JP7634969B2 - Method for classifying and disposing of electronic component scraps - Google Patents

Description

The present invention relates to a method for classifying scrap electronic components and a method for processing scrap electronic components, for example, a method for classifying scrap electronic components and a method for processing scrap electronic components that can be used in the recycling process of used electronic and electrical equipment.

In recent years, from the perspective of resource conservation, it has become increasingly common to recover valuable metals from discarded home appliances and scrap electronic parts such as PCs and mobile phones, and efficient methods for recovering these metals have been studied and proposed.

For example, Japanese Patent Laid-Open Publication No. 9-78151 (Patent Document 1) describes a method for recycling valuable metals that includes a process 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 that remains in the furnace.

JP 2018-123380 A (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.

Japanese Patent Application Publication No. 9-78151 JP 2018-123380 A

Ideally, electronic component scraps are crushed and effectively separated 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, crushed materials are classified and processed on the assumption that they are in a state suitable for recovering valuables. For example, when processing electronic component scraps, one of the objectives may be to properly recover circuit boards that contain a relatively large amount of valuable metals.

However, currently, there exists mixed waste that contains multiple types of scrap parts to be sorted, such as scrap parts containing substrates attached to components such as capacitors and IC chips, and scrap parts containing substrates attached to plastic. In the case of such mixed waste, it is difficult to separate using the conventional sorting methods. 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 to it may be determined to be a capacitor depending on the detection sensitivity of the sorter, and therefore not be recovered as a substrate, resulting in a decrease in the efficiency of substrate recovery. As such, the content ratio of valuable metals contained in electronic component waste varies depending on the raw material, and therefore, currently, efficient sorting of mixed waste cannot be performed using conventional sorting methods that rely on the detection sensitivity of the sorter.

In view of the above problems, the present disclosure provides a method for classifying electronic component scraps and a method for processing electronic component scraps that can appropriately identify mixed scraps containing multiple component types from among the electronic component scraps.

In order to solve the above problem, the inventors conducted extensive research and came to the conclusion 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. The inventors then came to the conclusion that it is effective to use an identification means to identify the position and shape of each scrap electronic component from among multiple 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 two or more characteristics associated with scrap electronic components of the same shape and in the same position.

In one aspect, an embodiment of the present invention, which was completed based on the above findings, is a method for classifying electronic component scraps, which includes a position and shape identification process for 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 process for analyzing at least two or more features of each electronic component scrap and obtaining feature analysis information, and a classification process for classifying each electronic component scrap into a predetermined component type based on the position and shape identification information and the feature analysis information, using two or more features associated with an electronic component scrap of the same shape and in the same position.

In another aspect, an embodiment of the present invention is a method for processing electronic component scraps, which includes a position and shape identification process for identifying the position and shape of each electronic component scrap from among multiple 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 process for analyzing at least two or more features of each electronic component scrap and obtaining feature analysis information associated with the position and shape identification information; a classification process for classifying each electronic component scrap into predetermined component types using two or more features associated with an electronic component scrap of the same shape and located in the same position based on the position and shape identification information and the feature analysis information; and an extraction process for extracting electronic component scraps to be extracted from the multiple electronic component scraps based on the classification result of the classification process and the position and shape identification information.

The present disclosure provides a method for classifying scrap electronic components and a method for processing scrap electronic components that can appropriately identify mixed scrap containing multiple component types from among scrap electronic components.

1 is a schematic diagram illustrating an example of a sorting system according to an embodiment of the present invention. FIG. 2 is a block diagram showing an example of an image analysis means according to the embodiment of the present invention. FIG. 4 is a flow diagram showing an example of an image analysis process according to an embodiment of the present invention. 1 is a photograph showing an example of area detection data. 1 is a photograph showing an example of multispectral imaging data. 5 is a photograph showing an example in which an inspection area is assigned to data obtained by binarizing the area detection data in FIG. 4. 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. 1 is a plan view illustrating an example of a picking device according to an embodiment of the present invention. 1 is a perspective view of a robot hand provided in a picking device according to an embodiment of the present invention. 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. 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. 1 is a photograph showing an example of single scrap (aluminum scrap) of electronic component scrap. 1 is a photograph showing an example of single scrap (Fe scrap) of electronic components. 1 is a photograph showing an example of mixed scraps (Aluminum scraps) of electronic component scraps. 1 is a photograph showing an example of mixed scraps (Fe scraps) of electronic component scraps.

The following describes the embodiments of the present invention with reference to the drawings. Note that the embodiments shown below are merely examples of devices and methods for embodying the technical ideas of the present invention, and the technical ideas of the present invention do not limit the structure, arrangement, etc. of the components to those described below.

(Sorting system)
As shown in FIG. 1 , the sorting system 100 according to an embodiment of the present invention comprises 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.

Electronic component scrap 5 refers to scraps of crushed electronic and electrical equipment such as discarded home appliances, PCs, and mobile phones, which have been collected and then crushed to an appropriate size. The crushing to obtain electronic component scrap 5 may be performed by the processor himself, but crushed items may also be purchased from the market.

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 shape as much as possible. Therefore, this does not include devices that belong to the category of crushers intended for fine crushing. 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 particle size of 10 mm or more and 100 mm or less, more typically 15 mm or more and 50 mm or less.

As the electronic component scraps 5, electronic component scraps 5 that have been sorted using a magnetic sorter, a color sorter, a metal sorter, an optical sorter including an infrared sensor, or a plastic sorter can be suitably used.

In particular, 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, or 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 piece (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.

Examples of single piece scrap include, but are not limited to, aluminum scraps including silver-white heat sinks or housings as shown in FIG. 12(a), aluminum scraps including black heat sinks or housings as shown in FIG. 12(b), cylindrical capacitors that are black or blue as shown in FIG. 12(c), iron scraps including black or gray iron cores as shown in FIG. 13(a), and silver screws and springs as shown in FIG. 13(b).

Mixed scrap includes heat sinks with ICs as shown in Figure 14(a), capacitors with substrates as shown in Figure 14(b), aluminum scraps with copper wires as shown in Figure 14(c), iron cores with copper coils as shown in Figure 15(a), iron scraps or iron cores with substrates as shown in Figure 15(b), substrates with lead wires as shown in Figure 5(c), etc. Individual scraps as shown in Figures 12(a) to 13(b) can be properly sorted to a certain extent by appropriately adjusting the sorting accuracy of the sorting machine, but mixed scraps as shown in Figures 14(a) to 15(c) can be difficult to properly sort because they contain a mixture of smelting raw materials and external raw materials.

The electronic component scraps 5 are transported from the transport source along the transport direction on the transport surface 30, and the 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.

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 the multispectral imaging data captured by the multispectral imaging unit and stores it in the multispectral imaging data storage means 211.

The image recognition unit 2 may include an area detection unit 23 that irradiates the electronic component scraps 5 in the imaging 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 imaging 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 imaging area including the electronic component scraps 5, is obtained by binarizing, thereby making clear the position, number, contour (shape) and area of the electronic component scraps 5 present in the imaging area. 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 item.

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 onto objects in the imaging area, and a detector that detects objects in the imaging area illuminated by the area detection light. Area detection data is stored in the area detection data storage means 212. Note that if the area detection data can be generated by the multispectral illumination unit and multispectral imaging unit provided in the imaging means 21 instead of the area detection unit 23, the area detection unit 23 may be omitted.

The control means 200 includes, for example, an imaging control means 201 for controlling the imaging means 21 and the area detection unit 23, a position and shape identification means 202 for identifying the position and shape of each electronic component scrap 5 from among a plurality of electronic component scraps 5 having different shapes and obtaining position and shape identification information including position information and shape information of each electronic component scrap 5, a feature analysis means 203 for analyzing at least two or more features of each electronic component scrap 5 and obtaining feature analysis information, a classification means 204 for classifying each electronic component scrap 5 into a predetermined component type using two or more features associated with an electronic component scrap 5 of the same shape and located in the same position based on the position and shape identification information and the feature analysis information, an identification information creation means 205 for creating identification information for each electronic component scrap 5, and a movement tracking means 207.

The storage device 210 includes a multispectral imaging data storage means 211 that stores multispectral imaging data for the electronic component scraps 5, an area detection data storage means 212 that stores area detection data, a color characteristic information storage means 213 that stores color characteristic information based on the colors possessed by the electronic component scraps 5 that 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 that creates identification information for each electronic component scrap based on the characteristics of the electronic component scraps 5.

The control means 200 may be connected to a server 25 or another sorting system 24 via a network 22, and may be configured to mutually share the image analysis results of the electronic component scraps 5 analyzed by the image analysis means 20 in FIG. 2.

The image analysis means 20 can proceed with the image analysis process, for example, in accordance with 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 for 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.

In step S101, the multispectral illumination unit of the imaging means 21 irradiates the electronic component scraps 5 in the imaging area with multispectral illumination light of different wavelengths, and the multispectral imaging unit obtains multiple pieces of multispectral imaging data with different illumination colors (see Figures 5(a) to 5(h)). Here, for example, multispectral imaging data of eight colors of illumination light, 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.

In step S102, the position and shape identification means 202 in FIG. 2 detects the position and shape of the 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 the electronic component scraps 5 to be detected. For example, in the example in FIG. 6, there are three lumps 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.

In step S103, the characteristic analysis means 203 sets the first to third inspection areas 51, 52, 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 the first, second, and third inspection areas 51, 52, 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 multiple component types contained in the electronic component scrap 5 that is registered in advance in the color characteristic information storage means 213, and obtains characteristic analysis information.

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 smelting raw materials or external raw materials. The extracted color area information includes information on the area threshold (set value of area ratio) for judging the electronic component scraps 5 as smelting raw materials or external raw materials when the electronic component scraps 5 in the inspection area contain an "extracted color" preset by the user. The feature analysis means 203 can analyze the features of the smelting raw materials or external raw materials 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 materials or external raw materials. The area threshold information for judging the smelting raw materials or external raw materials may be input in advance.

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 that are not suitable for recovery in the smelting process, such as iron, aluminum, or stainless steel (shiny silver), plastics (white, black, brown), and capacitors and heat sinks that do not contain a certain amount or more of valuable metals (silver, white, black).

In order to reduce misrecognition of these refining raw materials and external raw materials as much as possible and to increase recognition accuracy, it is preferable that the extracted color information includes at least the white, green, black, gold, and copper colors of the refining raw materials and 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.

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 extraction 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 a 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 two or more threshold values for the black area ratio 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.

For example, when "refining raw materials" such as circuit board scraps are selected as the objects to be sorted (items to be picked up (extracted) and removed from the transport surface 30), the sorting means 204 classifies whether or not the electronic component scraps 5 in the inspection area satisfy the conditions for "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 for "refining raw materials", the electronic component scraps 5 are classified as "refining raw materials" to be removed in the sorting process described below.

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

In step S105, the identification information creation means 205 creates identification information based on the results of setting the objects to be sorted and the objects to be excluded from sorting. 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, orientation of the major and minor axes 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.

According to an embodiment of the present invention, by providing an imaging means 21 equipped with multispectral lighting 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 preregistered color characteristic information of the smelting raw materials and the external raw materials, 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 when a conventional imaging means using a color camera is provided.

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

According to this embodiment, the imaging means 21 equipped with a multispectral illumination unit can obtain multiple multispectral imaging data with reduced effects of halation. 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.

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 color differences, suppressing the effects of shadows and making the recognition area of the silver color of the metal closer to reality.

In addition, in conventional color cameras, the spectral intensity of black and silver-colored materials such as stainless steel is reduced because the image passes through a color filter, and the recognition area of the silver color is reduced, which can result in a lower recognition rate. According to this embodiment, the imaging means 21 equipped with a multispectral illumination unit makes it possible to recognize black and silver-colored metals such as stainless steel. In addition, 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 range.

Figure 7 shows an example of a comparison result when a conventional color camera is used as the imaging means 21 and when a camera that emits 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 in Figure 1.

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.

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 an 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 an imaging means 21 that irradiates multispectral illumination light is used, it can be recognized as plastic, which is an external raw material.

As shown in FIG. 2, the control means 200 may have a movement tracking means 207. When the conveying unit 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 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 process 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 unit 3 can be operated continuously, thereby improving processing efficiency.

After image analysis processing is performed by the image recognition unit 2, the electronic component scraps 5 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, etc. can be used. In one embodiment, the sorting unit 1 includes a picking robot 10 that transports 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 smelting materials or non-system materials as objects from the electronic component scraps 5. The picking robot 10 picks out the objects based on the identification information created by the image recognition unit 2.

The picking robot 10 is not particularly limited as long as it is an industrial robot that has the function of grasping 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, or a parallel link type. A straight type robot is a simple robot composed of two to three sliding axes. A multi-joint robot can be 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, allowing for three-dimensional movement with a high degree of freedom. A horizontal type robot has all of 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 with a parallel link structure in which the joints are arranged in parallel.

Of the various industrial robots that can be used as picking robots 10, parallel link robots are particularly suitable, as they use a parallel link mechanism to move to the target location in the shortest distance, can move quickly and accurately to the location of the object to be extracted, grasp the material, and send it to the specified location at high speed.

Although not limited to the following aspect, as shown in FIG. 8, the picking robot 10 can pick up an object from the conveying unit 3 toward the 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. The conveying unit 4 can be configured with a conveyor or the like.

In this way, the transport 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 transport direction of the transport unit 3, so that the target object can be accurately removed from the electronic component scraps 5 in a short time and transported.

(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 sucking 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 an 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 conveying surface 30).

There are no particular limitations on the clamping unit 14 as long as it is configured to be able to clamp an object. The clamping unit 14 may, for example, include a first arm 14a, a second arm 14b, a third arm 14c, and a fourth arm 14d. The base ends of the first to fourth arm sections 14a to 14d are each connected to a drive mechanism (not shown) within the fixed unit 12.

As shown in FIG. 9(b), the first arm portion 14a and the second arm portion 14b can open and close in a direction V approaching or a direction W receding from the central axis X of the suction pad 13a by receiving power from the drive mechanism. The third arm portion 14c and the fourth arm portion 14d located toward the back of the page in FIG. 9(b) can also open and close in a direction V approaching or a direction W receding from the central axis X of the suction pad 13a by 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 each open and close at the same timing, thereby clamping or releasing an object sucked into the tip of the suction pad 13a.

The tips of the first to fourth arm sections 14a to 14d are preferably provided with claws 141a to 141d, respectively, that protrude toward the center where the suction pad 13a is located. By providing the first to fourth arm sections 14a to 14d with claws 141a to 141d, respectively, the object can be grasped more accurately while preventing it from falling.

The claws 141a to 141d are preferably shaped to taper toward the suction pad 13a. This allows the claws 141a to 141d to come into contact with the bottom surface of the object, making it easier to scoop up the object.

As shown in FIG. 9(b), the tips (lowest ends) of the claws 141a to 141d are preferably positioned relatively lower than the tip of the suction pad 13a, i.e., 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.

Also, as shown in FIG. 10, the claw portion 141a of the first arm portion 14a and the claw portion 141d of the fourth arm portion 14d may be connected, and the claw portion 141b of the second arm portion 14b and the claw portion 141c of the third arm portion 14c may be connected. This configuration allows for more appropriate gripping of long objects, including circuit boards and the like, in the electronic component scraps 5.

In addition, 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 and closing direction of the first to fourth arm parts 14a to 14d is preferably 5 mm or more when processing the electronic component scraps 5 according to this embodiment, and more preferably 10 mm or more, and even more preferably 15 mm or more. The upper limit depends on the dimensions of the robot hand 11, but can be, for example, 40 mm or less, or 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 and closing direction of the first to fourth arm parts 14a to 14d is preferably 5 mm or more when processing the electronic component scraps 5 according to this embodiment, and more preferably 10 mm or more, and even more preferably 20 mm or more. The upper limit depends on the dimensions of the robot hand 11, but can be, for example, 40 mm or less, or even 30 mm or less.

The robot hand 11 provided in the picking robot 10 according to the embodiment of the present invention includes a suction pad 13a that sucks in an object, and a clamping unit 14 that includes first to fourth arm units 14a to 14d that clamp the object sucked by the suction pad 13a. The object is first sucked in by the suction pad 13a, and then grasped by the first to fourth arm units 14a to 14d (see FIG. 9(c)). This allows heavy objects that are difficult to pick with the suction force of the suction pad 13a to be clamped and transported by the clamping unit 14, making it possible to transport electronic component scraps 5 of various shapes more reliably and appropriately.

In particular, circuit board scraps and the like have ICs and wiring laid on the boards, and are heavy, so if they are grasped only by the suction pad 13a or the first to fourth arm portions 14a to 14d, they may fall during transportation. 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 large quantities of the desired objects.

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 even 10 mm or more. This allows the object to be more appropriately grasped using the first to fourth arm sections 14a to 14d.

(Method of Disposing of Scrap Electronic Components)
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 scrap circuit boards by a metal sorting step (S6) using a metal sorter.

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 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 using a metal sorter (S6), which requires time for processing, it is possible to increase the amount of electronic component scraps 5 processed while removing smelting inhibitors and efficiently recovering valuable metals.

In one embodiment, the method for processing electronic component scraps 5 according to the embodiment of the present invention can include a pre-sorting process (S1) for removing lump copper wire scraps from electronic component scraps 5, an air sorting process (S2) for air sorting the electronic component scraps 5 after the pre-sorting to move powdery 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 from the electronic component scraps 5 after the linear copper wire scraps have been removed using a color sorter, and a metal sorting process (S6) for further removing circuit board scraps containing valuable metals such as copper from the electronic component scraps 5 after the color sorting process using a metal sorter.

By providing a screening 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 screening process, it is preferable to use a screening machine with a slit-shaped sieve. In the screening process (S3), powdery matter can be removed in addition to wire scraps by screening. The powdery matter and copper wire scraps after screening 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.

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

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 outside the smelting process. After the magnetic sorting process (S8), an eddy current sorting process (S9) is carried out, and further, a pre-sorting process (S10) is carried out to remove scraps containing aluminum, synthetic resins (plastics), and SUS, and to send the remaining circuit board scraps to the smelting process.

In the method for processing electronic component scraps 5 according to the embodiment of the present invention, the sorting system 100 shown in FIG. 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), so that smelting raw materials containing valuable metals that can be processed in the smelting process, or external raw materials that do not contain precious metals and contain any of iron, aluminum, stainless steel, and synthetic resin, can be efficiently and quickly processed from the electronic component scraps 5. This makes it possible to sort and process the electronic component scraps 5 more efficiently than when manual sorting is applied, and makes it possible to mechanically process a larger amount of electronic component scraps 5.

The method for processing electronic component scraps 5 according to this embodiment is not limited to the above pre-sorting steps (S1, S7, S10), but 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 the target objects are extracted using a sorting device equipped with a robot hand 11 shown in Figures 9(a) to 10.

In particular, the raw materials selected as external raw materials in the pre-sorting process (S7), magnetic sorting process (S8), eddy current sorting process (S9), and pre-sorting process (S10) contain a large amount of mixed scrap containing smelting raw materials and external raw materials in addition to single raw materials. By performing the image recognition process according to this embodiment on this composite scrap and carrying out 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 scrap is individual scrap or individual scrap from the mixed scrap containing the individual scrap in the external raw materials and the smelting raw materials.

For example, part scraps containing Al in the heavy objects sorted in the wind sorting process (S4) are sorted as Al scraps to the external raw material side in the eddy current sorting process (S9). This Al scrap includes not only single Al 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, by analyzing the characteristics of the part scraps using the sorting method according to this embodiment for the Al scraps sorted in the eddy current sorting process (S9), and further performing a sorting process to extract the target objects using a sorting device equipped with a robot hand 11 shown in Figs. 9(a) to 10, it is possible to sort single Al scraps as shown in Figs. 12(a) to 12(c) and mixed scraps containing Al as shown in Figs. 14(a) to 14(c). Then, by inputting the sorted mixed scraps into the smelting process as a smelting raw material containing valuables, the recovery efficiency of valuables can be improved.

The Fe-containing scrap parts in the heavy objects separated in the wind separation process (S4) are separated as Fe scrap, which is an external raw material, in the subsequent magnetic separation process (S8). The Fe scraps separated here include single iron scraps containing Fe alone as shown in Figures 13(a) and 13(b), as well as mixed iron scraps such as iron cores with copper coils, iron scraps or iron cores with boards, or boards with lead wires, in which other scrap parts are attached to Fe as shown in Figures 15(a) to 15(c). Therefore, by using the processing method according to this embodiment for the Fe scraps processed in the magnetic separation process (S8) and discriminating them by part characteristics, it is possible to extract a predetermined mixed iron scrap from the Fe scraps containing single iron scraps and mixed iron scraps. The scraps determined to be mixed iron scraps can be sorted into valuable materials (smelting raw material side) by picking, etc., thereby improving the recovery efficiency of valuable materials.

(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).

When copper is recovered as a valuable metal, smelting is performed using a smelting furnace. The smelting process includes, for example, a process of incinerating the scrap electronic components 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 scrap electronic components 5 may be omitted.

In the smelting process, the process of crushing and sieving the electronic component scraps 5 can be performed by any method that can mold 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 the following smelting inhibitors, iron, aluminum, stainless steel, and synthetic resin, out of the system.

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 this embodiment. In a copper smelting process using a flash smelting furnace method, for example, copper concentrate, 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 in the settler of the flash smelting furnace into matte containing, for example, 50 to 68% copper and slag floating above the matte. Valuable metals such as copper, gold, and silver in electronic and electrical equipment parts are absorbed into the matte retained in the flash smelting furnace, and the valuable metals can be recovered from the scrap electronic components 5.

In copper smelting, in order to produce copper and recover as many precious metals 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 the products and by-products produced in copper smelting and/or smelting inhibitors that affect the copper smelting process. For example, if a large amount of substances containing elements such as Sb and Ni are input into the smelting furnace, the quality of the electrolytic copper obtained by copper smelting may decrease.

In non-ferrous metal smelting processes such as copper smelting, sulfuric acid is produced from sulfur dioxide generated by the oxidation of concentrates, but if hydrocarbons are mixed into the sulfuric oxide, 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.

Furthermore, if Al, Fe, etc. are present in the smelting furnace at a certain concentration or higher, it can change the slag composition in the copper smelting process, for example, and affect the loss of valuable metals to the slag, or so-called slag loss. Also, if halogen elements such as Cl, Br, F, etc. are present in large amounts in the electronic component scrap 5 fed into the smelting furnace, they can cause corrosion of the copper smelting exhaust gas treatment equipment 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 scrap 5 being processed increases, resulting in problems such as a burden on the smelting process.

According to the method for processing scrap electronic components 5 according to an embodiment of the present invention, a physical separation process for scrap electronic components 5 as shown in FIG. 11 is provided prior to the smelting process. 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 is processed, and increase the proportion of scrap electronic components 5 that contains copper and valuable metals, thereby enabling efficient recovery of copper and valuable metals.

The present invention has been described using the above embodiments, but the descriptions and drawings that form part of this disclosure should not be understood as limiting this invention. In other words, this disclosure is not limited to the above embodiments, and the components can be modified and embodied without departing from the spirit of the disclosure.

1...Sorting section 2...Image recognition section 3...Transport section 4...Transport section 5...Electronic component scraps 10...Picking robot 11...Robot hand 12...Fixing section 13...Suction section 14...Clamping section 13a...Suction pad 13b...Vacuum generators 14a to 14d...Arm section 20...Image analysis means 21...Imaging means 22...Network 23...Area detection section 24...Sorting system 25...Server 30...Transport surfaces 51, 52, 53...Inspection area 100...Sorting system 1 20...input means 130...output means 141a to 141d...claw portion 200...control means 201...imaging control means 202...position and 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 (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 associated with an electronic component scrap having the same shape and being in the same position, based on the position/shape identification information and the characteristic analysis information, and the specific color and the ratio of the area occupied by the specific color ,
the extracted color area information includes two or more thresholds of an area ratio for a specific extracted color;
The method for sorting electronic component scraps includes, when the specific extracted color is extracted for electronic component scraps of the same shape and in the same position, sorting the electronic component scraps 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 . 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. The method for classifying electronic component scraps according to claim 1 or 2, wherein the characteristic analysis step includes obtaining two or more pieces of spectral information for each electronic component scrap using a multispectral imaging means. 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. The method for sorting electronic component scraps according to any one of claims 1 to 4, wherein the feature analysis step includes using a multispectral imaging means to irradiate the plurality of electronic component scraps with a reference light of white light, and correcting positional deviations of other multispectral imaging data due to movement of the electronic component scraps based on the multispectral imaging data irradiated with the reference light. The method for sorting electronic component scraps according to any one of claims 1 to 5, wherein the specific extracted colour includes black. The method for sorting scrap electronic components according to any one of claims 1 to 5, wherein extraction colors used for extracting and judging the objects to be sorted for sorting the scrap electronic components into the objects to be sorted and the objects not to be sorted include at least white, green, black, gold, and copper. 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 features 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 feature 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 ,
the extracted color area information includes two or more thresholds 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 .