WO2024121970A1 - Quality evaluation device and quality evaluation method - Google Patents

Quality evaluation device and quality evaluation method Download PDF

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Publication number
WO2024121970A1
WO2024121970A1 PCT/JP2022/045088 JP2022045088W WO2024121970A1 WO 2024121970 A1 WO2024121970 A1 WO 2024121970A1 JP 2022045088 W JP2022045088 W JP 2022045088W WO 2024121970 A1 WO2024121970 A1 WO 2024121970A1
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WO
WIPO (PCT)
Prior art keywords
substrate
image
component
machine
teacher
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PCT/JP2022/045088
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French (fr)
Japanese (ja)
Inventor
健二 杉山
雅史 天野
博史 大池
壮太 横山
Original Assignee
株式会社Fuji
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Application filed by 株式会社Fuji filed Critical 株式会社Fuji
Priority to PCT/JP2022/045088 priority Critical patent/WO2024121970A1/en
Publication of WO2024121970A1 publication Critical patent/WO2024121970A1/en

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  • This specification discloses technology relating to a quality determination device and a quality determination method.
  • a CPU Central Processing Unit
  • the CPU temporarily saves the image after the component is picked up and the image while the head is moving as normal images in the HDD (hard disk) of the management server.
  • the image after the component is picked up and the image while the head is moving are then reclassified from normal images to abnormal images.
  • the image finally classified as a normal image is used when editing component-related data (such as component shape data).
  • a machine learning learning model will be generated using teacher images of objects to be placed on a board by a board-related processing machine, and the quality of the board-related processing performed by the board-related processing machine will be judged.
  • the learning model is used as is, there is a possibility that the quality of the board-related processing will be erroneously judged.
  • this specification discloses a quality determination device and a quality determination method that can reduce erroneous determinations of substrate-related work using a learning model.
  • This specification discloses a quality determination device that includes an acquisition unit and a learning unit.
  • the acquisition unit acquires, in a production environment in which the product boards are produced, teacher images to be used for machine learning, which are multiple images of objects placed on the board by a substrate-related operation machine that performs a specified substrate-related operation on the board to produce a product board.
  • the learning unit uses the teacher images acquired by the acquisition unit to re-learn a quality determination model that is a learning model generated using the teacher images acquired in an environment different from the production environment and that determines the quality of the substrate-related operation performed by the substrate-related operation machine.
  • This specification also discloses a quality determination method including an acquisition step and a learning step.
  • the acquisition step acquires, in a production environment in which the product substrate is produced, a plurality of images of an object placed on the substrate by a substrate-related operation machine that performs a predetermined substrate-related operation on the substrate to produce a product substrate, and teacher images to be used for machine learning.
  • the learning step uses the teacher images acquired in the acquisition step to re-learn a quality determination model that is a learning model generated using the teacher images acquired in an environment different from the production environment and that determines the quality of the substrate-related operation performed by the substrate-related operation machine.
  • the quality determination device described above allows the quality determination model to be re-learned using teacher images acquired in the production environment where the product boards are produced. This reduces erroneous determinations of work on boards compared to when the quality determination model is used as is. What has been described above about the quality determination device also applies to the quality determination method.
  • FIG. 1 is a configuration diagram showing an example of a production line.
  • FIG. 2 is a plan view showing a configuration example of a component mounting machine.
  • FIG. 2 is a perspective view showing an example of a bulk feeder.
  • FIG. 4 is a plan view seen in the direction of arrow IV in FIG. 3 .
  • FIG. 11 is a plan view showing an example of a cavity unit to which components are supplied.
  • FIG. 6 is a schematic diagram showing an example of a state in which components are accommodated in the three cavities in FIG. 5 .
  • FIG. 13 is a flowchart illustrating an example of a control procedure performed by an acquisition unit.
  • FIG. 13 is a schematic diagram showing an example of a pre-mounting image.
  • FIG. 13 is a schematic diagram showing an example of an image after mounting.
  • FIG. 13 is a schematic diagram showing an example of relearning of a pass/fail determination model.
  • Embodiment 1-1 Configuration example of production line WL0
  • the quality determination device 80 can be applied to various production lines WL0 that produce product substrates 900.
  • a substrate-related operation machine WM0 performs a predetermined substrate-related operation on a substrate 90 to produce the product substrate 900.
  • the type and number of substrate-related operation machines WM0 are not limited.
  • the production line WL0 of the embodiment includes a plurality (five) of substrate-related operation machines WM0, including a printer WM1, a print inspection machine WM2, a component mounting machine WM3, a reflow oven WM4, and a visual inspection machine WM5, and the substrate 90 is transported by a substrate transport device in the above order.
  • the printer WM1 prints solder at the mounting position of the component 91 on the board 90.
  • the print inspection machine WM2 inspects the printing condition of the solder printed by the printer WM1.
  • the component mounting machine WM3 mounts multiple components 91 on the board 90 on which the solder has been printed by the printer WM1. There may be one component mounting machine WM3 or multiple component mounting machines WM3. When multiple component mounting machines WM3 are provided, multiple components 91 can be mounted by sharing the load between the multiple component mounting machines WM3.
  • the reflow furnace WM4 heats the board 90 on which components 91 have been mounted by the component mounting machine WM3, melting the solder and performing soldering.
  • the appearance inspection machine WM5 inspects the mounting state of the components 91 mounted by the component mounting machine WM3.
  • the production line WL0 can use multiple (five) substrate-related work machines WM0 to transport the boards 90 in sequence and perform production processes including inspection processes to produce the product boards 900.
  • the production line WL0 can also be equipped with substrate-related work machines WM0 such as functional inspection machines, buffer devices, substrate supply devices, substrate reversing devices, shield mounting devices, adhesive application devices, and ultraviolet ray irradiation devices as necessary.
  • the multiple (five) substrate-related operation machines WM0 and the management device HC0 are connected to each other so that they can communicate with each other via a wired or wireless communication unit.
  • the multiple (five) substrate-related operation machines WM0 and the management device HC0 form an on-site information and communication network (LAN: Local Area Network). This allows the multiple (five) substrate-related operation machines WM0 to communicate with each other via the communication unit.
  • the multiple (five) substrate-related operation machines WM0 can also communicate with the management device HC0 via the communication unit.
  • the management device HC0 controls the multiple (five) substrate-related work machines WM0 that make up the production line WL0, and monitors the operating status of the production line WL0.
  • the management device HC0 stores various control data for controlling the multiple (five) substrate-related work machines WM0.
  • the management device HC0 transmits control data to each of the multiple (five) substrate-related work machines WM0.
  • each of the multiple (five) substrate-related work machines WM0 transmits its operating status and production status to the management device HC0.
  • the management device HC0 is provided with a storage device 80s.
  • the storage device 80s can store, for example, acquired data acquired by the substrate-related operation machine WM0 in relation to substrate-related operations. For example, image data of an image captured by the substrate-related operation machine WM0 is included in the acquired data.
  • the teacher image 70 described below is included in the acquired data. Records (log data) of the operating status acquired by the substrate-related operation machine WM0 are included in the acquired data.
  • the component mounting machine WM3 mounts a plurality of components 91 on a board 90. As shown in Fig. 2, the component mounting machine WM3 includes a board transport device 11, a component supply device 12, a component transfer device 13, a component camera 14, a board camera 15, and a control device 20.
  • the board transport device 11 is, for example, a belt conveyor, and transports the board 90 in the transport direction (X-axis direction).
  • the board 90 is a circuit board on which electronic circuits, electric circuits, magnetic circuits, etc. are formed.
  • the board transport device 11 transports the board 90 into the component mounting machine WM3 and positions the board 90 at a predetermined position within the machine. After the component mounting machine WM3 has completed the mounting process of multiple components 91, the board transport device 11 transports the board 90 out of the component mounting machine WM3.
  • the component supply device 12 supplies a plurality of components 91 to be mounted on the board 90.
  • the component supply device 12 includes, for example, a plurality of feeders 12b arranged along the transport direction (X-axis direction) of the board 90. Each of the plurality of feeders 12b is detachably attached to the slot 12a.
  • a tape feeder can be used for the feeder 12b. The tape feeder pitch-feeds a carrier tape containing the plurality of components 91, and supplies the components 91 at the supply position so that they can be picked up.
  • the component camera 14 and the board camera 15 capture images based on control signals sent from the control device 20.
  • Image data of the images captured by the component camera 14 and the board camera 15 is sent to the control device 20.
  • the control device 20 is equipped with a known arithmetic device and storage device, and constitutes a control circuit. Information and image data output from various sensors provided in the component mounting machine WM3 are input to the control device 20.
  • the control device 20 sends control signals to each device based on a control program and predetermined mounting conditions that have been set in advance.
  • the control device 20 causes the board camera 15 to capture an image of the board 90 positioned by the board transport device 11.
  • the control device 20 processes the image captured by the board camera 15 to recognize the positioning state of the board 90.
  • the control device 20 also causes the part camera 14 to capture an image of the part 91 supplied by the part supply device 12.
  • the control device 20 causes the holding member 13d to pick up and hold the part 91 supplied by the part supply device 12, and causes the part camera 14 to capture an image of the part 91 held by the holding member 13d.
  • the control device 20 processes the image captured by the part camera 14 to recognize the supply state of the part 91 and the holding posture of the part 91.
  • the control device 20 moves the holding member 13d toward above the intended mounting position that is set in advance by a control program or the like.
  • the control device 20 also corrects the intended mounting position based on the positioning state of the board 90, the holding posture of the component 91, and the like, and sets the mounting position where the component 91 will actually be mounted.
  • the intended mounting position and mounting position include a rotation angle in addition to the position (X-axis coordinate and Y-axis coordinate).
  • the control device 20 corrects the target position (X-axis coordinates and Y-axis coordinates) and rotation angle of the holding member 13d to match the mounting position.
  • the control device 20 lowers the holding member 13d at the corrected rotation angle in the corrected target position to mount the component 91 on the board 90.
  • the control device 20 repeats the above pick-and-place cycle to perform the mounting process of mounting multiple components 91 on the board 90.
  • the bulk feeder 30 supplies supply components 91s, which are a plurality of components 91 discharged from a component case 32, to the component mounting machine WM3.
  • the bulk feeder 30 of the embodiment includes a feeder main body 31, a component case 32, a discharge device 33, a cover 34, a track member 40, a cavity unit 50, a vibration device 60, and a feeder control device 30c.
  • the feeder main body 31 is formed in a flat box shape.
  • the feeder main body 31 is detachably mounted in a slot 12a of the component supply device 12.
  • a component case 32 that stores multiple components 91 in bulk is removably attached to the feeder body 31.
  • the component case 32 can discharge the multiple components 91 stored therein.
  • the component case 32 is an external device of the bulk feeder 30. For example, an operator selects a component case 32 that stores a component 91 to be used in the mounting process from among the multiple component cases 32, and attaches the selected component case 32 to the feeder body 31.
  • the ejection device 33 adjusts the number of parts 91 ejected from the part case 32.
  • the ejection device 33 ejects the supply parts 91s into the receiving area Ar0 of the track member 40 shown in FIG. 4.
  • the supply parts 91s are part of the multiple parts 91 that are ejected from the part case 32 and supplied to the part mounting machine WM3.
  • the cover 34 is removably attached to the upper tip side of the supply parts 91s in the transport direction. The cover 34 prevents the supply parts 91s transported along the transport path Rd0 of the track member 40 shown in FIG. 4 from scattering to the outside.
  • the track member 40 has a transport path Rd0 along which supply parts 91s, which are multiple parts 91 discharged from the part case 32, are transported.
  • the track member 40 is provided at the upper end of the transport direction of the supply parts 91s.
  • the track member 40 is formed so as to extend in the transport direction of the supply parts 91s (left-right direction on the paper in FIG. 4).
  • a pair of side walls 41, 41 that protrude upward are formed on both edges of the width direction of the transport path Rd0 (top-bottom direction on the paper in FIG. 4).
  • the pair of side walls 41, 41, together with the tip 42 of the track member 40 surround the periphery of the transport path Rd0 and prevent leakage of the supply parts 91s transported along the transport path Rd0.
  • the track member 40 has a receiving area Ar0, a supply area As0, and a transport path Rd0.
  • the receiving area Ar0 is an area that receives supply parts 91s in bulk. In this embodiment, the receiving area Ar0 is provided below the discharge outlet of the part case 32.
  • the supply area As0 is an area where the part mounting machine WM3 can pick up the supply parts 91s. Specifically, the supply area As0 is an area where the supply parts 91s can be picked up by the holding member 13d supported by the mounting head 13c, and is included in the movable range of the mounting head 13c.
  • the cavity unit 50 has a plurality of cavities 51 (120 in this embodiment) in the supply area As0, each of which is to accommodate one of the supply parts 91s transported to the supply area As0.
  • each of the plurality (120) cavities 51 is intended to accommodate one part 91.
  • the plurality (120) cavities 51 are arranged in a matrix in the supply area As0.
  • the cavity unit 50 has a total of 120 cavities 51, with 10 arranged in the transport direction of the supply parts 91s and 12 arranged in the width direction of the transport path Rd0.
  • the track member 40 is provided so as to be vibrated relative to the feeder main body 31.
  • the vibration device 60 vibrates the track member 40 to transport the supply parts 91s on the transport path Rd0 to the supply area As0 from which the component mounting machine WM3 can pick them up.
  • the vibration device 60 causes the track member 40 to perform an elliptical motion clockwise or counterclockwise in a horizontal direction perpendicular to the transport direction of the supply parts 91s.
  • the vibration device 60 vibrates the track member 40 so that an external force is applied to the supply parts 91s on the transport path Rd0 from the tip side in the supply direction (the right side of the paper in FIG. 4) and directed upward, or from the base side in the supply direction (the left side of the paper in FIG. 4) and directed upward.
  • Whether the component mounting machine WM3 can pick up the component 91 supplied to the cavity unit 50 can be determined using machine learning. Specifically, as shown in the left diagram of FIG. 6, a machine learning learning model is generated using a teacher image 70 in which a component 91 housed in the cavity 51 in the correct orientation is captured, and the quality of the picking operation of the component 91 by the component mounting machine WM3 is determined. However, since there is a difference between the environment in which the teacher image 70 was acquired (e.g., the development environment) and the production environment in which the product board 900 is produced, if the learning model is used as is, there is a possibility that the quality of the picking operation will be erroneously determined.
  • the environment in which the teacher image 70 was acquired e.g., the development environment
  • the production environment in which the product board 900 is produced, if the learning model is used as is, there is a possibility that the quality of the picking operation will be erroneously determined.
  • the production line WL0 is provided with a quality determination device 80.
  • the quality determination device 80 re-learns the quality determination model 80m using teacher images 70 acquired in the production environment where the product boards 900 are produced. Therefore, erroneous determinations in the collection work are reduced compared to when the quality determination model 80m is used as is.
  • the quality determination device 80 when considered as a control block, includes an acquisition unit 81 and a learning unit 82.
  • the quality determination device 80 can also include a memory unit 83.
  • the quality determination device 80 of the embodiment includes an acquisition unit 81, a learning unit 82, and a storage unit 83. At least one of the acquisition unit 81, the learning unit 82, and the storage unit 83 can be provided in various control devices, management devices, etc. At least one of the acquisition unit 81, the learning unit 82, and the storage unit 83 can also be formed on the cloud. In the embodiment, the acquisition unit 81, the learning unit 82, and the storage unit 83 are all provided in the management device HC0.
  • the acquisition unit 81 acquires teacher images 70 in a production environment in which the product substrate 900 is produced (step S11 shown in FIG. 8).
  • the teacher images 70 refer to images used for machine learning, which are a plurality of images capturing an object 91t provided on the substrate 90 by a substrate-related operation machine WM0 that performs a predetermined substrate-related operation on the substrate 90 to produce the product substrate 900.
  • the control device 20 of the component mounting machine WM3 can make the above-mentioned correction during mounting and mount the component 91.
  • the control device 20 judges the actual work on the board (the work of mounting the component 91) to be defective.
  • the mounting of the component 91 is performed by the control device 20 of the component mounting machine WM3 (step S11c shown in FIG. 9).
  • the post-mounting image 70b2 can also be acquired by the visual inspection machine WM5.
  • the visual inspection machine WM5 can judge the quality of the actual work on the board (the work of mounting the component 91) in the same way as the component mounting machine WM3.
  • the component mounting machine WM3 of the embodiment includes a bulk feeder 30.
  • the bulk feeder 30 includes a track member 40 and a vibration device 60.
  • the track member 40 includes a transport path Rd0 along which a supply part 91s, which is a part 91 discharged from a part case 32 that contains the parts 91 in bulk, is transported to a supply area As0 where the supply part 91s can be picked up by the component mounting machine WM3.
  • the vibration device 60 vibrates the track member 40 to transport the supply part 91s to the supply area As0.
  • the pre-picking image 70a is an image of the supply component 91s transported to the supply area As0 of the bulk feeder 30.
  • the pre-picking image 70a shown in the left diagram of FIG. 6 can be acquired, for example, by the board camera 15 of the component mounting machine WM3 (step S11a shown in FIG. 9).
  • the component 91 is accommodated in the cavity 51 in the correct orientation, and the actual substrate work (the supply work of the component 91) is good.
  • the acquisition unit 81 acquires the pre-picking image 70a as the teacher image 70 if the actual substrate work when acquiring the pre-picking image 70a, the pre-mounting image 70b1, and the post-mounting image 70b2 are all good.
  • the control device 20 may erroneously recognize that one component 91 is housed in one cavity 51, and erroneously determine that the actual substrate work (the supply work of the components 91) is good.
  • the holding member 13d it is difficult for the holding member 13d to pick up the component 91 housed in the cavity 51, and it is highly likely that the actual substrate work when the before-mounting image 70b1 and the after-mounting image 70b2 are acquired will be judged to be poor.
  • the image shown in the right diagram in FIG. 6 is not adopted as the teacher image 70, and erroneous acquisition of the teacher image 70 is suppressed.
  • the bulk feeder 30 of the embodiment also includes a cavity unit 50.
  • the pick-up position of the part 91 by the holding member 13d is fixed to the position of the cavity 51.
  • the bulk feeder 30 may also omit the cavity unit 50.
  • the pick-up position of the part 91 by the holding member 13d is any position in the supply area As0, and the control device 20 needs to recognize the position and rotation angle of the part 91.
  • the pass/fail judgment device 80 can be applied to either form.
  • the storage unit 83 stores the teacher image 70 acquired by the acquisition unit 81 in the storage device 80s (step S12 shown in FIG. 8). This allows the storage device 80s to store and accumulate the teacher images 70 acquired in the production environment in which the product boards 900 are produced.
  • the storage unit 83 may take various forms as long as it is capable of storing the teacher images 70 in the storage device 80s. For example, the storage unit 83 may store the teacher images 70 in the storage device 80s sequentially each time the acquisition unit 81 acquires a teacher image 70. Furthermore, when a predetermined number of teacher images 70 are acquired by the acquisition unit 81, the storage unit 83 may store the predetermined number of teacher images 70 collectively in the storage device 80s.
  • the storage device 80s is required to be capable of storing at least the teacher image 70, and a known storage device, database, etc. may be used. Furthermore, at least when the object 91t is different, the applicable teacher image 70 is different. For example, when the object 91t is a component 91, when the component type is different, the applicable teacher image 70 is different. Therefore, the storage device 80s can store identification information for identifying the object 91t in association with the teacher image 70. Furthermore, for example, when the substrate-related work machine WM0 is different due to individual differences in the substrate-related work machine WM0, the appropriate teacher image 70 may also be different. Differences in the substrate-related work machine WM0 include differences in the devices and equipment (for example, the component supply device 12 of the component mounting machine WM3) that the substrate-related work machine WM0 is equipped with.
  • the appropriate teacher image 70 may differ if the imaging device 80c is different. For example, if the lighting device of the imaging device 80c is different, the appropriate teacher image 70 may differ. Also, if the imaging conditions when the imaging device 80c captures the teacher image 70 are different, the appropriate teacher image 70 may differ. For example, if at least one of the lighting direction, exposure time, and aperture of the imaging device 80c is different, the appropriate teacher image 70 may differ.
  • the storage device 80s may therefore store, in association with the teacher image 70, identification information identifying the target object 91t, identification information identifying at least one of the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, and the imaging conditions under which the imaging device 80c acquired the teacher image 70. This allows the storage device 80s to store the teacher image 70 that matches the production environment.
  • the component camera 14, board camera 15, and imaging devices of the component mounting machine WM3 and the visual inspection machine WM5 described above are included in the imaging device 80c.
  • a manufacturer of the substrate-related operation machine WM0 may prepare teacher images 70 in advance, generate a machine learning learning model, and distribute the learning model to a user of the substrate-related operation machine WM0. This allows the user of the substrate-related operation machine WM0 to omit the work of acquiring teacher images 70 and generating a learning model.
  • the target object 91t is a component 91
  • the external dimensions of the component 91 may differ slightly if the vendor is different. This may result in a different teacher image 70, and the learning model generated by the manufacturer may be an inappropriate learning model.
  • the environment in which the manufacturer acquired the teacher image 70 e.g., the development environment
  • the production environment in which the user produces the product board 900 e.g., the production environment in which the user produces the product board 900
  • the learning unit 82 uses the teacher images 70 acquired by the acquisition unit 81 to re-learn the pass/fail judgment model 80m, which is a learning model generated using the teacher images 70 acquired in an environment different from the production environment and judges whether the substrate-related operation performed by the substrate-related operation machine WM0 is pass/fail.
  • the manufacturer of the substrate-related operation machine WM0 acquires the teacher images 70 in a development environment and generates the pass/fail judgment model 80m.
  • the pass/fail judgment model 80m only needs to be able to judge whether the work on the substrate is pass/fail, and a publicly known learning model can be used.
  • the pass/fail judgment model 80m can be generated in accordance with various machine learning algorithms such as a support vector machine and a neural network.
  • the acquisition unit 81 acquires the teacher image 70 in the production environment in which the product substrate 900 is produced.
  • the learning unit 82 uses the teacher image 70 acquired by the acquisition unit 81 to re-learn the pass/fail judgment model 80m.
  • the learning unit 82 can re-learn the pass/fail judgment model 80m at any timing.
  • the learning unit 82 can also re-learn the pass/fail judgment model 80m at a predetermined timing (if Yes in step S13 and step S14 shown in FIG. 8). For example, immediately after the substrate-related operation machine WM0 is introduced, the number of teacher images 70 acquired in the production environment is small, and re-learning of the pass/fail judgment model 80m may not be performed appropriately.
  • the learning unit 82 can therefore re-learn the pass/fail determination model 80m when a predetermined time has passed since the substrate-related operation machine WM0 was introduced and production of the product substrate 900 was started. This allows the learning unit 82 to re-learn the pass/fail determination model 80m using the teacher images 70 acquired during the production of the product substrate 900 for the predetermined time, at the timing when a predetermined time has passed since the substrate-related operation machine WM0 was introduced and production of the product substrate 900 was started.
  • the predetermined time can be set to any time, and can be set to a time at which re-learning of the pass/fail determination model 80m can be appropriately executed.
  • the learning unit 82 can re-learn the pass/fail judgment model 80m using only the teacher images 70 acquired by the acquisition unit 81.
  • the learning unit 82 can also re-learn the pass/fail judgment model 80m using a combination of teacher images 70 acquired by the acquisition unit 81 in the production environment and teacher images 70 acquired in an environment different from the production environment (e.g., a development environment).
  • the learning unit 82 can also re-learn the pass/fail judgment model 80m by increasing the proportion of teacher images 70 acquired by the acquisition unit 81 as the above-mentioned specified time becomes longer.
  • the learning unit 82 can re-learn the pass/fail judgment model 80m when the erroneous judgment rate, where the judgment result of judging the pass/fail of substrate-related work using the pass/fail judgment model 80m differs from the actual pass/fail of the substrate-related work, exceeds an allowable value.
  • the learning unit 82 can re-learn the pass/fail judgment model 80m at the timing when the erroneous judgment rate exceeds the allowable value.
  • the erroneous judgment rate refers to the ratio of the number of times that an erroneous judgment was made to the number of times that the pass/fail of substrate-related work was judged.
  • the learning unit 82 can also re-learn the pass/fail judgment model 80m when a predetermined time has elapsed since the substrate-related processing machine WM0 was introduced and production of the product substrates 900 began, and the above-mentioned erroneous judgment rate exceeds an allowable value. Furthermore, if the predetermined timing has not yet arrived (No in step S13 shown in FIG. 8), the acquisition unit 81 continues to acquire the teacher image 70, and the memory unit 83 continues to store the teacher image 70, until the predetermined timing described above arrives.
  • the pass/fail judgment model 80m can be newly provided when the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, the imaging conditions when the imaging device 80c acquired the teacher image 70, and at least the object 91t among the objects 91t are different. This allows the pass/fail judgment model 80m to be provided which is more suited to the production environment.
  • the storage device 80s may store identification information for identifying the target object 91t, identification information for identifying at least one of the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, and the imaging conditions under which the imaging device 80c acquired the teacher image 70, in association with the teacher image 70. This allows the storage device 80s to store the necessary information in accordance with the pass/fail judgment model 80m.
  • the component supplying device 12 includes a bulk feeder 30.
  • the component supplying device 12 may also include a tape feeder.
  • the component supplying device 12 may also supply electronic components (e.g., lead components) that are relatively large compared to chip components and the like, in a state in which they are arranged on a tray.
  • the acquiring unit 81 can acquire the teacher image 70 in the same manner as the bulk feeder 30.
  • the substrate-related operation machine WM0 is described taking the component mounting machine WM3 as an example.
  • the substrate-related operation machine WM0 is not limited to the component mounting machine WM3.
  • the substrate-related work machine WM0 may be a printer WM1 that prints solder, which is the target object 91t, on the substrate 90.
  • the teacher image 70 is an image of the solder printed on the substrate 90.
  • the learning unit 82 can use the teacher image 70 acquired by the acquisition unit 81 to re-learn the pass/fail judgment model 80m that judges whether the substrate-related work (solder printing work) performed by the printer WM1 is pass/fail.
  • the matters described in this specification can be selected and applied as appropriate.
  • the matters described in this specification can be combined as appropriate.
  • the pass/fail determination method includes an acquisition process and a learning process.
  • the acquisition process corresponds to the control performed by the acquisition unit 81.
  • the learning process corresponds to the control performed by the learning unit 82.
  • the pass/fail determination method may also include a storage process.
  • the storage process corresponds to the control performed by the storage unit 83.
  • the quality determination model 80m can be re-learned using the teacher image 70 acquired in the production environment where the product substrate 900 is produced. Therefore, erroneous determination of the substrate-related work is reduced compared to the case where the quality determination model 80m is used as is.
  • the above description of the quality determination device 80 also applies to the quality determination method.
  • 13d holding member, 30: bulk feeder, 32: parts case, 40: track member, 60: vibration device, 70: teacher image, 70a: pre-collection image, 70b: post-collection image, 70b1: pre-attachment image, 70b2: post-attachment image, 80: quality determination device, 80c: imaging device, 80m: quality determination model, 80s: storage device, 81: acquisition unit, 82: learning unit, 83: storage unit, 90: board, 91: component, 91s: supply component, 91t: object, 900: product substrate, As0: supply area, Rd0: transport path, WM0: Board-related work machine, WM3: Component placement machine.

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Abstract

A quality evaluation device comprises an acquisition unit and a learning unit. The acquisition unit acquires, in an environment for producing product substrates, training images that are a plurality of images in which is captured a subject provided on a substrate by a substrate working machine that performs predetermined substrate work on substrates to produce the product substrates, the training images being used in machine learning. The training unit uses the training images acquired by the acquisition unit and performs retraining of a quality evaluation model that is a trained model generated by using training images acquired in an environment different from the production environment, the quality evaluation model evaluating the quality of the substrate work performed by the substrate working machine.

Description

良否判定装置および良否判定方法Apparatus and method for determining quality
　本明細書は、良否判定装置および良否判定方法に関する技術を開示する。 This specification discloses technology relating to a quality determination device and a quality determination method.
　特許文献１に記載の画像保存処理では、ＣＰＵ（中央演算処理装置）は、部品実装モジュールから送信された画像を、部品の識別情報および判定結果と共に受信する。そして、ＣＰＵは、画像に付されている判定結果が正常の場合に、受信した画像を正常画像として保存する。また、ＣＰＵは、画像に付されている判定結果が異常の場合に、受信した画像を異常画像として保存する。さらに、ＣＰＵは、判定結果が異常の部品の識別情報に対応付けられている正常画像を異常画像に再分類する。 In the image saving process described in Patent Document 1, a CPU (Central Processing Unit) receives an image sent from a component mounting module along with the component identification information and the judgment result. Then, if the judgment result attached to the image is normal, the CPU saves the received image as a normal image. Also, if the judgment result attached to the image is abnormal, the CPU saves the received image as an abnormal image. Furthermore, the CPU reclassifies normal images associated with the identification information of components with abnormal judgment results as abnormal images.
　具体的には、部品吸着後の画像とヘッド移動中の画像が正常であり、部品装着後の画像が異常の場合に、ＣＰＵは、一旦、部品吸着後の画像とヘッド移動中の画像を正常画像として管理サーバのＨＤＤ（ハードディスク）に保存する。そして、部品吸着後の画像とヘッド移動中の画像は、正常画像から異常画像に再分類される。最終的に正常画像として分類された画像は、部品関連データ（例えば、部品のシェイプデータなど）を編集する際に用いられる。 Specifically, if the image after the component is picked up and the image while the head is moving are normal, but the image after the component is mounted is abnormal, the CPU temporarily saves the image after the component is picked up and the image while the head is moving as normal images in the HDD (hard disk) of the management server. The image after the component is picked up and the image while the head is moving are then reclassified from normal images to abnormal images. The image finally classified as a normal image is used when editing component-related data (such as component shape data).
国際公開第２０２０／２６１４８９号International Publication No. 2020/261489
　対基板作業機によって基板に設けられる対象物が撮像されている教師画像を使用して機械学習の学習モデルを生成して、対基板作業機による対基板作業の良否を判定する形態が想定される。しかしながら、教師画像を取得した環境と、製品基板を生産する生産環境には差異があるため、学習モデルをそのまま使用すると、対基板作業の良否を誤判定する可能性がある。 It is envisioned that a machine learning learning model will be generated using teacher images of objects to be placed on a board by a board-related processing machine, and the quality of the board-related processing performed by the board-related processing machine will be judged. However, because there is a difference between the environment in which the teacher images were acquired and the production environment in which the product boards are produced, if the learning model is used as is, there is a possibility that the quality of the board-related processing will be erroneously judged.
　このような事情に鑑みて、本明細書は、学習モデルを使用した対基板作業の誤判定を低減可能な良否判定装置および良否判定方法を開示する。 In light of these circumstances, this specification discloses a quality determination device and a quality determination method that can reduce erroneous determinations of substrate-related work using a learning model.
　本明細書は、取得部と、学習部とを備える良否判定装置を開示する。前記取得部は、基板に所定の対基板作業を行って製品基板を生産する対基板作業機によって前記基板に設けられる対象物が撮像されている複数の画像であって機械学習に使用される教師画像を、前記製品基板を生産する生産環境において取得する。前記学習部は、前記生産環境と異なる環境において取得された前記教師画像を使用して生成された学習モデルであって前記対基板作業機による前記対基板作業の良否を判定する良否判定モデルの再学習を、前記取得部によって取得された前記教師画像を使用して行う。 This specification discloses a quality determination device that includes an acquisition unit and a learning unit. The acquisition unit acquires, in a production environment in which the product boards are produced, teacher images to be used for machine learning, which are multiple images of objects placed on the board by a substrate-related operation machine that performs a specified substrate-related operation on the board to produce a product board. The learning unit uses the teacher images acquired by the acquisition unit to re-learn a quality determination model that is a learning model generated using the teacher images acquired in an environment different from the production environment and that determines the quality of the substrate-related operation performed by the substrate-related operation machine.
　また、本明細書は、取得工程と、学習工程とを備える良否判定方法を開示する。前記取得工程は、基板に所定の対基板作業を行って製品基板を生産する対基板作業機によって前記基板に設けられる対象物が撮像されている複数の画像であって機械学習に使用される教師画像を、前記製品基板を生産する生産環境において取得する。前記学習工程は、前記生産環境と異なる環境において取得された前記教師画像を使用して生成された学習モデルであって前記対基板作業機による前記対基板作業の良否を判定する良否判定モデルの再学習を、前記取得工程によって取得された前記教師画像を使用して行う。 This specification also discloses a quality determination method including an acquisition step and a learning step. The acquisition step acquires, in a production environment in which the product substrate is produced, a plurality of images of an object placed on the substrate by a substrate-related operation machine that performs a predetermined substrate-related operation on the substrate to produce a product substrate, and teacher images to be used for machine learning. The learning step uses the teacher images acquired in the acquisition step to re-learn a quality determination model that is a learning model generated using the teacher images acquired in an environment different from the production environment and that determines the quality of the substrate-related operation performed by the substrate-related operation machine.
　なお、本明細書には、願書に最初に添付した請求の範囲（以下、当初請求の範囲という。）に記載の請求項５において、「請求項１に記載の良否判定装置」を「請求項１～請求項４のいずれか一項に記載の良否判定装置」に変更した技術的思想が開示されている。また、本明細書には、当初請求の範囲に記載の請求項７において、「請求項１に記載の良否判定装置」を「請求項１～請求項６のいずれか一項に記載の良否判定装置」に変更した技術的思想が開示されている。 This specification discloses the technical idea of changing "the quality determination device according to claim 1" to "the quality determination device according to any one of claims 1 to 4" in claim 5 of the claims originally attached to the application (hereinafter referred to as the initial claims). This specification also discloses the technical idea of changing "the quality determination device according to claim 1" to "the quality determination device according to any one of claims 1 to 6" in claim 7 of the initial claims.
　さらに、本明細書には、当初請求の範囲に記載の請求項８において、「請求項１に記載の良否判定装置」を「請求項１～請求項７のいずれか一項に記載の良否判定装置」に変更した技術的思想が開示されている。また、本明細書には、当初請求の範囲に記載の請求項９において、「請求項１に記載の良否判定装置」を「請求項１～請求項８のいずれか一項に記載の良否判定装置」に変更した技術的思想が開示されている。 Furthermore, this specification discloses the technical idea of changing "the quality determination device according to claim 1" to "the quality determination device according to any one of claims 1 to 7" in claim 8 originally claimed. Also, this specification discloses the technical idea of changing "the quality determination device according to claim 1" to "the quality determination device according to any one of claims 1 to 8" in claim 9 originally claimed.
　上記の良否判定装置によれば、製品基板を生産する生産環境において取得された教師画像を使用して良否判定モデルの再学習を行うことができる。そのため、良否判定モデルをそのまま使用する場合と比べて、対基板作業の誤判定が低減される。良否判定装置について上述されていることは、良否判定方法についても同様に言える。 The quality determination device described above allows the quality determination model to be re-learned using teacher images acquired in the production environment where the product boards are produced. This reduces erroneous determinations of work on boards compared to when the quality determination model is used as is. What has been described above about the quality determination device also applies to the quality determination method.
生産ラインの一例を示す構成図である。FIG. 1 is a configuration diagram showing an example of a production line. 部品装着機の構成例を示す平面図である。FIG. 2 is a plan view showing a configuration example of a component mounting machine. バルクフィーダの一例を示す斜視図である。FIG. 2 is a perspective view showing an example of a bulk feeder. 図３の矢印IV方向視の平面図である。FIG. 4 is a plan view seen in the direction of arrow IV in FIG. 3 . 部品が供給されたキャビティユニットの一例を示す平面図である。FIG. 11 is a plan view showing an example of a cavity unit to which components are supplied. 図５の３つのキャビティに収容されている部品の収容状態の一例を示す模式図である。FIG. 6 is a schematic diagram showing an example of a state in which components are accommodated in the three cavities in FIG. 5 . 良否判定装置の制御ブロックの一例を示すブロック図である。FIG. 2 is a block diagram showing an example of a control block of the quality determination device; 良否判定装置による制御手順の一例を示すフローチャートである。5 is a flowchart showing an example of a control procedure performed by the quality determination device. 取得部による制御手順の一例を示すフローチャートである。13 is a flowchart illustrating an example of a control procedure performed by an acquisition unit. 装着前画像の一例を示す模式図である。FIG. 13 is a schematic diagram showing an example of a pre-mounting image. 装着後画像の一例を示す模式図である。FIG. 13 is a schematic diagram showing an example of an image after mounting. 良否判定モデルの再学習の一例を示す模式図である。FIG. 13 is a schematic diagram showing an example of relearning of a pass/fail determination model.
　１．実施形態
　１－１．生産ラインＷＬ０の構成例
　良否判定装置８０は、製品基板９００を生産する種々の生産ラインＷＬ０に適用することができる。生産ラインＷＬ０では、対基板作業機ＷＭ０が基板９０に所定の対基板作業を行って製品基板９００を生産する。対基板作業機ＷＭ０の種類および数は、限定されない。図１に示すように、実施形態の生産ラインＷＬ０は、印刷機ＷＭ１、印刷検査機ＷＭ２、部品装着機ＷＭ３、リフロー炉ＷＭ４および外観検査機ＷＭ５の複数（５つ）の対基板作業機ＷＭ０を備えており、基板９０は、基板搬送装置によって、上記の順に搬送される。 1. Embodiment 1-1. Configuration example of production line WL0 The quality determination device 80 can be applied to various production lines WL0 that produce product substrates 900. In the production line WL0, a substrate-related operation machine WM0 performs a predetermined substrate-related operation on a substrate 90 to produce the product substrate 900. The type and number of substrate-related operation machines WM0 are not limited. As shown in FIG. 1, the production line WL0 of the embodiment includes a plurality (five) of substrate-related operation machines WM0, including a printer WM1, a print inspection machine WM2, a component mounting machine WM3, a reflow oven WM4, and a visual inspection machine WM5, and the substrate 90 is transported by a substrate transport device in the above order.
　印刷機ＷＭ１は、基板９０の部品９１の装着位置に、はんだを印刷する。印刷検査機ＷＭ２は、印刷機ＷＭ１によって印刷されたはんだの印刷状態を検査する。部品装着機ＷＭ３は、印刷機ＷＭ１によってはんだが印刷された基板９０に複数の部品９１を装着する。部品装着機ＷＭ３は、一つであっても良く、複数であっても良い。部品装着機ＷＭ３が複数設けられる場合は、複数の部品装着機ＷＭ３が分担して、複数の部品９１を装着することができる。 The printer WM1 prints solder at the mounting position of the component 91 on the board 90. The print inspection machine WM2 inspects the printing condition of the solder printed by the printer WM1. The component mounting machine WM3 mounts multiple components 91 on the board 90 on which the solder has been printed by the printer WM1. There may be one component mounting machine WM3 or multiple component mounting machines WM3. When multiple component mounting machines WM3 are provided, multiple components 91 can be mounted by sharing the load between the multiple component mounting machines WM3.
　リフロー炉ＷＭ４は、部品装着機ＷＭ３によって部品９１が装着された基板９０を加熱し、はんだを溶融させて、はんだ付けを行う。外観検査機ＷＭ５は、部品装着機ＷＭ３によって装着された部品９１の装着状態などを検査する。このように、生産ラインＷＬ０は、複数（５つ）の対基板作業機ＷＭ０を用いて、基板９０を順に搬送して、検査処理を含む生産処理を実行して製品基板９００を生産することができる。なお、生産ラインＷＬ０は、例えば、機能検査機、バッファ装置、基板供給装置、基板反転装置、シールド装着装置、接着剤塗布装置、紫外線照射装置などの対基板作業機ＷＭ０を必要に応じて備えることもできる。 The reflow furnace WM4 heats the board 90 on which components 91 have been mounted by the component mounting machine WM3, melting the solder and performing soldering. The appearance inspection machine WM5 inspects the mounting state of the components 91 mounted by the component mounting machine WM3. In this way, the production line WL0 can use multiple (five) substrate-related work machines WM0 to transport the boards 90 in sequence and perform production processes including inspection processes to produce the product boards 900. Note that the production line WL0 can also be equipped with substrate-related work machines WM0 such as functional inspection machines, buffer devices, substrate supply devices, substrate reversing devices, shield mounting devices, adhesive application devices, and ultraviolet ray irradiation devices as necessary.
　複数（５つ）の対基板作業機ＷＭ０および管理装置ＨＣ０は、有線または無線の通信部によって通信可能に接続されている。実施形態では、複数（５つ）の対基板作業機ＷＭ０および管理装置ＨＣ０によって、構内情報通信網（ＬＡＮ：Ｌｏｃａｌ　Ａｒｅａ　Ｎｅｔｗｏｒｋ）が構成されている。これにより、複数（５つ）の対基板作業機ＷＭ０は、通信部を介して、互いに通信することができる。また、複数（５つ）の対基板作業機ＷＭ０は、通信部を介して、管理装置ＨＣ０と通信することができる。 The multiple (five) substrate-related operation machines WM0 and the management device HC0 are connected to each other so that they can communicate with each other via a wired or wireless communication unit. In the embodiment, the multiple (five) substrate-related operation machines WM0 and the management device HC0 form an on-site information and communication network (LAN: Local Area Network). This allows the multiple (five) substrate-related operation machines WM0 to communicate with each other via the communication unit. The multiple (five) substrate-related operation machines WM0 can also communicate with the management device HC0 via the communication unit.
　管理装置ＨＣ０は、生産ラインＷＬ０を構成する複数（５つ）の対基板作業機ＷＭ０の制御を行い、生産ラインＷＬ０の動作状況を監視する。管理装置ＨＣ０には、複数（５つ）の対基板作業機ＷＭ０を制御する種々の制御データが記憶されている。管理装置ＨＣ０は、複数（５つ）の対基板作業機ＷＭ０の各々に制御データを送信する。また、複数（５つ）の対基板作業機ＷＭ０の各々は、管理装置ＨＣ０に動作状況および生産状況を送信する。 The management device HC0 controls the multiple (five) substrate-related work machines WM0 that make up the production line WL0, and monitors the operating status of the production line WL0. The management device HC0 stores various control data for controlling the multiple (five) substrate-related work machines WM0. The management device HC0 transmits control data to each of the multiple (five) substrate-related work machines WM0. In addition, each of the multiple (five) substrate-related work machines WM0 transmits its operating status and production status to the management device HC0.
　管理装置ＨＣ０には、記憶装置８０ｓが設けられている。記憶装置８０ｓは、例えば、対基板作業機ＷＭ０が対基板作業に関して取得した取得データを保存することができる。例えば、対基板作業機ＷＭ０によって撮像された画像の画像データは、取得データに含まれる。後述されている教師画像７０は、取得データに含まれる。対基板作業機ＷＭ０によって取得された稼働状況の記録（ログデータ）などは、取得データに含まれる。 The management device HC0 is provided with a storage device 80s. The storage device 80s can store, for example, acquired data acquired by the substrate-related operation machine WM0 in relation to substrate-related operations. For example, image data of an image captured by the substrate-related operation machine WM0 is included in the acquired data. The teacher image 70 described below is included in the acquired data. Records (log data) of the operating status acquired by the substrate-related operation machine WM0 are included in the acquired data.
　また、記憶装置８０ｓは、製品基板９００の生産に関する種々の生産情報を保存することもできる。例えば、部品９１の種類ごとの形状に関する情報、部品９１の電気的特性に関する情報、部品９１の取り扱い方法に関する情報などの部品データは、生産情報に含まれる。また、印刷検査機ＷＭ２、外観検査機ＷＭ５などの検査機による検査結果は、生産情報に含まれる。 The storage device 80s can also store various production information related to the production of the product board 900. For example, component data such as information on the shape of each type of component 91, information on the electrical properties of the component 91, and information on how to handle the component 91 are included in the production information. Inspection results from inspection machines such as the print inspection machine WM2 and the appearance inspection machine WM5 are also included in the production information.
　１－２．部品装着機ＷＭ３の構成例
　部品装着機ＷＭ３は、基板９０に複数の部品９１を装着する。図２に示すように、部品装着機ＷＭ３は、基板搬送装置１１、部品供給装置１２、部品移載装置１３、部品カメラ１４、基板カメラ１５および制御装置２０を備えている。 1-2. Example of configuration of component mounting machine WM3 The component mounting machine WM3 mounts a plurality of components 91 on a board 90. As shown in Fig. 2, the component mounting machine WM3 includes a board transport device 11, a component supply device 12, a component transfer device 13, a component camera 14, a board camera 15, and a control device 20.
　基板搬送装置１１は、例えば、ベルトコンベアなどによって構成され、基板９０を搬送方向（Ｘ軸方向）に搬送する。基板９０は、回路基板であり、電子回路、電気回路、磁気回路などが形成される。基板搬送装置１１は、部品装着機ＷＭ３の機内に基板９０を搬入し、機内の所定位置に基板９０を位置決めする。基板搬送装置１１は、部品装着機ＷＭ３による複数の部品９１の装着処理が終了した後に、基板９０を部品装着機ＷＭ３の機外に搬出する。 The board transport device 11 is, for example, a belt conveyor, and transports the board 90 in the transport direction (X-axis direction). The board 90 is a circuit board on which electronic circuits, electric circuits, magnetic circuits, etc. are formed. The board transport device 11 transports the board 90 into the component mounting machine WM3 and positions the board 90 at a predetermined position within the machine. After the component mounting machine WM3 has completed the mounting process of multiple components 91, the board transport device 11 transports the board 90 out of the component mounting machine WM3.
　部品供給装置１２は、基板９０に装着される複数の部品９１を供給する。部品供給装置１２は、例えば、基板９０の搬送方向（Ｘ軸方向）に沿って設けられる複数のフィーダ１２ｂを備えている。複数のフィーダ１２ｂの各々は、スロット１２ａに着脱可能に取り付けられている。例えば、フィーダ１２ｂは、テープフィーダを用いることができる。テープフィーダは、複数の部品９１が収納されているキャリアテープをピッチ送りして、供給位置において部品９１を採取可能に供給する。 The component supply device 12 supplies a plurality of components 91 to be mounted on the board 90. The component supply device 12 includes, for example, a plurality of feeders 12b arranged along the transport direction (X-axis direction) of the board 90. Each of the plurality of feeders 12b is detachably attached to the slot 12a. For example, a tape feeder can be used for the feeder 12b. The tape feeder pitch-feeds a carrier tape containing the plurality of components 91, and supplies the components 91 at the supply position so that they can be picked up.
　フィーダ１２ｂは、バルクフィーダ３０を用いることもできる。バルクフィーダ３０は、複数の部品９１をバルク状態（複数の部品９１の姿勢が不規則な状態）で収容する部品ケース３２から排出された複数の部品９１である供給部品９１ｓ（部品ケース３２に収容されている複数の部品９１の一部）を採取可能に供給する。また、部品供給装置１２は、チップ部品などと比べて比較的大型の電子部品（例えば、リード部品など）を、トレイ上に配置した状態で供給することもできる。 Feeder 12b may also be a bulk feeder 30. Bulk feeder 30 supplies and picks up supply components 91s (a part of the components 91 housed in component case 32), which are multiple components 91 discharged from a component case 32 that houses multiple components 91 in a bulk state (a state in which the multiple components 91 are in an irregular position). Component supply device 12 may also supply electronic components (e.g., lead components) that are relatively large compared to chip components and the like, while being arranged on a tray.
　部品移載装置１３は、ヘッド駆動装置１３ａ、移動台１３ｂ、装着ヘッド１３ｃおよび保持部材１３ｄを備えている。ヘッド駆動装置１３ａは、直動機構によって移動台１３ｂを、Ｘ軸方向およびＹ軸方向（水平面においてＸ軸方向と直交する方向）に移動可能に構成されている。移動台１３ｂには、クランプ部材によって装着ヘッド１３ｃが着脱可能（交換可能）に設けられている。装着ヘッド１３ｃは、少なくとも一つの保持部材１３ｄを用いて、部品供給装置１２によって供給される部品９１を採取し保持して、基板搬送装置１１によって位置決めされた基板９０に部品９１を装着する。保持部材１３ｄは、例えば、吸着ノズル、チャックなどを用いることができる。 The component transfer device 13 includes a head drive device 13a, a moving table 13b, a mounting head 13c, and a holding member 13d. The head drive device 13a is configured to be able to move the moving table 13b in the X-axis direction and the Y-axis direction (direction perpendicular to the X-axis direction in a horizontal plane) using a linear motion mechanism. The mounting head 13c is detachably (replaceably) attached to the moving table 13b using a clamp member. The mounting head 13c uses at least one holding member 13d to pick up and hold the component 91 supplied by the component supply device 12, and mounts the component 91 on the board 90 positioned by the board transport device 11. The holding member 13d can be, for example, a suction nozzle, a chuck, etc.
　部品カメラ１４および基板カメラ１５は、公知の撮像装置を用いることができる。部品カメラ１４は、光軸が鉛直方向（Ｘ軸方向およびＹ軸方向と直交するＺ軸方向）の上向きになるように、部品装着機ＷＭ３の基台に固定されている。部品カメラ１４は、保持部材１３ｄに保持されている部品９１を下方から撮像することができる。基板カメラ１５は、光軸が鉛直方向（Ｚ軸方向）の下向きになるように、部品移載装置１３の移動台１３ｂに設けられている。基板カメラ１５は、基板９０などを上方から撮像することができる。 The component camera 14 and the board camera 15 may be publicly known imaging devices. The component camera 14 is fixed to the base of the component mounting machine WM3 so that its optical axis faces upward in the vertical direction (the Z-axis direction perpendicular to the X-axis and Y-axis directions). The component camera 14 can image the component 91 held by the holding member 13d from below. The board camera 15 is mounted on the moving stage 13b of the component transfer device 13 so that its optical axis faces downward in the vertical direction (the Z-axis direction). The board camera 15 can image the board 90 and the like from above.
　部品カメラ１４および基板カメラ１５は、制御装置２０から送出される制御信号に基づいて撮像を行う。部品カメラ１４および基板カメラ１５によって撮像された画像の画像データは、制御装置２０に送信される。制御装置２０は、公知の演算装置および記憶装置を備えており、制御回路が構成されている。制御装置２０には、部品装着機ＷＭ３に設けられる各種センサから出力される情報、画像データなどが入力される。制御装置２０は、制御プログラムおよび予め設定されている所定の装着条件などに基づいて、各装置に対して制御信号を送出する。 The component camera 14 and the board camera 15 capture images based on control signals sent from the control device 20. Image data of the images captured by the component camera 14 and the board camera 15 is sent to the control device 20. The control device 20 is equipped with a known arithmetic device and storage device, and constitutes a control circuit. Information and image data output from various sensors provided in the component mounting machine WM3 are input to the control device 20. The control device 20 sends control signals to each device based on a control program and predetermined mounting conditions that have been set in advance.
　例えば、制御装置２０は、基板搬送装置１１によって位置決めされた基板９０を基板カメラ１５に撮像させる。制御装置２０は、基板カメラ１５によって撮像された画像を画像処理して、基板９０の位置決め状態を認識する。また、制御装置２０は、部品供給装置１２によって供給された部品９１を部品カメラ１４に撮像させる。制御装置２０は、部品供給装置１２によって供給された部品９１を保持部材１３ｄに採取させ保持させて、保持部材１３ｄに保持されている部品９１を部品カメラ１４に撮像させる。制御装置２０は、部品カメラ１４によって撮像された画像を画像処理して、部品９１の供給状態および部品９１の保持姿勢を認識する。 For example, the control device 20 causes the board camera 15 to capture an image of the board 90 positioned by the board transport device 11. The control device 20 processes the image captured by the board camera 15 to recognize the positioning state of the board 90. The control device 20 also causes the part camera 14 to capture an image of the part 91 supplied by the part supply device 12. The control device 20 causes the holding member 13d to pick up and hold the part 91 supplied by the part supply device 12, and causes the part camera 14 to capture an image of the part 91 held by the holding member 13d. The control device 20 processes the image captured by the part camera 14 to recognize the supply state of the part 91 and the holding posture of the part 91.
　制御装置２０は、制御プログラムなどによって予め設定される装着予定位置の上方に向かって、保持部材１３ｄを移動させる。また、制御装置２０は、基板９０の位置決め状態、部品９１の保持姿勢などに基づいて、装着予定位置を補正して、実際に部品９１を装着する装着位置を設定する。装着予定位置および装着位置は、位置（Ｘ軸座標およびＹ軸座標）の他に回転角度を含む。 The control device 20 moves the holding member 13d toward above the intended mounting position that is set in advance by a control program or the like. The control device 20 also corrects the intended mounting position based on the positioning state of the board 90, the holding posture of the component 91, and the like, and sets the mounting position where the component 91 will actually be mounted. The intended mounting position and mounting position include a rotation angle in addition to the position (X-axis coordinate and Y-axis coordinate).
　制御装置２０は、装着位置に合わせて、保持部材１３ｄの目標位置（Ｘ軸座標およびＹ軸座標）および回転角度を補正する。制御装置２０は、補正された目標位置において補正された回転角度で保持部材１３ｄを下降させて、基板９０に部品９１を装着する。制御装置２０は、上記のピックアンドプレースサイクルを繰り返すことによって、基板９０に複数の部品９１を装着する装着処理を実行する。 The control device 20 corrects the target position (X-axis coordinates and Y-axis coordinates) and rotation angle of the holding member 13d to match the mounting position. The control device 20 lowers the holding member 13d at the corrected rotation angle in the corrected target position to mount the component 91 on the board 90. The control device 20 repeats the above pick-and-place cycle to perform the mounting process of mounting multiple components 91 on the board 90.
　１－３．バルクフィーダ３０の構成例
　バルクフィーダ３０は、部品ケース３２から排出された複数の部品９１である供給部品９１ｓを部品装着機ＷＭ３に供給する。図３に示すように、実施形態のバルクフィーダ３０は、フィーダ本体部３１と、部品ケース３２と、排出装置３３と、カバー３４と、軌道部材４０と、キャビティユニット５０と、加振装置６０と、フィーダ制御装置３０ｃとを具備している。フィーダ本体部３１は、扁平な箱状に形成されている。フィーダ本体部３１は、部品供給装置１２のスロット１２ａに着脱可能に装備される。 1-3. Example of configuration of bulk feeder 30 The bulk feeder 30 supplies supply components 91s, which are a plurality of components 91 discharged from a component case 32, to the component mounting machine WM3. As shown in Fig. 3, the bulk feeder 30 of the embodiment includes a feeder main body 31, a component case 32, a discharge device 33, a cover 34, a track member 40, a cavity unit 50, a vibration device 60, and a feeder control device 30c. The feeder main body 31 is formed in a flat box shape. The feeder main body 31 is detachably mounted in a slot 12a of the component supply device 12.
　フィーダ本体部３１は、供給部品９１ｓの搬送方向の先端側に、コネクタ３１ａおよび複数（同図では、２つ）のピン３１ｂ，３１ｂを備えている。コネクタ３１ａは、バルクフィーダ３０がスロット１２ａに装備されたときに、制御装置２０と通信可能に設けられる。また、バルクフィーダ３０は、コネクタ３１ａを介して給電される。複数（２つ）のピン３１ｂ，３１ｂは、フィーダ本体部３１がスロット１２ａに装備される際の位置決めに用いられる。 The feeder body 31 is provided with a connector 31a and multiple (two in the figure) pins 31b, 31b at the tip side in the conveying direction of the supply part 91s. The connector 31a is provided so as to be able to communicate with the control device 20 when the bulk feeder 30 is installed in the slot 12a. The bulk feeder 30 is also powered via the connector 31a. The multiple (two) pins 31b, 31b are used for positioning the feeder body 31 when it is installed in the slot 12a.
　フィーダ本体部３１には、複数の部品９１をバルク状態で収容する部品ケース３２が着脱可能に取り付けられている。部品ケース３２は、収容している複数の部品９１を排出することができる。実施形態の部品ケース３２は、バルクフィーダ３０の外部装置である。例えば、作業者は、複数の部品ケース３２の中から装着処理に使用される部品９１を収容する部品ケース３２を選択して、選択した部品ケース３２をフィーダ本体部３１に取り付ける。 A component case 32 that stores multiple components 91 in bulk is removably attached to the feeder body 31. The component case 32 can discharge the multiple components 91 stored therein. In the embodiment, the component case 32 is an external device of the bulk feeder 30. For example, an operator selects a component case 32 that stores a component 91 to be used in the mounting process from among the multiple component cases 32, and attaches the selected component case 32 to the feeder body 31.
　排出装置３３は、部品ケース３２から排出する部品９１の数量を調整する。排出装置３３は、図４に示す軌道部材４０の受容領域Ａｒ０に供給部品９１ｓを排出する。供給部品９１ｓは、部品ケース３２から排出されて部品装着機ＷＭ３に供給される複数の部品９１の一部である。カバー３４は、供給部品９１ｓの搬送方向の先端側上部に着脱可能に取り付けられる。カバー３４は、図４に示す軌道部材４０の搬送路Ｒｄ０を搬送する供給部品９１ｓが外部へ飛散することを抑制する。 The ejection device 33 adjusts the number of parts 91 ejected from the part case 32. The ejection device 33 ejects the supply parts 91s into the receiving area Ar0 of the track member 40 shown in FIG. 4. The supply parts 91s are part of the multiple parts 91 that are ejected from the part case 32 and supplied to the part mounting machine WM3. The cover 34 is removably attached to the upper tip side of the supply parts 91s in the transport direction. The cover 34 prevents the supply parts 91s transported along the transport path Rd0 of the track member 40 shown in FIG. 4 from scattering to the outside.
　軌道部材４０は、部品ケース３２から排出された複数の部品９１である供給部品９１ｓが搬送される搬送路Ｒｄ０を備える。軌道部材４０は、供給部品９１ｓの搬送方向の先端側上部に設けられる。図４に示すように、軌道部材４０は、供給部品９１ｓの搬送方向（図４の紙面左右方向）に延伸するように形成されている。搬送路Ｒｄ０の幅方向（図４の紙面上下方向）の両縁には、上方に突出する一対の側壁４１，４１が形成されている。一対の側壁４１，４１は、軌道部材４０の先端部４２と共に搬送路Ｒｄ０の周縁を囲い、搬送路Ｒｄ０を搬送する供給部品９１ｓの漏出を抑制する。 The track member 40 has a transport path Rd0 along which supply parts 91s, which are multiple parts 91 discharged from the part case 32, are transported. The track member 40 is provided at the upper end of the transport direction of the supply parts 91s. As shown in FIG. 4, the track member 40 is formed so as to extend in the transport direction of the supply parts 91s (left-right direction on the paper in FIG. 4). A pair of side walls 41, 41 that protrude upward are formed on both edges of the width direction of the transport path Rd0 (top-bottom direction on the paper in FIG. 4). The pair of side walls 41, 41, together with the tip 42 of the track member 40, surround the periphery of the transport path Rd0 and prevent leakage of the supply parts 91s transported along the transport path Rd0.
　図４に示すように、軌道部材４０は、受容領域Ａｒ０、供給領域Ａｓ０および搬送路Ｒｄ０を備えている。受容領域Ａｒ０は、バルク状態の供給部品９１ｓを受容する領域をいう。実施形態の受容領域Ａｒ０は、部品ケース３２の排出口の下方に設けられている。供給領域Ａｓ０は、部品装着機ＷＭ３が供給部品９１ｓを採取可能な領域をいう。具体的には、供給領域Ａｓ０は、装着ヘッド１３ｃに支持された保持部材１３ｄによって供給部品９１ｓを採取可能な領域であり、装着ヘッド１３ｃの可動範囲に含まれる。 As shown in FIG. 4, the track member 40 has a receiving area Ar0, a supply area As0, and a transport path Rd0. The receiving area Ar0 is an area that receives supply parts 91s in bulk. In this embodiment, the receiving area Ar0 is provided below the discharge outlet of the part case 32. The supply area As0 is an area where the part mounting machine WM3 can pick up the supply parts 91s. Specifically, the supply area As0 is an area where the supply parts 91s can be picked up by the holding member 13d supported by the mounting head 13c, and is included in the movable range of the mounting head 13c.
　搬送路Ｒｄ０は、受容領域Ａｒ０と供給領域Ａｓ０との間に設けられ、受容領域Ａｒ０と供給領域Ａｓ０との間で供給部品９１ｓが搬送される。実施形態の搬送路Ｒｄ０は、底面が水平な溝形状に形成されている。搬送路Ｒｄ０の側面は、一対の側壁４１，４１によって形成される。搬送路Ｒｄ０の上方の開口部は、カバー３４によって概ね閉塞されている。軌道部材４０は、フィーダ本体部３１に対して鉛直方向（Ｚ軸方向）に僅かに変位可能（振動可能）に支持される。 The transport path Rd0 is provided between the receiving area Ar0 and the supply area As0, and the supply parts 91s are transported between the receiving area Ar0 and the supply area As0. In this embodiment, the transport path Rd0 is formed in a groove shape with a horizontal bottom. The sides of the transport path Rd0 are formed by a pair of side walls 41, 41. The upper opening of the transport path Rd0 is mostly closed by a cover 34. The track member 40 is supported so as to be slightly displaceable (vibrable) in the vertical direction (Z-axis direction) relative to the feeder main body 31.
　キャビティユニット５０は、供給領域Ａｓ０に搬送された供給部品９１ｓのうちの一つの部品９１が収容されるべきキャビティ５１を供給領域Ａｓ０に複数（実施形態では、１２０個）備える。つまり、複数（１２０個）のキャビティ５１の各々は、一つの部品９１を収容することが予定されている。具体的には、複数（１２０個）のキャビティ５１は、供給領域Ａｓ０においてマトリックス状に配列されている。例えば、図５に示すように、キャビティユニット５０は、供給部品９１ｓの搬送方向に１０個、搬送路Ｒｄ０の幅方向に１２個それぞれ配列された合計１２０個のキャビティ５１を備えている。 The cavity unit 50 has a plurality of cavities 51 (120 in this embodiment) in the supply area As0, each of which is to accommodate one of the supply parts 91s transported to the supply area As0. In other words, each of the plurality (120) cavities 51 is intended to accommodate one part 91. Specifically, the plurality (120) cavities 51 are arranged in a matrix in the supply area As0. For example, as shown in FIG. 5, the cavity unit 50 has a total of 120 cavities 51, with 10 arranged in the transport direction of the supply parts 91s and 12 arranged in the width direction of the transport path Rd0.
　また、複数（１２０個）のキャビティ５１の各々は、搬送路Ｒｄ０の上方に開口しており、例えば、四角柱形状の部品９１の高さ方向が鉛直方向（Ｚ軸方向）と一致する姿勢（正規の姿勢）で部品９１を収容することが予定されている。キャビティ５１の開口部は、鉛直方向（Ｚ軸方向）視における部品９１の外形寸法よりも若干大きく形成されている。キャビティ５１の深さは、部品９１の種類（形状、質量など）に応じて適宜設定される。また、キャビティ５１の形状、必要数、搬送性に影響し得る密集度を加味して、キャビティ５１の数が適宜設定される。 Furthermore, each of the multiple (120) cavities 51 opens above the transport path Rd0, and is intended to accommodate the part 91 in a posture (normal posture) in which the height direction of the rectangular prism-shaped part 91 coincides with the vertical direction (Z-axis direction). The opening of the cavity 51 is formed slightly larger than the outer dimensions of the part 91 when viewed in the vertical direction (Z-axis direction). The depth of the cavity 51 is set appropriately depending on the type of part 91 (shape, mass, etc.). Furthermore, the number of cavities 51 is set appropriately taking into account the shape of the cavities 51, the required number, and the density that may affect transportability.
　また、キャビティ５１の数は、一回のピックアンドプレースサイクルにおいて採取される部品９１の最大数よりも多く設定されると良い。なお、上記の最大数は、装着ヘッド１３ｃが支持する保持部材１３ｄの数に相当する。例えば、装着ヘッド１３ｃが２４本の吸着ノズルを支持する場合、キャビティ５１の数は、少なくとも２４個より多くなるように設定されると良い。 Furthermore, the number of cavities 51 is preferably set to be greater than the maximum number of components 91 that can be picked up in one pick-and-place cycle. Note that the above maximum number corresponds to the number of holding members 13d supported by the mounting head 13c. For example, if the mounting head 13c supports 24 suction nozzles, the number of cavities 51 is preferably set to be greater than at least 24.
　軌道部材４０は、フィーダ本体部３１に対して振動可能に設けられる。加振装置６０は、軌道部材４０を加振して搬送路Ｒｄ０上の供給部品９１ｓを部品装着機ＷＭ３が採取可能な供給領域Ａｓ０に搬送する。具体的には、加振装置６０は、供給部品９１ｓの搬送方向に直交する水平方向において、軌道部材４０に時計回りまたは反時計回りの楕円運動をさせる。このとき、加振装置６０は、搬送路Ｒｄ０上の供給部品９１ｓに対して、供給方向先端側（図４の紙面右側）かつ上方に向かう外力または供給方向基端側（図４の紙面左側）かつ上方に向かう外力が加えられるように軌道部材４０を加振する。 The track member 40 is provided so as to be vibrated relative to the feeder main body 31. The vibration device 60 vibrates the track member 40 to transport the supply parts 91s on the transport path Rd0 to the supply area As0 from which the component mounting machine WM3 can pick them up. Specifically, the vibration device 60 causes the track member 40 to perform an elliptical motion clockwise or counterclockwise in a horizontal direction perpendicular to the transport direction of the supply parts 91s. At this time, the vibration device 60 vibrates the track member 40 so that an external force is applied to the supply parts 91s on the transport path Rd0 from the tip side in the supply direction (the right side of the paper in FIG. 4) and directed upward, or from the base side in the supply direction (the left side of the paper in FIG. 4) and directed upward.
　加振装置６０は、例えば、フィーダ本体部３１と軌道部材４０を連結する支持部材と、支持部材に設けられる圧電素子と、圧電素子に給電する駆動部とを備えている。駆動部は、フィーダ制御装置３０ｃの指令に基づいて、圧電素子に供給する交流電力の印加電圧および周波数を変動させる。これにより、軌道部材４０に付与される振動の振幅および周波数が調整され、軌道部材４０の楕円運動の回転方向が規定される。軌道部材４０の振動の振幅、周波数、振動による楕円運動の回転方向が変動すると、搬送される供給部品９１ｓの搬送速度、分散度合い、搬送方向などが変動する。 The vibration device 60 includes, for example, a support member that connects the feeder main body 31 and the track member 40, a piezoelectric element provided on the support member, and a drive unit that supplies power to the piezoelectric element. The drive unit varies the applied voltage and frequency of the AC power supplied to the piezoelectric element based on commands from the feeder control device 30c. This adjusts the amplitude and frequency of the vibration imparted to the track member 40, and determines the direction of rotation of the elliptical motion of the track member 40. When the amplitude, frequency, and direction of rotation of the elliptical motion caused by the vibration of the track member 40 vary, the transport speed, degree of dispersion, transport direction, etc. of the transported supply parts 91s vary.
　ここで、搬送路Ｒｄ０上の供給部品９１ｓを供給領域Ａｓ０に向かって搬送する際の加振装置６０の動作を送り動作とする。また、搬送路Ｒｄ０上の供給部品９１ｓを受容領域Ａｒ０に向かって搬送する際の加振装置６０の動作を戻し動作とする。加振装置６０の送り動作および戻し動作の切り替えによって、軌道部材４０の楕円運動の方向が切り替わる。加振装置６０は、供給部品９１ｓをキャビティユニット５０の少なくとも一部のキャビティ５１に収容させる収容装置として機能する。 Here, the operation of the vibration device 60 when transporting the supply part 91s on the transport path Rd0 towards the supply area As0 is referred to as the feed operation. The operation of the vibration device 60 when transporting the supply part 91s on the transport path Rd0 towards the receiving area Ar0 is referred to as the return operation. The direction of the elliptical motion of the track member 40 is switched by switching between the feed operation and the return operation of the vibration device 60. The vibration device 60 functions as a storage device that stores the supply part 91s in at least some of the cavities 51 of the cavity unit 50.
　フィーダ制御装置３０ｃは、公知の演算装置および記憶装置を備えており、制御回路が構成されている。フィーダ制御装置３０ｃは、バルクフィーダ３０がスロット１２ａに装備された状態において、コネクタ３１ａを介して給電され、部品装着機ＷＭ３の制御装置２０と通信可能な状態になる。フィーダ制御装置３０ｃは、供給領域Ａｓ０に供給部品９１ｓを供給する供給処理において、加振装置６０の送り動作および戻し動作を実行する。 The feeder control device 30c is equipped with a known arithmetic device and storage device, and a control circuit is configured. When the bulk feeder 30 is installed in the slot 12a, the feeder control device 30c is powered via the connector 31a and is in a state where it can communicate with the control device 20 of the component mounting machine WM3. The feeder control device 30c executes the sending operation and the returning operation of the vibration device 60 in the supply process of supplying the supply component 91s to the supply area As0.
　具体的には、フィーダ制御装置３０ｃは、送り動作を実行する場合に、加振装置６０の駆動部に対して指令を送出する。これにより、駆動部が圧電素子に所定の電力を供給し、支持部材を介して軌道部材４０が加振される。その結果、搬送路Ｒｄ０上の供給部品９１ｓは、搬送方向先端側に移動するように外力を受けて搬送される。 Specifically, when the feeder control device 30c executes a feeding operation, it sends a command to the drive unit of the vibration device 60. This causes the drive unit to supply a predetermined amount of power to the piezoelectric element, and the track member 40 is vibrated via the support member. As a result, the supply part 91s on the conveying path Rd0 is conveyed by receiving an external force so as to move toward the tip end of the conveying direction.
　また、フィーダ制御装置３０ｃは、加振装置６０の送り動作および戻し動作を組み合わせることにより種々の搬送態様を実現する。例えば、フィーダ制御装置３０ｃは、搬送路Ｒｄ０上の供給部品９１ｓの少なくとも一部が供給領域Ａｓ０に到達した後に、図４に示す供給部品９１ｓが軌道部材４０の先端部４２の付近に到達するまで送り動作を継続する。このとき、フィーダ制御装置３０ｃは、戻し動作および送り動作を繰り返し実行して、軌道部材４０が振動した状態で供給領域Ａｓ０に供給部品９１ｓを滞留させるようにしても良い。 The feeder control device 30c also realizes various conveying modes by combining the sending and returning operations of the vibration device 60. For example, after at least a portion of the supply parts 91s on the conveying path Rd0 reaches the supply area As0, the feeder control device 30c continues the sending operation until the supply parts 91s shown in FIG. 4 reach the vicinity of the tip 42 of the track member 40. At this time, the feeder control device 30c may repeatedly execute the returning and sending operations to cause the supply parts 91s to remain in the supply area As0 while the track member 40 is vibrating.
　その後に、フィーダ制御装置３０ｃは、搬送路Ｒｄ０上の供給部品９１ｓの少なくとも一部が複数のキャビティ５１に収容された状態で戻し動作を実行して、残りの供給部品９１ｓを供給領域Ａｓ０から受容領域Ａｒ０の側に退避させる。これにより、キャビティユニット５０の複数（１２０個）のキャビティ５１のうちの所定数以上のキャビティ５１に部品９１が適正に収容される。フィーダ制御装置３０ｃは、送り動作および戻し動作の実行時間、収容工程における滞留の動作時間、繰り返し動作の実行回数を適宜設定することができる。また、フィーダ制御装置３０ｃは、部品ケース３２に収容されている部品９１の種類に応じて、加振装置６０が軌道部材４０を加振する際の振動の振幅、周波数などを調整しても良い。 Then, the feeder control device 30c executes a return operation when at least some of the supply parts 91s on the conveying path Rd0 are accommodated in the multiple cavities 51, and retreats the remaining supply parts 91s from the supply area As0 to the receiving area Ar0. This allows the parts 91 to be properly accommodated in at least a predetermined number of the multiple (120) cavities 51 in the cavity unit 50. The feeder control device 30c can appropriately set the execution time of the sending operation and the returning operation, the operation time of the retention in the accommodation process, and the number of times the repeat operation is performed. The feeder control device 30c may also adjust the amplitude and frequency of the vibration when the vibration device 60 vibrates the track member 40 according to the type of part 91 accommodated in the part case 32.
　１－４．良否判定装置８０の構成例
　図５は、供給部品９１ｓが供給されたキャビティユニット５０の一例を示している。同図は、合計１２０個のキャビティ５１における供給部品９１ｓの収容状態の一例を示している。図６は、図５の３つのキャビティ５１に収容されている部品９１の収容状態の一例を示している。例えば、図５の領域ＡＲ１および図６の左図のキャビティ５１に収容されている部品９１のように、正規の姿勢（例えば、四角柱形状の部品９１の高さ方向が鉛直方向（Ｚ軸方向）と一致する姿勢）でキャビティ５１に収容されている部品９１が存在する。 1-4. Example of the configuration of the quality determination device 80 Fig. 5 shows an example of a cavity unit 50 to which a supply part 91s is supplied. The figure shows an example of the accommodation state of the supply parts 91s in a total of 120 cavities 51. Fig. 6 shows an example of the accommodation state of the parts 91 accommodated in the three cavities 51 in Fig. 5. For example, like the part 91 accommodated in the area AR1 in Fig. 5 and the cavity 51 in the left diagram in Fig. 6, there is a part 91 accommodated in the cavity 51 in a normal orientation (for example, an orientation in which the height direction of the rectangular prism-shaped part 91 coincides with the vertical direction (Z-axis direction)).
　また、図５の領域ＡＲ２のように、部品９１が収容されていないキャビティ５１が存在する。さらに、図５の領域ＡＲ３および図６の中央図のように、収容されている部品９１の上に他の部品９１が堆積して重なっているキャビティ５１が存在する。また、図５の領域ＡＲ４および図６の右図のキャビティ５１に収容されている部品９１のように、一つのキャビティ５１に複数（同図では、二つ）の部品９１が嵌まり込んでいるキャビティ５１が存在する。これらのキャビティ５１では、部品装着機ＷＭ３が部品９１を採取することが困難である。 Also, there are cavities 51 that do not contain a component 91, as in area AR2 in Figure 5. Furthermore, there are cavities 51 in which other components 91 are piled up on top of the components 91 contained therein, as in area AR3 in Figure 5 and the center diagram in Figure 6. There are also cavities 51 in which multiple components 91 (two in this figure) are fitted into one cavity 51, as in the components 91 contained in cavity 51 in area AR4 in Figure 5 and the right diagram in Figure 6. It is difficult for the component mounting machine WM3 to pick up components 91 from these cavities 51.
　部品装着機ＷＭ３がキャビティユニット５０に供給された部品９１を採取可能であるか否かの判定は、機械学習を用いて行うことができる。具体的には、図６の左図に示すように、正規の姿勢でキャビティ５１に収容されている部品９１が撮像されている教師画像７０を使用して機械学習の学習モデルを生成して、部品装着機ＷＭ３による部品９１の採取作業の良否を判定する形態が想定される。しかしながら、教師画像７０を取得した環境（例えば、開発環境）と、製品基板９００を生産する生産環境には差異があるため、学習モデルをそのまま使用すると、採取作業の良否を誤判定する可能性がある。 Whether the component mounting machine WM3 can pick up the component 91 supplied to the cavity unit 50 can be determined using machine learning. Specifically, as shown in the left diagram of FIG. 6, a machine learning learning model is generated using a teacher image 70 in which a component 91 housed in the cavity 51 in the correct orientation is captured, and the quality of the picking operation of the component 91 by the component mounting machine WM3 is determined. However, since there is a difference between the environment in which the teacher image 70 was acquired (e.g., the development environment) and the production environment in which the product board 900 is produced, if the learning model is used as is, there is a possibility that the quality of the picking operation will be erroneously determined.
　そこで、実施形態の生産ラインＷＬ０には、良否判定装置８０が設けられている。良否判定装置８０は、製品基板９００を生産する生産環境において取得された教師画像７０を使用して良否判定モデル８０ｍの再学習を行う。そのため、良否判定モデル８０ｍをそのまま使用する場合と比べて、採取作業の誤判定が低減される。具体的には、良否判定装置８０は、制御ブロックとして捉えると、取得部８１と、学習部８２とを備える。良否判定装置８０は、記憶部８３を備えることもできる。 Therefore, in the embodiment, the production line WL0 is provided with a quality determination device 80. The quality determination device 80 re-learns the quality determination model 80m using teacher images 70 acquired in the production environment where the product boards 900 are produced. Therefore, erroneous determinations in the collection work are reduced compared to when the quality determination model 80m is used as is. Specifically, when considered as a control block, the quality determination device 80 includes an acquisition unit 81 and a learning unit 82. The quality determination device 80 can also include a memory unit 83.
　図７に示すように、実施形態の良否判定装置８０は、取得部８１と、学習部８２と、記憶部８３とを備えている。また、取得部８１、学習部８２および記憶部８３のうちの少なくとも一つは、種々の制御装置、管理装置などに設けることができる。取得部８１、学習部８２および記憶部８３のうちの少なくとも一つは、クラウド上に形成することもできる。実施形態では、取得部８１、学習部８２および記憶部８３は、いずれも管理装置ＨＣ０に設けられている。 As shown in FIG. 7, the quality determination device 80 of the embodiment includes an acquisition unit 81, a learning unit 82, and a storage unit 83. At least one of the acquisition unit 81, the learning unit 82, and the storage unit 83 can be provided in various control devices, management devices, etc. At least one of the acquisition unit 81, the learning unit 82, and the storage unit 83 can also be formed on the cloud. In the embodiment, the acquisition unit 81, the learning unit 82, and the storage unit 83 are all provided in the management device HC0.
　実施形態の良否判定装置８０は、図８に示すフローチャートに従って、制御を実行する。取得部８１は、ステップＳ１１に示す処理を行う。学習部８２は、ステップＳ１３およびステップＳ１４に示す判断および処理を行う。記憶部８３は、ステップＳ１２に示す処理を行う。また、取得部８１は、図９に示すフローチャートに従って、制御を実行することができる。図９に示すフローチャートは、ステップＳ１１に示す処理の詳細を示している。 The pass/fail judgment device 80 of the embodiment executes control according to the flowchart shown in FIG. 8. The acquisition unit 81 executes the process shown in step S11. The learning unit 82 executes the judgment and process shown in steps S13 and S14. The storage unit 83 executes the process shown in step S12. The acquisition unit 81 can also execute control according to the flowchart shown in FIG. 9. The flowchart shown in FIG. 9 shows details of the process shown in step S11.
　１－４－１．取得部８１および記憶部８３
　取得部８１は、製品基板９００を生産する生産環境において教師画像７０を取得する（図８に示すステップＳ１１）。教師画像７０は、基板９０に所定の対基板作業を行って製品基板９００を生産する対基板作業機ＷＭ０によって基板９０に設けられる対象物９１ｔが撮像されている複数の画像であって機械学習に使用される画像をいう。 1-4-1. Acquisition unit 81 and storage unit 83
The acquisition unit 81 acquires teacher images 70 in a production environment in which the product substrate 900 is produced (step S11 shown in FIG. 8). The teacher images 70 refer to images used for machine learning, which are a plurality of images capturing an object 91t provided on the substrate 90 by a substrate-related operation machine WM0 that performs a predetermined substrate-related operation on the substrate 90 to produce the product substrate 900.
　教師画像７０は、上記の画像であれば良く、限定されない。例えば、図６の左図に示す画像は、教師画像７０に含まれる。この場合、対基板作業機ＷＭ０は、部品装着機ＷＭ３である。既述されているように、部品装着機ＷＭ３は、保持部材１３ｄによって対象物９１ｔである部品９１を採取して基板９０に装着する。教師画像７０は、公知の種々の機械学習に使用することができる。例えば、教師画像７０は、サポートベクターマシン、ニューラルネットワークなどの種々の機械学習に使用することができる。 The teacher image 70 is not limited to the above images. For example, the image shown in the left diagram of FIG. 6 is included in the teacher image 70. In this case, the substrate-related operation machine WM0 is a component mounting machine WM3. As already described, the component mounting machine WM3 picks up a component 91, which is an object 91t, by using a holding member 13d and mounts it on the substrate 90. The teacher image 70 can be used for various known machine learning techniques. For example, the teacher image 70 can be used for various machine learning techniques such as a support vector machine and a neural network.
　取得部８１は、製品基板９００を生産する生産環境において教師画像７０を取得することができれば良く、種々の形態をとり得る。例えば、保持部材１３ｄによって採取される前の部品９１が撮像された画像を採取前画像７０ａとする。また、保持部材１３ｄによって採取された後の当該部品９１が撮像された画像を採取後画像７０ｂとする。採取前画像７０ａを画像処理して認識される部品９１の供給状態が良好の場合、採取後画像７０ｂを画像処理して認識される部品９１の保持状態および装着状態も良好になり易く、部品装着機ＷＭ３による対基板作業（部品９１の採取作業および装着作業）も良好になり易い。 The acquisition unit 81 may take various forms as long as it can acquire the teacher image 70 in the production environment in which the product board 900 is produced. For example, an image of the component 91 before it is picked up by the holding member 13d is defined as a pre-picking image 70a. An image of the component 91 after it is picked up by the holding member 13d is defined as a post-picking image 70b. If the supply state of the component 91 recognized by image processing of the pre-picking image 70a is good, the holding state and mounting state of the component 91 recognized by image processing of the post-picking image 70b are also likely to be good, and the work on the board by the component mounting machine WM3 (picking up and mounting the component 91) is also likely to be good.
　このように、採取前画像７０ａおよび採取後画像７０ｂは、部品装着機ＷＭ３による対基板作業において密接に関連している。そこで、取得部８１は、採取前画像７０ａおよび採取後画像７０ｂを取得した際の実際の対基板作業がいずれも良好であった場合に、採取前画像７０ａを教師画像７０として取得すると良い。これにより、取得部８１は、部品装着機ＷＭ３による対基板作業が良好な場合の教師画像７０を取得することができる。 In this way, the pre-picking image 70a and the post-picking image 70b are closely related in the substrate work performed by the component mounting machine WM3. Therefore, the acquisition unit 81 may acquire the pre-picking image 70a as the teacher image 70 if the actual substrate work performed when the pre-picking image 70a and the post-picking image 70b were acquired was both good. This allows the acquisition unit 81 to acquire the teacher image 70 when the substrate work performed by the component mounting machine WM3 is good.
　なお、取得部８１は、採取前画像７０ａおよび採取後画像７０ｂのうちの少なくとも一方を取得した際の実際の対基板作業が不良であった場合に、採取前画像７０ａを部品装着機ＷＭ３による対基板作業が不良な場合の教師画像７０として取得することもできる。また、実際の対基板作業の良否は、例えば、部品装着機ＷＭ３、外観検査機ＷＭ５などによって判断することができる。 Note that if the actual substrate work performed when at least one of the pre-collection image 70a and the post-collection image 70b was acquired was defective, the acquisition unit 81 can also acquire the pre-collection image 70a as a teacher image 70 for when the substrate work performed by the component mounting machine WM3 was defective. In addition, the quality of the actual substrate work can be determined, for example, by the component mounting machine WM3, the appearance inspection machine WM5, etc.
　例えば、図１０および図１１に示すように、採取後画像７０ｂは、装着前画像７０ｂ１および装着後画像７０ｂ２を含む。装着前画像７０ｂ１は、保持部材１３ｄによって採取され保持されている部品９１が撮像された画像をいう。図１０に示す装着前画像７０ｂ１は、例えば、部品装着機ＷＭ３の部品カメラ１４によって取得することができる（図９に示すステップＳ１１ｂ）。部品装着機ＷＭ３の制御装置２０は、保持部材１３ｄの採取部（例えば、吸着ノズルの先端部）によって部品９１が保持されるべき理想位置（例えば、部品９１の中心）と、実際の保持位置との偏差Ｄ１が許容範囲内の場合に、既述されている装着の際の補正を行い、部品９１を装着することができる。 For example, as shown in Figs. 10 and 11, the post-picking image 70b includes a pre-mounting image 70b1 and a post-mounting image 70b2. The pre-mounting image 70b1 refers to an image of the component 91 picked up and held by the holding member 13d. The pre-mounting image 70b1 shown in Fig. 10 can be acquired, for example, by the component camera 14 of the component mounting machine WM3 (step S11b shown in Fig. 9). When the deviation D1 between the ideal position (e.g., the center of the component 91) where the component 91 should be held by the picking portion (e.g., the tip of the suction nozzle) of the holding member 13d and the actual holding position is within an allowable range, the control device 20 of the component mounting machine WM3 can make the above-mentioned correction during mounting and mount the component 91.
　同様に、制御装置２０は、保持部材１３ｄに対して部品９１が保持されるべき理想角度に対する回転角度θ１が許容範囲内の場合に、既述されている装着の際の補正を行い、部品９１を装着することができる。よって、制御装置２０は、偏差Ｄ１が許容範囲内であり、且つ、回転角度θ１が許容範囲内の場合に、実際の対基板作業（部品９１の採取作業）を良好と判断する。また、制御装置２０は、偏差Ｄ１および回転角度θ１のうちの少なくとも一つが許容範囲を超える場合に、実際の対基板作業（部品９１の採取作業）を不良と判断する。 Similarly, when the rotation angle θ1 with respect to the ideal angle at which component 91 should be held by holding member 13d is within the allowable range, control device 20 can make the above-mentioned corrections during mounting and mount component 91. Thus, when deviation D1 is within the allowable range and rotation angle θ1 is also within the allowable range, control device 20 judges the actual substrate work (component 91 picking work) to be good. Furthermore, when at least one of deviation D1 and rotation angle θ1 exceeds the allowable range, control device 20 judges the actual substrate work (component 91 picking work) to be bad.
　装着後画像７０ｂ２は、基板９０に装着された部品９１が撮像された画像をいう。図１１に示す装着後画像７０ｂ２は、例えば、部品装着機ＷＭ３の基板カメラ１５によって取得することができる（図９に示すステップＳ１１ｄ）。制御装置２０は、部品９１が基板９０に装着されるべき理想位置（図１１に示す領域ＡＲ５の中心）と、実際の装着位置（部品９１の中心）との偏差Ｄ２が許容範囲内であり、且つ、部品９１が装着されるべき理想角度に対する回転角度θ２が許容範囲内の場合に、実際の対基板作業（部品９１の装着作業）を良好と判断する。 The post-mounting image 70b2 refers to an image of the component 91 mounted on the board 90. The post-mounting image 70b2 shown in FIG. 11 can be acquired, for example, by the board camera 15 of the component mounting machine WM3 (step S11d shown in FIG. 9). The control device 20 judges the actual board-to-board operation (mounting of the component 91) to be good when the deviation D2 between the ideal position at which the component 91 should be mounted on the board 90 (the center of the area AR5 shown in FIG. 11) and the actual mounting position (the center of the component 91) is within an acceptable range and the rotation angle θ2 relative to the ideal angle at which the component 91 should be mounted is within an acceptable range.
　また、制御装置２０は、偏差Ｄ２および回転角度θ２のうちの少なくとも一つが許容範囲を超える場合に、実際の対基板作業（部品９１の装着作業）を不良と判断する。なお、既述されているように、部品９１の装着は、部品装着機ＷＭ３の制御装置２０によって行われる（図９に示すステップＳ１１ｃ）。また、装着後画像７０ｂ２は、外観検査機ＷＭ５によって取得することもできる。外観検査機ＷＭ５は、部品装着機ＷＭ３と同様にして、実際の対基板作業（部品９１の装着作業）の良否を判断することができる。 Furthermore, if at least one of the deviation D2 and the rotation angle θ2 exceeds the allowable range, the control device 20 judges the actual work on the board (the work of mounting the component 91) to be defective. As already described, the mounting of the component 91 is performed by the control device 20 of the component mounting machine WM3 (step S11c shown in FIG. 9). The post-mounting image 70b2 can also be acquired by the visual inspection machine WM5. The visual inspection machine WM5 can judge the quality of the actual work on the board (the work of mounting the component 91) in the same way as the component mounting machine WM3.
　既述されているように、実施形態の部品装着機ＷＭ３は、バルクフィーダ３０を備えている。バルクフィーダ３０は、軌道部材４０と、加振装置６０とを具備する。軌道部材４０は、部品９１をバルク状態で収容する部品ケース３２から排出された部品９１である供給部品９１ｓが部品装着機ＷＭ３によって採取可能な供給領域Ａｓ０に搬送される搬送路Ｒｄ０を備える。加振装置６０は、軌道部材４０を加振して供給領域Ａｓ０に供給部品９１ｓを搬送する。 As described above, the component mounting machine WM3 of the embodiment includes a bulk feeder 30. The bulk feeder 30 includes a track member 40 and a vibration device 60. The track member 40 includes a transport path Rd0 along which a supply part 91s, which is a part 91 discharged from a part case 32 that contains the parts 91 in bulk, is transported to a supply area As0 where the supply part 91s can be picked up by the component mounting machine WM3. The vibration device 60 vibrates the track member 40 to transport the supply part 91s to the supply area As0.
　上記の形態では、採取前画像７０ａは、バルクフィーダ３０の供給領域Ａｓ０に搬送された供給部品９１ｓが撮像された画像である。図６の左図に示す採取前画像７０ａは、例えば、部品装着機ＷＭ３の基板カメラ１５によって取得することができる（図９に示すステップＳ１１ａ）。図６の左図に示す採取前画像７０ａは、部品９１が正規の姿勢でキャビティ５１に収容されており、実際の対基板作業（部品９１の供給作業）は、良好である。取得部８１は、採取前画像７０ａ、装着前画像７０ｂ１および装着後画像７０ｂ２を取得した際の実際の対基板作業がいずれも良好であった場合に、採取前画像７０ａを教師画像７０として取得する。 In the above embodiment, the pre-picking image 70a is an image of the supply component 91s transported to the supply area As0 of the bulk feeder 30. The pre-picking image 70a shown in the left diagram of FIG. 6 can be acquired, for example, by the board camera 15 of the component mounting machine WM3 (step S11a shown in FIG. 9). In the pre-picking image 70a shown in the left diagram of FIG. 6, the component 91 is accommodated in the cavity 51 in the correct orientation, and the actual substrate work (the supply work of the component 91) is good. The acquisition unit 81 acquires the pre-picking image 70a as the teacher image 70 if the actual substrate work when acquiring the pre-picking image 70a, the pre-mounting image 70b1, and the post-mounting image 70b2 are all good.
　なお、図５の領域ＡＲ４および図６の右図のキャビティ５１に収容されている部品９１のように、一つのキャビティ５１に複数（同図では、二つ）の部品９１が嵌まり込んでいるキャビティ５１が存在する。この場合、破線の矩形で示すように、制御装置２０は、一つのキャビティ５１に一つの部品９１が収容されていると誤認識して、実際の対基板作業（部品９１の供給作業）が良好であると誤判定する可能性がある。しかしながら、保持部材１３ｄは、当該キャビティ５１に収容されている部品９１を採取することが困難であり、装着前画像７０ｂ１および装着後画像７０ｂ２を取得した際の実際の対基板作業が不良と判断される可能性が高い。その結果、図６の右図に示す画像は、教師画像７０として採用されず、教師画像７０の誤取得が抑制される。 Note that there are cavities 51 in which multiple components 91 (two in this figure) are fitted, such as the components 91 housed in the cavity 51 in area AR4 in FIG. 5 and the right diagram in FIG. 6. In this case, as shown by the dashed rectangle, the control device 20 may erroneously recognize that one component 91 is housed in one cavity 51, and erroneously determine that the actual substrate work (the supply work of the components 91) is good. However, it is difficult for the holding member 13d to pick up the component 91 housed in the cavity 51, and it is highly likely that the actual substrate work when the before-mounting image 70b1 and the after-mounting image 70b2 are acquired will be judged to be poor. As a result, the image shown in the right diagram in FIG. 6 is not adopted as the teacher image 70, and erroneous acquisition of the teacher image 70 is suppressed.
　また、実施形態のバルクフィーダ３０は、キャビティユニット５０を備えている。この場合、保持部材１３ｄによる部品９１の採取位置は、キャビティ５１の位置に固定される。バルクフィーダ３０は、キャビティユニット５０を省略することもできる。この場合、保持部材１３ｄによる部品９１の採取位置は、供給領域Ａｓ０の任意の位置であり、制御装置２０は、部品９１の位置および回転角度を認識する必要がある。しかしながら、上記の点以外は、同様であり、いずれの形態においても、良否判定装置８０を適用することができる。 The bulk feeder 30 of the embodiment also includes a cavity unit 50. In this case, the pick-up position of the part 91 by the holding member 13d is fixed to the position of the cavity 51. The bulk feeder 30 may also omit the cavity unit 50. In this case, the pick-up position of the part 91 by the holding member 13d is any position in the supply area As0, and the control device 20 needs to recognize the position and rotation angle of the part 91. However, apart from the above points, they are similar, and the pass/fail judgment device 80 can be applied to either form.
　記憶部８３は、取得部８１によって取得された教師画像７０を記憶装置８０ｓに記憶させる（図８に示すステップＳ１２）。これにより、記憶装置８０ｓは、製品基板９００を生産する生産環境において取得された教師画像７０を記憶して、蓄積していくことができる。記憶部８３は、教師画像７０を記憶装置８０ｓに記憶させることができれば良く、種々の形態をとり得る。例えば、記憶部８３は、取得部８１によって教師画像７０が取得される度に逐次、教師画像７０を記憶装置８０ｓに記憶させることができる。また、記憶部８３は、取得部８１によって所定数の教師画像７０が取得された場合に、所定数の教師画像７０をまとめて記憶装置８０ｓに記憶させることもできる。 The storage unit 83 stores the teacher image 70 acquired by the acquisition unit 81 in the storage device 80s (step S12 shown in FIG. 8). This allows the storage device 80s to store and accumulate the teacher images 70 acquired in the production environment in which the product boards 900 are produced. The storage unit 83 may take various forms as long as it is capable of storing the teacher images 70 in the storage device 80s. For example, the storage unit 83 may store the teacher images 70 in the storage device 80s sequentially each time the acquisition unit 81 acquires a teacher image 70. Furthermore, when a predetermined number of teacher images 70 are acquired by the acquisition unit 81, the storage unit 83 may store the predetermined number of teacher images 70 collectively in the storage device 80s.
　記憶装置８０ｓは、少なくとも教師画像７０を記憶することができれば良く、公知の記憶装置、データベースなどを用いることができる。また、少なくとも対象物９１ｔが異なると、適用可能な教師画像７０も異なる。例えば、対象物９１ｔが部品９１の場合、部品種が異なると、適用可能な教師画像７０も異なる。そのため、記憶装置８０ｓは、対象物９１ｔを識別する識別情報と、教師画像７０とを関連付けて記憶することができる。さらに、例えば、対基板作業機ＷＭ０の個体差により、対基板作業機ＷＭ０が異なると、適切な教師画像７０も異なる可能性がある。対基板作業機ＷＭ０の相違には、対基板作業機ＷＭ０が具備する装置、機器（例えば、部品装着機ＷＭ３の部品供給装置１２）の相違が含まれる。 The storage device 80s is required to be capable of storing at least the teacher image 70, and a known storage device, database, etc. may be used. Furthermore, at least when the object 91t is different, the applicable teacher image 70 is different. For example, when the object 91t is a component 91, when the component type is different, the applicable teacher image 70 is different. Therefore, the storage device 80s can store identification information for identifying the object 91t in association with the teacher image 70. Furthermore, for example, when the substrate-related work machine WM0 is different due to individual differences in the substrate-related work machine WM0, the appropriate teacher image 70 may also be different. Differences in the substrate-related work machine WM0 include differences in the devices and equipment (for example, the component supply device 12 of the component mounting machine WM3) that the substrate-related work machine WM0 is equipped with.
　同様に、例えば、教師画像７０を取得する撮像装置８０ｃの個体差により、撮像装置８０ｃが異なると、適切な教師画像７０も異なる可能性がある。例えば、撮像装置８０ｃの照明装置が異なると、適切な教師画像７０も異なる可能性がある。また、撮像装置８０ｃが教師画像７０を取得したときの撮像条件が異なると、適切な教師画像７０も異なる可能性がある。例えば、撮像装置８０ｃの照明方向、露光時間および絞りのうちの少なくとも一つが異なると、適切な教師画像７０も異なる可能性がある。 Similarly, for example, due to individual differences in the imaging device 80c that captures the teacher image 70, the appropriate teacher image 70 may differ if the imaging device 80c is different. For example, if the lighting device of the imaging device 80c is different, the appropriate teacher image 70 may differ. Also, if the imaging conditions when the imaging device 80c captures the teacher image 70 are different, the appropriate teacher image 70 may differ. For example, if at least one of the lighting direction, exposure time, and aperture of the imaging device 80c is different, the appropriate teacher image 70 may differ.
　そこで、記憶装置８０ｓは、対象物９１ｔを識別する識別情報と、対基板作業機ＷＭ０、教師画像７０を取得する撮像装置８０ｃ、および、撮像装置８０ｃが教師画像７０を取得したときの撮像条件のうちの少なくとも一つを識別する識別情報と、教師画像７０とを関連付けて記憶すると良い。これにより、記憶装置８０ｓは、生産環境に合致した教師画像７０を記憶することができる。なお、既述されている部品装着機ＷＭ３の部品カメラ１４、基板カメラ１５および外観検査機ＷＭ５の撮像装置は、撮像装置８０ｃに含まれる。 The storage device 80s may therefore store, in association with the teacher image 70, identification information identifying the target object 91t, identification information identifying at least one of the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, and the imaging conditions under which the imaging device 80c acquired the teacher image 70. This allows the storage device 80s to store the teacher image 70 that matches the production environment. The component camera 14, board camera 15, and imaging devices of the component mounting machine WM3 and the visual inspection machine WM5 described above are included in the imaging device 80c.
　１－４－２．学習部８２
　例えば、対基板作業機ＷＭ０の製造者は、事前に教師画像７０を用意して、機械学習の学習モデルを生成し、学習モデルを対基板作業機ＷＭ０の使用者に配布する場合がある。これにより、対基板作業機ＷＭ０の使用者は、教師画像７０を取得して学習モデルを生成する作業を省略することができる。 1-4-2. Learning unit 82
For example, a manufacturer of the substrate-related operation machine WM0 may prepare teacher images 70 in advance, generate a machine learning learning model, and distribute the learning model to a user of the substrate-related operation machine WM0. This allows the user of the substrate-related operation machine WM0 to omit the work of acquiring teacher images 70 and generating a learning model.
　しかしながら、例えば、対象物９１ｔが部品９１の場合、部品種が同じであっても、ベンダが異なると、部品９１の外形寸法などが若干、異なる場合がある。そのため、教師画像７０が相違し、製造者が生成した学習モデルが不適切な学習モデルになる可能性がある。このように、製造者が教師画像７０を取得した環境（例えば、開発環境）と、使用者が製品基板９００を生産する生産環境には差異があるため、学習モデルをそのまま使用すると、対基板作業の良否を誤判定する可能性がある。 However, for example, when the target object 91t is a component 91, even if the component type is the same, the external dimensions of the component 91 may differ slightly if the vendor is different. This may result in a different teacher image 70, and the learning model generated by the manufacturer may be an inappropriate learning model. As such, there is a difference between the environment in which the manufacturer acquired the teacher image 70 (e.g., the development environment) and the production environment in which the user produces the product board 900, and therefore, if the learning model is used as is, there is a possibility that the quality of the work on the board may be erroneously determined.
　そこで、学習部８２は、生産環境と異なる環境において取得された教師画像７０を使用して生成された学習モデルであって対基板作業機ＷＭ０による対基板作業の良否を判定する良否判定モデル８０ｍの再学習を、取得部８１によって取得された教師画像７０を使用して行う。例えば、図１２に示すように、対基板作業機ＷＭ０の製造者は、開発環境において教師画像７０を取得して、良否判定モデル８０ｍを生成する。 Then, the learning unit 82 uses the teacher images 70 acquired by the acquisition unit 81 to re-learn the pass/fail judgment model 80m, which is a learning model generated using the teacher images 70 acquired in an environment different from the production environment and judges whether the substrate-related operation performed by the substrate-related operation machine WM0 is pass/fail. For example, as shown in FIG. 12, the manufacturer of the substrate-related operation machine WM0 acquires the teacher images 70 in a development environment and generates the pass/fail judgment model 80m.
　良否判定モデル８０ｍは、対基板作業の良否を判定することができれば良く、公知の学習モデルを用いることができる。例えば、良否判定モデル８０ｍは、サポートベクターマシン、ニューラルネットワークなどの種々の機械学習のアルゴリズムに合わせて生成することができる。既述されているように、取得部８１は、製品基板９００を生産する生産環境において教師画像７０を取得する。そして、学習部８２は、取得部８１によって取得された教師画像７０を使用して、良否判定モデル８０ｍの再学習を行う。 The pass/fail judgment model 80m only needs to be able to judge whether the work on the substrate is pass/fail, and a publicly known learning model can be used. For example, the pass/fail judgment model 80m can be generated in accordance with various machine learning algorithms such as a support vector machine and a neural network. As described above, the acquisition unit 81 acquires the teacher image 70 in the production environment in which the product substrate 900 is produced. The learning unit 82 then uses the teacher image 70 acquired by the acquisition unit 81 to re-learn the pass/fail judgment model 80m.
　学習部８２は、任意のタイミングにおいて、良否判定モデル８０ｍの再学習を行うことができる。また、学習部８２は、所定のタイミングにおいて、良否判定モデル８０ｍの再学習を行うこともできる（図８に示すステップＳ１３でＹｅｓの場合およびステップＳ１４）。例えば、対基板作業機ＷＭ０が導入された直後ほど、生産環境において取得された教師画像７０の数が少なく、良否判定モデル８０ｍの再学習を適切に実行できない可能性がある。 The learning unit 82 can re-learn the pass/fail judgment model 80m at any timing. The learning unit 82 can also re-learn the pass/fail judgment model 80m at a predetermined timing (if Yes in step S13 and step S14 shown in FIG. 8). For example, immediately after the substrate-related operation machine WM0 is introduced, the number of teacher images 70 acquired in the production environment is small, and re-learning of the pass/fail judgment model 80m may not be performed appropriately.
　そこで、学習部８２は、対基板作業機ＷＭ０が導入されて製品基板９００の生産を開始してから所定時間が経過した場合に、良否判定モデル８０ｍの再学習を行うことができる。これにより、学習部８２は、対基板作業機ＷＭ０が導入されて製品基板９００の生産を開始してから所定時間が経過したタイミングで、所定時間の製品基板９００の生産によって取得された教師画像７０を使用して、良否判定モデル８０ｍの再学習を行うことができる。なお、所定時間は、任意の時間に設定することができ、良否判定モデル８０ｍの再学習を適切に実行可能な時間に設定することができる。 The learning unit 82 can therefore re-learn the pass/fail determination model 80m when a predetermined time has passed since the substrate-related operation machine WM0 was introduced and production of the product substrate 900 was started. This allows the learning unit 82 to re-learn the pass/fail determination model 80m using the teacher images 70 acquired during the production of the product substrate 900 for the predetermined time, at the timing when a predetermined time has passed since the substrate-related operation machine WM0 was introduced and production of the product substrate 900 was started. The predetermined time can be set to any time, and can be set to a time at which re-learning of the pass/fail determination model 80m can be appropriately executed.
　学習部８２は、取得部８１によって取得された教師画像７０のみを使用して、良否判定モデル８０ｍの再学習を行うことができる。また、学習部８２は、生産環境において取得部８１によって取得された教師画像７０と、生産環境と異なる環境（例えば、開発環境）において取得された教師画像７０とを併用して、良否判定モデル８０ｍの再学習を行うこともできる。さらに、学習部８２は、上記の所定時間が長くなるほど、取得部８１によって取得された教師画像７０の割合を増加させて、良否判定モデル８０ｍの再学習を行うこともできる。 The learning unit 82 can re-learn the pass/fail judgment model 80m using only the teacher images 70 acquired by the acquisition unit 81. The learning unit 82 can also re-learn the pass/fail judgment model 80m using a combination of teacher images 70 acquired by the acquisition unit 81 in the production environment and teacher images 70 acquired in an environment different from the production environment (e.g., a development environment). Furthermore, the learning unit 82 can also re-learn the pass/fail judgment model 80m by increasing the proportion of teacher images 70 acquired by the acquisition unit 81 as the above-mentioned specified time becomes longer.
　また、良否判定モデル８０ｍを使用して対基板作業の良否を判定した判定結果が実際の対基板作業の良否と異なる誤判定率が高いほど、良否判定モデル８０ｍが生産環境に合致していない可能性が高い。そこで、学習部８２は、良否判定モデル８０ｍを使用して対基板作業の良否を判定した判定結果が実際の対基板作業の良否と異なる誤判定率が許容値を超えた場合に、良否判定モデル８０ｍの再学習を行うこともできる。これにより、学習部８２は、誤判定率が許容値を超えたタイミングで、良否判定モデル８０ｍの再学習を行うことができる。なお、誤判定率は、対基板作業の良否を判定した判定回数に対して、誤判定した回数の割合をいう。 Furthermore, the higher the erroneous judgment rate, where the judgment result of judging the pass/fail of substrate-related work using the pass/fail judgment model 80m differs from the actual pass/fail of the substrate-related work, the more likely it is that the pass/fail judgment model 80m does not match the production environment. Therefore, the learning unit 82 can re-learn the pass/fail judgment model 80m when the erroneous judgment rate, where the judgment result of judging the pass/fail of substrate-related work using the pass/fail judgment model 80m differs from the actual pass/fail of the substrate-related work, exceeds an allowable value. In this way, the learning unit 82 can re-learn the pass/fail judgment model 80m at the timing when the erroneous judgment rate exceeds the allowable value. The erroneous judgment rate refers to the ratio of the number of times that an erroneous judgment was made to the number of times that the pass/fail of substrate-related work was judged.
　学習部８２は、対基板作業機ＷＭ０が導入されて製品基板９００の生産を開始してから所定時間が経過し、且つ、上記の誤判定率が許容値を超えた場合に、良否判定モデル８０ｍの再学習を行うこともできる。また、所定のタイミングでない場合（図８に示すステップＳ１３でＮｏの場合）、既述されている所定のタイミングが到来するまで、取得部８１による教師画像７０の取得が継続され、記憶部８３による教師画像７０の記憶が継続される。 The learning unit 82 can also re-learn the pass/fail judgment model 80m when a predetermined time has elapsed since the substrate-related processing machine WM0 was introduced and production of the product substrates 900 began, and the above-mentioned erroneous judgment rate exceeds an allowable value. Furthermore, if the predetermined timing has not yet arrived (No in step S13 shown in FIG. 8), the acquisition unit 81 continues to acquire the teacher image 70, and the memory unit 83 continues to store the teacher image 70, until the predetermined timing described above arrives.
　既述されているように、対基板作業機ＷＭ０、教師画像７０を取得する撮像装置８０ｃ、撮像装置８０ｃが教師画像７０を取得したときの撮像条件、および、対象物９１ｔのうちの少なくとも対象物９１ｔが異なると、適切な教師画像７０が相違する可能性がある。そこで、良否判定モデル８０ｍは、対基板作業機ＷＭ０、教師画像７０を取得する撮像装置８０ｃ、撮像装置８０ｃが教師画像７０を取得したときの撮像条件、および、対象物９１ｔのうちの少なくとも対象物９１ｔが異なる場合に、新たに設けることができる。これにより、生産環境により合致した良否判定モデル８０ｍが設けられる。 As already mentioned, if the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, the imaging conditions when the imaging device 80c acquired the teacher image 70, and at least the object 91t among the objects 91t are different, the appropriate teacher image 70 may differ. Therefore, the pass/fail judgment model 80m can be newly provided when the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, the imaging conditions when the imaging device 80c acquired the teacher image 70, and at least the object 91t among the objects 91t are different. This allows the pass/fail judgment model 80m to be provided which is more suited to the production environment.
　また、この場合、記憶装置８０ｓは、対象物９１ｔを識別する識別情報と、対基板作業機ＷＭ０、教師画像７０を取得する撮像装置８０ｃ、および、撮像装置８０ｃが教師画像７０を取得したときの撮像条件のうちの少なくとも一つを識別する識別情報と、教師画像７０とを関連付けて記憶すると良い。これにより、記憶装置８０ｓは、良否判定モデル８０ｍに合わせて必要な情報を記憶することができる。 In this case, the storage device 80s may store identification information for identifying the target object 91t, identification information for identifying at least one of the substrate-related operation machine WM0, the imaging device 80c that acquires the teacher image 70, and the imaging conditions under which the imaging device 80c acquired the teacher image 70, in association with the teacher image 70. This allows the storage device 80s to store the necessary information in accordance with the pass/fail judgment model 80m.
　１－４－３．その他の形態
　実施形態の部品装着機ＷＭ３では、部品供給装置１２は、バルクフィーダ３０を備えている。しかしながら、部品供給装置１２は、テープフィーダを備えることもできる。また、部品供給装置１２は、チップ部品などと比べて比較的大型の電子部品（例えば、リード部品など）を、トレイ上に配置した状態で供給することもできる。いずれの場合も、取得部８１は、バルクフィーダ３０と同様にして、教師画像７０を取得することができる。また、実施形態では、対基板作業機ＷＭ０は、部品装着機ＷＭ３を例に説明されている。しかしながら、対基板作業機ＷＭ０は、部品装着機ＷＭ３に限定されない。 1-4-3. Other Forms In the component mounting machine WM3 of the embodiment, the component supplying device 12 includes a bulk feeder 30. However, the component supplying device 12 may also include a tape feeder. Furthermore, the component supplying device 12 may also supply electronic components (e.g., lead components) that are relatively large compared to chip components and the like, in a state in which they are arranged on a tray. In either case, the acquiring unit 81 can acquire the teacher image 70 in the same manner as the bulk feeder 30. Furthermore, in the embodiment, the substrate-related operation machine WM0 is described taking the component mounting machine WM3 as an example. However, the substrate-related operation machine WM0 is not limited to the component mounting machine WM3.
　例えば、対基板作業機ＷＭ０は、対象物９１ｔであるはんだを基板９０に印刷する印刷機ＷＭ１であっても良い。この場合、教師画像７０は、基板９０に印刷されたはんだを撮像した画像である。また、学習部８２は、印刷機ＷＭ１による対基板作業（はんだの印刷作業）の良否を判定する良否判定モデル８０ｍの再学習を、取得部８１によって取得された教師画像７０を使用して行うことができる。なお、本明細書において記載されている事項は、適宜、取捨選択して適用することができる。また、本明細書において記載されている事項は、適宜、組み合わせることができる。 For example, the substrate-related work machine WM0 may be a printer WM1 that prints solder, which is the target object 91t, on the substrate 90. In this case, the teacher image 70 is an image of the solder printed on the substrate 90. The learning unit 82 can use the teacher image 70 acquired by the acquisition unit 81 to re-learn the pass/fail judgment model 80m that judges whether the substrate-related work (solder printing work) performed by the printer WM1 is pass/fail. The matters described in this specification can be selected and applied as appropriate. The matters described in this specification can be combined as appropriate.
　２．良否判定方法
　良否判定装置８０について既述されていることは、良否判定方法についても同様に言える。具体的には、良否判定方法は、取得工程と、学習工程とを備える。取得工程は、取得部８１が行う制御に相当する。学習工程は、学習部８２が行う制御に相当する。また、良否判定方法は、記憶工程を備えることもできる。記憶工程は、記憶部８３が行う制御に相当する。 2. Pass/fail determination method What has already been described about the pass/fail determination device 80 also applies to the pass/fail determination method. Specifically, the pass/fail determination method includes an acquisition process and a learning process. The acquisition process corresponds to the control performed by the acquisition unit 81. The learning process corresponds to the control performed by the learning unit 82. The pass/fail determination method may also include a storage process. The storage process corresponds to the control performed by the storage unit 83.
　３．実施形態の効果の一例
　良否判定装置８０によれば、製品基板９００を生産する生産環境において取得された教師画像７０を使用して良否判定モデル８０ｍの再学習を行うことができる。そのため、良否判定モデル８０ｍをそのまま使用する場合と比べて、対基板作業の誤判定が低減される。良否判定装置８０について上述されていることは、良否判定方法についても同様に言える。 3. Example of Effects of the Embodiment According to the quality determination device 80, the quality determination model 80m can be re-learned using the teacher image 70 acquired in the production environment where the product substrate 900 is produced. Therefore, erroneous determination of the substrate-related work is reduced compared to the case where the quality determination model 80m is used as is. The above description of the quality determination device 80 also applies to the quality determination method.
１３ｄ：保持部材、３０：バルクフィーダ、３２：部品ケース、
４０：軌道部材、６０：加振装置、７０：教師画像、７０ａ：採取前画像、
７０ｂ：採取後画像、７０ｂ１：装着前画像、７０ｂ２：装着後画像、
８０：良否判定装置、８０ｃ：撮像装置、８０ｍ：良否判定モデル、
８０ｓ：記憶装置、８１：取得部、８２：学習部、８３：記憶部、
９０：基板、９１：部品、９１ｓ：供給部品、９１ｔ：対象物、
９００：製品基板、Ａｓ０：供給領域、Ｒｄ０：搬送路、
ＷＭ０：対基板作業機、ＷＭ３：部品装着機。 13d: holding member, 30: bulk feeder, 32: parts case,
40: track member, 60: vibration device, 70: teacher image, 70a: pre-collection image,
70b: post-collection image, 70b1: pre-attachment image, 70b2: post-attachment image,
80: quality determination device, 80c: imaging device, 80m: quality determination model,
80s: storage device, 81: acquisition unit, 82: learning unit, 83: storage unit,
90: board, 91: component, 91s: supply component, 91t: object,
900: product substrate, As0: supply area, Rd0: transport path,
WM0: Board-related work machine, WM3: Component placement machine.

Claims (10)

  1. 　基板に所定の対基板作業を行って製品基板を生産する対基板作業機によって前記基板に設けられる対象物が撮像されている複数の画像であって機械学習に使用される教師画像を、前記製品基板を生産する生産環境において取得する取得部と、
    　前記生産環境と異なる環境において取得された前記教師画像を使用して生成された学習モデルであって前記対基板作業機による前記対基板作業の良否を判定する良否判定モデルの再学習を、前記取得部によって取得された前記教師画像を使用して行う学習部と、
    を備える良否判定装置。     an acquisition unit that acquires, in a production environment in which the product substrates are produced, a plurality of images of an object provided on the substrate by a substrate-related operation machine that performs a predetermined substrate-related operation on the substrate to produce a product substrate, the image being used as a teacher image for machine learning;
    a learning unit that uses the teacher images acquired by the acquisition unit to re-learn a pass/fail judgment model that is a learning model generated using the teacher images acquired in an environment different from the production environment and that judges whether the substrate-related operation performed by the substrate-related operation machine is pass/fail; and
    A quality determination device comprising:
  2. 　前記対基板作業機は、保持部材によって前記対象物である部品を採取して前記基板に装着する部品装着機であり、
    　前記取得部は、前記保持部材によって採取される前の前記部品が撮像された採取前画像、および、前記保持部材によって採取された後の当該部品が撮像された採取後画像を取得した際の実際の前記対基板作業がいずれも良好であった場合に、前記採取前画像を前記教師画像として取得する請求項１に記載の良否判定装置。     the substrate-related operation machine is a component mounting machine that picks up a component, which is the object, by a holding member and mounts the component on the substrate,
    The pass/fail determination device of claim 1, wherein the acquisition unit acquires a pre-collection image of the part before it is picked up by the holding member as the teacher image if the actual substrate-related work was good when acquiring a pre-collection image of the part before it is picked up by the holding member and a post-collection image of the part after it is picked up by the holding member.
  3. 　前記採取後画像は、前記保持部材によって採取され保持されている前記部品が撮像された装着前画像、および、前記基板に装着された当該部品が撮像された装着後画像である請求項２に記載の良否判定装置。 The pass/fail determination device according to claim 2, wherein the post-collection image is a pre-mounting image of the component collected and held by the holding member, and a post-mounting image of the component mounted on the board.
  4. 　前記部品装着機は、
    　前記部品をバルク状態で収容する部品ケースから排出された前記部品である供給部品が前記部品装着機によって採取可能な供給領域に搬送される搬送路を備える軌道部材と、
    　前記軌道部材を加振して前記供給領域に前記供給部品を搬送する加振装置と、
    を具備するバルクフィーダを備え、
    　前記採取前画像は、前記バルクフィーダの前記供給領域に搬送された前記供給部品が撮像された画像である請求項２または請求項３に記載の良否判定装置。     The component mounting machine includes:
    a track member including a conveying path along which a supply component, which is the component discharged from a component case that accommodates the components in a bulk state, is conveyed to a supply area where the supply component can be picked up by the component mounting machine;
    a vibration device that vibrates the track member to transport the supply part to the supply area;
    The present invention relates to a bulk feeder comprising:
    4. The quality determination device according to claim 2, wherein the pre-picking image is an image of the supply component transported to the supply area of the bulk feeder.
  5. 　前記取得部によって取得された前記教師画像を記憶装置に記憶させる記憶部を備える請求項１に記載の良否判定装置。 The quality determination device according to claim 1, further comprising a storage unit that stores the teacher image acquired by the acquisition unit in a storage device.
  6. 　前記記憶装置は、
    　前記対象物を識別する識別情報と、
    　前記対基板作業機、前記教師画像を取得する撮像装置、および、前記撮像装置が前記教師画像を取得したときの撮像条件のうちの少なくとも一つを識別する識別情報と、
    　前記教師画像と、
    を関連付けて記憶する請求項５に記載の良否判定装置。     The storage device includes:
    Identification information for identifying the object;
    Identification information for identifying at least one of the substrate-related-operation performing apparatus, an imaging device that acquires the teacher image, and an imaging condition when the imaging device acquired the teacher image; and
    The teacher image;
    6. The quality determining device according to claim 5, wherein the above-mentioned items are stored in association with each other.
  7. 　前記学習部は、前記対基板作業機が導入されて前記製品基板の生産を開始してから所定時間が経過した場合に、前記良否判定モデルの前記再学習を行う請求項１に記載の良否判定装置。 The quality determination device according to claim 1, wherein the learning unit performs the re-learning of the quality determination model when a predetermined time has elapsed since the substrate-related processing machine was introduced and production of the product substrates was started.
  8. 　前記学習部は、前記良否判定モデルを使用して前記対基板作業の良否を判定した判定結果が実際の前記対基板作業の良否と異なる誤判定率が許容値を超えた場合に、前記良否判定モデルの前記再学習を行う請求項１に記載の良否判定装置。 The quality determination device according to claim 1, wherein the learning unit performs the re-learning of the quality determination model when a rate of erroneous determinations, in which the result of determining the quality of the substrate-related work using the quality determination model differs from the actual quality of the substrate-related work, exceeds an allowable value.
  9. 　前記良否判定モデルは、前記対基板作業機、前記教師画像を取得する撮像装置、前記撮像装置が前記教師画像を取得したときの撮像条件、および、前記対象物のうちの少なくとも前記対象物が異なる場合に、新たに設けられる請求項１に記載の良否判定装置。 The quality determination device according to claim 1, in which the quality determination model is newly established when at least one of the substrate-related operating machine, the imaging device that acquires the teacher image, the imaging conditions under which the imaging device acquires the teacher image, and the object is different.
  10. 　基板に所定の対基板作業を行って製品基板を生産する対基板作業機によって前記基板に設けられる対象物が撮像されている複数の画像であって機械学習に使用される教師画像を、前記製品基板を生産する生産環境において取得する取得工程と、
    　前記生産環境と異なる環境において取得された前記教師画像を使用して生成された学習モデルであって前記対基板作業機による前記対基板作業の良否を判定する良否判定モデルの再学習を、前記取得工程によって取得された前記教師画像を使用して行う学習工程と、
    を備える良否判定方法。     an acquisition step of acquiring, in a production environment in which the product substrates are produced, a plurality of images of objects to be provided on the substrate by a substrate-related operation machine that performs a predetermined substrate-related operation on the substrate to produce a product substrate, the teacher images being used for machine learning;
    a learning process in which, using the teacher images acquired in the acquisition process, a quality judgment model is re-learned, the quality judgment model being a learning model generated using the teacher images acquired in an environment different from the production environment, and the quality judgment model judges whether the substrate-related operation performed by the substrate-related operation machine is quality or not;
    The method includes the steps of:
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JP2014153906A (en) * 2013-02-08 2014-08-25 Honda Motor Co Ltd Inspection device, inspection method and program
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