Production support system, production support method and program

Abstract

To provide a production support system and the like capable of preventing mounting errors (defects) more reliably.SOLUTION: A production support system 1 capable of communication with a mounting line 20 including a component mounting unit 21 that mounts a component includes: an acquisition unit 31 that acquires operational performance information regarding a component from the mounting line 20; a determination unit 33 that determines whether the cause of component failure is a parameter related to the component based on the acquired operational performance information; a selection unit (e.g., a determination unit 42) that, when the factor is determined to be a parameter of the component, selects the parameter to be adjusted from multiple parameters included in the component-related parameters based on the operational performance information; and an execution unit (e.g., generation unit 43) that executes a series of processing to adjust the selected parameters to be adjusted.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a production support system, a production support method, and a program. [Background technology]

[0002] Patent Document 1 discloses a method for dealing with mounting deviations, which stores mounting deviations as mounting results, sets a correction amount for the mounting position deviation so as to cancel out the mounting deviation, and starts mounting processing with the set correction amount. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2022-175215 Summary of the Invention [Problem to be solved by the invention]

[0004] Incidentally, there are multiple parameters related to components, and when a mounting error (defect) occurs, the parameters related to the components may be adjusted to prevent the mounting error. This parameter adjustment work requires a high level of expertise in component mounting, and there is a concern that the efficiency of the parameter adjustment work may decrease if an unskilled worker adjusts the parameters. For example, an unskilled worker may not know which parameters of the components should be adjusted to prevent the mounting error, and may end up repeating trial and error due to inappropriate data adjustment.

[0005] Therefore, it is desirable to automatically adjust the parameters related to the components, including the selection of the parameters to be adjusted. However, Patent Document 1 does not disclose a technique for automatically selecting the parameters to be adjusted.

[0006] Therefore, the present disclosure provides a production support system, a production support method, and a program that can automatically select parameters related to components that can suppress mounting errors (defects). [Means for solving the problem]

[0007] A production support system according to one aspect of the present disclosure is a production support system capable of communicating with a mounting line including a component mounting device that mounts components, and includes an acquisition unit that acquires operation performance information related to the components from the mounting line, a determination unit that determines whether a cause of failure of the component is a parameter related to the component based on the acquired operation performance information, a selection unit that, when it is determined that the cause is a parameter related to the component, selects a parameter to be adjusted from a plurality of parameters included in the parameters related to the component based on the operation performance information, and an execution unit that executes processing to adjust the selected parameter to be adjusted.

[0008] A production support method according to one aspect of the present disclosure is a production support method executed by a production support system capable of communicating with a mounting line including a component mounting device that mounts components, the production support method acquiring operation performance information related to the components from the mounting line, determining whether a cause of failure of the components is a parameter related to the components based on the acquired operation performance information, and if it is determined that the cause is a parameter related to the components, selecting a parameter to be adjusted from a plurality of parameters included in the parameters related to the components based on the operation performance information, and executing a process to adjust the selected parameter to be adjusted.

[0009] A program according to one aspect of the present disclosure is a program for causing a computer to execute the above-described production support method. Effect of the Invention

[0010] According to one aspect of the present disclosure, it is possible to realize a production support system or the like that can automatically select parameters related to components that can suppress mounting errors (defects). [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing a functional configuration of a production support system according to an embodiment. [Diagram 2] FIG. 2 is a block diagram illustrating a functional configuration of an estimation unit according to an embodiment. [Diagram 3] FIG. 3 is a diagram for explaining the determination in the determining unit according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a table stored in the storage unit according to the embodiment. [Diagram 5] FIG. 5 is a flowchart showing the operation of the defect monitoring device according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing the operation of the online adjustment device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of parameters related to a part. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] (Background to this disclosure) Prior to describing the present disclosure, the background to the present disclosure will be described with reference to FIG. 7. FIG. 7 is a diagram showing an example of parameters related to components. The parameters shown in FIG. 7 are merely exemplary, and parameters other than those shown in FIG. 7 may be included. Also, FIG. 7 shows parameters related to component mounting devices included in a mounting line, but parameters related to other production devices included in the mounting line may be included. Examples of other production devices include printing devices, processing devices, etc.

[0013] As shown in Fig. 7, the parameters related to components include many parameters. The parameters related to components include mounting condition parameters that indicate parameters related to a component mounting device when mounting a component on a board or the like, and component shape parameters that indicate parameters related to the component itself. Parameters are further classified into quantitative variables that adjust values ​​and qualitative variables that select appropriate ones.

[0014] Examples of mounting condition system parameters whose classification is a qualitative variable include eight parameters, such as the nozzle number and an automatic height measurement enable / disable flag, and examples of mounting condition system parameters whose classification is a quantitative variable include 13 parameters, such as the suction pressure allowance and the mounting pressure allowance.

[0015] The component mounting operation performed by the component mounting device consists of various operations, such as a pickup operation in which a component is picked up from a component supply unit by a pickup nozzle, a recognition operation in which the picked component is photographed and recognized, a mounting operation in which the component is transferred to a board and mounted, etc. Since these operations require precise operations for minute components to be performed with high accuracy and efficiency, mounting condition system parameters for performing each operation in a good operating mode are preset according to the type of component.

[0016] Examples of part shape system parameters whose classification is a qualitative variable include 10 parameters including tilt range check and center range check, and examples of part shape system parameters whose classification is a quantitative variable include 17 parameters including transmitted lamp offset and recognition speed.

[0017] Then, component data (for example, component data D2 shown in Fig. 1 described later) in which the mounting condition parameters and component shape parameters are associated with the type of component is generated as a component library. This component data is not necessarily set to optimal values ​​that allow the work operation to be performed in an optimal operating mode, and is required to be corrected as needed in response to malfunctions such as mounting errors that occur during the execution of the component mounting work.

[0018] This type of parameter adjustment work (part data correction work) requires a high level of expertise, including specialized knowledge of part mounting and proficiency based on experience. For example, if a mounting error (defective work) occurs, it is necessary to adjust the part-related parameters during production to prevent the mounting error, but the reality is that which parameters among the multiple parameters shown in Figure 7 should be adjusted and to what extent is largely dependent on the know-how of the worker. For this reason, when unskilled workers adjust parameters, they end up repeating trial and error due to inappropriate data adjustment, which reduces the efficiency of the parameter adjustment work and takes time for production to stabilize, raising concerns about operational losses.

[0019] Therefore, there is a demand for automatic adjustment of part-related parameters, including the selection of parameters to be adjusted. Note that "automatically" means that part-related parameters are adjusted without the intervention of an operator (human).

[0020] Therefore, the present inventors have conducted extensive research into production support systems etc. that can automatically select parameters related to parts that can suppress mounting errors (defects), and have invented the production support system etc. described below. For example, the present inventors have invented a production support system that can automate the adjustment of parameters related to parts that can suppress mounting errors (defects) by selecting parameters to be adjusted based on operation performance information (e.g., part recognition logs) and further determining the adjustment amount (in the case of a quantitative variable) or the setting value (in the case of a qualitative variable) of the selected parameters.

[0021] A production support system according to a first aspect of the present disclosure is a production support system capable of communicating with a mounting line including a component mounting device that mounts components, and includes an acquisition unit that acquires operation performance information related to the components from the mounting line, a determination unit that determines whether a cause of a defect in the component is a parameter related to the component based on the acquired operation performance information, a selection unit that, when it is determined that the cause is a parameter related to the component, selects a parameter to be adjusted from a plurality of parameters included in the parameters related to the component based on the operation performance information, and an execution unit that executes processing to adjust the selected parameter to be adjusted.

[0022] In this way, when it is determined that the cause of the defect is a parameter related to a part, the selection unit selects the parameter to be adjusted. Therefore, it is possible to realize a production support system that can automatically select a parameter related to a part that can suppress mounting errors (defects).

[0023] Also, for example, a production support system according to a second aspect may be the production support system according to the first aspect, wherein the operation performance information includes a mounting log of the mounting line, and the determination unit determines whether the cause relates to the component or to a unit of the component mounting device based on the mounting log.

[0024] This makes it possible to distinguish whether a mounting error is caused by a component or equipment based on the mounting log. In other words, since the cause can be accurately determined, parameter selection can be performed automatically and accurately.

[0025] Also, for example, a production support system according to a third aspect may be the production support system according to the first aspect, wherein the operation performance information includes an event log of the mounting line, and the determination unit determines, based on the event log, whether the cause is a parameter related to the component or an event included in the event log.

[0026] This makes it possible to distinguish whether a mounting error is caused by a component or an event based on the event log. In other words, because the cause can be accurately determined, parameter selection can be performed automatically and accurately.

[0027] Also, for example, a production support system according to a fourth aspect is the production support system according to the second aspect, wherein the operation performance information includes an event log of the mounting line, and when the determination unit determines that the cause is related to the component, the determination unit may further determine, based on the event log, whether the cause is a parameter related to the component or an event included in the event log.

[0028] This makes it possible to determine the cause of a mounting error using both the mounting log and the event log. In other words, since the cause can be determined more accurately, parameter selection can be performed automatically and more accurately.

[0029] Also, for example, a production support system according to a fifth aspect may be the production support system according to the third or fourth aspect, wherein the operation performance information includes first time information indicating a time when the defect occurred, the event log includes second time information indicating a time when the event occurred, and the determination unit uses a time difference between the first time information and the second time information to determine whether the cause is a parameter related to the part or the event.

[0030] This makes it possible to automatically determine whether a cause is a parameter related to a part or an event cause simply by checking the time difference.

[0031] Also, for example, a production support system according to a sixth aspect may be the production support system according to the third or fourth aspect, and the determination unit may determine that the factor is a parameter related to the part when a failure rate within a predetermined time from a time when a production model is switched on the mounting line becomes equal to or exceeds a predetermined threshold.

[0032] This makes it possible to automatically determine whether a problem is due to a part-related parameter or an event, simply by checking the defect rate.

[0033] Also, for example, a production support system according to a seventh aspect is a production support system according to any one of the third to sixth aspects, and the event may include at least one of model switching for switching a production model on the mounting line, data editing for editing parameters related to the component, and material supply for supplying the component to the component mounting device.

[0034] This makes it possible to accurately determine the cause of a defect when an event includes at least one of a model change, data editing, and material supply.

[0035] Also, for example, a production support system according to an eighth aspect is a production support system according to any one of the first to seventh aspects, wherein the operation performance information includes a component recognition log indicating a result of recognition of the components by an imaging device provided in the component mounting device of the mounting line, and the selection unit may select the parameter to be adjusted based on the component recognition log.

[0036] This makes it possible to automatically select more appropriate parameters based on the component recognition results as parameters to be adjusted.

[0037] Also, for example, a production support system according to a ninth aspect is the production support system according to the eighth aspect, wherein the operation performance information includes part specification information indicating specifications of the part, and the selection unit may determine at least one of an adjustment amount and a setting value of the selected parameter to be adjusted based on the part specification information of the part.

[0038] This makes it possible to automatically determine at least one of the adjustment amount and the set value that is more appropriate according to the specifications of the part.

[0039] Also, for example, a production support system according to a tenth aspect is the production support system according to the ninth aspect, and the part specification information may include manufacturer information indicating a manufacturer of the part.

[0040] This makes it possible to automatically determine a more appropriate adjustment amount when the adjustment amount varies depending on the manufacturer.

[0041] Also, for example, a production support system according to an eleventh aspect may be the production support system according to the ninth or tenth aspect, and may include a memory unit that stores an adjustment rule in which the part recognition log and the part specification information are associated with at least one of the adjustment amount and the set value.

[0042] In this way, by using the table read from the storage unit, at least one of the adjustment amount and the set value can be determined from the part recognition log and the part specification information without communicating with the outside. For example, even in a poor communication environment, at least one of the adjustment amount and the set value can be automatically determined.

[0043] Also, for example, a production support system according to a twelfth aspect is a production support system according to any one of the eighth to eleventh aspects, and the selection unit may select the suction pressure of the component mounting device as the parameter to be adjusted when a result of the recognition of the component included in the component recognition log is no component suction.

[0044] As a result, when the component recognition log includes a recognition result in which a component is not picked up, the suction pressure parameter can be automatically selected.

[0045] Also, for example, a production support system according to a thirteenth aspect may be a production support system according to any one of the first to twelfth aspects, and may include an output section that outputs the adjusted parameter of the adjustment target to the mounting line.

[0046] This makes it possible to automatically select parameters relating to components in a component mounting device.

[0047] Also, for example, a production support system according to a fourteenth aspect is a production support system according to any one of the first to thirteenth aspects, and the production support system may repeatedly execute processes from obtaining the operation performance information to adjusting the parameters to be adjusted.

[0048] This makes it possible to automatically select the parameters to be adjusted according to the situation at that time, even if the defect situation changes over time.

[0049] Also, for example, a production support system according to a fifteenth aspect is a production support system according to the fourteenth aspect, and the production support system may repeatedly perform operations from acquiring the operation performance information to adjusting the parameters to be adjusted until the defect rate of the part becomes equal to or lower than a predetermined threshold value.

[0050] This makes it possible to automatically and reliably keep the defect rate below a predetermined threshold value.

[0051] Also, for example, a production support system according to a 16th aspect is a production support system according to any one of the 1st to 15th aspects, and the defects may include at least one of a pickup error when picking up the part, a part recognition error when recognizing the part, and a mounting error when mounting the part.

[0052] This makes it possible to realize a production support system that can automatically suppress the occurrence of at least one of a pickup error, a component recognition error, and a mounting error.

[0053] A production support method according to a seventeenth aspect of the present disclosure is a production support method executed by a production support system capable of communicating with a mounting line including a component mounting device that mounts components, which acquires operation performance information related to the components from the mounting line, determines whether a cause of the component's failure is a parameter related to the components based on the acquired operation performance information, and if it is determined that the cause is a parameter related to the components, selects a parameter to be adjusted from a plurality of parameters included in the parameters related to the components based on the operation performance information, and executes a process for adjusting the selected parameter to be adjusted. A program according to an eighteenth aspect of the present disclosure is a program for causing a computer to execute the above production support method.

[0054] This provides the same effects as the above-mentioned production support system.

[0055] These general or specific aspects may be realized by a system, a method, an integrated circuit, a computer program, or a non-transitory recording medium such as a computer-readable CD-ROM, or may be realized by any combination of the system, the method, the integrated circuit, the computer program, or the recording medium. The program may be stored in the recording medium in advance, or may be supplied to the recording medium via a wide area communication network including the Internet.

[0056] Hereinafter, the embodiment will be specifically described with reference to the drawings.

[0057] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, components, component arrangement and connection forms, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure. Furthermore, among the components in the following embodiments, components that are not described in the independent claims are described as optional components.

[0058] In addition, each figure is a schematic diagram and is not necessarily illustrated precisely. Therefore, for example, the scales in each figure do not necessarily match. In addition, in each figure, substantially the same configurations are given the same reference numerals, and duplicated explanations are omitted or simplified.

[0059] Furthermore, in this specification, terms indicating relationships between elements such as "same" and numerical values ​​and numerical ranges are not expressions that only indicate a strict meaning, but are expressions that include a substantially equivalent range, for example, a difference of about a few percent (or about 10%).

[0060] In addition, in this specification, ordinal numbers such as "first" and "second" do not refer to the number or order of components, unless otherwise specified, but are used for the purpose of avoiding confusion between and distinguishing between components of the same type.

[0061] (Embodiment) Hereinafter, a production support system including a defect monitoring device and an online adjustment device according to the present embodiment will be described with reference to FIGS. 1 to 6. FIG.

[0062] [1. Configuration of production support system] First, the configuration of a production support system according to the present embodiment will be described with reference to Figures 1 and 2. Figure 1 is a block diagram showing the functional configuration of a production support system 1 according to the present embodiment.

[0063] 1, the production support system 1 is a system for producing mounted boards by mounting components on an object such as a board using a mounting line 20. The production support system 1 includes at least a defect monitoring device 30 and an online adjustment device 40. The production support system 1 may further include at least one of an offline adjustment device 10 and the mounting line 20.

[0064] The offline adjustment device 10 is an information processing device for generating component data D2 (adjusted component data) to be used on the mounting line 20 by adjusting past component data D1 (pre-adjustment component data) before production on the mounting line 20. The offline adjustment device 10 learns adjustment patterns for parameters included in the component data D1, and estimates initial values ​​of an optimal parameter set based on the learning.

[0065] The offline adjustment device 10 includes an explanatory variable preprocessing unit 11, an estimating unit 12, and an adjustment pattern learning unit 14.

[0066] The explanatory variable pre-processing unit 11 extracts explanatory variables used as input information for the trained model 13 from the pre-adjustment part data D1, and outputs the explanatory variables to the estimation unit 12. The explanatory variable pre-processing unit 11 may perform a predetermined calculation on the explanatory variables. The part data D1 includes part specification information on parts to be used in the next production run and various parameters (for example, the parameters shown in FIG. 7), and the explanatory variable pre-processing unit 11 may extract the part specification information as the explanatory variables. The explanatory variables include, for example, at least one of the part type, dimensions, manufacturer, and mounting correction amount of the part. The dimensions and mounting correction amount are, for example, actual measurement values.

[0067] Note that the various parameters included in the part data D1 may be set to default values, or may be set to values ​​that have been used in the past (for example, last time).

[0068] The estimation unit 12 estimates (calculates) various parameters to be used in the next production based on the trained model 13 generated by the learning unit 17 and the explanatory variables output from the explanatory variable preprocessing unit 11, and outputs component data D2 including the estimated various parameters to the mounting line 20. In this way, the estimation unit 12 estimates parameters related to the parts in the next production using the trained model 13 generated by an estimation method based on the part specification information of the parts and parameters related to the parts.

[0069] The adjustment pattern learning unit 14 generates a trained model 13 by machine learning using a teacher parts data set S including a past production program. The past production program may include part data that has been adjusted in the past. The adjustment pattern learning unit 14 has a response variable preprocessing unit 15, an explanatory variable preprocessing unit 16, and a learning unit 17.

[0070] The objective variable preprocessing unit 15 generates preprocessed data by performing a predetermined preprocessing on the objective variables included in the teacher part data set S, and outputs the preprocessed data to the learning unit 17. It can also be said that the objective variable preprocessing unit 15 outputs objective variables corresponding to the explanatory variables input to the explanatory variable preprocessing unit 16 to the learning unit 17. The objective variables here are, for example, setting values ​​of parameters related to parts (for example, each parameter shown in FIG. 7).

[0071] The explanatory variable preprocessing unit 16 performs a predetermined preprocessing on the explanatory variables included in the teacher parts data set S to generate preprocessed data, and outputs the preprocessed data to the learning unit 17. The explanatory variables include quantitative variables and qualitative variables. The explanatory variables here are, for example, information about parts (for example, part specification information).

[0072] The learning unit 17 uses the explanatory variables (e.g., part specification information) output from the objective variable preprocessing unit 15 as input information and the objective variables (e.g., parameters related to parts) output from the explanatory variable preprocessing unit 16 as correct answer information, and generates a learning model showing the relationship between the part specification information and the parameters by a learning algorithm using machine learning or the like. As the learning algorithm, a neural network (including deep learning using a multi-layered neural network), genetic programming, a decision tree, a Bayesian network, a support vector machine (SVM), a Gaussian process, or the like may be used. The generated trained model 13 is output to the estimation unit 12. The generated trained model 13 may be stored in a storage unit (not shown).

[0073] The mounting line 20 is composed of a plurality of production facilities including one or more component mounting devices 21, and produces mounted boards. The component mounting device 21 mounts components on an object such as a board. The component mounting device 21 is composed of a plurality of units. In this embodiment, the component mounting device 21 has, as units, a drive control unit, a component mounting head unit, and a component supply unit. The drive control unit controls the rotation and movement of the component mounting head unit. The drive control unit has a head spindle that controls the rotation of the component mounting head unit by driving a motor. The component mounting head unit mounts (mounts) components supplied to a component supply position (component suction position) of the feeder on the board. The component mounting head unit is composed of a mounting head (head) equipped with a component suction nozzle (nozzle) that can pick up components from the feeder and move up and down individually. For example, a plurality of nozzles are attached to the head. The component supply unit has one or more feeders arranged side by side, and each feeds components to the component supply position. In this embodiment, the feeder is a tape feeder, but it may be another feeder, such as a bulk feeder.

[0074] The mounting line 20 (or a control device (not shown) that controls the mounting line 20) stores the mounting log L1, event log L2, and component recognition log L3 acquired during production, as well as component specification information P related to the components used in production. The mounting log L1, event log L2, and component recognition log L3 are examples of operation performance information related to components. Note that the operation performance information may include at least one of the mounting log L1, event log L2, and component recognition log L3.

[0075] The component recognition log L3 indicates the result of component recognition by an imaging device (not shown) provided in the component mounting device 21 during mounting. The component recognition log L3 includes information indicating the presence or absence of a component, information indicating the presence or absence (detected or not detected) of a component, information indicating various positions or dimensions, etc. The information indicating various positions includes, for example, the position of a picked-up component, the position of the electrodes of the component, etc. Furthermore, the information indicating various dimensions includes, for example, the electrode pitch of the component, the component size, etc.

[0076] The mounting line 20 starts production using the component data D2 generated by the offline adjustment device 10. As described above, the component data D2 is not necessarily set to an optimal value, and defects such as mounting errors may occur during production.

[0077] The components are electronic components, such as, but not limited to, resistors, capacitors, etc. Furthermore, the target object is not limited to a substrate, and may be any workpiece that can be subjected to a predetermined processing.

[0078] The component mounting device 21 has various sensors that directly or indirectly measure the states (for example, operations) of the drive control unit, the component mounting head unit, and the component supply unit.

[0079] The defect monitoring device 30 is capable of communicating with the mounting line 20, monitors defects on the mounting line 20, and when a defect occurs, outputs an instruction to adjust parameters related to the components in the component data to the online adjustment device 40. Specifically, when a defect such as a mounting error occurs during production using the component data D2, the defect monitoring device 30 estimates the cause of the defect. If the result of this estimation shows that the cause is the component data, an instruction to adjust parameters of the component data is output to the online adjustment device 40. The defect monitoring device 30 includes an acquisition unit 31, a calculation unit 32, a determination unit 33, an estimation unit 34, and an output unit 35.

[0080] The acquisition unit 31 acquires various logs acquired during production on the mounting line 20 from the mounting line 20. The acquisition unit 31 is configured to be able to acquire at least the mounting log L1 and the event log L2. The acquisition unit 31 is configured to include, for example, a communication circuit (communication module).

[0081] The mounting log L1 includes information on the control of each unit acquired during the period from the operation of picking up a component from a feeder to the operation of mounting the component on a board in the component mounting device 21, and information indicating production results. Information on the control of the units includes, for example, the amount of component mounting correction. Information indicating production results includes information on the number of productions (for example, the number of pickup attempts) and the number of mounting errors. The mounting log L1 includes actual values ​​measured during production.

[0082] The calculation unit 32 calculates the defect rate of defects that occurred in the component mounting device 21 based on the mounting log L1. In the present embodiment, the calculation unit 32 calculates the mounting error rate of mounting errors that occurred in the component mounting device 21 as the defect rate. The calculation unit 32 may calculate the mounting error rate by, for example, dividing the number of mounting errors by the number of pickup attempts. Below, an example will be described in which the defects are mounting errors and the defect rate is the mounting error rate.

[0083] The determination unit 33 executes various determination processes in the defect monitoring device 30. For example, based on the mounting log L1, the determination unit 33 determines whether the defect rate (here, mounting error rate) is equal to or greater than a threshold. The threshold may be set in advance and stored in a storage unit (not shown) included in the defect monitoring device 30. For example, based on the mounting log L1, the determination unit 33 determines whether the cause of the defect is a component or a unit of the component mounting device 21, that is, whether the occurrence of the defect is due to a component factor or an equipment factor. For example, based on the event log L2, the determination unit 33 determines whether the cause of the defect is a parameter related to the component or an event included in the event log L2, that is, whether the cause is a component factor or an event factor.

[0084] The estimation unit 34 executes an estimation process for determining whether the cause of the mounting error is a factor of the production equipment (e.g., the component mounting device 21, etc.) or a factor related to the component, based on the mounting log L1. The estimation unit 34 outputs an estimation result of the cause of the mounting error, based on the mounting log L1. The mounting log L1 includes a log of a measurement target (estimation target) acquired during production by the component mounting device 21. In parallel with production by the mounting line 20, the estimation unit 34 outputs an estimation result of the cause of the mounting error, using the mounting log L1 acquired during production on the mounting line 20.

[0085] FIG. 2 is a block diagram showing a functional configuration of the estimation unit 34 according to the present embodiment.

[0086] 2, the estimation unit 34 has an error count preprocessing unit 34a and a mounting error cause estimation unit 34b. Based on the number of defects in a certain period (e.g., a work shift), the estimation unit 34 identifies whether the cause of a defect (such as a mounting error) that occurred in the period is a feeder, a nozzle, a spindle, or a component. Identifying includes, for example, calculating a cause probability that the cause of the defect is a feeder, a nozzle, a spindle, or a component.

[0087] The miss count preprocessing unit 34a acquires the mounting log L1 from the mounting line 20 via the acquisition unit 31, generates preprocessed data from the acquired mounting log L1, and outputs it to the mounting miss cause estimation unit 34b. The preprocessed data includes, for example, "feeder serial number," "head spindle number," "nozzle serial number," "component serial number," "component size," "number of pickup attempts," and "number of mounting misses."

[0088] The feeder serial number is identification information (eg, an identification number) of each feeder.

[0089] The head spindle number is identification information (e.g., an identification number) for identifying the head spindle that controlled the rotation of the nozzle. One component mounting head unit is equipped with multiple head spindles, and each of the multiple head spindles is assigned different identification information.

[0090] The nozzle serial number is identification information (for example, an identification number) for each nozzle. A plurality of nozzles are mounted on one component mounting head unit, and each of the plurality of nozzles is assigned a different identification number.

[0091] The part size indicates the size of the part used in production. The part size may be, for example, the width of the part when viewed from above (the length in the direction perpendicular to the direction in which the part moves in the feeder).

[0092] The part serial number is identification information (for example, an identification number) for identifying a part supplied by a feeder.

[0093] The number of pickup attempts indicates the number of times a nozzle picks up a component, that is, the number of times a nozzle picks up a component during a predetermined period or in a predetermined production unit (e.g., 1 LOT).

[0094] The number of mounting errors indicates the number of times that a mounting error occurred among the number of pickup attempts. Mounting errors include errors in picking up a component by the nozzle, errors in placing a component on a board, etc.

[0095] In this way, the preprocessed data includes information for identifying the unit in which the components were mounted, information for identifying the components to be mounted, and information related to production performance.

[0096] The above "feeder serial", "head spindle number" and "nozzle serial" are information for determining whether the cause of the mounting error is an equipment factor, and the "component serial" and "component size" are information for determining whether the cause of the mounting error is a component factor. Furthermore, the preprocessed data is not limited to the above exemplified information as long as it contains information that can determine whether the cause of the mounting error is an equipment factor or a component-related factor.

[0097] The mounting error cause estimation unit 34b estimates the cause probability of the unit for the mounting error included in the preprocessed data based on the preprocessed data from the error count preprocessing unit 34a and the regression coefficient estimated in advance. The mounting error cause estimation unit 34b estimates the cause probability of the unit in the mounting log L1 of the measurement target using an estimation model 34b1 based on the regression coefficient.

[0098] Here, the regression coefficients of each element are estimated using the Markov Chain Monte Carlo (MCMC) method from the distribution of the number of pickup trials and the number of mounting errors for combinations of the elements of, for example, feeders, head spindles, nozzles, and component sizes. The MCMC method is a method for obtaining samples (generating random numbers) from a multivariate probability distribution, and is a method that is often used when performing maximum likelihood estimation of multiple parameters (here, regression coefficients) that make up a statistical model, as in the present disclosure. Each element includes, for example, a feeder, a head spindle, a nozzle, and a component (component size).

[0099] The mounting error cause estimation unit 34b calculates the error cause probability that each element (e.g., each unit) is the cause of an error that has occurred, using a binomial distribution derived based on the pre-processed data and the regression coefficients. The mounting error cause estimation unit 34b calculates the error cause probability for each component serial number (e.g., each component size), the error cause probability for each nozzle serial number, the error cause probability for each head spindle number, and the error cause probability for each feeder serial number.

[0100] In addition, the estimation of the error cause probability in the implementation error cause estimation unit 34b may use, for example, the technology disclosed in the applicant's unpublished patent application "Patent Application No. 2022-184454".

[0101] 1 again, the determination unit 33 determines whether the cause of the component failure is related to the component or the unit of the component mounting device 21 based on each error cause probability from the mounting error cause estimation unit 34b. The determination unit 33 makes the above determination based on the error cause probability for the component size, the error cause probability for the nozzle, the error cause probability for the head spindle, and the error cause probability for the feeder from the mounting error cause estimation unit 34b. For example, the determination unit 33 may determine that the cause of the component failure is the component (i.e., the component cause) when the error cause probability for the component size among the four error cause probabilities is the highest or higher than a predetermined value, and may determine that the cause of the component failure is the unit of the component mounting device 21 (i.e., the equipment cause) when any of the remaining three error cause probabilities is the highest or higher than a predetermined value.

[0102] Furthermore, when the determination unit 33 determines that the cause is related to the part, it further determines, based on the event log L2, whether the cause is a parameter related to the part or an event included in the event log L2. Specifically, the determination unit 33 makes the determination based on both the mounting log L1 and the event log L2. Fig. 3 is a diagram for explaining the determination made by the determination unit 33 according to this embodiment. Fig. 3 shows a chart for determining the influence of an operation event by a worker or the like.

[0103] 3, defects include a pickup error when picking up a component, a component recognition error when recognizing a component, and a mounting error when mounting a component. Note that defects may include at least one of a pickup error, a component recognition error, and a mounting error, and may also include other errors.

[0104] 3 shows events included in the event log L2 as model switching, in which an operator switches the production model on the mounting line 20, data editing, in which an operator edits parameters related to components, and material supply, in which an operator supplies (replenishes or changes) components to be used in the component mounting device 21. These events include operation events performed on the production equipment. Note that the events may include at least one of model switching, data editing, and material supply, and may also include other events. Furthermore, material supply may be, for example, supplying the same components.

[0105] 3 shows a judgment chart for three lots, LOT-1, LOT-2, and LOT-3, in one component mounting device 21, with the horizontal axis indicating time. Also, in FIG. 3, "x" indicates the timing at which a defect occurred, and "●" indicates the timing at which an operation event occurred.

[0106] As shown in Fig. 3, in lot LOT-1, defects occur sporadically at certain intervals. In this case, it is considered that there is no relationship between the event and the defect. In this case, the judgment unit 33 may not judge the cause of the defect, or may judge that the cause of the defect is a parameter related to the part.

[0107] In lot: LOT-2, defects (here, mounting errors) frequently occur within a predetermined time (e.g., 30 minutes) after material supply is performed. In this case, since the defects are considered to be due to the material supply, the judgment unit 33 judges that the cause of the defects is poor material supply. Also, in lot: LOT-2, a model change is performed as an event at 10:00, but since there is a long time until the timing when the defects occur, it is not judged to be a cause of the defects.

[0108] In lot LOT-3, defects (here, pick-up errors) frequently occur within a predetermined time (e.g., 30 minutes) after the model change. When the model change is performed, the production program is changed, and therefore the part data is also changed. In this case, since it is considered that the defect is due to the model change, the determination unit 33 determines that the cause of the defect is defective part parameters. Furthermore, when the defect rate within a predetermined time (e.g., 30 minutes) after the model change is performed is equal to or exceeds a predetermined threshold, the determination unit 33 may determine that the cause of the defect is a parameter related to the part. In other words, the determination unit 33 may determine that the parameter related to the part newly inserted after the model change is the cause of the defect.

[0109] Also, for example, if defects occur frequently within a predetermined time (e.g., 30 minutes) after data editing, the defects are considered to be due to data editing, so the determination unit 33 may determine that the cause of the defects is data editing. Note that the predetermined time may be a different value for each event.

[0110] In this way, the mounting log L1 includes first time information indicating the time when the defect occurred, and the event log L2 includes second time information indicating the time when the event occurred. Then, the determination unit 33 uses the time difference between the first time information and the second time information to determine whether the cause is a part (a parameter related to the part) or an event.

[0111] The determination method of the determination unit 33 is not limited to the method shown in Fig. 3. For example, the determination unit 33 may determine whether the cause is a part or an event depending on the type of event. Specifically, the determination unit 33 may determine that a parameter related to the new part is the cause of the defect in an event such as inputting a new part (model change), and may determine that the event itself, not a parameter related to the part, is the cause of the defect in an event such as replenishing the same part (material supply).

[0112] 1 again, when the determination unit 33 determines that the cause of the defect is a parameter related to the part, the output unit 35 outputs a parameter adjustment instruction for adjusting the parameter related to the part to the online adjustment device 40. The output unit 35 is configured to include, for example, a communication circuit (communication module).

[0113] The online adjustment device 40 is capable of communicating with the mounting line 20 and the defect monitoring device 30, and updates component data (i.e., parameters related to the components) during production on the mounting line 20. When it is determined that the cause of the defect is a parameter related to the component, the online adjustment device 40 selects a parameter to be adjusted from a plurality of parameters (see FIG. 7, for example) included in the parameters related to the component based on operation performance information, and adjusts the selected parameter to be adjusted. The adjustment here means changing the parameter to be adjusted so as to suppress the defect. The online adjustment device 40 includes an acquisition unit 41, a determination unit 42, a generation unit 43, an output unit 44, and a storage unit 45.

[0114] The acquisition unit 41 acquires various pieces of information acquired during production on the mounting line 20 from the mounting line 20. The acquisition unit 41 is configured to be able to acquire at least one of a component recognition log L3 and component specification information P (see FIG. 4 described later). The acquisition unit 41 is configured to include, for example, a communication circuit (communication module).

[0115] The determination unit 42 selects a parameter to be adjusted according to the defect from among a plurality of parameters based on the operation performance information. The determination unit 42 selects the parameter to be adjusted based on, for example, a table shown in Fig. 4 described later and at least one of the part recognition log L3 and the part specification information P.

[0116] The generating unit 43 generates part data D3 reflecting the adjustment content of the parameter to be adjusted selected by the determining unit 42. It can also be said that the generating unit 43 updates the part data D2 to the part data D3. The generating unit 43 is an example of an executing unit, and generating the part data D3 is an example of executing a process for adjusting the parameter to be adjusted selected.

[0117] The output unit 44 outputs the component data including the adjusted parameters to the mounting line 20 (for example, the component mounting device 21). The output unit 44 includes, for example, a communication circuit (communication module).

[0118] The storage unit 45 stores information related to a process for updating part data in the online adjustment device 40. The storage unit 45 stores a table (see FIG. 4 described later) in which the part recognition log L3 and part specification information P are associated with at least one of an adjustment amount and a set value. The storage unit 45 may be realized by, for example, a semiconductor memory.

[0119] FIG. 4 is a diagram showing an example of a table stored in storage unit 45 according to the present embodiment.

[0120] As shown in FIG. 4, the table includes a determination condition ID, part specification information P, part recognition results included in the part recognition log L3, estimation factors, and parameter adjustment contents.

[0121] The judgment condition ID indicates identification information for identifying the judgment condition.

[0122] The part specification information P includes information indicating the part type, dimensions (length (L), width (W), thickness (T)), and the manufacturer that produced the part. Even if the part type and dimensions are the same, if the manufacturer is different, the parameter adjustment contents (e.g., "value") may be different. For this reason, it is desirable for the part specification information P to include the manufacturer.

[0123] In this way, even if the part recognition log L3 is the same, the value (adjustment amount) may differ depending on the part specification information P. Therefore, it is preferable to determine the parameter adjustment content using both the part specification information P and the part recognition log L3.

[0124] The component recognition result includes the result of the component recognition and details indicating supplementary information regarding the result. The results include "no component pickup" indicating that the nozzle has not picked up the component, "not detected target for recognition" indicating that the nozzle has not detected the target component for recognition, and "abnormal electrode position" indicating that the position of the electrode of the component is abnormal. The details include "n / a" indicating that there is no match, "contour determination impossible" indicating that the contour is too light or dark to determine, and "contour angle deviation (e.g., 45°)" indicating that the contour angle is deviated.

[0125] The inferred factors indicate supplementary information to explain why that parameter adjustment content was selected. Inferred factors include: parts not being picked up, lamp setting abnormality: low or high (lamp is too dim or too bright), pick-up abnormality, etc. Including the inferred factors in the table makes it possible to support workers in understanding the content selected as the parameter adjustment content that corresponds to the defect.

[0126] The parameter adjustment content is associated with the part specification information P and the part recognition result, and one parameter adjustment content is determined by identifying the part specification information P and the part recognition result. The parameter adjustment content includes a parameter, a parameter category, a sign, a value, and a unit.

[0127] The parameter is information indicating which of a plurality of parameters included in the part-related parameters is to be adjusted, including the suction pressure, the transmission lamp offset, the nozzle to be used, and the like.

[0128] The parameter class indicates whether the parameter is a quantitative or qualitative variable. The parameter class is provided for informational purposes only.

[0129] The sign indicates whether the numerical value indicated in the value is to be added (plus) or subtracted (minus) from the value of the part data D2. The sign is set when the parameter category is a quantitative variable.

[0130] The value indicates how much the parameter is adjusted. The value may be set based on past experience, for example. The unit indicates the unit of the numerical value indicated in the value.

[0131] For example, in FIG. 4, when the judgment condition ID is "CC0402T001", it indicates that the parameter is adjusted to increase the suction pressure margin height by 0.05 mm. In this case, the generation unit 43 generates the component data D3 by increasing the suction pressure margin height in the component data by 0.05 mm. Also, for example, when the judgment condition ID is "BGA1010R002", it indicates that the nozzle to be used is adjusted to switch to the "185" nozzle. In this case, the generation unit 43 generates the component data D3 by changing the information specifying the nozzle to be used in the component data to the "185" nozzle. Note that the "185" nozzle indicates the type of nozzle and is an example of a parameter setting value (qualitative variable) related to the component.

[0132] Note that the value (eg, adjustment amount) may be fixed to, for example, the minimum adjustment unit.

[0133] The determination unit 42 may select a parameter to be adjusted for a component based on, for example, the component recognition log L3 shown in Fig. 4. For example, when the component recognition result indicates that the component is not picked up, the determination unit 42 may set the parameter of the parameter adjustment content (the parameter to be adjusted) to the suction hold-down allowance, and when the component recognition result indicates that the recognition target is not detected, the determination unit 42 may set the parameter of the parameter adjustment content to the transmitted lamp offset.

[0134] The determination unit 42 may select at least one of the adjustment amount and the set value of the parameter of the selected part based on the part specification information P. The determination unit 42 may select the value of the parameter adjustment content based on the manufacturer, for example. For example, when the part types and dimensions are the same but the manufacturers are different (for example, Company A and Company B shown in FIG. 4), the determination unit 42 may set the value to 0.05 mm when the manufacturer is Company A and to 0.04 mm when the manufacturer is Company B.

[0135] In this way, the determination unit 42 may select which parameter to adjust based on the part recognition log L3, and further determine the amount of adjustment based on the part specification information P.

[0136] [2. Operation of the production support system] Next, the operation of the production support system 1 configured as above will be described with reference to Fig. 5 and Fig. 6. The operation of the defect monitoring device 30 will be described with reference to Fig. 5, and the operation of the online adjustment device 40 will be described with reference to Fig. 6. Fig. 5 is a flowchart showing the operation (production support method) of the defect monitoring device 30 according to this embodiment. The operation shown in Fig. 5 is executed one or more times (e.g., multiple times) during production (e.g., during production of one lot). Note that step S18 in Fig. 5 shows an example of determining whether the cause is an event cause or a part cause, in which it is determined whether the cause is a poor material supply or a part parameter.

[0137] 5, the acquisition unit 31 of the defect monitoring device 30 acquires a mounting log L1 acquired during production of the mounting line 20 from the mounting line 20 (S11). The acquisition unit 31 may, for example, acquire the mounting log L1 sequentially from the mounting line 20. In this manner, the acquisition unit 31 acquires the mounting log L1 acquired during production of the mounting line 20 during that production.

[0138] Next, the calculation unit 32 calculates the defect rate based on the mounting log L1 acquired by the acquisition unit 31 (S12). In this embodiment, the calculation unit 32 calculates a mounting error rate as the defect rate. The mounting error rate may be, for example, a probability in a predetermined period of time, or a probability in a predetermined production quantity.

[0139] Next, the determination unit 33 determines whether or not the defect rate calculated by the calculation unit 32 is equal to or greater than a preset threshold value (S13). The determination unit 33 determines whether or not the mounting error rate is equal to or greater than a threshold value.

[0140] Next, when the determination unit 33 determines that the failure rate is equal to or greater than the threshold (Yes in S13), the estimation unit 34 identifies the cause of the failure (S14). In order to identify the cause of the failure, the estimation unit 34 estimates a failure cause probability, which is the probability that a mounting error occurs due to a part, and a failure cause probability, which is the probability that a mounting error occurs due to equipment, using a pre-generated estimation model 34b1. The estimation unit 34 outputs the estimated failure cause probability (for example, the four failure cause probabilities shown in FIG. 2) to the determination unit 33. In this way, the estimation unit 34 executes a process for determining whether the cause of the failure is a part cause or an equipment cause, using the pre-generated estimation model 34b1. Note that the part cause here means that the cause of the failure is a part-related cause, and the equipment cause means that the cause of the failure is an equipment-related cause (i.e., a cause other than a part).

[0141] Moreover, when the judgment unit 33 judges that the defect rate is less than the threshold value (No in S13), the defect monitoring device 30 returns to step S11 and continues the process.

[0142] Next, the determination unit 33 determines whether the defect is due to a part factor or an equipment factor based on the error factor probability (defect factor probability) estimated by the estimation unit 34 (S15). The determination unit 33 may determine that the defect is due to a part factor when the error factor probability, which is the probability that a mounting error occurs due to a part factor, is equal to or greater than the error factor probability, which is the probability that a mounting error occurs due to an equipment factor, and may determine that the defect is due to an equipment factor if this is not the case.

[0143] Next, when the determination unit 33 determines that the defect is due to a component (component cause in S15), the acquisition unit 31 acquires the event log L2 from the mounting line 20 (S16). The event log L2 includes, for example, events that occurred in the mounting line 20 during the period in which the defect occurred.

[0144] The acquisition unit 31 may acquire both the mounting log L1 and the event log L2 in step S11. The event log L2 may be acquired by the time the determination in step S17 is performed.

[0145] Next, the determination unit 33 determines the effect of the event (operation event) included in the event log L2 on the occurrence of the defect based on the event log L2 (S17). In other words, the determination unit 33 determines whether the cause of the defect is an event. The determination unit 33 makes the determination in step S17 using the time difference between the time the defect occurred and the time the event occurred (see FIG. 3).

[0146] If the judgment unit 33 judges that the cause of the defect is a part parameter (the part parameter is the cause in S18), the output unit 35 outputs a parameter adjustment instruction to the online adjustment device 40 to adjust the parameters related to the part (part parameters) (S19).

[0147] Furthermore, when the determination unit 33 determines that the defect is due to equipment (equipment cause in S15), and when the determination unit 33 determines that the cause of the defect is a parameter related to the part (caused by poor material supply in S18), the output unit 35 outputs an inspection instruction (S20). The output unit 35 presents the inspection instruction, for example, to a terminal device of a worker or a presentation device provided in the mounting line 20. The presentation device is, for example, a display device, but may also be a sound output device or the like.

[0148] Fig. 6 is a flowchart showing the operation (production support method) of the online adjustment device 40 according to the present embodiment. Fig. 6 shows the operation of the online adjustment device 40 that is executed after a parameter adjustment instruction is acquired.

[0149] First, when the acquisition unit 41 of the online adjustment device 40 acquires a parameter adjustment instruction from the defect monitoring device 30, it acquires the component specification information P and the component recognition log L3 from the mounting line 20 (S31, S32). The component specification information P and the component recognition log L3 may be acquired simultaneously or at different times.

[0150] Next, the determination unit 42 determines parameter adjustment contents for the parts based on the part specification information P, the part recognition log L3, and the table (see FIG. 4) read from the storage unit 45 (S33). The determination unit 42 determines the parameter adjustment contents corresponding to the part specification information P and the part recognition log L3, among the parameter adjustment contents in the table shown in FIG. 4, as the parameter adjustment contents for defects. The table is an example of an adjustment rule.

[0151] For example, a case will be described in which the component specification information P includes information that the component type is "ceramic capacitor", the dimensions are L 0.4 mm, W 0.2 mm, and T 0.2 mm, the manufacturer is Company A, and the component recognition log L3 includes information that the result is no component suction and the details are n / a. In this case, the determination unit 42 selects the parameter as the suction pressure allowance as the parameter adjustment content corresponding to the component specification information P and the component recognition log L3, and determines that the value (adjustment amount) is +0.05 mm. It can also be said that the determination unit 42 selects the suction pressure allowance as the parameter to be adjusted from among multiple parameters based on the component specification information P and the component recognition log L3. In this way, the determination unit 42 functions as a selection unit that selects the parameter to be adjusted.

[0152] The determination unit 42 may select at least the parameters to be adjusted based on the operation result information (for example, the part recognition log L3).

[0153] Next, the generation unit 43 generates component data D3 (S34) in accordance with the parameter adjustment content determined by the determination unit 42. For example, if the determination unit 42 determines that the parameter is the suction pressure margin and the value (adjustment amount) is +0.05 mm, the generation unit 43 generates component data D3 by changing the value of the suction pressure margin, which is a parameter of the component data D2 currently used in the mounting line 20, to a value that increases by 0.05 mm from the current value.

[0154] Next, the output unit 44 outputs the component data D3 generated by the generation unit 43 to the mounting line 20 in production (S35). The component data D3 includes at least one of the adjusted mounting condition system parameters and the component shape system parameters.

[0155] As a result, by switching the component data used in production on the mounting line 20 from the component data D2 to the component data D3, it is possible to suppress the occurrence of defects during the production.

[0156] The defect monitoring device 30 and the online adjustment device 40 repeatedly execute the processes from obtaining operation result information to adjusting parameters shown in Fig. 5 and Fig. 6. For example, the defect monitoring device 30 and the online adjustment device 40 may repeatedly execute the processes from obtaining operation result information to adjusting parameters until the defect rate of the parts becomes equal to or less than a predetermined threshold, or may execute the processes at predetermined time intervals. The predetermined time interval may be, for example, within 30 minutes, within 1 hour, or within 2 hours.

[0157] If the result of the part recognition log L3 changes while the online adjustment device 40 is repeating the adjustment, the online adjustment device 40 may select and adjust another parameter of the same part. The other parameter may also be included in the table shown in FIG.

[0158] (Other embodiments) Although the production support system according to one or more aspects has been described based on the embodiment, the present disclosure is not limited to this embodiment. As long as it does not deviate from the gist of the present disclosure, various modifications conceived by a person skilled in the art to this embodiment and forms constructed by combining components in different embodiments may also be included in the present disclosure.

[0159] For example, in the above embodiment, the online adjustment device 40 outputs the component data D3 to, for example, the component mounting device 21 of the mounting line 20, but, for example, the online adjustment device 40 may output component data corresponding to another device to the other device. The other devices may include, for example, a printing device that performs printing, a processing device that performs processing such as bending, etc. In this case, the mounting line 20 includes a printing device, etc., and the online adjustment device 40 may update the component data used in the printing device, etc.

[0160] Furthermore, the operation result information (e.g., mounting log L1) in the above embodiment may include information on control amounts for controlling a plurality of units. The information on the control amounts may include, for example, information on control amounts (correction amounts) for controlling the movement and rotation of a unit (e.g., nozzle) (e.g., adsorption correction amount and recognition correction amount).

[0161] Furthermore, each of the defect monitoring device 30 and the online adjustment device 40 in the above-described embodiment may be a terminal device disposed in a factory or the like where the mounting line 20 is located, or may be a server device disposed remotely from the factory.

[0162] Furthermore, the defect monitoring device 30 according to the above embodiment may acquire only the event log L2 from the mounting log L1 and the event log L2. In this case, the determination unit 33 does not perform a determination using the mounting log L1, but determines whether the cause of the defect is a parameter related to the part or an event included in the event log L2, based on the event log L2.

[0163] Furthermore, when the number of mounting errors increases as a result of adjusting the parameters related to the parts, the online adjustment device 40 according to the above embodiment may readjust the parameters to be adjusted by reversing the sign (plus / minus) of the adjustment amount while keeping the parameters to be adjusted the same. Furthermore, the defect monitoring device 30 may compare the defect rate before and after the adjustment of the parameters related to the parts, and when the defect rate is higher after the adjustment, output a parameter adjustment instruction to the online adjustment device 40 that includes reversing the sign of the adjustment amount and making the adjustment.

[0164] In the above embodiment, an example has been described in which online adjustment device 40 adjusts parameters related to one part, but two or more parameters may be adjusted simultaneously. In other words, in the table shown in Fig. 4, two or more sets of parameters and values ​​may be associated with one part recognition log L3 and one part specification information P.

[0165] Furthermore, the execution unit that executes the process for adjusting the selected parameter to be adjusted according to the above embodiment may be realized as, for example, a reception unit that receives input of at least one of an adjustment amount and a setting value from an operator for the parameter to be adjusted selected by the determination unit 42. The reception unit may be a button, a mouse, a touch panel, or the like, or may be configured to receive input by voice. In this case, the table shown in FIG. 4 may not include "values."

[0166] In the above embodiments, each component may be implemented by dedicated hardware or by executing a software program suitable for each component. Each component may be implemented by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0167] In addition, the order in which each step is performed in the flowchart is merely an example for specifically explaining the present disclosure, and an order other than the above may be used. In addition, some of the steps may be performed simultaneously (in parallel) with other steps, or some of the steps may not be performed.

[0168] In addition, the division of functional blocks in the block diagram is an example, and multiple functional blocks may be realized as one functional block, one functional block may be divided into multiple blocks, or some functions may be transferred to another functional block. Furthermore, the functions of multiple functional blocks having similar functions may be processed in parallel or in a time-sharing manner by a single piece of hardware or software.

[0169] Moreover, each of the defect monitoring device 30 and the online adjustment device 40 according to the above embodiment may be realized as a single device or may be realized by multiple devices. When each of the defect monitoring device 30 and the online adjustment device 40 is realized by multiple devices, each component of the defect monitoring device 30 and the online adjustment device 40 may be distributed in any manner among the multiple devices. When each of the defect monitoring device 30 and the online adjustment device 40 is realized by multiple devices, the communication method between the multiple devices is not particularly limited, and may be wireless communication or wired communication. Moreover, wireless communication and wired communication may be combined between the devices. Moreover, the defect monitoring device 30 and the online adjustment device 40 according to the above embodiment may be realized by one information processing device (computer).

[0170] Moreover, each of the components described in the above embodiments may be realized as software, or may be realized as an LSI, which is typically an integrated circuit. These may be individually integrated into one chip, or may be integrated into one chip to include some or all of them. Here, LSI is used, but it may be called IC, system LSI, super LSI, or ultra LSI depending on the degree of integration. Furthermore, the method of integration is not limited to LSI, and may be realized by a dedicated circuit (a general-purpose circuit that executes a dedicated program) or a general-purpose processor. After LSI manufacture, a programmable FPGA (Field Programmable Gate Array) or a reconfigurable processor that can reconfigure the connection or setting of the circuit cells inside the LSI may be used. Furthermore, if an integrated circuit technology that replaces LSI appears due to the progress of semiconductor technology or a different derived technology, it is natural that the components may be integrated using that technology.

[0171] A system LSI is an ultra-multifunctional LSI manufactured by integrating multiple processing units on a single chip, and specifically, is a computer system that includes a microprocessor, a ROM (Read Only Memory), a RAM (Random Access Memory), etc. Computer programs are stored in the ROM. The system LSI achieves its functions by the microprocessor operating in accordance with the computer program.

[0172] Moreover, one aspect of the present disclosure may be a computer program that causes a computer to execute each of the characteristic steps included in the production support method shown in either FIG. 5 or FIG.

[0173] Also, for example, the program may be a program to be executed by a computer. Also, one aspect of the present disclosure may be a computer-readable non-transitory recording medium on which such a program is recorded. For example, such a program may be recorded on a recording medium and distributed or circulated. For example, the distributed program may be installed in a device having another processor, and the program may be executed by the processor, thereby making it possible to cause the device to perform each of the above processes. [Industrial Applicability]

[0174] The present disclosure is useful for a support system for supporting production using a component mounting device, etc. [Explanation of symbols]

[0175] 1. Production Support System 10 Offline Adjustment Device 11, 16 Explanatory variable preprocessing section 12, 34 Estimation part 13 Pre-trained models 14 Adjustment pattern learning section 15 Target variable preprocessing section 17 Learning Department 20 Mounting Line 21 Component mounting equipment 30 Waste monitoring device 31, 41 Acquisition Department 32 Calculation section 33 Judgment section 34a Miss count preprocessing section 34b Implementation Error Cause Estimation Department 34b1 Estimation model 35, 44 Output section 40 Online Adjustment Device 42 Decision Section (Selection Section) 43 Generation unit (execution unit) 45 Storage section D1, D2, D3 parts data L1 Implementation log (operational performance information) L2 Event log (operational performance information) L3 Parts recognition log (operational performance information) P Parts specification information S Teacher parts data set

Claims

1. A production support system capable of communicating with a mounting line including a component mounting device that mounts components, an acquisition unit that acquires operation performance information related to the component from the mounting line; a determination unit that determines whether a cause of the defect of the part is a parameter related to the part based on the acquired operation result information; a selection unit that selects a parameter to be adjusted from a plurality of parameters included in the parameters related to the part based on the operation record information when the cause is determined to be a parameter related to the part; and an execution unit that executes a process for adjusting the selected parameter to be adjusted. Production support system.

2. the operation record information includes a mounting log of the mounting line, the determination unit determines whether the cause is related to the component or to a unit of the component mounting device based on the mounting log. The production support system according to claim 1 .

3. the operation record information includes an event log of the mounting line, the determination unit determines, based on the event log, whether the cause is a parameter related to the part or an event included in the event log. The production support system according to claim 1 .

4. the operation record information includes an event log of the mounting line, When the determination unit determines that the cause is related to the part, the determination unit further determines, based on the event log, whether the cause is a parameter related to the part or an event included in the event log. The production support system according to claim 2 .

5. The operation result information includes first time information indicating a time when the defect occurred, the event log includes second time information indicating a time when the event occurred; the determination unit determines whether the cause is a parameter related to the part or the event, using a time difference between the first time information and the second time information. The production support system according to claim 3 or 4.

6. the determination unit determines that the cause is a parameter related to the component when a defect rate within a predetermined time from a time when a production model is switched on the mounting line becomes equal to or greater than a predetermined threshold value. The production support system according to claim 3 or 4.

7. the event includes at least one of a model changeover for changing a production model in the mounting line, a data editing for editing parameters related to the component, and a material supply for supplying the component to the component mounting device; The production support system according to claim 3 or 4.

8. the operation performance information includes a component recognition log indicating a result of recognition of the components by an imaging device provided in the component mounting device of the mounting line, The selection unit selects the parameter to be adjusted based on the part recognition log. The production support system according to any one of claims 1 to 4.

9. The operation record information includes part specification information indicating specifications of the part, the selection unit determines at least one of an adjustment amount and a setting value of the selected parameter to be adjusted based on the part specification information of the part. The production support system according to claim 8.

10. The part specification information includes manufacturer information indicating a manufacturer of the part. The production support system according to claim 9.

11. a storage unit that stores an adjustment rule in which the part recognition log and the part specification information are associated with at least one of the adjustment amount and the setting value, The production support system according to claim 9.

12. the selection unit selects a suction and pressure margin of the component mounting device as the parameter to be adjusted when a result of the component recognition included in the component recognition log indicates no component suction. The production support system according to claim 8.

13. an output unit that outputs the adjusted parameter to be adjusted to the mounting line; The production support system according to any one of claims 1 to 4.

14. the production support system repeatedly executes a process from acquiring the operation result information to adjusting the parameter to be adjusted. The production support system according to any one of claims 1 to 4.

15. the production support system repeatedly executes a process from acquiring the operation result information to adjusting the parameter to be adjusted until the defective rate of the part becomes equal to or less than a predetermined threshold value. The production support system according to claim 14.

16. The defect includes at least one of a pickup error when picking up the component, a component recognition error when recognizing the component, and a mounting error when mounting the component. The production support system according to any one of claims 1 to 4.

17. A production support method executed by a production support system capable of communicating with a mounting line including a component mounting device that mounts components, comprising: acquiring operation record information relating to the component from the mounting line; determining whether a cause of the defect of the part is a parameter related to the part based on the acquired operation result information; selecting a parameter to be adjusted from a plurality of parameters included in the parameters related to the part based on the operation record information when it is determined that the cause is a parameter related to the part; Executing a process for adjusting the selected adjustment target parameter; Production support methods.

18. A program for causing a computer to execute the production support method according to claim 17.

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