Object work system and determination method

Abstract

This object work system comprises a mounting-related device that executes mounting-related processing relating to mounting processing for mounting a component on an object that is conveyed by a conveyance device, the conveyance device comprising a detection unit for detecting the object, a conveyance member on which the object is placed and conveyed, and a drive unit for driving the conveyance member. The object work system comprises a determination unit that determines a defective state of the conveyance device from a determination factor that includes one or more of a detection factor of the detection unit, a state factor of the conveyance member, and a drive factor of the drive unit.

Description

Object Working System and Judgment Method

[0001] This specification discloses an object working system and a judgment method.

[0002] Conventionally, as a work management device for managing a mounting device that mounts components on an object such as a substrate, a problem detection unit that detects that a problem has occurred in any of a plurality of mounting devices, a coping method database that stores coping methods for problems, an update unit that updates the coping methods for problems at any time, and a work instruction unit that extracts a coping method for a problem from the coping method database and instructs an operator when the problem detection unit detects that a problem has occurred have been proposed (see, for example, Patent Document 1). In this device, when a problem occurs in the mounting device, the operator can appropriately cope with the problem in a short time by implementing the coping method instructed by the work instruction unit.

[0003] International Publication No. 2018 / 142604

[0004] However, in Patent Document 1 described above, although it is possible to cope with a problem by storing a coping method for the problem in a coping method database, it is still not sufficient, and it has been required to cope with the problem as early as possible before the occurrence of a defect.

[0005] The present disclosure has been made in view of such problems, and the main object is to provide an object working system and a judgment method capable of coping with a defect as early as possible.

[0006] The object working system and judgment method disclosed in this specification have taken the following means to achieve the above main object.

[0007] The object handling system of this disclosure is an object handling system having an object handling device that performs mounting-related processing for mounting components onto an object transported by a transport device comprising a detection unit for detecting an object, a transport member for placing and transporting the object, and a drive unit for driving the transport member, wherein the object handling system includes a determination unit that determines a defective state of the transport device from a determination factor that includes one or more of the detection factors of the detection unit, the state factors of the transport member, and the drive factors of the drive unit.

[0008] In this object handling system, the malfunction state of the conveying device is determined from a determination factor that includes one or more of the following: a detection factor from a detection unit that detects the object, a state factor from a conveying member that places and transports the object, and a drive factor from a drive unit that drives the conveying member. Therefore, this control device can determine the malfunction state according to the determination factor and, by using the determination result, can address any malfunctions that may occur in the conveying device as early as possible.

[0009] A schematic diagram showing an example of the implementation system 10. A schematic diagram showing an example of the implementation device 13. A schematic diagram showing an example of the transport device 40. A diagram showing an example of factor recording information 24 stored in the memory unit 22. A diagram showing an example of the information of the judgment factor 25. A flowchart showing an example of the defective state estimation model construction processing routine. A flowchart showing an example of the implementation processing routine. A flowchart showing an example of the defective state determination processing routine. A diagram showing an example of the information display screen 70.

[0010] This embodiment will be described below with reference to the drawings. Figure 1 is a schematic diagram showing an example of the mounting system 10. Figure 2 is a schematic diagram showing an example of the mounting device 13. Figure 3 is a schematic diagram showing an example of the transport device 40, where Figure 3A is a perspective view and Figure 3B is a side view cross-sectional view. Figure 4 is an explanatory diagram showing an example of factor recording information 24 stored in the storage unit 22. Figure 5 is an explanatory diagram showing an example of the information of the judgment factor 25, where Figure 5A is an example of distance information 24a, Figure 5B is an example of intensity information 24b, Figure 5C is an example of looseness information 24c, Figure 5D is an example of stop position information 24d, Figure 5E is an example of load information 24f, and Figure 5F is an example of operation information 24g. In this embodiment, the left-right direction (X-axis), front-back direction (Y-axis), and up-down direction (Z-axis) are as shown in Figures 1 to 3.

[0011] The mounting system 10 is configured as an object work system that functions as a production line, in which mounting devices 13 for mounting components P onto a substrate S (the object) are arranged in the transport direction D of the substrate S. Here, the object to be mounted is described as a substrate S, but it is not particularly limited as long as it mounts components P, and may be a three-dimensional base material. This mounting system 10 is configured to be able to exchange information with a control unit 60 via a network 19. The control unit 60 is connected to mounting systems 10, as well as mounting systems 10B, 10C, etc., via the network 19 so that they can exchange information. Note that mounting systems 10B and 10C may have different configurations from mounting system 10, but a detailed explanation is omitted as they are assumed to include a configuration similar to mounting system 10.

[0012] As shown in Figure 1, the mounting system 10 includes a printing device 11, a printing inspection device 12, a mounting device 13, a mounting inspection device 14, and a management device 18. The printing device 11 is a device that prints a viscous fluid such as solder paste onto a substrate S. The printing device 11 includes a transport device for transporting the substrate S, as well as a printing head with a squeegee that moves the viscous fluid for printing as a work unit for working on the substrate S as the target object. The printing device 11 may also be a device that prints adhesives or conductive pastes as the viscous fluid. The printing inspection device 12 is a device that inspects the state of the printed viscous fluid. The printing inspection device 12 includes a transport device for transporting the substrate S, as well as an inspection head with an imaging unit that images the printed material as a work unit for working on the substrate S. The mounting device 13 is a device that mounts components P onto the substrate S. The mounting inspection device 14 is a device that inspects the state of the components P mounted by the mounting device 13. The mounting inspection device 14 includes a transport device for transporting the substrate S, as well as an inspection head with an imaging unit for imaging the substrate S, which serves as a work unit for performing operations on the substrate S. The mounting device 13 may also be a mounting-inspection device that has the functions of the mounting inspection device 14. The mounting system 10 may also include a separate transport device 40. The management device 18 is a computer that manages information on each device of the mounting system 10. The printing device 11, printing inspection device 12, mounting device 13, mounting inspection device 14, and management device 18 are mounting-related devices that perform mounting-related processing related to the mounting process. The mounting-related devices include a transport device for transporting the substrate S, but here we will mainly describe the transport device 40 of the mounting device 13.

[0013] The mounting device 13 is a device that performs a mounting process to mount components P onto a substrate S transported by the transport device 40. The mounting device 13 takes components P from the component supply unit 27 that supplies the components P and mounts them onto the substrate S, which is the target object before or after the printing process. As shown in Figure 2, the mounting device 13 comprises a control device 20, a component supply unit 27, a mounting imaging unit 29, a mounting unit 30, an operation panel 36, and a transport device 40.

[0014] The control device 20 is configured as a microprocessor centered on the control unit 21 and controls the entire device. This control device 20 includes a storage unit 22 for storing various data. The control device 20 has the function of controlling the entire mounting device 13. The control device 20 outputs control signals to each unit of the mounting device 13 and inputs signals from each of these units. The control unit 21 has the function of a determination unit 21a that determines the faulty state of the transport device 40 from a faulty state estimation model 26 using a predetermined determination factor 25. Note that "faulty state" refers to a state that is not in good condition, but it may be a state in which the transport process can be executed and is prior to an abnormal state (error state) that hinders the transport process.

[0015] As shown in Figure 4, the memory unit 22 stores mounting condition information 23, factor recording information 24, and a defect state estimation model 26. The mounting condition information 23 includes information such as the mounting order in which components P are mounted on the substrate S, the sampling position of components P, the placement position, and the type of sampling member 33 from which components P can be collected. The factor recording information 24 includes information on the judgment factors 25 as learning data used when determining the state of the transport device 40. New information is added to the factor recording information 24 as the mounting process by the mounting unit 30 progresses. The factor recording information 24 includes information on each factor associated with the elapsed time (see Figure 5). The judgment factors 25 include, for example, one or more of the following: detection factors 24A of the detection units 44 and 45, state factors 24B of the conveyor belt 42 as a transport member, and drive factors 24C of the drive unit 43 that drives the conveyor belt 42. The factor recording information 24 includes, as shown in Figure 4, distance information 24a and intensity information 24b as detection factors 24A, looseness information 24c, stopping position information 24d and deterioration information 24e as state factors 24B, and load information 24f and operation information 24g as driving factors 24C. Distance information 24a is, for example, factor information relating to the distance between the detection units 44 and 45 of the transport device 40 and the object, as shown in Figure 5A. Intensity information 24b is, for example, factor information relating to the intensity of the detected wave detected by the light receiving units of the detection units 44 and 45, as shown in Figure 5B. Looseness information 24c is, for example, factor information relating to the looseness of the conveyor belt 42 as a transport member of the transport device 40, as shown in Figure 5C. Stopping position information 24d is, for example, factor information relating to the stopping position of the substrate S detected by the detection units 44 and 45, as shown in Figure 5D. The stopping position information 24d is assumed to include the amount of positional deviation (distance) relative to the reference position, but it may also include the absolute position of the substrate S. The deterioration information 24e is information on factors related to the deterioration state of the conveyor belt 42. The load information 24f is information on factors related to the load on the drive unit 43 of the conveying device 40 when it is being driven. The load information 24f may include the torque command value when the drive unit 43 is being driven, or it may be the torque value output from the drive unit 43. The operation information 24g is information on factors related to the operation of the drive unit 43.The operation information 24g may include position information from the encoder of the drive unit 43. This factor recording information 24 also includes state information regarding malfunctions that have occurred in the conveying device 40. This state information may be, for example, input by an operator after checking the state of the conveying device 40, or it may be an estimated value by the control unit 21. This state information may include information regarding malfunctions such as the distance and detection wave intensity of the detection units 44 and 45, looseness or deviation of the stopping position of the conveyor belt 42, and deviations in the output and position control of the drive unit 43.

[0016] The malfunction state estimation model 26 is constructed based on information from a determination factor 25 that includes one or more of the detection factors 24A of the detection units 44 and 45, the state factor 24B of the conveyor belt 42, and the drive factor 24C of the drive unit 43. The malfunction state estimation model 26 is a learning model that determines the malfunction state of the conveyor device 40 using the information of the determination factor 25 acquired from the conveyor device 40 as training data. This malfunction state estimation model 26 is constructed by machine learning using the determination factor 25. Examples of machine learning include supervised learning, unsupervised learning, reinforcement learning, deep reinforcement learning, and semi-supervised learning, of which supervised learning and semi-supervised learning are preferred. Machine learning techniques include support vector machines (SVM), logistic regression, random forests (RF), neural networks (NN), feedforward neural networks (FFNN), recurrent neural networks (RNN), convolutional neural networks (CNN), naive Bayes, principal component analysis, k-nearest neighbors, k-means, and generative adversarial networks (GAN). The appropriate machine learning technique should be selected from the above depending on the 25 decision factors and the content of the decision.

[0017] The malfunction state estimation model 26 may, for example, be constructed using training data consisting of information on each factor obtained using a transport device 40 that experimentally creates a deteriorated state in the initial stage, and information on the malfunction state at that time. Alternatively, the malfunction state estimation model 26 may be updated thereafter using factor recording information 24 obtained when the transport device 40 is used by a user. Examples of deteriorated states include a state in which the distance deviation of the detection units 44 and 45 to the object is large, a state in which the light intensity of the detection units 44 and 45 is reduced when they operate, a state in which the conveyor belt 42 is loose, a state in which there is a stop position deviation, a state in which the conveyor belt 42 is deteriorated, a state in which the driving force of the drive unit 43 is unstable, a state in which the rotational operation is unstable, and combinations of two or more of these states. The malfunction state estimation model 26 is constructed using training data that includes information on each factor in this deteriorated state and good / bad states. The malfunction state estimation model 26 is constructed using a number of training data that satisfies a predetermined allowable accuracy when determining the state of the transport device 40 using this model. The malfunction state estimation model 26 is constructed using a determination factor 25 that includes the factors described above. Given the factors of the transport device 40, it is configured to identify which of its states is malfunctioning and to pinpoint the cause.

[0018] The parts supply unit 27 has multiple feeders 28 equipped with reels and tray units, and is detachably attached to the front side of the mounting device 13. Tape, which serves as a holding member, is wound around each reel, and multiple parts P are held along the longitudinal direction of the tape. This holding member is unwound from the reel toward the rear, and with the parts P exposed, it is fed by the feeder 28 to the picking position where it will be picked up by the picking member 33. The mounting imaging unit 29 is a unit that images the mounting unit 30 side, as shown in Figure 2. The mounting imaging unit 29 is a parts camera that captures images of one or more parts P picked up by the mounting head 32. The mounting imaging unit 29 includes an illumination unit that irradiates light onto the mounting unit 30 side and an image sensor that converts the input light into an electrical signal. The mounting imaging unit 29 captures an image of the mounting head 32 holding the parts P as it passes above the mounting imaging unit 29, and outputs the captured image data to the control device 20. The control device 20 processes the captured image to inspect whether the shape and location of part P are normal, and to detect the amount of displacement such as the position and rotation of part P at the time of collection.

[0019] The mounting unit 30 is configured as a work unit that performs work on the substrate S, which is the object. The mounting unit 30 is a unit that picks up components P from the component supply unit 27 using a picking member 33 and places them on the substrate S fixed to the transport device 40. The mounting unit 30 includes a head moving unit 31, a mounting head 32, a picking member 33, an object imaging unit 34, and a lifting unit 35. The head moving unit 31 includes a slider that moves in the XY direction guided by a guide rail and a motor that drives the slider. The mounting head 32 is detachably mounted on the slider and moves in the XY direction by the head moving unit 31. One or more picking members 33 are detachably mounted on the lower side of the mounting head 32. The picking member 33 may be a suction nozzle that picks up components P using negative pressure, or a mechanical chuck that grips components P. The object imaging unit 34 is a unit that images the substrate S, which is the object. The object imaging unit 34 is a mark camera positioned on the underside of the mounting head 32, which images the substrate S and marks formed on the substrate S from above. The object imaging unit 34 moves in the XY direction as the mounting head 32 moves. The object imaging unit 34 can image the substrate S on the transport device 40, as well as the conveyor belt 42. The object imaging unit 34 is used in the process of detecting the deterioration state of the conveyor belt 42. The lifting unit 35 is a unit that moves up and down a cylinder to which a sampling member 33 is attached. The lifting unit 35 is a mechanism that moves linearly along the Z axis, and may be a ball screw mechanism or a linear motor.

[0020] The control panel 36 is a unit configured as a notification unit that receives input from the operator and presents and notifies the operator of information. This control panel 36 is located on the front of the mounting device 13 and includes a display unit which is a display and an operation unit which has both a touch panel and buttons.

[0021] The transport device 40 is a unit that loads, transports, fixes at the mounting position, and unloads substrates S along a transport path extending in the left-right direction (X-axis direction). As shown in Figure 3, the transport device 40 comprises a side frame 41, a conveyor belt 42, a drive unit 43, detection units 44 and 45, a support base 47, a lifting unit 48, and a support plate 49. The transport device 40 also includes a clamping device for fixing the substrates S. The side frame 41 is a pair of support members arranged along the transport direction D at a predetermined distance in the Y-axis direction. One of the side frame 41 is fixed to the end of the support base 47, and the other moves along a guide formed on the support base 47, allowing the transport width to be changed. The conveyor belt 42 is provided on each of the pair of side frames 41 and is a transport member that carries objects on it. The conveyor belt 42 is stretched between a drive shaft and a driven shaft and is driven to circumferentially along the transport direction D by the rotation of the drive shaft. The drive unit 43 is a motor connected to the drive shaft and rotates the drive shaft. The drive unit 43 may be, for example, a stepping motor capable of driving in both forward and reverse rotation directions. The detection units 44 and 45 are sensors provided on the transport path of the transport device 40, as shown in Figure 3B, and are used to detect objects. The detection units 44 and 45 may be, for example, transmissive optical sensors comprising a light-emitting unit and a light-receiving unit provided at opposing positions on either side of the transport path of the substrate S. The detection units 44 and 45 may also be reflective optical sensors. The light-emitting unit emits light as a detection wave. The light-receiving unit receives the light as a detection wave emitted by the light-emitting unit. The detection units 44 and 45 output a detection signal according to the light-receiving state of the light-receiving unit. The detection unit 44 is provided on the entrance side of the transport path, and the detection unit 45 is provided in the central part between the entrance and exit of the transport path. The control device 20 acquires the movement and stopping position of the substrate S based on detection signals from the detection units 44 and 45. The support base 47 is a base that supports the components of the transport device 40 from below. The lifting unit 48 is a mechanism that is installed on the support base 47 and raises and lowers the support plate 49. The support plate 49 is a support member that supports the substrate S from below, and for example, backup pins are arranged on it.

[0022] The management device 18 is configured as a server for managing the mounting system 10. The management device 18 transmits signals to mounting-related devices such as the mounting device 13 provided by the mounting system 10 and receives signals from these mounting-related devices. As shown in Figure 1, the management device 18 includes a control unit 51, a storage unit 52, a display device 57, an input device 58, and a communication unit 59. The control unit 51 is configured as a microprocessor and controls the entire device. The storage unit 52 is a storage medium for storing various data and programs. The storage unit 52 stores mounting condition information 53, factor recording information 54, and a defect state estimation model 56, which include information similar to the mounting condition information 23, factor recording information 24, and defect state estimation model 26 stored in the storage unit 22. The factor recording information 54 may include judgment factors 55 obtained during manufacturing and their state information obtained from the mounting-related devices of the mounting system 10. The defect state estimation model 56 may be pre-built before being used in the mounting system 10. The information stored in the memory unit 52 will not be described in detail here, as the information stored in the memory unit 22 has already been described. The control unit 51 acquires the implementation condition information 53, acquires and stores the factor recording information 54 from the implementation-related equipment, and performs processing to apply it to the faulty state estimation model 56. The display device 57 is a liquid crystal screen that serves as a notification unit to inform the user of various information by displaying it. The input device 58 includes a keyboard and mouse, etc., for the user to input various commands. The communication unit 59 is an interface for exchanging information with external devices included in the implementation system 10.

[0023] The central control unit 60 is configured as a server for a central control system that manages multiple implementation systems, including implementation systems 10, 10B, 10C, etc. The central control unit 60 may be owned by the developer or provider of the implementation systems 10, 10B, 10C, etc. The central control unit 60 transmits signals to a management device 18, etc., provided by the implementation system 10, and receives signals from this management device 18, etc. As shown in Figure 1, the central control unit 60 comprises a control unit 61, a storage unit 62, a display device 67, an input device 68, and a communication unit 69. The control unit 61 is configured as a microprocessor and controls the entire device. The storage unit 62 is a storage medium that stores various data and programs. The storage unit 62 stores implementation condition information 63, factor recording information 64, and a failure state estimation model 66, which include information similar to the implementation condition information 23, factor recording information 24, and failure state estimation model 26 stored in the storage unit 22. The factor recording information 64 may include a determination factor 65 that includes information on each factor obtained using the transport device 40 which experimentally created a deteriorated state, and information on the defect state at that time. Alternatively, the factor recording information 64 may include the determination factor 65 obtained during manufacturing and its state information obtained from the implementation systems 10, 10B, 10C, etc. The defect state estimation model 66 may be pre-constructed using the factor recording information 64 before being used in the implementation system 10. Note that the information stored in the storage unit 62 will not be explained here, as the explanation of the information stored in the storage unit 22 is provided. The control unit 61 creates implementation condition information 63, obtains and saves the factor recording information 64 from the implementation system 10, and executes a process to construct the initial state of the defect state estimation model 66. The display device 67 is a liquid crystal screen that serves as a notification unit to inform the user by displaying various information. The input device 68 includes a keyboard and mouse, etc., for the user to input various commands. The communication unit 69 is an interface for exchanging information with external devices such as the management device 18 of the implementation system 10.

[0024] Next, the operation of the implementation system 10 of this embodiment, as configured in this way, will be explained, starting with the process of constructing the defect state estimation model 26. The construction of the defect state estimation model 26 may be performed by the control unit 60, the management unit 18, or the control device 20. Here, the case in which the control unit 60 newly constructs the defect state estimation model 66 will be explained as an example. Figure 6 is a flowchart of an example of a defect state estimation model construction processing routine. This routine is stored in the storage unit 62 and executed by instruction from the user using the control unit 60. This routine may be executed when the defect state estimation model 26 is pre-constructed before the implementation system 10 is put into operation, or it may be executed when the defect state estimation model 26 is updated and constructed after the implementation system 10 has been put into operation and data for the judgment factors 65 have been accumulated in the production process.

[0025] When this routine is started, the control unit 61 of the central unit 60 first obtains the determination factor 25 used in the fault state estimation model 66 to be constructed and the state information when the determination factor 25 was acquired from the factor recording information 64 as training data (S100). The control unit 21 uses the relationship between each factor, the detection factor 24A, the state factor 24B, and the drive factor 24C, and the fault state at that time as training data. Here, for example, the state of the conveying device 40, including faulty states and good states, and the information of each factor are used as training data. Faulty states include, for example, faulty distance or light intensity of the detection units 44 and 45, faulty looseness or deterioration of the conveyor belt 42, and faulty torque or rotational fluctuation of the drive unit 43. Examples of data for each factor include distance information 24a, intensity information 24b, looseness information 24c, stop position information 24d, deterioration information 24e, load information 24f, and operation information 24g. Next, the control unit 61 incorporates the acquired information into the fault state estimation model 66 (S110) and updates the fault state estimation model 66 to incorporate the judgment factors 65 (S120). Subsequently, the control unit 61 determines whether or not a standard number of judgment factors 65 have been incorporated (S130), and if the standard number of judgment factors 65 have not been incorporated, it executes the processing from S100 onwards. Here, the "standard number" may be, for example, a value that indicates a predetermined acceptable accuracy (such as 80% or more or 90% or more) when determining the state of the transport device 40 using the fault state estimation model 66, which is determined by experience and set to the obtained value. When the standard number of judgment factors 65 have been incorporated in S130, the control unit 61 outputs the fault state estimation model 66 constructed here to the implementation system that uses it (S140), and terminates this routine. The implementation system 10 stores the acquired fault state estimation model 66 as fault state estimation model 26 in the storage unit 22. In the implementation system 10, a fault state estimation model 26 is used to determine if the configuration of the transport device 40 is deviating from a good state.

[0026] Next, the process of placing components P onto the substrate S using the mounting device 13 will be described. Figure 7 is a flowchart of an example of a mounting process routine executed by the control device 20 of the mounting device 13. This routine is stored in the storage unit 22 of the mounting device 13 and executed by the control unit 21 of the control device 20 after the mounting system 10 is started. When this routine is started, the control unit 21 first reads and acquires the mounting condition information 23 for the substrate S to be produced this time from the storage unit 22 (S200). The control unit 21 may also acquire the mounting condition information 23 from the management device 18.

[0027] Next, the control unit 21 determines whether or not it is time to acquire factors (S210). This acquisition timing may be a periodic timing, such as exceeding a predetermined period (e.g., one day) or a predetermined number of conveyed items (e.g., one thousand items). When it is time to acquire factors, the control unit 21 executes the factor acquisition operation and stores the obtained information in the factor recording information 24 (S220). The control unit 21 drives the conveyor belt 42 in a circular motion and controls the drive unit 43 to stop at a predetermined conveying distance. The control unit 21 also moves the object imaging unit 34 onto the conveyor belt 42 and acquires an image of the conveyor belt 42. The control unit 21 acquires deterioration information 24e of the conveyor belt 42, load information 24f of the drive unit 43, operation information 24g, etc. The deterioration information 24e may include information such as the deterioration of the shape and material of the conveyor belt 42. The deterioration of the material of the conveyor belt 42 may be determined, for example, from information such as the brightness of the image. The load information 24f may include, for example, the torque command value when the drive unit 43 is driven, or the torque value output from the drive unit 43. The operation information 24g may include position information corresponding to the passage of time, measured by the encoder of the drive unit 43.

[0028] After S220, or when it is not a factor acquisition timing in 210, the control unit 21 outputs a command to the transport device 40 to transport the substrate S (S230). Control of the drive unit 43, such as driving and stopping, may be performed by a controller provided in the transport device 40, or it may be performed directly by the control unit 21. Upon receiving this command, the transport device 40 drives the drive unit 43 and drives the conveyor belt 42 in a circular motion. Next, the control unit 21 determines whether the substrate S has reached the stopping position, for example, based on the value of the encoder of the drive unit 43 or the sensor values ​​of the detection units 44 and 45 (S240). If the substrate S has not reached the stopping position, the control unit 21 executes the process in S230 and continues transporting the substrate S. On the other hand, when the substrate S has reached the stopping position, the control unit 21 executes the substrate S stopping process (S250) and fixes the substrate S in place (S260). At this time, the control unit 21 acquires distance information 24a, intensity information 24b, looseness information 24c, and stopping position information 24d from the detection units 44 and 45 as determination factors 25, and stores them in the factor recording information 24. Distance information 24a may include, for example, the distance value between the detection units 44 and 45 and the substrate S obtained from the detected wave. Intensity information 24b may include, for example, the intensity value of the detected wave input by the light receiving unit of the detection units 44 and 45. Looseness information 24c may be obtained, for example, from the difference value between the position accuracy obtained by the detection factor 24A and the drive control by the drive factor 24C. Stopping position information 24d may include, for example, the amount of positional deviation of the stopping position of the substrate S from the theoretical position.

[0029] Next, the control unit 21 sets the components P to be placed on the substrate S based on the mounting condition information 23 (S270). The control unit 21 sets the components P to be placed in the mounting order, for example, using the number of sampling members 33 attached to the mounting head 32. Next, the control unit 21 moves the mounting head 32 to the component supply unit 27 and has the sampling members 33 collect the components P set as the target of placement (S280). Next, the control unit 21 moves the mounting head 32 above the mounting imaging unit 29 and has the mounting imaging unit 29 image the components P collected by the mounting head 32 (S290). Next, the control unit 21 obtains a correction value to cancel out the amount of misalignment in the sampling state based on the captured image and temporarily stores this correction value (S300). Subsequently, the control unit 21 moves the mounting head 32 to the placement position and uses this correction value to correct the positional misalignment and place the components P on the substrate S (S310).

[0030] After S310, the control unit 21 determines whether there is a component P to be picked and placed next (S320). If there is a component P to be picked and placed next, it executes the process from S270 onwards. That is, the control unit 21 sets the component P to be picked next and places the component P on the substrate S while correcting for misalignment. On the other hand, if there is no component P to be picked and placed next in S320, the control unit 21 determines whether the production of the substrate S is complete (S330). If the production of the substrate S is not complete, the control unit 21 executes the process from S210 onwards. On the other hand, if the production of the substrate S is completed in S330, this routine is terminated. In this way, the control unit 21 executes the mounting process while acquiring detection factors 24A, state factors 24B, and driving factors 24C, etc.

[0031] Next, the process by which the control device 20 determines whether a defect has occurred in the transport device 40 will be described. Figure 8 is a flowchart showing an example of a defect condition determination processing routine executed by the control device 20 of the mounting device 13. This routine is stored in the storage unit 22 of the mounting device 13 and is repeatedly executed by the control unit 21 of the control device 20 in parallel with the mounting processing routine. When this routine is started, the control unit 21 first determines whether or not the defect condition determination timing has been reached (S400). The defect condition determination timing may be one or more of the following, for example, the start of production of the substrate S, the elapsed time of a predetermined period, or the production of a predetermined number of substrates S. Currently, if the defect condition determination timing has been reached, the control unit 21 acquires the most recent determination factor 25 (S410). The control unit 21 acquires the determination factor 25 obtained in S220 and S250 described above. Next, the control unit 21 applies the acquired determination factor 25 as learning data to the defect condition estimation model 26 and uses the defect condition estimation model 26 to determine the defect condition of the transport device 40 (S420). The control unit 21 uses one or more of the following as learning data—distance and strength of the detection units 44 and 45, slack in the conveyor belt 42, stopping position, output and operation of the drive unit 43—to determine the state of the detection units 44 and 45, the conveyor belt 42, and the drive unit 43 using the fault state estimation model 26. At this time, the control unit 21 also uses the fault state estimation model 26 to determine the cause of the fault. Next, the control unit 21 determines whether or not there is a fault, i.e., an unfavorable state in the conveying device 40 (S430). If there is no fault, or if it is not the fault state determination timing in S400, the control unit 21 terminates this routine. On the other hand, if it is determined that there is a fault, i.e., an unfavorable state in the conveying device 40, the control unit 21 obtains the location of the fault and the cause of the fault from this determination result (S440), outputs the location of the fault and the cause of the fault (S450), and terminates this routine.

[0032] The control unit 21 may determine the malfunction of the detection units 44 and 45 based, for example, on a distance factor to the substrate S and an intensity factor of the detection wave irradiated onto the substrate S. Alternatively, the control unit 21 may determine the malfunction of the transport device 40 based on at least two of the detection factor 24A, state factor 24B, and drive factor 24C. For example, the control unit 21 may determine malfunctions of the transport device 40 based on the detection of the arrival of the substrate S by the detection units 44 and 45 (detection factor 24A) and the image captured by the object imaging unit 34 (state factor 24B), malfunctions of the transport device 40 based on the degree of agreement between the passage time of the substrate S by the detection units 44 and 45 (detection factor 24A) and the drive speed of the drive unit 43 (drive factor 24C), and malfunctions of the transport device 40 based on the looseness of the conveyor belt 42 (state factor 24B) and the load on the drive unit 43 (drive factor 24C).

[0033] Figure 9 is an explanatory diagram showing an example of an information display screen 70 displayed on the display unit of the operation panel 36 of the mounting device 13. The information display screen 70 includes an information display area 71 and a message display area 72. The information display area 71 is a field where information such as the status of the device is displayed, and also includes the message display area 72. The message display area 72 is a field where messages to the operator, such as instructions for work on the mounting device 13, are displayed. When the control unit 21 determines that the transport device 40 is in a defective state before it becomes an abnormal state that makes it difficult to operate the device, it displays a message on the operation panel 36 indicating that an inspection should be performed, along with information such as the determined location and the cause of the defective state. When the operator confirms this message, they can perform work to resolve the defective state at a relatively early stage.

[0034] Generally, when judging deterioration, a threshold is set for a single factor, and superiority or inferiority is determined based on whether the threshold is exceeded. In this case, the threshold is set closer to the abnormal state due to the accuracy of the judgment, making it difficult to accurately determine a defective state. This control device 20 uses a defective state estimation model 26 that has been machine-trained with one or more factors as training data, so it can accurately determine a defective state that will subsequently become an abnormal state. In particular, the control device 20 machine-trains a combination of detection factor 24A, state factor 24B, and drive factor 24C, which are considered to be causes of misalignment of the stopping position of the substrate S, so it can more accurately determine the location and cause of the defect.

[0035] Next, the process by which the control device 20 updates the defect state estimation model 26 will be described. The control device 20 executes the defect state estimation model construction processing routine shown in Figure 6 and updates the defect state estimation model 26 using the new judgment factor 25 and the state information, which is information about the defect state, as training data. The state information may include, for example, information about the actual defect state and its cause as confirmed by the worker. Examples of state information include, "the cause is looseness in the conveyor belt 42 and the looseness value is such and such," and "the drive unit 43 is malfunctioning and the phase variation with respect to the axis rotation is within a specific range." In this way, the control device 20 updates the defect state estimation model 26 in accordance with the mounting process of the mounting device 13, enabling more appropriate judgments to be made.

[0036] Here, the correspondence between the components of this embodiment and the components of the present disclosure will be clarified. The conveying device 40 of this embodiment is an example of the conveying device of the present disclosure, the mounting system 10 is an example of the object handling system, the determination unit 21a is an example of the determination unit, the conveyor belt 42 is an example of the conveying member, the drive unit 43 is an example of the drive unit, and the detection units 44 and 45 are examples of the detection unit. Furthermore, detection factor 24A is an example of a detection factor, distance information 24a is an example of distance factor information, intensity information 24b is an example of intensity factor information, state factor 24B is an example of a state factor, looseness information 24c is an example of looseness factor information, stop position information 24d is an example of position factor information, deterioration information 24e is an example of deterioration factor information, driving factor 24C is an example of a driving factor, load information 24f is an example of load factor information, operation information 24g is an example of operation factor information, judgment factor 25, judgment factor 55, and judgment factor 65 are examples of judgment factors, and defective state estimation model 26, defective state estimation model 56, and defective state estimation model 66 are examples of defective state estimation models. In addition, printing device 11, printing inspection device 12, mounting device 13, and mounting inspection device 14 are examples of mounting-related devices, component P is an example of a component, and substrate S is an example of an object. In this embodiment, an example of the determination method of this disclosure is also clarified by explaining the operation of the mounting device 13.

[0037] The determination unit 21a of the control device 20 of this embodiment, as described above, is used in a mounting system 10 as an object work system, which has a mounting-related device that performs mounting-related processing for mounting components P onto a substrate S transported by a transport device 40, which is equipped with detection units 44 and 45 for detecting a substrate S as an object, a conveyor belt 42 as a transport member for placing and transporting the substrate S, and a drive unit 43 for driving the transport member. The determination unit 21a of this control device 20 determines a defective state of the transport device 40 from a determination factor 25 that includes one or more of the detection factors 24A of the detection units 44 and 45, the state factor 24B of the conveyor belt 42, and the drive factor 24C of the drive unit 43. In this mounting system 10, a defective state according to the determination factor is determined, and by using the determination result, it is possible to deal with any malfunctions that may occur in the transport device 40 as early as possible.

[0038] Furthermore, the determination unit 21a determines the defective state of the conveying device using the determination factor 25 and a defective state estimation model 26 constructed by machine learning using the determination factor 25. By using the defective state estimation model 26 constructed using machine learning, it is possible to determine any malfunctions that may occur in the conveying device 40 as early as possible. Moreover, the determination unit 21a uses the defective state estimation model 26 constructed based on the information of the determination factor 25, which includes one or more of the detection factor 24A of the detection units 44 and 45, the state factor 24B of the conveyor belt 42, and the drive factor 24C of the drive unit 43. Using the information of the determination factor 25 as training data, the determination unit 21a determines the defective state of the conveying device 40 from the defective state estimation model 26. In this implementation system 10, by using the defective state estimation model 26 constructed based on the determination factor 25, the determination factor 25 can be obtained and the state of the conveying device 40 can be determined.

[0039] Furthermore, the detection factor 24A includes one or more of the distance factor to the substrate S and the intensity factor of the detection wave irradiated onto the substrate S; the state factor 24B includes one or more of the loosening factor related to the loosening of the conveyor belt 42, the position factor related to the movement or stopping position of the substrate S and the deterioration factor related to the deterioration of the conveyor belt 42; and the drive factor 24C includes one or more of the load factor related to the load on the drive unit 43 and the operation factor related to the amount of movement of the drive unit 43. By using distance factors, intensity factors, loosening factors, position factors, deterioration factors, load factors, operation factors, etc., this control device 20 can address any malfunctions that may occur in the conveying device 40 as early as possible. Furthermore, the control unit 21 uses a malfunction state estimation model 26 constructed based on two or more pieces of information from the detection factor 24A, state factor 24B, and drive factor 24C to acquire two or more pieces of information from the transport device 40, and uses the information from the detection factor 24A, state factor 24B, and drive factor 24C as training data to determine the malfunction state of the transport device 40. This control unit 20 can comprehensively determine the occurrence of a malfunction based on two or more pieces of information from the detection factor 24A, state factor 24B, and drive factor 24C.

[0040] Furthermore, the determination unit 21a determines the faulty state of the detection units 44 and 45 based on information such as the distance factor to the substrate S and the intensity factor of the detection wave irradiated onto the substrate S, thereby enabling earlier detection of malfunctions in the detection units 44 and 45. Moreover, the determination unit 21a determines the faulty state of the transport device 40 based on at least two of the detection factor 24A, the state factor 24B, and the drive factor 24C, enabling the determination of malfunctions in the transport device 40 using more complex factors. Furthermore, the determination unit 21a determines the faulty state of the transport device 40 related to the misalignment of the stopping position of the substrate S based on the detection factor 24A related to the detection of the substrate S by the detection units 44 and 45 and the state factor 24B related to the deterioration of the conveyor belt 42. In this mounting system 10, malfunctions in the transport device 40 can be determined more accurately using more complex factors. The determination unit 21a then periodically acquires the state factor 24B from the transport device 40 and determines the faulty state of the transport device 40 based on the state factor 24B. This implementation system 10 can periodically acquire state factors 24B and determine a defective state based on the state factors 24B. Furthermore, the implementation system 10 is equipped with an operation panel 36 that serves as a notification unit to notify the operator of the determination of the defective state of the transport device 40, making it easy for the operator to confirm the determination.

[0041] Furthermore, the determination unit 21a uses the determination factor 25 as training data to output information regarding the cause of the defective state of the transport device 40 from the defective state estimation model 26. In this implementation system 10, by outputting information regarding the cause of the defective state, it is possible to address any malfunctions that may occur in the transport device 40 as early as possible. In addition, the determination unit 21a acquires information on the defective state of the transport device 40 and information on the determination factor 25 of the transport device 40 and updates the defective state estimation model 26. In this implementation system 10, by reconstructing and updating the defective state estimation model 26, it is possible to address any malfunctions that may occur in the transport device 40 with greater accuracy. Furthermore, the determination unit 21a acquires information on the detection factor 24A from the transport device 40 each time a substrate S is transported, acquires information on the state factor 24B from the transport device 40 each time a substrate S is transported or periodically, and acquires information on the drive factor 24C from the transport device 40 periodically. This control device 20 can more efficiently determine potential malfunctions in the transport device by acquiring information on factors that can be obtained during processing each time, and by periodically acquiring information on factors that should be acquired in other processes.

[0042] Furthermore, the mounting apparatus 13, as a mounting-related device, comprises a transport device 40 for transporting the substrate S, a mounting unit 30 as a work unit for performing work on the substrate S, and the control device 20 described above. Similar to the control device 20 described above, this mounting apparatus 13 can address any malfunctions that may occur in the transport device 40 as early as possible.

[0043] It goes without saying that the object handling system and determination method disclosed herein are not limited in any way to the embodiments described above, and can be implemented in various forms as long as they fall within the technical scope of the present invention.

[0044] For example, in the above-described embodiment, it is assumed that the control unit 21 constructs the failure state estimation model 26 using the information of the detection factor 24A, the state factor 24B, and the driving factor 24C. However, if the information of the determination factor 25 is used, it is not particularly limited thereto, and one or more of these may be omitted, or information of other factors may be used instead of or in addition to these. Further, the control unit 21 determines the state of the transfer device 40 using the failure state estimation model 26 based on the information of the determination factor 25 including the detection factor 24A, the state factor 24B, and the driving factor 24C. However, if the information of the factor for which the failure state estimation model 26 is constructed is used, it is not particularly limited thereto. Also in this control device 20, the failure state of the transfer device 40 can be determined using the failure state estimation model 26.

[0045] In the above-described embodiment, it is assumed that the detection factor 24A includes the distance information 24a and the state factor 24B. However, it is not particularly limited thereto, and any one or more of these may be omitted, or other factors may be included instead of or in addition to these. Similarly, it is assumed that the state factor 24B includes the loosening information 24c, the stop position information 24d, and the deterioration information 24e. However, it is not particularly limited thereto, and any one or more of these may be omitted, or other factors may be included instead of or in addition to these. Further, it is assumed that the driving factor 24C includes the load information 24f and the operation information 24g. However, it is not particularly limited thereto, and any one or more of these may be omitted, or other factors may be included instead of or in addition to these. Also in this control device 20, the failure state of the transfer device 40 can be determined using the failure state estimation model 26.

[0046] In the above-described embodiment, the control unit 21 outputs information regarding the cause of the failure state of the transfer device 40 from the failure state estimation model 26 using the determination factor 25 as learning data. However, it is not particularly limited thereto, and the output of the information regarding the cause of this failure state may be omitted. Also in this control device 20, the failure state of the transfer device 40 can be determined using the failure state estimation model 26.

[0047] In the above-described embodiment, the control unit 21 determines the defective state of the transfer device 40 using the defective estimation model 26 constructed by machine learning using the determination factor 25 and the information of the determination factor 25. However, it is not particularly limited to this, and the defective state estimation model 26 may be omitted. Also in this control device 20, the defective state of the transfer device 40 can be determined from the determination factor 25. Note that it is preferable for the control device 20 to use the defective state estimation model 26 in terms of early detection of defects and improvement of determination accuracy.

[0048] In the above-described embodiment, the mounting device 13 has been described as a main example of the mounting-related device. However, it is not particularly limited to this, and in a printing device 11, a printing inspection device 12, a mounting inspection device 14, etc., the control device of each device may determine the defective state of the transfer device of each device. Also, in the above-described embodiment, the control device 20 of the mounting-related device determines the defective state of the transfer device 40. However, it is not particularly limited to this. For example, the management device 18 may determine the defective state of each transfer device 40 of the mounting system 10, or the general control device 60 may determine the defective state of each transfer device 40, or other control devices may determine the defective state of the transfer device 40. Further, the control device 20 has been described as updating the defective state estimation model 26. However, it is not particularly limited to this, and the update of the defective state estimation model 26 may be executed by other devices such as the general control device 60 or the management device 18.

[0049] In the above-described embodiment, the present disclosure has been described with the mounting system 10, the mounting device 13, and the control device 20. However, it is not particularly limited to this, and it may also be a control method and a determination method for mounting-related devices, and a program for realizing these methods.

[0050] Herein, the determination method of the present disclosure may be configured as follows. For example, the determination method of the present disclosure is a determination method executed by a computer of an object work system having an object work system having an object work system having a detection unit for detecting an object, a transport member for placing and transporting the object, and a drive unit for driving the transport member, the determination method being executed by a computer of an object work system having an object work system having a detection unit for detecting an object, a transport member for placing and transporting the object, and a drive unit for driving the transport member, the determination step being to determine the defective state of the transport system using a defective state estimation model from a determination factor that includes one or more of the detection factors of the detection unit, the state factors of the transport member, and the drive factors of the drive unit.

[0051] This determination method, similar to the control device described above, determines a faulty state that fits the faulty state estimation model, and by using the determination result, it is possible to address potential malfunctions in the transport device as early as possible. In this determination method, various forms of the object handling system and mounting-related equipment described above may be adopted, or steps that realize the functions of the object handling system and mounting-related equipment described above may be added.

[0052] This specification also discloses the following technical concepts: in claim 4 of the original application, "the object handling system described in claim 1 or 2" is changed to "the object handling system described in any one of claims 1 to 3"; in claim 5 of the original application, "the object handling system described in claim 1 or 2" is changed to "the object handling system described in any one of claims 1 to 4"; in claim 6 of the original application, "the object handling system described in claim 1 or 2" is changed to "the object handling system described in any one of claims 1 to 5"; in claim 9 of the original application, "the object handling system described in claim 1 or 2" is changed to "the object handling system described in any one of claims 1 to 8"; and in claim 10 of the original application, "the object handling system described in claim 1 or 2" is changed to "the object handling system described in any one of claims 1 to 9".

[0053] This disclosure is applicable to the technical field of equipment that performs processes such as sampling and placement of parts.

[0054] 10 Assembly system (object work system), 11 Printing device, 12 Printing inspection device, 13 Assembly device, 14 Assembly inspection device, 18 Management device, 19 Network, 20 Control device, 21 Control unit, 21a Judgment unit, 22 Storage unit, 23 Assembly condition information, 24 Factor recording information, 24A Detection factor, 24B State factor, 24C Drive factor, 24a Distance information, 24b Strength information, 24c Looseness information, 24d Stop position information, 24e Deterioration information, 24f Load information, 24g Operation information, 25 Judgment factor, 26 Defect state estimation model, 27 Parts supply unit, 28 Feeder, 29 Assembly imaging unit, 30 Assembly unit, 31 Head movement unit, 32 Assembly head, 33 Sampling material, 34 Object imaging unit, 35 Lifting unit, 36 Operation panel, 40 Conveying device, 41 Side frame, 42 conveyor belt, 43 drive unit, 44, 45 detection unit, 47 support base, 48 lifting unit, 49 support plate, 51 control unit, 52 memory unit, 53 mounting condition information, 54 factor recording information, 55 judgment factor, 56 defect state estimation model, 57 display device, 58 input device, 59 communication unit, 60 overall unit, 61 control unit, 62 memory unit, 63 mounting condition information, 64 factor recording information, 65 judgment factor, 66 defect state estimation model, 67 display device, 68 input device, 69 communication unit, 70 information display screen, 71 information display field, 72 message display field, D transport direction, P component, S circuit board.

Claims

1. An object work system having an object-related device that performs mounting-related processing for mounting components onto an object transported by a transport device comprising a detection unit for detecting an object, a transport member for placing and transporting the object, and a drive unit for driving the transport member, wherein the object work system comprises a determination unit that determines a defective state of the transport device from a determination factor that includes one or more of the detection factors of the detection unit, the state factors of the transport member, and the drive factors of the drive unit.

2. The object handling system according to claim 1, wherein the determination unit determines the defective state of the conveying device using a defect estimation model constructed by machine learning using the determination factor and the determination factor.

3. The object handling system according to claim 1 or 2, wherein the detection factor includes one or more of the distance factor to the object and the intensity factor of the detection wave irradiated onto the object; the state factor includes one or more of the loosening factor related to the loosening of the transport member, the position factor related to the movement or stopping position of the object and the deterioration factor related to the deterioration of the transport member; and the drive factor includes one or more of the load factor related to the load of the drive unit and the operation factor related to the amount of movement of the drive unit.

4. The object work system according to claim 1 or 2, wherein the determination unit determines the malfunction state of the detection unit based on a distance factor to the object and an intensity factor of the detection wave irradiated onto the object.

5. The object handling system according to claim 1 or 2, wherein the determination unit determines the defective state of the conveying device based on at least two of the detection factor, the state factor, and the drive factor.

6. The object handling system according to claim 1 or 2, wherein the determination unit determines a defective state of the conveying device relating to the misalignment of the stopping position of the object, based on the detection factor relating to the detection of the object by the detection unit and the state factor relating to the deterioration of the conveying member.

7. The object handling system according to claim 5, wherein the determination unit periodically obtains the state factors from the conveying device and determines the defective state of the conveying device based on the state factors.

8. The object handling system according to claim 2, wherein the determination unit acquires the defective state of the conveying device and the determination factors of the conveying device and updates the defective state estimation model.

9. The object handling system according to claim 1 or 2, wherein the determination unit obtains the detection factor from the transport device each time the object is transported or periodically, obtains the state factor from the transport device each time the object is transported or periodically, and obtains the drive factor from the transport device periodically.

10. An object handling system according to claim 1 or 2, comprising a notification unit that notifies the determination of the defective state of the conveying device.

11. A determination method executed by a computer of an object work system having an object work system having an object work system having an object work system comprising a detection unit for detecting an object, a transport member for placing and transporting the object, and a drive unit for driving the transport member, the determination method comprising: a determination step of determining a defective state of the transport system from a determination factor that includes one or more of the detection factors of the detection unit, the state factors of the transport member, and the drive factors of the drive unit.

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