 and an output means 130 that can input and output information necessary for the control means 200.

[0023] The imaging means 21 includes a multispectral illumination unit (not shown) that irradiates a plurality of electronic component scraps 5 in an imaging area with light of different wavelengths (multispectral light) to obtain two or more pieces of spectral information for the electronic component scraps 5, and a multispectral imaging unit (multi-camera unit: not shown) that images the electronic component scraps 5 in the imaging area irradiated with light of different wavelengths. The imaging control means 201 extracts multispectral imaging data captured by the multispectral imaging unit, and stores it in the multispectral imaging data storage means 211.

[0024] The image recognition unit 2 may include an area detection unit 23 that irradiates the electronic component scraps 5 in the image capture area with area detection light to obtain area detection data. Area detection includes detecting the area (area of ​​presence) of the electronic component scraps 5 present in the image capture area of ​​the image recognition unit 2 by image recognition processing, thereby making it possible to clearly recognize the contour of the target object. Specifically, the area detection data, which is an image of the image capture area including the electronic component scraps 5, is obtained by binarizing, thereby making the position, number, contour (shape) and area of ​​the electronic component scraps 5 present in the image capture area clear. For example, if the position of any electronic component scrap 5 in the sorting unit 1 matches the position of a specific electronic component scrap 5 in the area detection data obtained by the image recognition unit 2, it can be determined that they are the same object.

[0025] The specific configuration of the area detection unit 23 is not particularly limited, but may include, for example, a light source that irradiates area detection light such as visible light, infrared light, or ultraviolet light to an object in the imaging area, and a detector that detects an object in the imaging area illuminated by the area detection light. Area detection data is stored in area detection data storage means 212. Note that, when area detection data can be generated by a multispectral illumination unit and a multispectral imaging unit included in the imaging means 21 instead of the area detection unit 23, the area detection unit 23 may be omitted.

[0026] The control means 200 includes, for example, imaging control means 201 which controls the imaging means 21 and the area detection unit 23; position and shape identification means 202 which identifies the position and shape of each electronic component scrap 5 from a plurality of electronic component scraps 5 having different shapes, and obtains position and shape identification information including position information and shape information of each electronic component scrap 5; feature analysis means 203 which analyzes at least two or more characteristics of each electronic component scrap 5 and obtains feature analysis information; classification means 204 which classifies each electronic component scrap 5 into predetermined component types based on the position and shape identification information and the feature analysis information, using two or more characteristics associated with an electronic component scrap 5 of the same shape and in the same position; identification information creation means 205 which creates identification information for each electronic component scrap 5, and movement tracking means 207.

[0027] The storage device 210 comprises a multispectral imaging data storage means 211 which stores multispectral imaging data for the electronic component scraps 5, an area detection data storage means 212 which stores area detection data, a color characteristic information storage means 213 which stores color characteristic information based on the colors of the electronic component scraps 5 which are the subject of image analysis by the image analysis means 20, a classification information storage means 214, and an identification information storage means 215 which creates identification information for each electronic component scrap based on the characteristics of the electronic component scraps 5.

[0028] The control means 200 may be configured to be connected to a server 25 or another sorting system 24 via a network 22 so as to be able to mutually share the results of image analysis of the electronic component scraps 5 analyzed by the image analysis means 20 of FIG.

[0029] The image analysis means 20 can proceed with the image analysis process, for example, along the procedure shown in Fig. 3. For example, as shown in step S100 in Fig. 3, the imaging control means 201 controls the area detection unit 23 to irradiate area detection light onto electronic component scraps 5 transported into the imaging area, detect objects in the imaging area illuminated by the area detection light, and capture area detection data of the electronic component scraps 5 transported into the imaging area (see Fig. 4). The area detection data is stored in the area detection data storage means 212.

[0030] In step S101, the multispectral illumination unit included in the imaging means 21 irradiates electronic component scraps 5 in an imaging area with multispectral illumination light of different wavelengths, and the multispectral imaging unit obtains a plurality of pieces of multispectral imaging data with different illumination colors (see Figs. 5(a) to 5(h)). Here, for example, multispectral imaging data of illumination light of eight colors, namely white, ultraviolet (UV), blue, green, orange, red, far infrared (FR), and infrared (IR), can be obtained. The multispectral imaging data is stored in the multispectral imaging data storage means 211.

[0031] In step S102, the position and shape identification means 202 in FIG. 2 detects the position and shape of electronic component scraps 5 present within the imaging area based on the area detection data. For example, the position and shape identification means 202 digitizes the imaging data in FIG. 4 to clarify the shading between the outer diameter of the electronic component scraps 5 and the background (see FIG. 5), and based on the image with the shading clarified, extracts black shapes that stand out on a white background as electronic component scraps 5 to be detected. For example, in the example in FIG. 6, three lumps exist on a white background. Therefore, the position and shape identification means 202 detects three electronic component scraps 5 in the imaging data. By detecting the number of electronic component scraps 5 present within the imaging area using the area detection data, the position and shape (area (number of pixels)) of the electronic component scraps 5 can be more appropriately detected.

[0032] In step S103, the characteristic analysis means 203 sets first to third inspection areas 51, 52, and 53 so that each inspection area contains only one electronic component scrap 5. For example, in the example of Fig. 6, the characteristic analysis means 203 can set first, second, and third inspection areas 51, 52, and 53. The characteristic analysis means 203 further analyzes at least two or more characteristics of the electronic component scrap 5 in each of the first to third inspection areas 51 to 53 based on the multispectral imaging data and identification information for identifying a plurality of component types contained in the electronic component scrap 5, which is registered in advance in the color characteristic information storage means 213, to obtain characteristic analysis information.

[0033] The feature analysis information for analyzing the features of the electronic component scraps 5 may include color feature information, and the color feature information may include at least extracted color information and extracted color area information. The extracted color information includes information including the set values ​​of each value such as hue, saturation, and brightness of typical colors possessed by the smelting raw material or the external raw material. The extracted color area information includes information of an area threshold (a set value of an area ratio) for judging the electronic component scraps 5 as a smelting raw material or an external raw material when the electronic component scraps 5 in the inspection area include an "extracted color" preset by the user. The feature analysis means 203 can analyze the features of the smelting raw material or the external raw material by analyzing the specific color possessed by the electronic component scraps and the ratio of the area occupied by the specific color to the total area of ​​the electronic component scraps 5, and comparing it with the threshold for judging the smelting raw material or the external raw material. The area threshold information for judging the smelting raw material or the external raw material may be input in advance.

[0034] The color characteristic information includes valuable materials contained in the smelting raw materials to be recovered, such as wire scraps (copper, gold), metal scraps such as brass (copper, gold), ICs or LSIs (black, gold, green), circuit boards containing valuable metals (green, brown, black, white), connector sockets containing copper wires (white), capacitors and heat sinks containing a certain amount or more of valuable metals (silver, white, black), etc. External raw materials include metal scraps (shiny silver) that are not suitable for recovery in the smelting process, such as iron, aluminum, or stainless steel, plastics (white, black, brown), and capacitors and heat sinks that do not contain a certain amount or more of valuable metals (silver, white, black).

[0035] In order to reduce misrecognition of the smelting raw materials and the external raw materials as much as possible and to increase the recognition accuracy, it is preferable that the extracted color information includes at least the white, green, black, gold, and copper colors of the smelting raw materials and the external raw materials as information on the "extracted color" used to determine the extraction of the sorting object. In a more preferred embodiment, it is preferable that at least the white, green, black, gold, copper, brown, and silver colors are included as information on the "extracted color" used to determine the extraction of the sorting object.

[0036] Among various extraction colors, black is a color that can be included in both the smelting raw material and the external raw material, and is an extraction color that is particularly prone to misrecognition. Therefore, in this embodiment, it is preferable that the extracted color area information used as the feature analysis information has two or more thresholds for the area of ​​black. For example, when a first threshold and a second threshold greater than the first threshold are set, among the electronic component scraps 5 having black color, those below the first threshold are considered to be merely affected by the shadows created by the unevenness of the electronic component scraps 5, and can be set as "selection objects" to be extracted as smelting raw materials. Those between the first and second thresholds are considered to be detecting the regulator (IC) attached to the heat sink, and are set as "selection excluded objects" to be left on the conveying surface 30 as external raw materials. Those above the second threshold are considered to be black-painted metal scraps, and can be set as "selection objects" as smelting raw materials. In this way, by providing a threshold value of the black area ratio of 2 or more as the extracted color area information, it is possible to reduce erroneous recognition when separating refining raw materials from non-system raw materials from electronic component scraps 5, and to improve recognition accuracy.

[0037] For example, when "refining raw materials" such as circuit board scraps are selected as the objects to be sorted (those to be picked up (picked up) and removed from the conveying surface 30), the sorting means 204 classifies whether or not the electronic component scraps 5 in the inspection area satisfy the conditions of "refining raw materials" by comparing them with the multispectral imaging data based on the extracted color information and extracted color area information previously set as "refining raw materials". If the electronic component scraps 5 in the inspection area satisfy the conditions of "refining raw materials", the electronic component scraps 5 are classified as "refining raw materials" to be removed in the sorting process described below.

[0038] In step S104, the classification means 204 determines, for the electronic component scraps 5 in each inspection area, objects not to be sorted (items excluded from sorting: either smelting raw materials or external raw materials) based on the multispectral imaging data and color characteristic information for distinguishing between smelting raw materials and external raw materials that is registered in advance in the color characteristic information storage means 213.

[0039] In step S105, based on the results of setting the objects to be sorted and the objects to be excluded from sorting, the identification information creating means 205 creates identification information. The identification information includes information on whether the electronic component scraps 5 in each inspection area are objects to be sorted or objects not to be sorted, as well as information on the position, color, area, major and minor axis directions of the objects, center of gravity, etc. of the electronic component scraps 5. The identification information is stored in the identification information storage means 215.

[0040] According to the embodiment of the present invention, by providing an imaging means 21 equipped with a multispectral illumination for capturing images of electronic component scraps 5 within an imaging area to obtain multispectral imaging data, and an image analysis means 20 for identifying the smelting raw materials or the external raw materials based on the multispectral imaging data and the color characteristic information of the smelting raw materials and the external raw materials registered in advance, and obtaining identification information including position information of the smelting raw materials or the external raw materials, the recognition accuracy of the electronic component scraps 5 can be improved compared to the case where an imaging means using a conventional color camera is provided.

[0041] For example, among the electronic component scraps 5 considered as external raw materials, metal scraps such as iron, aluminum, and stainless steel have a metallic luster, so that they appear white due to halation in a conventional color camera, and the recognition area of ​​silver may become smaller, resulting in a lower recognition rate of the target. On the other hand, if the extraction color is expanded to white-silver in an attempt to reduce the false recognition rate, the white-silver color contained in the circuit board scraps, which are smelting raw materials, will be falsely detected.

[0042] According to this embodiment, the imaging means 21 equipped with a multispectral illumination unit can obtain a plurality of multispectral imaging data in which the effects of halation are suppressed. By using this multispectral imaging data to compare the electronic component scraps 5 contained therein with color characteristic information including information on extracted colors that have been registered in advance, the effects of halation can be reduced and erroneous recognition of the colors of the electronic component scraps 5 can be suppressed.

[0043] In addition, with conventional color cameras, metal objects with uneven surfaces appear black over a wide area due to the reflection of light, and the recognition area of ​​the silver color of the metal becomes smaller, which can result in a lower recognition rate. According to this embodiment, the imaging means 21 equipped with a multi-spectral illumination unit can recognize subtle differences in color, suppressing the effects of shadows and making the recognition area of ​​the silver color of the metal closer to reality.

[0044] Furthermore, in conventional color cameras, since the image passes through a color filter, the spectral intensity of black and silver-colored materials such as stainless steel is reduced and the recognition area of ​​the silver color is reduced, which may result in a lower recognition rate. According to this embodiment, black and silver-colored metals such as stainless steel can be recognized by the imaging means 21 equipped with a multispectral illumination unit. Furthermore, metal scraps painted black can be identified while ignoring the effects of the paint by evaluating them using multispectral imaging data with wavelengths in the infrared region.

[0045] FIG. 7 shows an example of a comparison result between a case where a conventional color camera is used as the imaging means 21 and a case where a camera that irradiates multispectral illumination light is used as the imaging means 21, when two types of metal scraps and two types of plastics are processed as the electronic component scraps 5 to be image-analyzed by the image recognition unit 2 of FIG. 1.

[0046] In the case of metal scrap (1) that emits white reflected light when irradiated with conventional illumination light, a color camera will confuse it with white plastic, which is an external raw material, resulting in a false recognition. On the other hand, when an imaging means 21 that irradiates multispectral illumination light is used, it can be recognized as substrate scraps, which are a raw material for smelting. Similarly, in the case of metal scrap (2) that emits green reflected light when irradiated with illumination light, a color camera will confuse it with green plastic substrates, which are an external raw material, resulting in a false recognition. On the other hand, when an imaging means 21 that irradiates multispectral illumination light is used, it can be recognized as substrate scraps, which are a raw material for smelting.

[0047] In the case of plastic (1) with a very dirty appearance, a color camera will confuse it with brown circuit board scraps, which are a raw material for smelting, resulting in a false recognition. On the other hand, when the imaging means 21 that irradiates multispectral illumination light is used, it can be recognized as plastic, which is an external raw material. In the case of red plastic (2), a color camera will confuse it with copper wire scraps, which are a raw material for smelting, resulting in a false recognition. On the other hand, when the imaging means 21 that irradiates multispectral illumination light is used, it can be recognized as plastic, which is an external raw material.

[0048] As shown in FIG. 2, the control means 200 may have a movement tracking means 207. When the conveying section 3 is continuously moved to continuously move the electronic component scraps 5 on the conveying surface 30, a positional deviation may occur between the first and last captured image data. In this embodiment, when the electronic component scraps 5 that are continuously moving along the conveying direction are identified by image analysis, the step of identifying by image analysis involves irradiating the electronic component scraps 5 in the image capturing area with a reference light immediately before and immediately after capturing the area detection light and the multispectral illumination light. The reference light may be white light from the multispectral illumination. By correcting the positional deviation of the image capturing data due to the movement of the electronic component scraps 5 based on the image capturing data irradiated with this reference light, the conveying section 3 can be continuously operated, thereby improving the processing efficiency.

[0049] The electronic component scraps 5 after the image analysis process in the image recognition unit 2 are sent to the sorting unit 1 shown in FIG. 1. The sorting unit 1 is not particularly limited as long as it has a device for sorting objects on the conveying surface 30. For example, a sorting device using an air jet, an electric paddle, a suction mechanism, a robot hand, or the like can be used. In one embodiment, the sorting unit 1 includes a picking robot 10 that transports the objects on the conveying surface 30 from the conveying unit 3 to the conveying unit 4, and a robot hand 11 that is connected to the picking robot 10 and grasps the smelting raw material or the external raw material as the object from the electronic component scraps 5. The picking robot 10 picks out the object based on the identification information created by the image recognition unit 2.

[0050] The picking robot 10 is not particularly limited as long as it is an industrial robot having the function of grabbing and transporting an object, and various types of industrial robots can be used. For example, the picking robot 10 can be a robot equipped with various types such as a straight type, a multi-joint type, a parallel link type, etc. A straight type robot is a simple robot composed of two to three slide shafts. A multi-joint robot is either a vertical type or a horizontal type, and a vertical type has a wide range of motion due to the rotation of the base and the movement of the arm, and is capable of three-dimensional movement with a high degree of freedom. A horizontal type robot has all the rotation axes of the joints aligned vertically, and has a simpler structure than a vertical multi-joint robot. A parallel link type robot is an industrial robot having a parallel link structure in which joints are arranged in parallel.

[0051] Among the various industrial robots that can be used as picking robots 10, parallel link robots are particularly suitable because they use a parallel link mechanism to move to the target location in the shortest distance, allowing them to move to the location of the object to be extracted with high speed and precision, grasp the material, and send it to the specified location at high speed.

[0052] Although not limited to the following embodiment, the picking robot 10 can pick up an object from the conveying unit 3 toward a destination of the conveying unit 4, which typically has a conveying direction perpendicular to the conveying direction, so as to cross the conveying surface 30 of the conveying unit 3 as shown in Fig. 8. The conveying unit 4 can be configured with a conveyor or the like.

[0053] In this way, the conveying unit 3 and the picking robot 10 are arranged close to each other, and the picking robot 10 is configured to discharge the target object in a direction crossing the conveying direction of the conveying unit 3, so that the target object can be accurately removed from the electronic component scraps 5 in a short time and conveyed.

[0054] (Robot Hand) 9(a), the robot hand 11 provided in the picking robot 10 includes a suction unit 13 at the center, the suction unit 13 including a suction pad 13a for suctioning an object and a vacuum generator 13b connected to the suction pad 13a, a clamping unit 14 (first to fourth arm units 14a to 14d) for clamping the object to be sucked by the suction pad 13a, and a fixing unit 12 for fixing the suction unit 13 and the clamping unit 14. One end of the fixing unit 12 is fixed to the picking robot 10, and the clamping unit 14 is fixed to the other end. The suction pad 13a is made of an elastic material such as rubber or silicone, and protrudes downward (toward the transport surface 30).

[0055] The clamping unit 14 is not particularly limited as long as it is configured to be able to clamp an object. The clamping unit 14 may include, for example, a first arm unit 14a, a second arm unit 14b, a third arm unit 14c, and a fourth arm unit 14d. The base ends of the first to fourth arm units 14a to 14d are each connected to a drive mechanism (not shown) in the fixed unit 12.

[0056] As shown in Fig. 9(b), the first arm portion 14a and the second arm portion 14b can open and close in conjunction with each other in a direction V approaching or a direction W moving away from the central axis X of the suction pad 13a upon receiving power from a drive mechanism. The third arm portion 14c and the fourth arm portion 14d located in the depth direction of the paper surface of Fig. 9(b) can also open and close in conjunction with each other in a direction V approaching or a direction W moving away from the central axis X of the suction pad 13a upon receiving power from an air chuck (not shown) connected to the first to fourth arm portions 14a to 14d. The first to fourth arm portions 14a to 14d can open and close at the same timing, thereby clamping or releasing an object sucked to the tip portion of the suction pad 13a.

[0057] It is preferable that the first to fourth arm portions 14a to 14d have claw portions 141a to 141d at their tips, respectively, which protrude toward the center portion where the suction pad 13a is disposed. By providing the first to fourth arm portions 14a to 14d with the claw portions 141a to 141d, respectively, the object can be gripped more accurately while preventing the object from falling.

[0058] The claws 141a to 141d are preferably formed to have a tapered shape toward the suction pad 13a side, which makes it easier for the claws 141a to 141d to come into contact with the bottom surface of the object and scoop up the object.

[0059] 9(b), it is preferable that the tips (lowest ends) of the claws 141a to 141d are disposed at a position relatively lower than the tip of the suction pad 13a, that is, at a position closer to the conveying surface 30. This makes it easier for the claws 141a to 141d to scoop up and clamp the object sucked by the suction pad 13a from the conveying surface 30.

[0060] 10, the claw 141a of the first arm 14a and the claw 141d of the fourth arm 14d may be connected, and the claw 141b of the second arm 14b and the claw 141c of the third arm 14c may be connected. With this configuration, long objects including circuit boards and the like in the electronic component scraps 5 can be more appropriately gripped.

[0061] Also, by changing the length of the claws 141a to 141d according to the shape of the object, it is possible to grip a smaller object more reliably. The length L (see FIG. 9(a)) of the claws 141a to 141d along a direction parallel to the opening / closing direction of the first to fourth arm parts 14a to 14d is preferably 5 mm or more, more preferably 10 mm or more, and even more preferably 15 mm or more when processing the electronic component scraps 5 according to this embodiment. The upper limit depends on the dimensions of the robot hand 11, but can be, for example, 40 mm or less, and even 30 mm or less. The length D (see FIG. 9(b)) of the claws 141a to 141d along a direction perpendicular to the opening / closing direction of the first to fourth arm parts 14a to 14d is preferably 5 mm or more, more preferably 10 mm or more, and even more preferably 20 mm or more when processing the electronic component scraps 5 according to this embodiment. The upper limit depends on the dimensions of the robot hand 11, but can be, for example, 40 mm or less, and even 30 mm or less.

[0062] The robot hand 11 provided in the picking robot 10 according to the embodiment of the present invention includes a suction pad 13a for sucking an object, and a clamping unit 14 having first to fourth arm units 14a to 14d for clamping the object sucked by the suction pad 13a, and the object is first sucked by the suction pad 13a, and then gripped by the first to fourth arm units 14a to 14d (see FIG. 9(c)). As a result, even heavy objects that are difficult to pick with the suction force of the suction pad 13a can be clamped and transported by the clamping unit 14, so that electronic component scraps 5 of various shapes can be transported more reliably and appropriately.

[0063] In particular, circuit board scraps and the like have ICs and wiring laid on the boards and are heavy, and may fall during transportation if they are held by either the suction pad 13a or the first to fourth arm portions 14a to 14d alone. The picking device according to the embodiment of the present invention can more reliably remove scraps such as circuit board scraps, which have a high specific gravity and a variety of sizes, from among electronic component scraps 5, which are a mixture of various raw materials, and can therefore properly select a large amount of the target objects.

[0064] The opening and closing speed of the first to fourth arm sections 14a to 14d can be adjusted according to the conveying speed of the conveying surface 30. In addition, it is preferable to set the distance between the object to be picked by the first to fourth arm sections 14a to 14d and another adjacent object to 5 mm or more, and further preferably 10 mm or more. This allows the object to be more appropriately gripped by the first to fourth arm sections 14a to 14d.

[0065] (Method of Disposal of Electronic Part Scrap) An example of a method for processing scrap electronic components 5 according to an embodiment of the present invention is shown in Fig. 11. The method for processing scrap electronic components according to an embodiment of the present invention can include at least a step of processing scrap electronic components 5 by at least two stages of air sorting (S2, S4), and a step of sorting board scraps by a metal sorting step (S6) using a metal sorter.

[0066] According to the processing method according to the embodiment of the present invention, in the initial stage of physical sorting, air sorting is first performed in two stages (S2, S4), which makes it possible to reduce the loss of valuable metals compared to when magnetic sorting is performed in the initial stage, and to sort and process a large amount of electronic component scraps 5 at once while concentrating more valuable metals. Then, by combining the two stages of air sorting with a sorting process (S6) using a metal sorter, which requires time for processing, it is possible to remove smelting inhibitors while increasing the processing amount of electronic component scraps 5, and to efficiently recover valuable metals.

[0067] In one embodiment, the method for processing electronic component scrap 5 according to the embodiment of the present invention includes a pre-sorting process (S1) for removing lump copper wire scraps from the electronic component scrap 5, an air sorting process (S2) for air sorting the electronic component scrap 5 after the pre-sorting to move powder-like and film-like scraps to the lighter side and remove them, a sieving process (S3) for sieving the heavy materials obtained by the air sorting and removing linear (long) copper wire scraps, a second-stage air sorting process (S4), a color sorting process (S5) for removing circuit board scraps containing valuable metals such as copper using a color sorter from the electronic component scrap 5 after the removal of the linear copper wire scraps, and a metal sorting process (S6) for further removing circuit board scraps containing valuable metals such as copper using a metal sorter from the electronic component scrap 5 after the color sorting process.

[0068] By providing a sieving process (S3) between the first stage wind sorting process (S2) and the second stage wind sorting process (S4), wire scraps contained in the electronic component scraps 5 can be removed. In the sieving process, it is preferable to use a sieving machine having a slit-shaped sieve. In the sieving process (S3), powdery matter can be removed in addition to wire scraps by sieving. The powdery matter and copper wire scraps after sieving can be sent to a smelting process via an incineration pretreatment process, so that valuable metals in the component scraps can be recovered more efficiently. In addition, by performing a color sorting process (S5) after the wind sorting process (S4), the metal content ratio of the processing object sent to the metal sorting process (S6) can be reduced, so that the sorting process in the metal sorting process (S6) can be made higher.

[0069] Furthermore, some of the heavy materials obtained in the second wind sorting step (S4) may be mixed with substrates to be processed in the copper smelting step. Therefore, by further sorting the heavy materials obtained in the second wind sorting step (S4) using magnetic sorting, eddy current sorting, color sorting, manual sorting, robots, etc., the substrates to be processed in the copper smelting step can be separated and sent to the smelting step, thereby increasing the recovery efficiency of valuable metals.

[0070] For example, the heavy objects obtained in the second stage wind sorting process (S4) are sent to the magnetic sorting process (S8) via the pre-sorting process (S7). In the magnetic sorting process (S8), raw materials containing iron are removed from the heavy objects as raw materials not required for the smelting process. After the magnetic sorting process (S8), an eddy current sorting process (S9) is performed, and further, a pre-sorting process (S10) is performed to remove scraps containing aluminum, synthetic resins (plastics), and SUS, and the remaining substrate scraps are sent to the smelting process.

[0071] 1 is introduced into the pre-sorting process (S1, S7) before or after the wind sorting process (S2, S4), or into the pre-sorting process (S10) after the eddy current sorting process (S9), to efficiently and quickly process refining materials containing valuable metals that can be processed in the smelting process, or external materials that do not contain precious metals and contain any of iron, aluminum, stainless steel, and synthetic resin, from the electronic component scraps 5. This makes it possible to sort and process the electronic component scraps 5 more efficiently than in the case of applying manual sorting, and allows mechanical processing of a larger amount of electronic component scraps 5.

[0072] Furthermore, the method for processing electronic component scraps 5 according to this embodiment is not limited to the above pre-sorting steps (S1, S7, S10), and can be used in appropriate combination with each sorting process. For example, it is also preferable to perform image recognition in the image recognition unit 2 before or after processing the electronic component scraps 5 in the various sorting steps (S3 to S6, S8 to S9) as needed, and to perform a sorting process in which targets are extracted using a sorting device equipped with a robot hand 11 shown in Figures 9(a) to 10.

[0073] In particular, in the raw materials sorted as external raw materials in the pre-sorting process (S7), the magnetic sorting process (S8), the eddy current sorting process (S9), and the pre-sorting process (S10), in addition to the single raw materials, there are many mixed scraps in which the smelting raw materials and the external raw materials are mixed. By performing the image recognition process according to this embodiment on this composite scrap and performing a sorting process to extract the target object using a sorting device equipped with a robot hand 11 shown in Figures 9(a) to 10, it is possible to mechanize the work that was previously performed manually, thereby speeding up the sorting process and making it possible to appropriately determine whether the mixed scraps or the individual scraps are from the single scraps in the external raw materials and the mixed scraps containing the smelting raw materials.

[0074] Also, for example, part scraps containing Al in the heavy objects sorted in the wind sorting step (S4) are sorted as Al scraps to the external raw material side by the eddy current sorting step (S9), and this Al scrap includes not only Al single scraps as shown in Figs. 12(a) to 12(c) but also mixed scraps containing Al as shown in Figs. 14(a) to 14(c). Therefore, for the Al scraps sorted in the eddy current sorting step (S9), the characteristics of the part scraps are analyzed using the sorting method according to this embodiment, and further, a sorting process is performed to extract the target objects using a sorting device equipped with a robot hand 11 shown in Figs. 9(a) to 10, so that it is possible to sort the Al single scraps as shown in Figs. 12(a) to 12(c) and the mixed scraps containing Al as shown in Figs. 14(a) to 14(c). Then, the sorted mixed scraps are input to the smelting step as a smelting raw material containing valuables, thereby improving the recovery efficiency of valuables.

[0075] The Fe-containing scraps in the heavy materials sorted in the wind sorting step (S4) are sorted as Fe scraps, which are external raw materials, in the subsequent magnetic sorting step (S8). The Fe scraps sorted here include, in addition to the single iron scraps containing Fe as a single element as shown in Fig. 13(a) and Fig. 13(b), mixed iron scraps such as iron cores with copper coils, iron scraps with boards or iron cores, or boards with lead wires, in which other scrap parts are attached to Fe as shown in Fig. 15(a) to (c). Therefore, the Fe scraps processed in the magnetic sorting step (S8) can be classified by the characteristics of the parts using the processing method according to this embodiment, and a predetermined mixed iron scrap can be extracted from the Fe scraps containing single iron scraps and mixed iron scraps. The mixed iron scraps determined to be mixed iron scraps can be sorted into valuable materials (smelting raw materials) by picking or the like, thereby improving the recovery efficiency of valuable materials.

[0076] (Smelting process) The method for processing scrap electronic components 5 according to the embodiment of the present invention further includes a smelting step for smelting the processing raw material containing valuable metals that have been sorted out in each of the physical sorting steps (S1 to S10).

[0077] When recovering copper as a valuable metal, smelting is performed using a smelting furnace. The smelting process includes, for example, a process of incinerating the electronic component scraps 5, a process of crushing and sieving the incinerated material, and a process of smelting the crushed and sieved material into copper. Depending on the processing capacity of the smelting process, the process of incinerating the electronic component scraps 5 may be omitted.

[0078] In the smelting process, any method can be selected for the crushing and sieving of the electronic component scraps 5 as long as it is a process for forming the electronic component scraps 5 into a size suitable for the smelting process. By performing the physical separation process shown in Fig. 11 before the incineration process and the crushing and sieving process in the smelting process, valuable metals can be recovered more efficiently while efficiently sending raw materials containing any of iron, aluminum, stainless steel, and synthetic resins that act as smelting inhibitors out of the system.

[0079] Although not limited to the following, a copper smelting process using a flash smelting furnace method can be suitably used as the smelting process according to the present embodiment. In the copper smelting process using the flash smelting furnace method, for example, copper concentrate, a solvent, and scrap electronic components 5 are charged from the ceiling of the shaft of a flash smelting furnace. The charged concentrate and scrap electronic components 5 are melted in the shaft of the flash smelting furnace, and separated into matte containing, for example, 50 to 68% copper and slag floating above the matte in the settler of the flash smelting furnace. Valuable metals such as copper, gold, and silver in electronic and electrical equipment components are absorbed into the matte retained in the flash smelting furnace, and the valuable metals can be recovered from the scrap electronic components 5.

[0080] In copper smelting, in order to produce copper and recover as much precious metal as possible, it is important to input and process as much electronic component scrap 5 as possible, which has a high content of valuable metals such as copper, gold, and silver. On the other hand, electronic component scrap 5 contains substances that affect the quality of products and by-products in copper smelting and / or smelting inhibitors that affect the copper smelting process. For example, if the amount of substances containing elements such as Sb and Ni input into the smelting furnace is large, the quality of the electrolytic copper obtained by copper smelting may decrease.

[0081] In non-ferrous metal smelting processes such as copper smelting, sulfuric acid is produced from sulfur dioxide generated by the oxidation of concentrate, but if hydrocarbons are mixed with the sulfuric acid, the sulfuric acid produced may become discolored. Sources of hydrocarbon contamination include synthetic resins such as plastics, and depending on the composition of the electronic component scraps 5 brought to the copper smelter, they may contain a large amount of such synthetic resins. Synthetic resins may cause sudden combustion and smoke leakage in the smelting furnace, as well as equipment deterioration due to localized heating.

[0082] Furthermore, if Al, Fe, etc. are present in the smelting furnace at a certain concentration or higher, it may change the slag composition in the copper smelting process, affecting the loss of valuable metals to the slag, i.e., the so-called slag loss. Also, if halogen elements such as Cl, Br, F, etc. are contained in large amounts in the electronic component scraps 5 fed into the smelting furnace, they may cause corrosion of the exhaust gas treatment equipment for copper smelting and deterioration of the sulfuric acid catalyst. The problem of the inclusion of such smelting inhibitors becomes more apparent as the amount of electronic component scraps 5 processed increases, causing problems such as a burden on the smelting process.

[0083] According to the method for processing scrap electronic components 5 according to the embodiment of the present invention, a physical separation process for scrap electronic components 5 is provided prior to the smelting process, as shown in Fig. 11. This makes it possible to minimize the proportion of smelting inhibitors brought into the smelting process, increase the amount of scrap electronic components 5 that can be processed, and increase the proportion of scrap electronic components 5 that contain copper and valuable metals, thereby enabling efficient recovery of copper and valuable metals.

[0084] Although the present invention has been described by the above embodiment, the description and drawings forming a part of this disclosure should not be understood as limiting the present invention. In other words, the present disclosure is not limited to the above embodiment, and the components can be modified and embodied without departing from the spirit of the present disclosure. [Explanation of symbols]

[0085] 1. Sorting section 2. Image recognition section 3...Transportation section 4. Conveyor section 5. Electronic parts scraps 10. Picking robot 11. Robot hand 12...Fixed part 13...Suction part 14...Holding part 13a…Suction pad 13b…Vacuum generator 14a~14d…Arm section 20...Image analysis methods 21...imaging means 22…Network 23...Area detection unit 24…Sorting system 25…Server 30...Transport surface 51, 52, 53... Inspection area 100...Sorting system 120...Input means 130...Output means 141a~141d...Claw part 200...Control means 201...imaging control means 202...Position shape identification means 203...Characteristic analysis means 204...Classification means 205...Identification information creation means 207...Movement tracking means 210...Storage device 211...Multispectral imaging data storage means 212...Area detection data storage means 213...Color characteristic information storage means 214...Classification information storage means 215...Identification information storage means

Claims

1. a position and shape identifying step of identifying a position and a shape of each electronic component scrap from among a plurality of electronic component scraps having different shapes, and obtaining position and shape identifying information including position information and shape information of each electronic component scrap; a feature analysis step of analyzing the features of each electronic component scrap using features including extracted color information including information on a specific color possessed by each electronic component scrap, and extracted color area information including information on the ratio of the area occupied by the specific color to the total area of ​​each electronic component scrap, to obtain feature analysis information; a classification process for classifying each electronic component scrap into a predetermined component type using characteristics including information on the specific color and the ratio of the area occupied by the specific color associated with one electronic component scrap having the same shape and at the same position based on the position / shape identification information and the characteristic analysis information; Including, the extracted color area information includes two or more threshold values ​​of an area ratio for a specific extracted color; The method for sorting scrap electronic components includes, when the specific extracted color is extracted for scrap electronic components of the same shape and in the same position, sorting the scrap electronic components into items to be sorted and items to be excluded from sorting based on information of two or more threshold values ​​of the area ratio that are preset for the specific extracted color.

2. The position and shape identification step includes:

2. The method for classifying electronic component scraps according to claim 1, further comprising: irradiating area detection light onto the plurality of electronic component scraps within an imaging area to obtain area detection data; and using the area detection data to create position and shape identification information including position information and shape information of each electronic component scrap.

3. 3. The method of claim 1, wherein the step of analyzing the characteristics includes obtaining two or more spectral information for each electronic component scrap using a multispectral imaging means.

4. The method for sorting scrap electronic components according to any one of claims 1 to 3, wherein the plurality of scrap electronic components having different shapes include scrap electronic components after a sorting process is performed using any one of a magnetic sorter, a color sorter, a metal sorter, an optical sorter including an infrared sensor, and a plastic sorter.

5. A method for classifying electronic component scraps as described in any one of claims 1 to 4, wherein the feature analysis step includes using a multispectral imaging means to irradiate a white reference light onto the plurality of electronic component scraps, and correcting positional shifts of other multispectral imaging data due to movement of the electronic component scraps based on the multispectral imaging data irradiated with the reference light.

6. A method for classifying electronic component scraps described in any one of claims 1 to 5, wherein the specific extracted color includes black.

7. A method for classifying electronic component scraps described in any one of claims 1 to 5, wherein the extraction colors used to determine the extraction of the sorting objects in order to classify the electronic component scraps into the sorting objects and the sorting exclusion objects include at least white, green, black, gold, and copper.

8. a position and shape identifying step of identifying a position and a shape of each electronic component scrap from among a plurality of electronic component scraps having different shapes, and obtaining position and shape identifying information including position information and shape information of each electronic component scrap; a feature analysis step of analyzing the features of each electronic component scrap using features including extracted color information including information on a specific color possessed by each electronic component scrap, and extracted color area information including information on the ratio of the area occupied by the specific color to the total area of ​​each electronic component scrap, to obtain feature analysis information; a classification process for classifying each electronic component scrap into a predetermined component type using characteristics including information on the specific color and the ratio of the area occupied by the specific color associated with one electronic component scrap having the same shape and located at the same position based on the position / shape identification information and the characteristic analysis information; an extraction step of extracting electronic component scraps to be extracted from the plurality of electronic component scraps based on a classification result of the classification step and the position and shape identification information; Including, the extracted color area information includes two or more threshold values ​​of an area ratio for a specific extracted color; The method for processing scrap electronic components includes, when the specific extracted color is extracted from scrap electronic components of the same shape and in the same position, classifying the scrap electronic components into items to be sorted and items to be excluded from sorting based on information of two or more threshold values ​​of the area ratio that are preset for the specific extracted color.

Images