Production support equipment

Abstract

This production assistance device comprises: a training data acquisition unit that acquires, as training data, arrangement data relating to arrangement of a plurality of component mounting machines and component type data indicating component types of a plurality of components to be mounted on a board by the component mounting machines, respectively; a trained model storage unit that stores a trained model generated by performing machine learning of a replacement pattern in which rewards are obtained by replacing a pair of component types representing component types distinguished by the component type data and mounting components by the plurality of component mounting machines; a production information acquisition unit that acquires production information including at least new arrangement data and component type data and giving an instruction to produce a board by mounting new components using the component mounting machines; and an inference unit that uses the trained model and the new arrangement data and component type data included in the production information to infer and output a pair of component types to be replaced among the new component types distinguished by the component type data.

Description

Technical Field

[0001] This specification relates to a production support device.

Background Art

[0002] Conventionally, for example, a parts feeder sorting method for an electronic component mounter disclosed in Patent Document 1 (hereinafter referred to as the "conventional method") is known. In the conventional method, a parts feeder row in which all parts feeders sorted to each electronic component mounter are arranged in an arbitrary order is determined, and for each parts feeder, an identifier of each electronic component mounter is associated by a random number to generate a predetermined number of individuals each consisting of an array of identifiers. Then, in the conventional method, a genetic algorithm is applied to each generated individual with the mounting time at each electronic component mounter as an evaluation value, and the sorting of the parts feeders to each electronic component mounter is optimized so that the evaluation value becomes minimum.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in the above-described conventional method, for all parts feeders forming the parts feeder row, a population consisting of a plurality of individuals of the next generation is generated by considering all combinations of the parts feeders, and by repeating the generation of the population, finally, the parts feeder with the minimum evaluation value is sorted. In this case, as the number of parts feeders increases, it takes time until the sorting of the parts feeders is finally optimized.

[0005] Furthermore, conventional methods do not pre-select the parts feeders to be optimized. Therefore, even if only a few parts feeders are different, it is necessary to generate the next generation by considering all combinations of parts feeders for all parts feeders forming the parts feeder row. Consequently, even if only a few parts are different, optimization takes time. Therefore, it is desirable to be able to perform optimization efficiently even when the number of parts to be optimized increases.

[0006] This specification aims to provide a production support device that can efficiently optimize the arrangement of component types. [Means for solving the problem]

[0007] This specification discloses a production support device comprising: a training data acquisition unit that acquires as training data placement data relating to the arrangement of multiple component mounting machines and component type data representing the component types of multiple components that each component mounting machine attaches to a substrate; a trained model storage unit that stores a trained model generated by performing machine learning on swapping patterns that provide a reward when multiple component mounting machines attach components by swapping component type pairs representing component types distinguished by component type data; a production information acquisition unit that acquires production information that includes at least new placement data and new component type data and instructs the production of a substrate by attaching new components using component mounting machines; and an inference unit that uses the new placement data and new component type data included in the production information and the trained model to infer and output component type pairs that are to be swapped among the new component types distinguished by component type data.

[0008] This specification also discloses the technical idea behind changing "the production support device described in claim 1 or 2" to "the production support device described in any one of claims 1-4" in claim 5 of the original application. Furthermore, this specification also discloses the technical idea behind changing "the production support device described in claim 1 or 2" to "the production support device described in any one of claims 1-6" in claim 7 of the original application. Moreover, this specification also discloses the technical idea behind changing "the production support device described in claim 1 or 2" to "the production support device described in any one of claims 1-8" in claim 9 of the original application. [Effects of the Invention]

[0009] According to the production support system, by using a pre-trained model that has learned replacement patterns that yield rewards for optimizing the placement of part types, it is possible to infer the part type pairs that are to be replaced, i.e., effective for optimization. This eliminates the need to sequentially consider all combinations of part types for multiple part types to determine effective part type pairs for optimization, and by using the pre-trained model, effective part type pairs for optimization can be selectively determined even for new part types. Therefore, by using the production support system, the placement of part types can be optimized efficiently. [Brief explanation of the drawing]

[0010] [Figure 1] This diagram shows the overall configuration of the production system. [Figure 2] This diagram illustrates the multiple component mounting machines that make up the production system shown in Figure 1. [Figure 3] Figure 2 is a schematic diagram showing the overall configuration of the component mounting machine. [Figure 4] Figure 1 is a schematic side view showing the main components of the feeder. [Figure 5] This is a schematic top view of the carrier tape. [Figure 6] This is a functional block diagram showing the configuration of the production support system. [Figure 7]This is a functional block diagram showing the configuration of the learning phase by the production support device (trained model generation unit). [Figure 8] This is a flowchart showing the learning process performed by the production support device (trained model generation unit). [Figure 9] This is a functional block diagram showing the configuration of the inference phase by the production support device (inference unit). [Figure 10] This flowchart shows the optimization program executed by the production support device. [Modes for carrying out the invention]

[0011] The production support device will be described below with reference to the drawings. In this embodiment, the production support device will be described as being provided in a production system in which a feeder is transported to a parts mounting machine by an automatic conveyor.

[0012] 1. Overall configuration of production system 1 First, the overall configuration of production system 1 will be described with reference to Figures 1, 2, and 3. Production system 1 comprises a plurality of component mounting machines 10 (four in this embodiment) arranged in the width direction, an automatic transport machine 20, a loader device 30, a feeder 40, and a production support device 100. The component mounting machine 10 is a substrate mounting machine that performs a mounting operation in which components P (for example, electronic components) are mounted onto a substrate K as a predetermined operation.

[0013] In the production system 1 formed by multiple component mounting machines 10, substrates K are sequentially transported into each component mounting machine 10, and a mounting process is performed in each component mounting machine 10 to mount predetermined components. In the following description, the X-axis direction is defined as the left-right direction (width direction) of the component mounting machine 10, the Y-axis direction is defined as the front-back direction (depth direction) of the component mounting machine 10, and the Z-axis direction is defined as the up-down direction (vertical direction) of the component mounting machine 10.

[0014] Furthermore, the production system 1 includes an automatic transporter 20 that transports and detaches (replaces) the feeder 40 for each component mounter 10. Here, examples of the automatic transporter 20 include an AGV (Automatic Guided Vehicle), which is an automated guided vehicle (unmanned transport robot) that automatically shuttles between an automated warehouse (not shown) and the component mounter 10 to transport a predetermined feeder 40. Although not shown, the automatic transporter 20 includes a detachment mechanism (e.g., a belt conveyor or an articulated robot) for detaching the feeder 40 from the component supply device 12 of the component mounter 10, which will be described later.

[0015] Furthermore, the production system 1 includes a loader device 30 that replenishes components P and changes the setup for the next production in accordance with the production schedule. The loader device 30 is disposed in front of the component mounter 10 (more specifically, the component supply device 12, which will be described later) in the Y-axis direction and is movable in the X-axis direction. In the present embodiment, the loader device 30 is also movable transversely in the X-axis direction with respect to the adjacent component mounter 10 (component supply device 12).

[0016] Furthermore, the loader device 30 moves the feeder 40 from the upper stage to the lower stage or from the lower stage to the upper stage in the slot 12S of the component supply device 12, which will be described later. Further, the loader device 30 moves and replaces the feeder 40 between two component mounters 10, that is, between the slots 12S of the two component supply devices 12. Specifically, the loader device 30 can temporarily store (recover) the feeder 40 set in the upper stage of the slot 12S, move in the X-axis direction, and then discharge and set the stored (recovered) feeder 40 in the lower stage. Also, the loader device 30 can temporarily store (recover) the feeder 40 set in the lower stage of the slot 12S, move in the X-axis direction, and then discharge and set the stored (recovered) feeder 40 in the upper stage.

[0017] Furthermore, the loader device 30 temporarily accommodates (recovers) the feeder 40 set in the slot 12S of the component supply device 12 of one component mounting machine 10, moves in the X-axis direction, and then discharges and sets the accommodated (recovered) feeder 40 in the slot 12S of the component supply device 12 of the other component mounting machine 10. That is, a plurality of feeders 40 can be exchanged between the component mounting machines 10. Thereby, the loader device 30 can automatically perform the supply of the component P and the setup change (including the exchange of the feeder 40).

[0018] Here, in the production system 1, as shown in FIG. 1, in addition to the above-described devices 10, 20, 30, 40, a management device H for controlling the whole production is provided. Examples of the management device H include a host computer, a buffer, etc. that are communicably connected to the above-described devices. Then, as will be described later, the management device H supplies various information including production information J related to production to the above-described devices 10, 20, 30, 40, 100 as necessary.

[0019] 2. Component mounting machine 10 As schematically shown in FIG. 3, the component mounting machine 10 mainly includes a substrate transfer device 11, a component supply device 12, a component transfer device 13, a component camera 14, a substrate camera 15, and a control device 16.

[0020] The substrate transfer device 11 is composed of a belt conveyor or the like and sequentially transfers the substrate K in the X-axis direction. The substrate transfer device 11 positions the substrate K at a predetermined position inside the component mounting machine 10. Then, when the mounting operation on the positioned substrate K is completed, the substrate transfer device 11 carries out the substrate K outside the component mounting machine 10 (for example, to an adjacent component mounting machine 10).

[0021] The component supply device 12 supplies components P (for example, electronic components) to be mounted on the substrate K. The component supply device 12 has a plurality of slots 12S arranged in the X-axis direction, and a feeder 40 is detachably set in each of the slots 12S. In this embodiment, the slots 12S are formed by an upper and lower section along the Z-axis direction (see Figure 2). The component supply device 12 uses the feeder 40 to feed and move a carrier tape 50 that supplies components P, which will be described later, and supplies components P to component supply positions Ps (see Figure 4) located at the tip side (upper side in Figure 3) of the feeder 40.

[0022] The component transfer device 13 holds the component P supplied to the component supply position Ps and mounts the held component P onto the positioned substrate K. The component transfer device 13 mainly comprises a head drive device 13A, a mobile table 13B, and a mounting head 13C. The head drive device 13A moves the mobile table 13B in the X-axis and Y-axis directions using a linear motion mechanism.

[0023] The mounting head 13C is a holding device for holding parts P and is detachably mounted on the movable table 13B. A nozzle holder 13D provided on the mounting head 13C is detachably equipped with a plurality of suction nozzles 13E capable of holding parts P. The suction nozzles 13E are supported on the mounting head 13C so as to be rotatable around an axis parallel to the Z-axis direction (the vertical direction of the parts mounting machine 10) and so as to be able to move up and down. The suction nozzles 13E hold parts P supplied to the parts supply position Ps by suction and mount the held parts P onto the positioned substrate K.

[0024] The component camera 14 and the substrate camera 15 are digital imaging devices having image sensors such as CCD or CMOS. The component camera 14 is fixed to the base of the component mounting machine 10 with its optical axis oriented in the Z-axis direction and images the component P held by the suction nozzle 13E from below. The substrate camera 15 is fixed to the mobile stage 13B with its optical axis oriented in the Z-axis direction and images the substrate K from above.

[0025] The control device 16 is a computer device whose main components are a CPU, ROM, RAM, and various interfaces, and it comprehensively controls the operation of the component mounting machine 10. Specifically, the control device 16 operates the component mounting machine 10 by executing a control program (not shown). As a result, the component mounting machine 10 performs the component mounting operation, for example, according to a pre-stored sequence.

[0026] For example, the control device 16 causes the substrate camera 15 to image the substrate K positioned by the substrate transport device 11. The control device 16 then processes the image captured by the substrate camera 15 to recognize the positioning state of the substrate K. In addition, the control device 16 has the suction nozzle 13E collect and hold the component P supplied by the component supply device 12, and then causes the component camera 14 to image the held component P. The control device 16 then processes the image captured by the component camera 14 to recognize the orientation of the component P.

[0027] The control device 16 executes a control program and moves the suction nozzle 13E (mounting head 13C) upwards towards a designated mounting position that is pre-set as the position for mounting the component P on the substrate K. The control device 16 also corrects the designated mounting position and mounting angle based on the positioning state of the substrate K and the orientation of the component P, and sets the actual mounting position and mounting angle for mounting the component P.

[0028] The control device 16 corrects the target position (X-axis coordinates and Y-axis coordinates) and rotation angle of the suction nozzle 13E according to the mounting position and mounting angle. Then, the control device 16 lowers the suction nozzle 13E at the corrected target position and the corrected rotation angle, and mounts the component P onto the substrate K. The control device 16 performs the mounting process of mounting multiple components P onto the substrate K by repeating the pick-and-place cycle as described above.

[0029] 3. Feeder 40 As shown in Figure 4, the feeder 40 comprises a feeder body 41, a drive sprocket 42, a tape presser 43, and a peeling 44. The feeder 40 holds a reel R on which a carrier tape 50 containing parts P for each part type is wound. The feeder 40 can communicate with the management device H, for example, when it is set in the slot 12S of the parts supply device 12 of the parts mounting machine 10, or when it is being transported by the automatic transport machine 20.

[0030] Here, we will explain the carrier tape 50 wound around the reel R. As shown in Figure 5, the carrier tape 50 comprises a base tape 51 and a cover tape 52. The base tape 51 is formed using a flexible material such as paper or resin. On one side of the base tape 51 in the width direction (the lower side in Figure 5), a plurality of cavities 511 capable of accommodating parts P are provided at equal intervals along the longitudinal direction of the base tape 51 (the left-right direction in Figure 5). On the other side of the base tape 51 in the width direction (the upper side in Figure 5), a plurality of feed holes 512 are provided at equal intervals along the longitudinal direction of the base tape 51. The plurality of feed holes 512 mesh with the drive sprocket 42.

[0031] The cover tape 52 is formed using a transparent polymer film or the like. As shown by the dashed line in Figure 5, the cover tape 52 covers the upper surface of the base tape 51 and prevents the component P housed in the cavity 511 from falling out. The base tape 51 and the cover tape 52 are joined to each other at joining portions 501 and 502, which are provided on both sides (one side and the other side) in the width direction of the carrier tape 50 that sandwiches the cavity 511. Here, joining portions 501 and 502 are provided on one side in the width direction of the carrier tape 50 from the feed hole 512.

[0032] Returning to the description of the feeder 40, the feeder body 41 is a thin, box-shaped component formed from a transparent or opaque resin plate or metal plate. The sides of the feeder body 41 are designed to be openable and closable (though not shown in the illustration), and inside the feeder body 41, as shown in Figure 4, a drive sprocket 42, a tape presser 43, and a peeling 44 are arranged.

[0033] The drive sprocket 42 is a sprocket that can mesh with the feed holes 512 provided in the base tape 51 of the carrier tape 50, and is rotatably mounted on the feeder body 41. A motor (e.g., a stepping motor) is connected to the drive sprocket 42 via a plurality of gears (not shown). As a result, the drive sprocket 42 is driven by the motor and feeds the carrier tape 50 by pitch, thereby transporting the parts P to the parts supply position Ps.

[0034] Here, the parts supply position Ps is located above the position where the drive sprocket 42 is positioned when viewed from the direction of the rotation axis of the drive sprocket 42 (X-axis direction). As a result, the feeder 40 can position the gear engagement position between the carrier tape 50 and the drive sprocket 42 close to the parts supply position Ps, thereby improving the positioning accuracy of the parts P transported to the parts supply position Ps.

[0035] The tape holding section 43 guides the carrier tape 50 pulled from the reel R so that the part P is transported to the part supply position Ps. The peeling section 44 peels the cover tape 52 from the base tape 51 before the part P reaches the part supply position Ps, making the part P housed in the cavity 511 ready to be picked up by the suction nozzle 13E (see Figure 3).

[0036] 4. Overview of the production support device 100 As described above, each component mounting machine 10 constituting the production system 1 mounts multiple components P of different types onto the substrate K, supplied from each of the multiple feeders 40 set in the multiple slots 12S of the component supply device 12 by an automatic transport machine 20 or loader device 30. That is, each component mounting machine 10 constituting the production system 1 performs the mounting process on the substrate K by sequentially picking and placing components P of different types, and then supplies the mounted substrate K to, for example, an adjacent component mounting machine 10.

[0037] Incidentally, in a production system 1 with multiple component mounting machines 10, the time required for pick-and-place may differ depending on the type of component P to be mounted on the substrate K. Therefore, in production system 1, there may be differences in cycle time, which represents the time required for each component mounting machine 10 to complete mounting of the component P onto the substrate K. If there is a large difference in cycle time in production system 1, the component mounting machine 10 with the longest cycle time may become a so-called bottleneck, potentially worsening the productivity when producing the substrate K.

[0038] In this context, to improve productivity, or in other words, to level out cycle times to prevent bottlenecks, the optimization of the arrangement of components P (component types) that each of the multiple component mounting machines 10 in the production system 1 attaches to the substrate K is usually considered, that is, the swapping of component types of components P in the production system 1. In other words, in order to optimize the mounting order of components P that the multiple component mounting machines 10 in the production system 1 sequentially attach to the substrate K, the swapping of component types, that is, the swapping of feeders 40 that supply component P for each component type, is considered.

[0039] However, when considering swapping parts (swapping feeders 40), a pair of parts representing parts from among all parts used in production system 1, or a pair of feeders representing feeders 40 that supply parts P of the parts to be swapped to the parts mounting machine 10, is tentatively determined. Then, for the tentatively determined pair of parts (or feeder) pairs, the mounting process of parts P when the parts (feeders 40) are swapped is simulated and the cycle time is measured.

[0040] Typically, when considering the optimization of the layout, the following steps are taken: a preliminary determination of such part type pairs (or feeder pairs), a simulation of the mounting process for the preliminary determined part type pairs (feeder pairs), and an evaluation of the cycle time based on the simulation results. This is done by combining, for example, all part types (feeders 40) used in production system 1. Therefore, as the number of part types, i.e., feeders 40 or part mounting machines 10, increases, the content of the layout optimization considerations becomes more complex, and an enormous amount of time is required to evaluate the cycle time, that is, to obtain the optimal solution that realizes the optimization of the part type layout (feeder 40 layout) that can eliminate bottlenecks.

[0041] Therefore, the production system 1 is equipped with a production support device 100 that infers part type pairs representing the aforementioned part types. The production support device 100 is provided to communicate with each part mounting machine 10 (feeder 40), automatic transport machine 20, loader device 30, and management device H that constitute the production system 1. The production support device 100 can also be, for example, a device incorporated into the management device H. The production support device 100 provides support to maximize evaluation with respect to a pre-set evaluation target. For example, cycle time can be used as an evaluation target. The production support device 100 infers and outputs part type pairs representing part types that are to be replaced among multiple part types of part P mounted by the part mounting machine 10, according to the evaluation results of the evaluation target.

[0042] Specifically, the production support device 100 stores a trained model generated by reinforcement learning. The production support device 100 then uses the trained model and production information J supplied from the management device H to infer and determine which pair of part types to be replaced from among multiple part types of parts P used in production, specifically, which pair of feeders to be replaced from among multiple feeders 40 set in the part mounting machine 10. This allows the production system 1 to optimize the arrangement (replacement) of part types, i.e., the arrangement (replacement) of the feeders 40. As a result, the cycle time of each part mounting machine 10 is leveled, and consequently, the impact of bottlenecks in the production system 1 on overall production can be reduced. Regarding bottlenecks, for example, if the leveling is above a certain standard, it can be considered that there are "no bottlenecks."

[0043] 4-1. Configuration of the production support device 100 Next, the configuration of the production support device 100 of this embodiment will be described. The production support device 100 is a device whose main components are a computer device having a CPU, ROM, RAM, and various interfaces, and as shown in Figure 6, it includes a learning data acquisition unit 110, a trained model storage unit 130, a production information acquisition unit 140, and an inference unit 150. Furthermore, as shown in Figure 6, the production support device 100 includes a trained model generation unit 120. In addition, as shown in Figure 6, the production support device 100 includes an optimizer 160 capable of performing optimization simulations using the inference results from the inference unit 150.

[0044] The learning data acquisition unit 110 acquires optimization information D, which is learning data, including placement data Da relating to the arrangement of multiple component mounting machines 10 constituting the production system 1, and component type data Dk representing the component types of multiple component P that each component mounting machine 10 attaches to the substrate K. In addition, the learning data acquisition unit 110 also acquires cycle time data Ds representing the cycle time required for the mounting process at each component mounting machine 10 when using the arrangement of the component mounting machines 10 represented by the placement data Da and the component P of the component type represented by the component type data Dk (feeders 40 that supply the component P of the corresponding component type), as well as repositioning restriction information Dj representing the mounting order of component P that must be strictly observed.

[0045] Furthermore, the placement data Da, part type data Dk, cycle time data Ds, and replacement restriction information Dj included in the optimization information D are supplied from the management device H or an external device (not shown). In this embodiment, the case where the data is supplied from the management device H is given as an example.

[0046] Here, the production information J output by the management device H includes the number and arrangement of component mounting machines 10 constituting the production system 1, which corresponds to the placement data Da; the number of feeders 40 set in each component mounting machine 10; the type and number of components P mounted in each component mounting machine 10, which corresponds to the component type data Dk; and the cycle time as actual or simulation results, which corresponds to the cycle time data Ds. Furthermore, the production information J includes control data including the specified mounting position and specified mounting angle of the component P on the substrate K; component information (shape, dimensions, maximum movement speed, imaging conditions, etc.); the degree of cycle time leveling (presence or absence of bottlenecks); and equipment information that affects the efficiency of the mounting process (mounting head 13C, suction nozzle 13E, etc.). Therefore, the learning data acquisition unit 110 can acquire the production information J output from the management device H as learning data, in addition to acquiring the above-mentioned placement data Da, component type data Dk, cycle time data Ds, and replacement restriction information Dj as optimization information D.

[0047] The trained model generation unit 120 generates a trained model M by repeatedly performing machine learning (reinforcement learning) on ​​multiple swapping patterns (for example, two component mounting machines 10 mounting components P to obtain a reward E, described later) which are distinguished by component type data Dk acquired by the training data acquisition unit 110. Here, as described above, each of the multiple components P is contained in a carrier tape 50 wound around a reel R, and each reel R is loaded into a feeder 40 that supplies the components P contained in the carrier tape 50 to the component mounting machine 10.

[0048] Therefore, a component type pair Cp corresponds to a feeder pair Cf, which represents feeders 40 loaded with reels R around which carrier tapes 50 containing components P of the component type that form the component type pair Cp are wound. For this reason, the trained model generation unit 120 can generate a trained model M by, in addition to or instead of, machine learning (reinforcement learning) on ​​the swapping patterns of component type pairs Cp, by swapping feeder pairs Cf and repeatedly performing machine learning (reinforcement learning) on ​​swapping patterns in which multiple component mounting machines 10 mount components P, thereby obtaining a reward E described later. The generation of the trained model M by the trained model generation unit 120 will be described in detail later.

[0049] The trained model storage unit 130 stores the trained model M generated by the trained model generation unit 120. Therefore, the trained model storage unit 130 can store the trained model M, which is updated as the trained model generation unit 120 repeatedly performs machine learning (reinforcement learning).

[0050] The production information acquisition unit 140 acquires production information J that includes at least new placement data Dan and new component type data Dkn, and instructs the production of the substrate K by mounting new component P using the component mounting machine 10. Specifically, the production information acquisition unit 140 acquires production information J from the control device H when it is necessary to optimize a new component type pair Cp (new feeder pair Cf) during the production of the substrate K.

[0051] The inference unit 150 uses the new placement data Dan and new part type data Dkn included in the production information J acquired by the production information acquisition unit 140, and the trained model M stored in the trained model storage unit 130, to infer and output the part type pair Cp (feeder pair Cf) to be replaced from among the new part types distinguished by the part type data Dkn. Here, the inference unit 150 outputs the inferred part type pair Cp (feeder pair Cf) to the management device H (more specifically, for example, a display device not shown in the illustration provided on the management device H) to guide workers, etc. The inference of the part type pair Cp (feeder pair Cf) by the inference unit 150 will be described in detail later.

[0052] Based on the component type pair Cp (feeder pair Cf) inferred by the inference unit 150, the optimizer 160 simulates the component mounting process of the component mounting machine 10 and the cycle time associated with the mounting process when the component type (feeder 40) is changed. Then, as will be described later, based on the simulation results, the optimizer 160 determines the reward E in machine learning (reinforcement learning) when the trained model generation unit 120 generates the trained model M, and updates the optimization information D (more specifically, for example, cycle time data Ds).

[0053] 4-2. Configuration of the trained model generation unit 120 that functions in the learning phase Next, with reference to Figure 7, the configuration of the trained model generation unit 120 of the production support device 100 that functions in the learning phase will be described. As shown in Figure 7, the trained model generation unit 120 mainly comprises a state information acquisition unit 121, an evaluation result acquisition unit 122, a reward calculation unit 123, a value function storage unit 124, an action decision unit 125, an action information output unit 126, and a value function update unit 127.

[0054] The status information acquisition unit 121 acquires at least one of the optimization information D and production information J, which are learning data, as status information. Specifically, the status information acquisition unit 121 acquires at least placement data Da and part type data Dk as learning data, and also acquires cycle time data Ds and replacement restriction information Dj as status information. Here, the status information acquisition unit 121 mainly acquires status information from the optimizer 160, but the learning data acquisition unit 110 can also acquire status information (learning data) from the management device H as needed.

[0055] The evaluation result acquisition unit 122 acquires evaluation results obtained from the mounting process after swapping component type pairs Cp among the component types represented by component type data Dk, or after swapping feeder pairs Cf among the multiple feeders 40, with respect to a pre-set evaluation target. The evaluation result acquisition unit 122 acquires evaluation results from the mounting process after swapping component type pairs Cp or feeder pairs Cf, such as the increase or decrease in cycle time, whether the components P were mounted on the substrate K in ascending order, or whether the components P were mounted on the substrate K in ascending order of their height in the Z-axis direction from the surface of the substrate K. The evaluation result acquisition unit 122 can acquire evaluation results for the evaluation target from the optimizer 160.

[0056] The reward calculation unit 123 calculates a reward E for the replacement of a component type pair Cp (or feeder pair Cf) based on the evaluation results of the evaluation target obtained by replacing a component type pair Cp (or feeder pair Cf), and based on the optimization information D (or production information J). The reward calculation unit 123 gives a positive reward E for the replacement of a component type pair Cp (or feeder pair Cf) if the evaluation result is good, while giving a negative reward (penalty) for the replacement of a component type pair Cp (or feeder pair Cf) if the evaluation result is not good.

[0057] For example, regarding the cycle time, which is one of the evaluation results, the reward calculation unit 123 gives a positive reward E if the cycle time decreases when the mounting process after swapping component type pairs Cp (or feeder pair Cf) is simulated (or when the mounting process is actually performed on the component mounting machine 10). On the other hand, the reward calculation unit 123 gives a negative reward E if the cycle time increases. Also, regarding the order in which components P are placed on the substrate K, which is one of the evaluation results, the reward calculation unit 123 gives a positive reward E if the components are placed (mounted) in order from smallest to largest, or from lowest to highest, when the mounting process after swapping component type pairs Cp (or feeder pair Cf) is simulated (or when the mounting process is actually performed on the component mounting machine 10). On the other hand, the reward calculation unit 123 gives a negative reward E if the components are placed (mounted) in order from largest to smallest, or from highest to lowest, or from highest to lowest.

[0058] In this way, the reward calculation unit 123 calculates a reward E for each evaluation target. The reward calculation unit 123 also assigns a reward E according to the difference between the evaluation result and the standard set for each evaluation target. That is, the reward calculation unit 123 assigns a larger reward E when the difference between the evaluation result and the standard is large in the positive direction than when the difference is small in the positive direction. Conversely, when the difference between the evaluation result and the standard is large in the negative direction, it assigns a larger penalty than when the difference is small in the negative direction.

[0059] Let's take cycle time, one of the evaluation results, as an example. For example, before replacing a component pair Cp (or replacing a feeder pair Cf) (performing a simulation), the cycle time represented by the cycle time data Ds included in the optimization information D is used as the base cycle time. Then, in a simulation of the mounting process after replacing a component pair Cp (or replacing a feeder pair Cf), the reward calculation unit 123 gives a larger reward E if the reduction time, which is the difference between the cycle time and the base cycle time, is large in the positive direction than if the reduction time is small in the positive direction. In other words, the reward calculation unit 123 gives a larger reward E as the reduction time of the cycle time increases (as the cycle time is shortened). Conversely, if the reduction time is large in the negative direction, that is, if the cycle time is longer than the base cycle time, the reward calculation unit 123 gives a negative reward E or no reward E.

[0060] The value function storage unit 124 generates a value function in reinforcement learning based on the state information (training data) acquired by the state information acquisition unit 121 and the reward E calculated by the reward calculation unit 123. Here, the value function is a function generated in the learning phase to obtain action information corresponding to the state information so that the evaluation result of the object being evaluated is optimized. The value function storage unit 124 then stores the generated value function, i.e., the trained model M, in an updatable manner. Therefore, the value function storage unit 124 also performs the function of the trained model storage unit 130.

[0061] In particular, the value function (trained model M) in this embodiment is an optimal action value function generated by DQN (Deep Q-Network) as a reinforcement learning algorithm. In this case, the optimal action value function is obtained as an approximation function using a neural network, and it gives the best action to take when the Q value (the value of the reward E obtained immediately according to the state) can be estimated for each action in a given state. That is, when the optimal action value function is the trained model M, the Q value is estimated using a neural network in which the component type pair Cp (feeder pair Cf) is the node of the output layer when the "state" represented by state information is taken as input, and as a result the component type pair Cp (feeder pair Cf) to be replaced as the "best action" is given.

[0062] Furthermore, the value function is not limited to the case where the optimal action value function is found using DQN. For example, it is also possible to generate a value function using reinforcement learning algorithms such as Q-learning, Sarsa, or Monte Carlo methods. In this case, a "policy" is determined based on the generated value function, and the "best action" is determined based on the "policy."

[0063] The action decision unit 125 determines a part type pair Cp of selectable part types from among multiple part types, or a feeder pair Cf of selectable feeders 40 from among multiple feeders 40, based on state information and a learned model M (optimal action value function). In this case, the action decision unit 125 can select a part type pair Cp (or feeder pair Cf) based on the optimal action value function (learned model M), or, if necessary, search for a part type pair Cp (or feeder pair Cf) without relying on the optimal action value function (learned model M).

[0064] The action information output unit 126 outputs the decision made by the action decision unit 125, i.e., the component type pair Cp (or feeder pair Cf) to be replaced, as action information A to the optimizer 160. In this case, the optimizer 160 acquires the action information A and performs a simulation of the installation process based on virtual installation conditions in which the component type pair Cp (or feeder pair Cf) is replaced according to the action information A. The optimizer 160 then estimates the evaluation result for the evaluation mode as the simulation result when the component type pair Cp (or feeder pair Cf) is replaced according to the action information A.

[0065] Subsequently, the state information acquisition unit 121 acquires the virtual mounting conditions as new optimization information D (or production information J), i.e., new state information, and the evaluation result acquisition unit 122 acquires the estimated evaluation result of the evaluation target by the optimizer 160. Next, the reward calculation unit 123 calculates the reward E for the new optimization information D (or production information J) based on the estimated evaluation result by the optimizer 160. In other words, the reward calculation unit 123 calculates the evaluation of the action information A that transitioned from the state information before the swap of the part type pair Cp (or feeder pair Cf) to the new state information after the swap of the part type pair Cp (or feeder pair Cf) as the reward E for the new state information, i.e., the optimization information D (or production information J).

[0066] The value function update unit 127 updates the optimal action value function stored in the value function update unit 127 based on the new state information, i.e., the optimized information D (or production information J), updated based on the action information A, and the reward E for the new state information (optimized information D reflecting the action information A). The value function update unit 127 only needs to update the optimal action value function based on the reinforcement learning algorithm (DQN), and for example, if a negative reward E is given, it is possible not to update the optimal action value function.

[0067] 4-3. Training process of the trained model generation unit 120 Here, referring to the flowchart shown in Figure 8, we will explain the learning process performed by the trained model generation unit 120 during the learning phase.

[0068] As shown in Figure 8, the trained model generation unit 120 performs a first learning step S1 as the first step in the learning process, in which it performs reinforcement learning using the estimation evaluation results from the optimizer 160. Subsequently, the trained model generation unit 120 performs a second learning step S2 in which it performs reinforcement learning using the actual evaluation results obtained by replacing the actual component type pair Cp (or feeder pair Cf) in the component mounting machine 10.

[0069] In the first learning process S1, when the trained model generation unit 120 performs machine learning using reinforcement learning, the value function storage unit 124 stores, for example, a provisional optimal action value function (value function) created by the worker. In addition, state information, i.e., optimization information D (or production information J), is provisionally created by the worker.

[0070] In other words, the optimal action value function (value function) stored in the value function memory unit 124 in the early stages of the learning phase has much room for improvement, and the action information A obtained from the initial optimal action value function (value function) is also immature. Therefore, for example, if multiple parts mounting machines 10 constituting the production system 1 swap parts type pairs Cp (or feeder pairs Cf) based on immature action information A and perform the mounting process, the evaluation results of the evaluated items are likely to be unsatisfactory. As a result, for example, there is a concern that a severe bottleneck may occur in the parts mounting machines 10 constituting the production system 1, leading to a deterioration in productivity.

[0071] Therefore, in the initial stage of the learning process, the trained model generation unit 120 performs reinforcement learning using the estimated evaluation results obtained from the simulation performed by the optimizer 160. In this case, the trained model generation unit 120 can perform reinforcement learning using only the simulation results without actually replacing the part type pair Cp (or feeder pair Cf) in the part mounting machine 10, thus avoiding a decrease in productivity. Furthermore, when using simulation results, evaluation results for the evaluation target can be obtained in a shorter time compared to when reinforcement learning is performed while actually replacing the part type pair Cp (or feeder pair Cf) in the part mounting machine 10. For this reason, the optimal action-value function (value function) can be updated in a shorter time during the first learning process.

[0072] Subsequently, the trained model generation unit 120 updates the optimal action-value function (value function), and once the accuracy of action information A has improved, it performs reinforcement learning using the actual evaluation results obtained by the mounting process after actually replacing the part type pair Cp (or feeder pair Cf) in the part mounting machine 10. This allows the trained model generation unit 120 to further improve action information A while suppressing the occurrence of a decrease in productivity.

[0073] 4-4. Configuration of the inference unit 150 that functions in the inference phase Next, with reference to Figure 9, the configuration of the inference unit 150 of the production support device 100 that functions in the inference phase will be described. As shown in Figure 9, the inference unit 150 mainly comprises a state information acquisition unit 151, a value function storage unit 152, an action decision unit 153, and an action information output unit 154. The state information acquisition unit 151, the value function storage unit 152, the action decision unit 153, and the action information output unit 154 have the same configuration as the state information acquisition unit 121, the value function storage unit 124, the action decision unit 125, and the action information output unit 126 of the trained model generation unit 120 described above.

[0074] 4-5. Optimization of the arrangement (rearrangement) of part type pairs Cp by the production support device 100 Next, referring to the flowchart of the optimization program shown in Figure 10, we will explain the optimization of swapping part type pairs Cp (or feeder pairs Cf) mainly performed by the inference unit 150 of the production support device 100. The optimization program starts in step S10. Then, in the following step S11, the production support device 100's production information acquisition unit 140 acquires production information J, for example, from the management device H, which instructs actual production. Then, as the "first step," the production support device 100 (inference unit 150) sets a part mounting machine pair Cm, which represents the part mounting machines 10 among the multiple part mounting machines 10 that constitute the production system 1, based on the production information J.

[0075] As described above, when optimization is performed, the multiple feeders 40 that can be set on each component mounting machine 10 are known, for example, by optimization information D and production information J. In other words, the component types of the components P to be mounted on each component mounting machine 10 are also known, for example, by optimization information D and production information J. That is, the relationship between each component type of component P and each component mounting machine 10 is also known. For this reason, when it is desired to optimize the component types, as a first step, for example, in accordance with the operator's instructions, the inference unit 150 appropriately sets component mounting machine pairs Cm representing the component mounting machines 10 among the multiple component mounting machines 10 that constitute the production system 1, based on production information J.

[0076] In the following step S12, the production support device 100, as the "second step," infers a part type pair Cp (or feeder pair Cf) using the optimal action-value function (trained model M). Specifically, the inference unit 150 obtains production information J as state information from the production information acquisition unit 140, which includes new placement data Dan and new part type data Dkn, from the state information acquisition unit 151. Then, the action decision unit 153 infers the part type pair Cp (or feeder pair Cf) to be replaced using the state information (production information J) obtained by the state information acquisition unit 151 and the optimal action-value function (trained model M) stored in the value function storage unit 152 (trained model storage unit 130).

[0077] In the subsequent step S13, the production support device 100 swaps the part type pair Cp (or feeder pair Cf) in the part mounting machine 10. Specifically, the action information output unit 156 of the production support device 100 outputs the part type pair Cp (or feeder pair Cf) inferred in step S11 as action information A to the management device H. The management device H then outputs a command based on the action information A, specifically a command to swap the feeders 40 that form the feeder pair Cf corresponding to the part type pair Cp, to, for example, multiple part mounting machines 10 and loader devices 30.

[0078] As a result, each component mounting machine 10 and loader device 30 swaps the two feeders 40 identified by the component type pair Cp, specifically the feeder pair Cf, which is identified in the action information A. The swapping of the feeders 40 in the component mounting machine 10 includes, for example, changing the identification number (a number corresponding to the order in which the component P is mounted) assigned to the slot 12S of the component supply device 12 in accordance with the swapping of the feeders 40.

[0079] In the following step S14, the production support device 100 acquires the cycle time required for the mounting process after the part type, i.e., the feeder 40, has been replaced, at the parts mounting machine 10. Specifically, the production support device 100 status information acquisition unit 151 acquires from the management device H the cycle time required for the mounting process at the parts mounting machine 10 after the feeder 40 has been replaced.

[0080] In the following step S15, the production support device 100 determines whether the cycle time acquired in step S14 has improved compared to before the feeder 40 was replaced. That is, if the cycle time acquired in step S14 after the replacement of the part type pair Cp (feeder pair Cf) is shorter than the reference cycle time before the replacement of the part type pair Cp (feeder pair Cf) included in the production information J (status information) acquired by the production information acquisition unit 140 in step S11, the production support device 100 determines "Yes" because the cycle time has improved. Then, the production support device 100 returns to step S12 and executes the processing of each step from step S12 onward.

[0081] On the other hand, if, for example, after performing multiple optimizations (replacements), the cycle time after replacing the part type pair Cp (feeder pair Cf) acquired in step S12 is not shorter than the reference cycle time, the production support device 100 determines "No" because the cycle time has not been improved. Then, in step S16, the production support device 100 returns the part type that was replaced in step S13, i.e., the feeder 40, to its state before replacement and proceeds to step S17.

[0082] In other words, the action information output unit 154 of the production support device 100 outputs, for example, a feeder pair Cf (part type pair Cp) that returns the corresponding feeder 40 to its previous state as action information A to the management device H. As a result, the management device H outputs a command based on action information A, specifically a command to return the feeders 40 that form the feeder pair Cf corresponding to the part type pair Cp to their previous state, to, for example, multiple part mounting machines 10 and loader devices 30.

[0083] As a result, each component mounting machine 10 and loader device 30 returns the two feeders 40 identified by the component type pair Cp, specifically the feeder pair Cf, identified in the action information A, to their state before replacement. Note that the replacement of the feeders 40 in the component mounting machine 10 includes, for example, changing the identification number (a number corresponding to the order in which the component P is mounted) assigned to the slot 12S of the component supply device 12 in accordance with the replacement of the feeders 40.

[0084] In step S17, the production support device 100 determines, based on the production information J, whether the replacement of the component type (feeder 40) described above, or in other words, the optimization study, has been completed for all possible component mounting machine pairs Cm that can be combined with the multiple component mounting machines 10 constituting the production system 1. That is, if the optimization study for all target component mounting machine pairs Cm has not been completed, the production support device 100 determines "No" and returns to step S11. Then, if the production support device 100 sets a new component mounting machine pair Cm in step S11, it executes the step processing from step S12 onwards as described above. On the other hand, if the optimization study for all target component mounting machine pairs Cm has been completed, the production support device 100 determines "Yes" and proceeds to step S18, and the execution of the optimization program ends in step S16.

[0085] Here, "all possible component mounting machine pairs Cm" may include, for example, setting all combinations of component mounting machines 10 that constitute production system 1 as component mounting machine pairs Cm. Alternatively, for example, if there are combinations of component mounting machines 10 that are expected to have an effect such as shortening cycle time, it is also possible to select the component mounting machines 10 that are expected to have such an effect from all of the component mounting machines 10 and set them as component mounting machine pairs Cm.

[0086] As can be understood from the above explanation, the production support device 100 includes a learning data acquisition unit 110 that acquires placement data Da related to the arrangement of multiple component mounting machines 10 and component type data Dk representing the component types of multiple component P that each component mounting machine 10 attaches to the substrate K, as optimization information D or production information J which are learning data, and a machine learning operation that performs swapping patterns on which a reward E is obtained when multiple component mounting machines 10 attach component P by swapping component type pairs Cp representing component types distinguished by component type data Dk. The production support device 100 includes a trained model storage unit 130 that stores the generated trained model M, a production information acquisition unit 140 that acquires production information J which includes at least new placement data Dan and new part type data Dkn and instructs the production of a substrate K by attaching new parts P using a parts mounting machine 10, and an inference unit that uses the new placement data Dan and new part type data Dkn included in the production information J and the trained model M to infer and output a pair of part types Cp to be replaced from among the new part types distinguished by the part type data Dkn. The production support device 100 also includes a trained model generation unit 120 that generates a trained model M by repeatedly performing machine learning on replacement patterns for which a reward E is obtained when replacing a pair of part types Cp distinguished by the part type data Dkn and supplying parts P to the parts mounting machine 10.

[0087] According to this, regarding the optimization of the arrangement of component types (feeder 40 swapping) for component P, by using a trained model M that has learned swapping patterns that yield a reward E, it is possible to infer the component type pairs Cp (feeder pairs Cf) that are subject to swapping, i.e., effective for optimization. This eliminates the need to sequentially consider all combinations of component types (feeder 40s) for multiple component types to determine the component type pairs Cp (feeder pairs Cf) that are effective for optimization. Furthermore, by using the trained model M, it is possible to selectively determine the component type pairs Cp (feeder pairs Cf) that are effective for optimization even for new component types. Therefore, by using the production support device 100, the arrangement of component types (feeder 40s) can be optimized efficiently.

[0088] 5. First variation In the above-described embodiment, for example, as a first step, a pair of parts mounting machines Cm is set by an operator or the like, and as a second step, the production support device 100 can replace the feeders 40 (part types) set in the selectively set pair of parts mounting machines Cm, i.e., make them the target of optimization. As a result, in the above-described embodiment, for example, the number of simulations performed by the optimizer 160 can be reduced, and the arrangement of part types can be optimized efficiently.

[0089] Incidentally, as mentioned above, the type of part P to be mounted in each part mounting machine 10 is known. Therefore, instead of inferring the part type pair Cp or feeder pair Cf as the target of replacement, as in the embodiment described above, the production support device 100 can also infer the part mounting machine pair Cm in the same way as inferring the part type pair Cp or feeder pair Cf, as shown in Figures 7 and 9. That is, in this case, as the first step, for example, in step S11 of the optimization program described above, the part mounting machine pair Cm is inferred for the part mounting machine 10 that is most likely to mount the feeder 40 (part type) to be replaced, based on the learned model M and the optimization information D (or production information J). As a result, as described above, in the second step, the production support device 100 can infer the feeder pair Cf, i.e., the part type pair Cp, that will actually be set in the part mounting machine pair Cm, and thus efficiently optimize the arrangement of part types.

[0090] 6. Second variation Furthermore, in the embodiment described above, during the learning phase, the reward calculation unit 123 calculates a reward E according to the evaluation result, regardless of the evaluation target. In addition, as shown by the dashed line in Figure 7, the trained model generation unit 120 may also include a weighting unit 128. The weighting unit 128 will be described below.

[0091] The weighting unit 128 weights the reward E that the reward calculation unit 123 gives to each of the multiple evaluation targets. In other words, if the importance of some of the multiple evaluation targets (e.g., cycle time) is higher than the importance of other evaluation targets (e.g., placement of parts P), the weighting unit 128 increases the reward E or the degree of penalty given to some of the evaluation targets compared to others. Therefore, the same effect as the embodiment described above can be obtained in the second modified example as well. The weighting of the reward E for each evaluation target can be set, for example, by the operator.

[0092] 7. Other variations In the embodiment described above, the production support device 100 infers part type pairs Cp (feeder pairs Cf) based on the learned model M and optimization information D (production information J). Alternatively, for example, as in the first modified example, if the production support device 100 infers part mounting machine pairs Cm, the worker may determine the part type pairs Cp and feeder pairs Cf for a limited number of feeders 40, i.e., part types, that are set in the part mounting machine 10 that form the part mounting machine pairs Cm. Even in this case, since the number of part types (feeders 40) that are subject to replacement is limited, even if the worker determines the part type pairs Cp and feeder pairs Cf, it is possible to optimize the placement of part types more efficiently than in the conventional method described above.

[0093] Furthermore, in the embodiment described above, the production support device 100 is equipped with a trained model generation unit 120. Alternatively, the trained model generation unit 120 can be installed in a device other than the production support device 100 installed in the production system 1 (for example, the management device H of the production system 1, or a computer device owned by the manufacturer that manufactures the production system 1 and the component mounting machine 10 and is capable of communicating with the management device H). In this case, the trained model generation unit 120 installed in a device other than the production support device 100 can generate a trained model M using, for example, optimization information D owned by the manufacturer. The generated trained model M is then supplied, for example, to the management device H of the production system 1, and from the management device H, it is supplied to the trained model storage unit 130 of the production support device 100 for storage. In this case as well, the same effects as in the embodiment described above can be obtained. [Explanation of Symbols]

[0094] 1…Production system, 10…Component mounting machine, 11…Substrate transport device, 12…Component supply device, 12S…Slot, 13…Component transfer device, 13A…Head drive device, 13B…Mobile platform, 13C…Mounting head, 13D…Nozzle holder, 13E…Suction nozzle, 14…Component camera, 15…Substrate camera, 16…Control device, 20…Automatic transport machine, 30…Loader device, 40…Feeder, 41…Feeder body, 42…Drive sprocket, 43…Tape presser, 44…Peeling section, 50…Carrier tape, 501…Joining section, 502…Joining section, 51…Base tape, 511…Cavity, 512…Feed hole, 52…Cover tape, 100…Production support device, 110…Training data acquisition unit, 120…Trained model generation unit, 121…Status information acquisition unit, 122…Evaluation result acquisition unit ,123...Reward calculation unit, 124...Value function storage unit, 125...Action decision unit, 126...Action information output unit, 127...Value function update unit, 128...Weighting unit, 130...Trained model storage unit, 140...Production information acquisition unit, 150...Inference unit, 151...State information acquisition unit, 152...Value function storage unit, 153...Action decision unit, 154...Action information output unit, 160...Optimizer, P...Part, Ps...Part supply position, R...Reel, D...Optimization information, Da...Placement data, Dan...New placement data, Dk...Part type data, Dkn...New part type data, Ds...Cycle time data, Dj...Replacement restriction information, J...Production information, M...Trained model, E...Reward, A...Action information, Cp...Part type pair, Cf...Feeder pair, Cm...Part mounting machine pair, H...Management device

Claims

1. A learning data acquisition unit acquires arrangement data relating to the arrangement of multiple component mounting machines and component type data representing the component types of multiple components that each of the component mounting machines mounts onto a circuit board, as learning data. A trained model storage unit stores a trained model generated by performing machine learning on swapping patterns in which a reward is obtained when multiple component mounting machines mount components by swapping component type pairs representing the component types distinguished by the component type data, A production information acquisition unit that acquires production information that includes at least new placement data and new component type data, and instructs the production of the circuit board by mounting the new components using the component mounting machine, An inference unit that uses the new placement data and new part type data included in the production information and the trained model to infer and output the part type pairs to be replaced from among the new part types distinguished by the part type data, A production support device equipped with these features.

2. The production support apparatus according to claim 1, further comprising a trained model generation unit that generates the trained model by repeatedly performing machine learning on replacement patterns for which a reward is obtained when replacing the parts type pairs distinguished by the parts type data and supplying the parts to the parts mounting machine.

3. The aforementioned compensation is, A production support device according to claim 1 or 2, provided in a simulation after swapping the aforementioned parts pairs, where the cycle time required for mounting the parts is shortened.

4. The aforementioned compensation is, The production support device according to claim 3, wherein the reduction in cycle time after the replacement of the parts type pair increases as the reduction in cycle time after the replacement of the parts type pair increases compared to the cycle time before the replacement of the parts type pair.

5. The aforementioned compensation is, The production support apparatus according to claim 1 or 2, provided that in a simulation after swapping the aforementioned pairs of component types, the components are mounted on the substrate in order from smallest to largest.

6. The aforementioned compensation is, The production support apparatus according to claim 5, provided that in a simulation after swapping the aforementioned pairs of component types, the components are mounted on the substrate in order from lowest to highest height from the surface of the substrate.

7. Each of the aforementioned components is housed in a carrier tape wound around a reel. The production support apparatus according to claim 1 or 2, wherein each reel is loaded into a feeder that supplies the components contained in the carrier tape to the component mounting machine.

8. The inference unit, The production support device according to claim 7, which infers and outputs a feeder pair representing the feeders that supply the aforementioned components to the component mounting machine as the component type pair.

9. A first step is to set up a parts mounting machine pair that represents the parts mounting machines among a plurality of parts mounting machines, A production support apparatus according to claim 1 or 2, which performs a second step of inferring and outputting the part type pair in the part mounting machine pair.

10. The inference unit, The production support device according to claim 9, which uses the production information and the trained model to infer and output the pair of component mounting machines in the first process.

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