Production line management system and production line management method

Abstract

To provide a production line management system capable of improving the production tact in a production line, and provide a production line management method.SOLUTION: A production line management system 1 manages a production line 4 including multiple pieces of component mounting equipment M4, M5 producing a product. The production line management system 1 includes: a second status monitoring unit 22 for monitoring the state of the component mounting equipment M4, M5; a second optimizing unit 24 that estimates the production tact of the production line 4 when executing the instructions by the component mounting equipment M4 and M5 for each of the multiple instructions extracted corresponding to the state of the component mounting equipment M4 and M5; a second countermeasure determination unit 23 that determines an instruction to be executed from among the multiple instructions based on the multiple production tacts estimated by the second optimizing unit 24; and an instruction output unit 26 that outputs the instruction determined by the second countermeasure determination unit 23.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present disclosure relates to a production line management system and a production line management method for managing a production line equipped with production equipment that produces products. [Background technology]

[0002] Patent Document 1 discloses a component mounting machine that includes a line tact acquisition means for acquiring line tact during component mounting on a production line, an identification means for identifying one component mounting machine that determines the acquired line tact, a mounting condition determination means for determining mounting conditions for the identified component mounting machine and other component mounting machines so as to reduce the line tact, and a component mounting means for mounting components under the determined conditions. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2009-123902 Summary of the Invention [Problem to be solved by the invention]

[0004] The mounter in Patent Document 1 is able to determine mounting conditions so as to reduce the line tact time for each mounter, but does not take any measures to improve the production tact time of a production line that includes multiple mounters.

[0005] Therefore, the present disclosure provides a production line management system and a production line management method that can improve the production takt time of a production line. [Means for solving the problem]

[0006] A production line management system according to one aspect of the present disclosure is a production line management system that manages a production line equipped with a plurality of production devices that produce products, and includes: a status monitoring unit that monitors the status of the production devices; a production takt estimation unit that estimates a production takt of the production line when a plurality of instructions are extracted corresponding to the status of the production devices and the production devices execute the plurality of instructions; an instruction determination unit that determines an instruction to be executed from the plurality of instructions based on the plurality of production takts estimated by the production takt estimation unit; and an instruction output unit that outputs the instruction determined by the instruction determination unit. The instruction determination unit sets priorities corresponding to the plurality of instructions from the plurality of production takt times estimated by the production takt time estimation unit, and determines a priority instruction, which is an instruction to be executed from among the plurality of instructions, based on the set priorities. The status monitoring unit monitors use restrictions or the lifting of the use restrictions on work units included in at least a portion of the production equipment, and further includes a storage unit that stores production data for producing the product before the priority instruction is executed. When the status monitoring unit detects the lifting of the use restriction on the production equipment after executing the priority instruction for the use restriction on the production equipment, the instruction determination unit determines an instruction to change the production data stored in the storage unit as the priority instruction. do.

[0007] These comprehensive or specific aspects may be realized by a system, an apparatus, a method, a recording medium, or a computer program, or may be realized by any combination of a system, an apparatus, a method, a recording medium, and a computer program. [Effects of the Invention]

[0008] According to the production line management system and the like according to the present disclosure, the production takt time of the production line can be improved. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is an explanatory diagram showing a production line management system according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing a production line management system according to an embodiment. [Figure 3] FIG. 3 is a flowchart showing a processing operation when the first state monitoring unit according to the embodiment finds a problem in the component mounting device. [Figure 4] FIG. 4 is a flowchart showing the process of policy decision in the first measure decision unit according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating a case where the first optimization unit according to the embodiment optimizes the suction nozzle. [Figure 6]FIG. 6 is a flowchart showing the processing operations of optimization by the second optimization unit and policy determination by the second measure determination unit according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating a case where the second optimization unit according to the embodiment optimizes the suction nozzle. [Figure 8] FIG. 8 is a flowchart showing the processing operation of the countermeasure arbitration unit according to the embodiment. [Figure 9] FIG. 9 is a flowchart showing processing operations after a countermeasure is executed by the instruction output unit, the effect determination unit, and the update unit according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] The production line management system disclosed herein is a production line management system that manages a production line equipped with a plurality of production devices that produce products, and includes: a status monitoring unit that monitors the status of the production devices; a production takt estimation unit that estimates the production takt of the production line when the production devices execute each of a plurality of instructions extracted corresponding to the status of the production devices; an instruction determination unit that determines an instruction to be executed from among the plurality of instructions based on the plurality of production takts estimated by the production takt estimation unit; and an instruction output unit that outputs the instruction determined by the instruction determination unit.

[0011] According to this, when a problem occurs in the state of a production device, the production takt of the production line including the production device where the problem occurred can be estimated, and instructions can be determined and output according to the estimated production takt. In other words, the production line management system can determine and output instructions according to the estimated production takt so as to prevent a decrease in the production takt.

[0012] Therefore, the production takt time of the production line can be improved. As a result, even if a problem occurs in the state of the production equipment, instructions according to the production takt time are output so that a decrease in the production takt time is suppressed, and therefore the production line management system can suppress a decrease in productivity on the production line.

[0013] The production line management method disclosed herein is a production line management method for managing a production line equipped with a plurality of production devices that produce products, which monitors the status of the production devices, estimates the production takt time of the production line when the production devices execute each of a plurality of instructions extracted corresponding to the status of the production devices, determines which instructions to execute from among the plurality of instructions based on the estimated production takt time, and outputs the determined instructions.

[0014] This production line management method also provides the same effects as those described above.

[0015] The production line management system disclosed herein is a production line management system including a production line equipped with a plurality of production devices that produce products, and a management device that manages the production line, wherein the production line includes a first optimization unit that optimizes mounting data corresponding to the respective states of the plurality of production devices and estimates a first production takt time when the optimized plurality of mounting data is executed, and a first instruction determination unit that determines first mounting data from among the plurality of mounting data for the production devices based on the plurality of first production takt times; the management device includes a second optimization unit that optimizes mounting data corresponding to the state of the production line and estimates a second production takt time when the optimized plurality of mounting data is executed, a second instruction determination unit that determines second mounting data from among the plurality of mounting data for the production line based on the plurality of second production takt times; and an instruction output unit that outputs the second mounting data determined by the second instruction determination unit, wherein the second optimization unit compares the first production takt time with the second production takt time, and the second instruction determination unit sets a priority based on the result of the comparison by the second optimization unit, and determines mounting data according to the set priority.

[0016] The instruction determination unit may also set priorities corresponding to the plurality of instructions from the plurality of production takts estimated by the production takt estimation unit, and determine priority instructions, which are instructions to be executed from among the plurality of instructions, based on the set priorities.

[0017] This allows priorities to be set according to production takt time, and instructions can be determined and output according to the priorities. As a result, the production line management system can prevent a decline in productivity on the production line.

[0018] The state monitoring unit may also monitor use restrictions or the release of use restrictions on task units included in at least a portion of the production equipment.

[0019] This allows the use of a problematic operation unit to be restricted immediately if there is a problem, and the restriction on use to be lifted immediately once the operation unit has been repaired or replaced, thereby preventing a decline in productivity, an increase in defective products, and a decrease in production takt time on the production line.

[0020] The production system may further include a countermeasure arbitration unit that arbitrates the execution of the priority instruction based on the availability of resources including the production device and at least one of the work units for executing the priority instruction.

[0021] This allows arbitration of the execution of priority instructions, so that, for example, a production device can execute a priority instruction after completing production of a product, allowing the production device to produce a product in accordance with the priority instruction at an appropriate time.

[0022] The instruction determination unit may set priorities corresponding to the instructions in ascending order of the estimated production takt times.

[0023] According to this, an instruction corresponding to the shortest production takt time is determined and the determined instruction is output, so that a decrease in the production takt time on the production line can be more reliably suppressed.

[0024] In addition, the countermeasure mediation unit may output the priority instruction to be mediated to the instruction output unit so that the priority instruction is executed for at least one of the production devices that executes the priority instruction after production of the product being produced in at least one of the production devices that executes the priority instruction is completed.

[0025] This makes it possible to prevent a priority instruction from being executed during the production of a product, thereby preventing the production of a product with duplicated components or a product with no components mounted, and as a result, preventing an increase in defective products.

[0026] The system may further include a memory unit that stores production data for producing the product before the priority instruction is executed, and when the status monitoring unit detects that the usage restriction on the production device has been lifted after the priority instruction for the usage restriction on the production device has been executed, the instruction determination unit may determine that the priority instruction is an instruction to change the production data stored in the memory unit.

[0027] In this way, when the usage restriction is lifted, the problem that occurred in the production equipment has been resolved, and the instruction determination unit can output instructions for producing the product using the production data before execution. As a result, the production line management system can more reliably prevent a decrease in productivity on the production line.

[0028] The plurality of instructions may also include a first instruction to cause the production device to change at least a portion of the work to produce the product, and a second instruction to cause the production line spanning the plurality of production devices to change at least a portion of the work to produce the product.

[0029] This allows tasks to be changed for a single production device or for a production line that spans multiple production devices. Therefore, it is possible to simultaneously suppress a decrease in production takt time for a single production device and a decrease in production takt time for a production line that spans multiple production devices. As a result, because it is sometimes possible to suppress a decrease in production takt time simply by changing tasks for a single production device, it is no longer necessary to perform processing such as estimating the production takt time for a production line in order to change tasks for the production line. In other words, it is possible to suppress an increase in the processing burden on the production line management system.

[0030] Furthermore, the instruction determination unit may determine the second instruction as the priority instruction when the production takt time after execution of the second instruction is improved compared to the production takt time after execution of the first instruction.

[0031] This allows the production line management system to determine and output priority instructions that correspond to the improved production takt time, thereby more reliably preventing a decline in productivity on the production line.

[0032] In addition, the production device may be a component mounting device that mounts components on a board, the work unit may be a suction nozzle that is a nozzle that picks up components, and a component supply device that supplies the components to the suction nozzle, and the status monitoring unit may monitor the use restriction or the release of the use restriction of the work unit.

[0033] This allows the production line management system to immediately restrict the use of a task unit when a problem occurs, and immediately release the restriction on the use of the task unit when the problem is resolved, thereby preventing a decrease in production takt time on the production line.

[0034] Hereinafter, the embodiments will be specifically described with reference to the drawings.

[0035] The embodiments described below are all comprehensive or specific examples. The numerical values, shapes, components, component placement and connection configurations, steps, and step order 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 not described in independent claims are described as optional components.

[0036] Furthermore, each figure is a schematic diagram and is not necessarily an exact illustration. Therefore, for example, the scales of the figures do not necessarily match. Furthermore, in each figure, substantially the same components are given the same reference numerals, and redundant explanations are omitted or simplified.

[0037] (Embodiment) First, the configuration of the production line management system 1 will be described with reference to FIG.

[0038] FIG. 1 is an explanatory diagram showing a production line management system 1 according to an embodiment.

[0039] 1, a production line management system 1 manages a production line 4 equipped with a plurality of production devices that produce products. The production devices are, for example, component mounting devices M4 and M5 that mount components on a board.

[0040] The production line management system 1 includes a production line 4 and a management device 7.

[0041] [Production Line 4] The production line 4 has a function of mounting components (electronic components) on boards to produce products (for example, mounted boards), and also has a function of supplying, delivering, and collecting the boards to be mounted.

[0042] Specifically, production line 4 is configured by connecting in series in the following order: board supply device M1, board transfer device M2, a plurality of electronic component mounting devices, namely, solder printing device M3, component mounting devices M4 and M5, reflow device M6, and board removal device M7. Each device from board supply device M1 to board removal device M7 is connected via communication network 2 to management device 7, which has a management computer.

[0043] For example, solder printing device M3, component mounting devices M4 and M5, and reflow device M6 perform component mounting work to mount components on boards transported along production line 4. That is, boards supplied by board supply device M1 are carried into solder printing device M3 via board delivery device M2. Solder printing device M3 performs solder printing work to screen-print solder for joining components on the carried-in boards.

[0044] The solder-printed boards are then handed over to component mounting devices M4 and M5. These mount the components onto the solder-printed boards. The boards with the components mounted are then transported to reflow device M6, where they are heated according to a predetermined heating profile. This melts and solidifies the solder printed on the heated board to join the components. The components are then solder-bonded to the board, completing the completed mounted board with the components mounted on it. The completed mounted board is then collected by board collection device M7.

[0045] Next, in this embodiment, the configuration of component mounting device M5 will be mainly described as an example of a production device. Also, since component mounting device M4 has the same configuration as component mounting device M5, the description of component mounting device M4 will be omitted.

[0046] The component mounting device M5 is equipped with a base, a board transport unit, a component supply device, and a mounting head. A board is placed on the base. The board transport unit can transport boards delivered from an upstream device to a downstream device. The component supply device can supply components to the mounting head. The component supply device is provided with multiple tape feeders for supplying components to the mounting head. The mounting head can pick up and remove components from the tape feeders, move above the board, and mount the components at the mounting position on the board. The mounting head is equipped with suction nozzles that pick up and hold components and can be raised and lowered individually.

[0047] 1 and 2, the configuration of the component mounting device M5 other than the configuration of the base, board transport unit, component supply device, mounting head, etc. Fig. 2 is a block diagram showing a production line management system 1 according to an embodiment.

[0048] The component mounting device M5 has a first acquisition unit 10, a first status monitoring unit 11, a first countermeasure decision unit 12, a first optimization unit 13, learning models 15 and 16, a resource database 17, mounting data 18, and a memory unit 14.

[0049] Here, the first state monitoring unit 11, the first countermeasure decision unit 12, and the first optimization unit 13 are realized by a processor operating in accordance with a program stored in a storage unit 14 such as a memory. The learning models 15 and 16, the resource database 17, and the implementation data 18 are stored in the storage unit 14. The storage units 14 in which the program, the learning models 15 and 16, the resource database 17, and the implementation data 18 are stored may be the same storage unit or different storage units.

[0050] The first acquisition unit 10 acquires status information, which is information indicating the status of the component mounting device M5, as a result of the process in which the component mounting device M5 produces a product. Here, the status of the component mounting device M5 includes the status of the mounting process, such as component pickup, component recognition, and component placement, as well as the status of production resources involved in the mounting process, such as the board, components, workers, mounting head, suction nozzle, and tape feeder. Taking the suction nozzle as an example, the status information is flow rate data acquired from a flow rate sensor that measures the flow rate of air flowing inside the suction nozzle.

[0051] The first acquisition unit 10 outputs the acquired state information to the first state monitoring unit 11.

[0052] The first state monitoring unit 11 monitors the state of the component mounting apparatus M5. That is, the first state monitoring unit 11 can detect, based on a first predetermined condition, that there is a problem with the state of the component mounting apparatus M5 indicated in the state information acquired from the first acquisition unit 10. Here, the first predetermined condition is a learning model 15 for detecting that there is a problem with the state of the component mounting apparatus M5.

[0053] If there is a problem with the condition of the component mounting device M5, the first condition monitoring unit 11 uses the learning model 15 to detect whether or not there is a mounting error, the amount of mounting, the quality of mounting, etc., and detects deterioration of the component mounting device M5, the suction nozzle, and the tape feeder, etc.

[0054] The learning model 15 is a model for detecting whether there is a problem with the state of the component mounting device M5. When state information is input, the learning model 15 is trained to output whether each of the operation units 5a constituting the component mounting device M5 is in a normal state or in a state with a problem. For example, if the state information is flow rate data, for a mounting head, which is one of the operation units 5a constituting the component mounting device M5, the learning model 15 can output whether the state of the mounting head is in a normal state or in a state with a problem, based on the flow rate of the mounting head indicated in the flow rate data. Here, the operation unit 5a is a suction nozzle that picks up components, and a component supply device that supplies components to the suction nozzle.

[0055] When first status monitoring unit 11 detects a problem with component mounting device M5 by using learning model 15, it outputs status information to first countermeasure decision unit 12. The status information includes information about working unit 5a of component mounting device M5 that has been detected as having a problem.

[0056] Here, the first measure determination unit 12 causes the first optimization unit 13 to analyze the problem existing in the component mounting device M5 so that it can be resolved. In other words, when the first measure determination unit 12 acquires status information from the first status monitoring unit 11, it causes the first optimization unit 13 to analyze the status information, mounting data 18, and resource database 17, which are all related to each other, as a measure for the component mounting device M5 in a problematic state. Here, the measure for the component mounting device M5 in a problematic state is a measure to suppress a decrease in production takt (an increase in takt loss) of the component mounting device M5.

[0057] Specifically, the first optimization unit 13 references the resource database 17, which stores production resources, and the mounting data 18 for the component mounting device M5 in a problematic state, to optimize the component arrangement, component pickup order, component placement order, mounting head path, mounting head operation, and the like. In other words, the first optimization unit 13 optimizes the mounting data 18 used by the component mounting device M5 in a problematic state. The resource database 17 includes information on whether or not resources are available to allocate the same type of equipment as the operation unit 5a that constitutes the component mounting device M5 in question. The mounting data 18 also includes data on each operation unit 5a that constitutes the component mounting device M5 and production data for producing the product. The simulation results obtained by the first optimization unit 13 optimizing the mounting data 18 are temporarily stored in memory as temporary data, and the mounting data 18 is not changed.

[0058] The first optimization unit 13 also analyzes the extent of the impact of the simulation results obtained by optimizing the mounting data 18. That is, the first optimization unit 13 determines whether the resulting impact of the optimization of the mounting data 18 for the problematic component mounting device M5 fits within the mounting data 18 in the component mounting device M5. For example, if the reduction in production takt time for the component mounting device M5 due to the optimization of the mounting data 18 is limited to a level that allows the production plan to be met, the first optimization unit 13 determines that the resulting impact of the optimization fits within the mounting data 18 in the component mounting device M5. On the other hand, if the reduction in production takt time for the component mounting device M5 due to the optimization of the mounting data 18 fails to meet the production plan and, for example, affects a component mounting device other than the component mounting device M5, for example, the first optimization unit 13 determines that the resulting impact of the optimization does not fit within the mounting data 18 in the component mounting device M5. In this case, the first optimization unit 13 also optimizes the mounting data 18 for, for example, the component mounting device M4. That is, for example, when there is an impact across multiple component mounting devices E4 and E5, first optimization unit 13 determines that there is an impact on production line 4. An impact on production line 4 refers to when all devices constituting production line 4 are affected, or when multiple devices are affected. Note that the affected devices are not limited to component mounting device M4, but also include other devices in production line 4 such as board supply device M1, board transfer device M2, reflow device M6, and board recovery device M7.

[0059] The first optimization unit 13 outputs the optimized simulation results to the first countermeasure determination unit 12. The simulation results optimized by the first optimization unit 13 include the result of optimizing the mounting data 18 of the component mounting device M5, the result of whether or not production of products by the component mounting device M5 can be continued, and the result of the extent of impact due to the optimization. A result in which production of products by the component mounting device M5 cannot be continued means, for example, a result in which the production takt of the component mounting device M5 is significantly reduced, causing disruption to the production plan for the production line 4. Furthermore, a result in which production of products by the component mounting device M5 can be continued means, for example, a result in which the production takt of the component mounting device M5 is reduced, but does not cause disruption to the production plan.

[0060] Next, if the acquired simulation result shows that there is no impact across multiple component mounting devices E4 and E5, the first measure determination unit 12 determines a measure to be executed from multiple measures extracted according to the state of the problematic component mounting device M5. Here, the measure to be executed from multiple measures is a measure to be executed in response to a priority instruction.

[0061] In this embodiment, the first measure determination unit 12 determines the measure to be executed using a learning model 16. The learning model 16 is a model for determining a measure according to the state of the problematic component mounting device M5. The learning model 16 is trained so that when state information is input, a measure according to the state of the problematic component mounting device M5 is output.

[0062] That is, the first measure determination unit 12 determines a priority instruction to execute a predetermined measure from among a plurality of measures based on the respective priorities corresponding to the plurality of measures. Here, the first measure determination unit 12 can set priorities for determining the order of priority corresponding to the measures and update the set priorities. The first measure determination unit 12 outputs the determined priority instruction to the component mounting device M5 via an instruction output unit (not shown).

[0063] On the other hand, if the result of the impact range in the acquired simulation result has an impact across multiple component mounting devices M4 and M5, the first measure decision unit 12 sends an event notification to the management device 7. The event notification triggers the management device 7 to perform a simulation to optimize the mounting data 33. The event notification also includes the simulation result optimized by the component mounting device M5, status information, etc.

[0064] [Management device 7] The management device 7 has a second acquisition unit 21, a second status monitoring unit 22, a second countermeasure decision unit 23, a second optimization unit 24, a countermeasure mediation unit 25, an instruction output unit 26, learning models 31, 34, a resource database 32, implementation data 33, a production plan 35, and a memory unit 30.

[0065] Here, the second status monitoring unit 22, the second countermeasure decision unit 23, the second optimization unit 24, and the countermeasure arbitration unit 25 are realized by a processor operating in accordance with a program stored in a storage unit 30 such as a memory. The learning models 31 and 34, the resource database 32, the implementation data 33, and the production plan 35 are stored in the storage unit 30. The storage units 30 storing the program, the learning models 31 and 34, the resource database 32, the implementation data 33, and the production plan 35 may be the same storage unit or different storage units. This storage unit 30 is provided in the management device 7.

[0066] The second acquisition unit 21 acquires an event notification from the component mounting apparatus M5. The second acquisition unit 21 outputs the acquired event notification to the second state monitoring unit 22.

[0067] The second status monitoring unit 22 monitors the status of the production line 4. Specifically, the second status monitoring unit 22 can detect a change in the status of the production line 4 indicated in the status information of the event notification acquired by the second acquisition unit 21 based on a second predetermined condition. Here, the second predetermined condition is a learning model 31 for detecting a change in the status of the production line 4. If there is a problem with the change in the status of the production line 4, the second status monitoring unit 22 uses the learning model 31 to detect, for example, that a change in the status of the component mounting device M5 has an impact on the production takt time, etc., of other component mounting devices M4. In other words, a change in the status of the production line 4 occurs when there is an increase or decrease in the production takt time of one or more devices constituting the production line 4. The second status monitoring unit 22 is an example of a status monitoring unit.

[0068] The learning model 31 is a model for detecting, for example, that a change in the state of the component mounting device M5 has an impact across multiple component mounting devices M4, E5, and that there is a possibility that the production takt time on the production line 4 will change (improve or decrease). For example, when state information is input, the learning model 31 is trained to output that there is an impact on multiple component mounting devices M4, E5, and that there is a possibility that the production takt time on the production line 4 will change.

[0069] In addition, when the second status monitoring unit 22 detects a change in the status of the production line 4 using the learning model 31, it outputs status information of the production line 4, including the multiple component mounting devices that have been detected as being in a changed state, to the second countermeasure decision unit 23.

[0070] When a change in the state of the production line 4 is a problem such as a decrease in production takt time or quality, the second measure decision unit 23 causes the second optimization unit 24 to analyze the problem so that it can be resolved. In other words, when the second measure decision unit 23 acquires state information from the second state monitoring unit 22, it causes the second optimization unit 24 to analyze the state information, mounting data 33, and resource database 32, which are all related to each other, as a measure for the production line 4 in a problematic state. Here, a measure for the production line 4 in a problematic state is a measure for suppressing a decrease in the production takt time (an increase in takt loss) of the production line 4. The second measure decision unit 23 is an example of an instruction decision unit.

[0071] Specifically, the second optimization unit 24 references the resource database 32, which stores production resources, and the mounting data 33 for the production line 4 in a problematic state, and optimizes the component arrangement, component pickup order, component placement order, mounting head path, and the like, thereby optimizing the mounting data 33 for the production line 4. The resource database 32 includes information on whether or not resources are available to allocate the same type of equipment as the operation unit 5a that constitutes the problematic component mounting device M5. The mounting data 33 also includes data on each operation unit 5a that constitutes the component mounting device M5 and production data for producing the product. The simulation results obtained by the second optimization unit 24 optimizing the mounting data 33 are temporarily stored in memory as temporary data, and the mounting data 33 is not changed. The second optimization unit 24 is an example of a production takt time estimation unit.

[0072] That is, after optimizing the mounting data 33 of production line 4, second optimization unit 24 compares the simulation results obtained by optimizing the mounting data 33 of production line 4, which includes multiple component mounting devices M4 and M5, with the simulation results obtained by optimizing the mounting data 33 of component mounting device M5. Comparing these simulation results makes it possible to compare the production takt of both, and therefore second measure decision unit 23 can select the mounting data 33 that has the better production takt, which is the optimized simulation result.

[0073] In this way, the second optimization unit 24 can estimate the production takt time of the production line 4 by optimizing the mounting data 33 of the production line 4. In other words, the second optimization unit 24 can estimate the production takt time of the production line 4 when the production equipment executes each of the multiple instructions extracted corresponding to the state of the production equipment. Here, the multiple instructions include a first instruction for changing at least a part of the work performed by the component mounting equipment M5 to produce the product, and a second instruction for changing at least a part of the work performed by the production line 4 across the multiple component mounting equipment M4 and M5 to produce the product.

[0074] The second optimization unit 24 compares the production takt times of both the parties and outputs the result to the second measure decision unit 23.

[0075] The second countermeasure decision unit 23 obtains the result of comparing the production takt of both from the second optimization unit 24. If the improvement rate of the comparison result is less than a predetermined value, the second countermeasure decision unit 23 determines that no improvement is necessary because no significant improvement in the production takt is expected. If the second countermeasure decision unit 23 determines that no improvement is necessary, it terminates the process without deciding on a countermeasure. This allows the production line management system 1 to continue operating in its current state. The predetermined value is a value that is set in advance and can be changed as desired.

[0076] On the other hand, if the improvement rate of the comparison result is equal to or greater than a predetermined value, the second optimization unit 24 determines that an improvement in the production takt is expected and therefore determines that an improvement should be made. As a result, if the production takt after execution of the second instruction is improved compared to the production takt after execution of the first instruction, the second measure determination unit 23 determines the second instruction as a priority instruction. In other words, when determining that an improvement should be made, the second measure determination unit 23 determines a priority instruction, which is an instruction to execute a measure, based on the multiple production takts estimated by the second optimization unit 24. In this way, upon obtaining the optimized simulation results from the second optimization unit 24, the second measure determination unit 23 determines a measure to be executed from among the multiple measures extracted according to the state of the problematic production line 4.

[0077] In this embodiment, the second measure decision unit 23 decides the measure to be executed using a learning model 34. The learning model 34 is a learning model 34 for deciding a measure according to the state of the problematic production line 4. The learning model 34 is trained so that when state information is input, a measure according to the state of the problematic production line 4 is output.

[0078] Furthermore, the second countermeasure decision unit 23 determines priority instructions, which are instructions to execute a predetermined countermeasure from among the multiple countermeasures, based on the priority of each of the multiple countermeasures. That is, the second countermeasure decision unit 23 sets priorities corresponding to multiple instructions from the multiple production takts estimated by the second optimization unit 24, and determines priority instructions to be executed based on the set priorities. Here, the second countermeasure decision unit 23 can set priorities corresponding to the multiple instructions in ascending order of the multiple production takts. Furthermore, the second countermeasure decision unit 23 can update the priorities for determining the priority order corresponding to the countermeasures, using the learning model 34.

[0079] In addition, when the production plan and the current production progress are compared and it is found that the remaining number of products is small, the period until production ends is short depending on the remaining number of products, or improving the production takt time will have almost no impact on the production plan, the second measure decision unit 23 may not implement the decided measure. A small remaining number of products means, for example, that the remaining number of products is within several tens of units. Furthermore, a short period until production ends means, for example, within several tens of minutes.

[0080] Furthermore, when status information is input, the learning model 34 may learn to lower the priority or not take any measures if improving the production takt time will have little impact on the production plan.

[0081] When the second measure decision section 23 decides on the measure to be taken, it outputs to the measure arbitration section 25 an instruction to execute the decided measure.

[0082] Countermeasure arbitration unit 25 arbitrates the execution of priority instructions according to the countermeasures decided by second countermeasure decision unit 23. Specifically, countermeasure arbitration unit 25 arbitrates the execution of priority instructions based on the presence or absence of resources including at least one of component mounting apparatus M5 and work unit 5a for executing the priority instructions. The presence or absence of resources is managed in resource database 32.

[0083] The countermeasure arbitration unit 25 outputs the arbitration priority instruction to the instruction output unit 26 so that the priority instruction is executed for at least one component mounting device M5 that executes the priority instruction after the production of the product being produced in at least one component mounting device M5 that executes the priority instruction is completed.

[0084] The instruction output unit 26 outputs a priority instruction. Specifically, the instruction output unit 26 outputs a priority instruction that is an instruction decided by the second measure decision unit 23 and that is arbitrated by the measure arbitration unit 25. In other words, the instruction output unit 26 outputs a measure that is a priority instruction in accordance with the arbitration by the measure arbitration unit 25.

[0085] As a result, after the production of the product being produced in the component mounting device M5 is completed, the production line switches the mounting data that was being used to mounting data in which the execution of priority instructions has been mediated, i.e., optimized new mounting data, and produces the product using the new mounting data after the switch.

[0086] The instruction output unit 26 may output instructions to the component mounting device M5 and a mobile terminal carried by the worker. The instruction output unit 26 may also output instructions to the production device and the worker via a management device 7 that manages the component mounting device M5 and a worker management device that manages the worker.

[0087] In addition, second status monitoring unit 22 monitors the use restriction or lifting of the use restriction on operation units 5a included in at least some of component mounting devices M4 and M5. Therefore, when outputting an instruction to implement the countermeasure, instruction output unit 26 outputs a restriction instruction to restrict the use of the problematic operation unit 5a included in component mounting device M5. As a result, component mounting device M5, whose use of the operation unit 5a is restricted, manufactures a product using temporarily changed mounting data 33. At this time, the original mounting data 33 stored in memory unit 30 is not changed. Note that when the operation unit 5a included in the problematic component mounting device M5 is repaired or replaced, instruction output unit 26 outputs a lift instruction to component mounting device M5 to lift the use restriction on the operation unit 5a.

[0088] Furthermore, when the second status monitoring unit 22 detects that the usage restriction on the production device has been lifted after executing a priority instruction for the usage restriction on the component mounting device M5, the second countermeasure decision unit 23 can decide that the change instruction to the production data stored in the memory unit 30 is the priority instruction.

[0089] <Processing operation> The processing operations of the production line management system 1 and the production line management method in this embodiment will be described. In this processing operation, the component mounting device M5 will be mainly described. Note that the other devices included in the production line 4 are similar, so their description will be omitted.

[0090] [Example 1] FIG. 3 is a flowchart showing the processing operation when the first state monitoring unit 11 according to the embodiment finds a problem with the component mounting device M5.

[0091] First, the first state monitoring unit 11 acquires state information indicating the state of the component mounting device M5 (step S11). In this way, the first state monitoring unit 11 monitors the state of the component mounting device M5 indicated in the state information.

[0092] Next, the first state monitoring unit 11 reads the learning model 15 (step S12). Specifically, by reading the learning model 15, the first state monitoring unit 11 detects the presence or absence of mounting errors, the amount of mounting, the quality of mounting, etc., and detects deterioration of the component mounting device M5, the suction nozzle, and the tape feeder, etc. The learning model 15 is a model for detecting whether there is a problem with the state of the component mounting device M5.

[0093] Here, the learning model 15 can be trained by setting the learning policy to emphasize quality or productivity. In other words, by training the learning model 15, the component mounting device M5 can produce products with a good balance between productivity and quality. For example, if a decrease in the flow rate of the suction nozzle is detected based on the flow rate of the suction nozzle associated with the learning model 15, learning is performed so that the decrease in the flow rate of the suction nozzle is detected early.

[0094] Next, the first state monitoring unit 11 determines whether or not a problem has been detected in the state of the component mounting device M5 using the learning model 15 (step S13). For example, the first state monitoring unit 11 determines whether or not a problem has been detected in the state of the component mounting device M5 based on whether or not it has been detected that the productivity of the component mounting device M5 is declining, that the quality is declining, that there is an increase in defects, or the like, or whether or not it has been detected that the production equipment is deteriorating, that there is an error in the work of the worker, or the like.

[0095] If no problem is detected in the state of the component mounting device M5 (No in step S13), the first state monitoring unit 11 repeats the processes from step S11 to step S13 until a problem is detected.

[0096] On the other hand, if the first state monitoring unit 11 detects a problem in the state of the component mounting device M5 (Yes in step S13), the first countermeasure decision unit 12 decides on a countermeasure policy for the detected problem.

[0097] [Example 2] Next, the first measure determination section 12 will be described in detail with reference to FIG.

[0098] FIG. 4 is a flowchart showing the processing operation of the countermeasure decision in the first countermeasure decision unit 12 according to the embodiment.

[0099] When a problem is detected in the status of the first status monitoring unit 11, the first countermeasure determination unit 12 causes the first optimization unit 13 to analyze the interrelated status information, mounting data 18, and resource database 17 (step S21). That is, the first optimization unit 13 references the resource database 17 of the production resources related to the component mounting device M5 in the problematic state. At this time, the first optimization unit 13 also references the mounting data 18 and the status information. For example, if the first status monitoring unit 11 detects a problem in the status of the component mounting device M5 based on the status information, such as deterioration of the pickup nozzle, the first optimization unit 13 analyzes that the pickup nozzle is the production resource that may be causing the problem. The first optimization unit 13 optimizes the mounting data 18 as a countermeasure to the analyzed pickup nozzle problem.

[0100] Here, a case where the first optimization unit 13 optimizes the suction nozzles A to D of the component mounting device M5 will be described with reference to Fig. 5. Fig. 5 is a diagram showing a case where the first optimization unit 13 according to the embodiment optimizes the suction nozzles A to D. Fig. 5 shows a case where suction nozzle A picks up component a from the first turn to the third turn, suction nozzle B picks up component b from the first turn to the third turn, suction nozzle c picks up component c from the first turn to the third turn, and suction nozzle D picks up component a in the first turn and picks up component c from the second turn to the third turn.

[0101] If a problem is detected with pickup nozzle D, the use of pickup nozzle D is restricted, so the first optimization unit 13 sets mounting data 18 for four turns, increasing the number of turns from three by one, as shown in the diagram at the end of the arrow in Figure 5. In this case, the first optimization unit 13 distributes components a, c, and c that were being handled by pickup nozzle D to pickup nozzles A to C. Specifically, the first optimization unit 13 optimizes the mounting data 18 so that pickup nozzle A picks up component a in the fourth turn, pickup nozzle B picks up component c in the fourth turn, and pickup nozzle C picks up component c in the fourth turn.

[0102] In this way, even if a problem occurs with suction nozzle D, the first optimization unit 13 performs optimization by allocating components a, c, and c that suction nozzle D was supposed to mount on the board to suction nozzles A to C. In this case, an increase in the number of turns of component mounting device M5 may result in a decrease in production takt time. Note that, based on mounting data 18, the first optimization unit 13 allocates components a, c, and c that suction nozzle D was supposed to mount to suction nozzles A to C that can pick them up.

[0103] Returning to the explanation of FIG. 4, the first optimization unit 13 then outputs the optimized simulation results to the first measure determination unit 12.

[0104] The first optimization unit 13 also analyzes the extent of influence in the simulation results obtained by optimizing the mounting data 18. The first optimization unit 13 determines whether the result of the extent of influence resulting from the optimization of the mounting data 18 for the component mounting device M5 having a problem fits within the mounting data 18 for the component mounting device M5. The first optimization unit 13 outputs the determined result of the extent of influence to the first countermeasure determination unit 12.

[0105] Next, the first measure determination unit 12 reads the learning model 16 (step S22). Specifically, the first measure determination unit 12 uses the read learning model 16 to determine the measure to be executed. The learning model 16 is a model for determining a measure according to the state of the component mounting device M5, and is a model for updating the priority for determining the priority order for the measure. The learning model 16 is trained to output a measure according to the state of the component mounting device M5 when the simulation results and the results of the impact range are input.

[0106] Next, the first measure decision unit 12 analyzes priorities to determine the priorities of the multiple measures (step S23). For example, when outputting candidate measures for the detected problem, the learning model 16 outputs each candidate measure with each priority associated with the measure. This allows the first measure decision unit 12 to grasp each measure and the priority associated with each measure.

[0107] Next, the first measure decision section 12 creates a measure candidate list that associates each measure with a priority corresponding to each measure (step S24). Here, the measure candidate list will be described.

[0108] For example, suppose a problem of an increase in the component pickup error rate due to the suction nozzle is detected. In this case, the learning model 16 outputs potential countermeasures for the problem of the increase in the pickup error rate, such as pickup position teaching, feeder replacement, and nozzle replacement, and outputs a priority rate as the priority of each potential countermeasure. This allows the first countermeasure decision unit 12 to create a countermeasure candidate list including the priority of each potential countermeasure candidate.

[0109] Next, the first measure determination unit 12 registers the measure candidate with the highest priority in the created measure candidate list in a priority measure list as a measure for the detected problem (step S25). For example, when the measure candidate list is created, the first measure determination unit 12 registers the measure "pickup position teach," which has the highest priority for the problem of worsening pickup error rate, in the priority measure list. In other words, the first measure determination unit 12 determines "pickup position teach" as a priority instruction to be executed from among multiple measures.

[0110] Furthermore, when the first measure determination unit 12 determines a measure that changes the mounting data 18 as a priority instruction, if the acquired simulation results show that the range of influence does not affect multiple component mounting devices, it outputs new mounting data 18, which is a simulation result obtained by optimizing the determined measure, to the component mounting device M5 that has the problem. This causes the component mounting device M5 to execute the new mounting data 18 as shown in FIG. 5. Here, if the component mounting device M5 is producing a product using the existing mounting data 18, once production of the product is completed, the component mounting device M5 switches from the existing mounting data 18 to the new mounting data 18 and executes the new mounting data 18. Note that if a measure that does not change the mounting data 18 is determined as a priority instruction, the first optimization unit 13 does not execute a simulation.

[0111] On the other hand, the first measure decision unit 12 notifies the management device 7 of an event when the result of the influence range in the acquired simulation result indicates that the influence extends across a plurality of component mounting devices M4 and M5.

[0112] [Example 3] Next, details of the second status monitoring unit 22 will be described with reference to Fig. 3. This operation example is the same as Fig. 3 which explains the details of the first status monitoring unit 11, and therefore is not shown.

[0113] First, the second status monitoring unit 22 acquires status information and the like, which is information relating to the status of the production line 4, in response to an event notification from the production line 4 (step S11).

[0114] Next, the second state monitoring unit 22 reads the learning model 31 (step S12). Specifically, by reading the learning model 31, the second state monitoring unit 22 detects, for example, that a change in the state of the component mounting device M5 has an impact on the other component mounting device M4. The learning model 31 is a learning model 31 for detecting, for example, that a change in the state of the component mounting device M5 may cause a decrease in the production takt time (have an impact) on the other component mounting devices M4.

[0115] Next, the second state monitoring unit 22 determines whether or not a change has been detected in the state of the production line 4, using the learning model 31. For example, the second state monitoring unit 22 determines whether or not a change in the production line 4 has been detected, such as a problem that may cause a decrease in production takt time (step S13).

[0116] If no change is detected (No in step S13), the second state monitoring unit 22 repeats the processes from step S11 to step S13 until a change is detected.

[0117] On the other hand, if the second state monitoring unit 22 detects a change (Yes in step S13), the second measure decision unit 23 decides on a countermeasure policy for the problem detected as the change.

[0118] At this time, instruction output unit 26 outputs a restriction instruction to restrict the use of problematic work unit 5a included in component mounting device M5. Once problematic work unit 5a included in component mounting device M5 is repaired or replaced, instruction output unit 26 outputs a lift instruction to component mounting device M5 to lift the use restriction on work unit 5a.

[0119] [Example 4] Next, details of the second measure decision unit 23 will be described with reference to Fig. 6. In this operation example, processing operations similar to those in Fig. 4, which describes the details of the first status monitoring unit 11, will be assigned the same reference numerals and descriptions thereof will be omitted.

[0120] FIG. 6 is a flowchart showing the processing operations of optimization by the second optimization unit 24 and policy determination by the second measure determination unit 23 according to the embodiment.

[0121] When second countermeasure decision unit 23 acquires the status information from second status monitoring unit 22, it analyzes the status information, mounting data 33, and resource database 32, which are all related to each other, as a countermeasure for the production line 4 in a problematic state. Specifically, second optimization unit 24 references resource database 32, which stores production resources, and mounting data 33 for the production line 4 in a problematic state, and optimizes the component arrangement, component pickup order, component mounting order, mounting head path, etc., thereby optimizing the mounting data 33 for production line 4 (step S121).

[0122] Here, a case where the second optimization unit 24 optimizes the suction nozzles A to D of the production line 4 will be described with reference to Fig. 7. Fig. 7 is a diagram showing a case where the second optimization unit 24 according to the embodiment optimizes the suction nozzles A to D. Fig. 7 illustrates a case where the production line 4 is made up of a first production device and a second production device.

[0123] Also, in the first production device included in production line 4, the following cases are shown: suction nozzle A picks up component a from the first turn to the third turn; suction nozzle B picks up component b from the first turn to the third turn; suction nozzle c picks up component c from the first turn to the third turn; and suction nozzle D picks up component a on the first turn and picks up component c from the second turn to the third turn.

[0124] Also, in the second production device included in production line 4, the following cases are shown: suction nozzle A picks up component a from the first turn to the second turn; suction nozzle B picks up component b from the first turn to the third turn; suction nozzle c picks up component c from the first turn to the second turn; and suction nozzle D picks up component d in the first turn and component c in the second turn.

[0125] The second optimization unit 24 optimizes the mounting data 33 of the production line 4, as shown in the diagram at the end of the arrow in Figure 7, that is, generates simulation results in which the mounting data 33 of each of the first production device and the second production device is optimized.

[0126] Here, a problem has been detected with suction nozzle D of the first production device, so the use of suction nozzle D is restricted. The second optimization unit 24 references the resource database 32 and mounting data 33 and extracts that the second production device has an available space in the third turn. In the third turn of the second production device, the second optimization unit 24 allocates components a, c, and c that were being handled by suction nozzle D of the first production device to suction nozzles A, C, and D.

[0127] In other words, the second optimization unit 24 optimizes the pickup nozzle A to pick up component a from the first turn to the third turn, the pickup nozzle B to pick up component b from the first turn to the third turn, the pickup nozzle C to pick up component c from the first turn to the third turn, the pickup nozzle D to pick up component d in the first turn, and the pickup nozzle D to pick up component c from the second turn to the third turn.

[0128] In this way, the second optimization unit 24 performs optimization so that even if a problem occurs with the suction nozzle D of the first production device, the product can be produced by allocating parts a, c, and c to the suction nozzles A, C, and D of the second production device. In this case, the number of turns of the first production device does not increase, so a decrease in the production takt time on the production line 4 is suppressed.

[0129] The second optimization unit 24 compares the simulation results of each device in the production line 4 optimized by the first optimization unit 13 with the simulation results of optimizing the production line 4. In other words, the second optimization unit 24 compares the case of FIG. 5 with the case of FIG. 7 and outputs the comparison result to the second measure decision unit 23.

[0130] Returning to the explanation of FIG. 6, next, the second measure decision unit 23 determines whether or not to execute improvement by determining whether or not the improvement rate of the result of comparison by the second optimization unit 24 is equal to or greater than a predetermined value (step S122).

[0131] If the improvement rate as a result of the comparison by the second optimization unit 24 is less than a predetermined value, the second measure decision unit 23 determines that improvement is unnecessary (No in step S122). If the second measure decision unit 23 determines that improvement is unnecessary, it ends the operation process without deciding on a measure. As a result, the operation of the production line management system 1 continues in the current state.

[0132] If the improvement rate as a result of the comparison by the second optimization unit 24 is equal to or greater than a predetermined value, the second measure decision unit 23 determines that improvement should be performed (Yes in step S122).

[0133] Next, the second measure decision unit 23 reads the learning model 34 (step S22). The learning model 34 is a model for deciding measures according to the state of the production line 4, and is a model for updating priorities for deciding the priorities of the measures. For example, the learning model 34 is trained so that, when a detected problem is input, it outputs measures for the detected problem as candidates.

[0134] Next, the second measure decision unit 23 analyzes the priorities to determine the order of priority of each of the multiple measures (step S23). For example, when outputting measures for the detected problem as candidates, the learning model 34 associates the priorities with the measures and outputs the candidate measures. This allows the second measure decision unit 23 to grasp each measure and the priority corresponding to each measure.

[0135] Next, the second measure decision section 23 creates a measure candidate list (step S24).

[0136] Next, the second measure decision section 23 registers the measure candidate with the highest priority in the created measure candidate list in the priority measure list as a measure for the detected problem (step S25).

[0137] [Example 5] Next, the countermeasure arbitration unit 25 will be described in detail with reference to FIG.

[0138] FIG. 8 is a flowchart showing the processing operation of the countermeasure arbitration unit 25 according to the embodiment.

[0139] First, the measure arbitration unit 25 reads the priority measure list (step S31) and selects a measure with a high priority (step S32). For example, if the priority measure list in which the pickup position teach has the highest priority rate is read, the measure arbitration unit 25 selects the pickup position teach as the measure with a high priority.

[0140] Next, the countermeasure mediation unit 25 reads the resource data from the resource database 32 (step S33).

[0141] For example, the resource data manages the presence or absence of each production resource, specifically, the presence or absence of a mounting head, a suction nozzle, and a tape feeder as each production resource.

[0142] Next, the countermeasure arbitration unit 25 determines whether or not the production resource is locked for the selected countermeasure (step S34). For example, it is assumed that the countermeasure arbitration unit 25 selects pickup position teach. Also, it is assumed that pickup position teach is a countermeasure performed by an operator. In this case, the countermeasure arbitration unit 25 determines whether or not the production resource is locked by checking the lock status of the operator in the read resource data.

[0143] If the countermeasure arbitration unit 25 determines that the production resource for the selected countermeasure is locked (Yes in step S34), it selects the countermeasure with the next highest priority (step S35) and performs the process again from step S33.

[0144] When the countermeasure arbitration unit 25 determines that the production resource for the selected countermeasure is not locked (No in step S34), it locks the production resource for the selected countermeasure (step S36).

[0145] Then, the countermeasure arbitration unit 25 determines whether or not the production resources for all the countermeasures included in the priority countermeasure list are locked (step S37). If the production resources for all the countermeasures are not locked (No in step S37), the process is repeated from step S32, excluding the countermeasure selected this time. If the production resources for all the countermeasures are locked (Yes in step S37), the countermeasures are executed for the selected countermeasures with the highest priority among the countermeasures in the priority countermeasure list whose production resources are not locked.

[0146] [Example 6] Next, the instruction output unit 26 and the effect determination unit and update unit (not shown) will be described in detail with reference to FIG.

[0147] 9 is a flowchart showing processing operations after countermeasures are executed by the instruction output unit 26, the effect determination unit, and the update unit according to the embodiment. Note that, hereinafter, a case where the countermeasure selected by the countermeasure arbitration unit 25 for the problem of worsening pickup error rate is pickup position teaching and the locked production resource is worker A is referred to as specific example 1, and a case where the countermeasure selected by the countermeasure arbitration unit 25 for the problem of poor feeder sliding is cleaning and the locked production resources are the feeder and worker B is referred to as specific example 2.

[0148] First, the instruction output unit 26 outputs an instruction to execute a countermeasure for the production resource locked by the countermeasure arbitration unit 25 (step S41). For example, in the case of Specific Example 1, the instruction output unit 26 outputs a first countermeasure (e.g., a priority instruction) that causes worker A to perform pickup position teaching. For example, in the case of Specific Example 2, the instruction output unit 26 outputs a second countermeasure (e.g., a priority instruction) that causes worker B to clean the tape feeder. In this way, the instruction is output according to the countermeasure, and the instruction is executed. Note that the instruction may be executed manually by a worker or the like, or automatically by the production line 4 or the like. If the above-mentioned instruction is to optimize the mounting data 33, the production device that received the instruction changes the mounting data 33 at a timing when it is possible to change the mounting data 33. For example, the production device changes the mounting data 33 after finishing production of the product that it is producing within itself and there is no product left in the production device.

[0149] Next, the effect determination unit acquires status information from the second status monitoring unit 22 (step S42). Specifically, the effect determination unit acquires status information related to the status of the production line 4 or status information related to the status of the production resources. The reason the effect determination unit acquires status information is to confirm changes in the status of the production line 4 due to the execution of instructions corresponding to the countermeasures, that is, to determine the effectiveness of the instructions corresponding to the executed countermeasures. For example, in the case of specific example 1, the effect determination unit acquires status information related to the results of the mounting process related to pickup. That is, the effect determination unit uses error information related to the pickup nozzle to be monitored as status information and monitors its trend. For example, in the case of specific example 2, the effect determination unit acquires status information related to the status of the tape feeder. That is, the effect determination unit uses error information related to the tape feeder to be monitored as status information and monitors its trend.

[0150] Next, the effect determination unit determines whether or not a problem has been detected in the state of the production line 4 (step S43). If a problem has been detected, it can be determined that the instructions corresponding to the executed measures have not been effective or that the effects of the instructions corresponding to the executed measures have not yet been seen, and if no problem has been detected, it can be determined that the instructions corresponding to the executed measures have been effective.

[0151] If no problem is detected (No in step S43), the update unit updates the learning models 31, 34 based on the determined effect (step S44). For example, in the case of specific example 1, if a problem is no longer detected, the update unit determines that the pickup position teach was effective for the problem and updates the learning models 31, 34 so that the priority of the pickup position teach is increased. For example, in the case of specific example 2, if a problem is no longer detected, the update unit determines that cleaning was effective for the problem and updates the learning models 31, 34 so that the priority of cleaning is increased.

[0152] Next, since the current instruction has been completed, the effect determination unit unlocks the production resources that were locked when executing the current instruction (step S45). For example, in the specific example 1, the locked worker A is unlocked. For example, in the specific example 2, the locked worker B and the tape feeder are unlocked.

[0153] Next, the effect determination unit deletes the measure executed in response to the current instruction from the priority measure list (step S46). For example, in the specific example 1, pickup position teaching is deleted from the priority measure list. For example, in the specific example 2, cleaning is deleted from the priority measure list.

[0154] Then, the effect determining unit deletes the countermeasure candidate list for the problem that is no longer detected as a result of the current instruction (step S47). For example, in specific example 1, the problem of the worsening pickup error rate is no longer detected, and therefore no countermeasure for the problem is necessary, and so the countermeasure candidate list is deleted. Deleting the countermeasure candidate list means that, when the effect determining unit determines that the execution of the first priority instruction (pickup position teach instruction) has resulted in an improvement in the pickup error rate after the execution of the pickup position teach instruction by a predetermined value or more compared to the state (pickup error rate) before the execution of the pickup position teach instruction, the instruction output unit 26 will not output the second priority instruction (feeder replacement instruction or nozzle replacement instruction) extracted in relation to the pickup error rate corresponding to the pickup position teach instruction before execution.

[0155] On the other hand, if a problem is detected (Yes in step S43), the effect determination unit determines whether a timeout has occurred (step S48). In other words, the effect determination unit determines whether the effect has not been determined for a predetermined time. Since it may take some time for the effect to appear after an instruction is executed, the processing in step S48 is performed. The predetermined time is set, for example, as the time required to reap the effect for each instruction. Furthermore, for instructions that are not executed immediately, such as measures added to the production plan 35 or maintenance plan, the production resources may be unlocked when a plan to execute the instruction is created, and the effect determination may be performed after the plan is executed.

[0156] If a timeout has not occurred (No in step S48), the processes in steps S42, S43 and S48 are repeated until a problem is no longer detected or a timeout occurs.

[0157] If a timeout occurs (Yes in step S48), the update unit updates the learning models 31, 34 based on the determined effectiveness (step S49). For example, in specific example 1, if a timeout occurs while a problem is still detected, the update unit determines that the pickup position teach was not effective for the problem and updates the learning models 31, 34 so that the priority of the pickup position teach is lowered. For example, in specific example 2, if a timeout occurs while a problem is still detected, the update unit determines that cleaning was not effective for the problem and updates the learning models 31, 34 so that the priority of cleaning is lowered. Furthermore, if a problem is detected but there is a trend of improvement in the status information (there is a trend of improvement below a predetermined level), the update unit updates the learning models 31, 34 so that the degree of priority reduction is reduced.

[0158] Next, since the current instruction has been completed, the effect determination unit unlocks the production resources that were locked when executing the current instruction (step S50). For example, in the specific example 1, the locked worker A is unlocked. For example, in the specific example 2, the locked worker B and the feeder are unlocked.

[0159] Next, the effect determination unit deletes the measure executed in response to the current instruction from the priority measure list (step S51). For example, in the specific example 1, pickup position teaching is deleted from the priority measure list. For example, in the specific example 2, cleaning is deleted from the priority measure list.

[0160] Next, the effectiveness determining unit updates the list of countermeasure candidates for problems that remain detected even after the current instruction is executed (step S52).

[0161] The effectiveness assessment unit registers the highest-priority candidate countermeasure in the created candidate countermeasure list in the priority countermeasure list as a countermeasure for the detected problem (step S53). For example, when the candidate countermeasure list is created, a countermeasure of nozzle replacement is registered in the priority countermeasure list for the problem of a worsening pickup error rate. In other words, a command to replace the nozzle may be output for the problem of a worsening pickup error rate that continues even after pickup position teaching has been performed.

[0162] (Other embodiments) While the production line management system of the present disclosure has been described above based on the embodiments, the present disclosure is not limited to the above-described embodiments. As long as it does not deviate from the spirit of the present disclosure, various modifications conceivable by those skilled in the art to the present embodiments and configurations constructed by combining components of different embodiments are also included within the scope of the present disclosure.

[0163] For example, in the above-described embodiment, in the production line management system, the first measure determination unit and the second optimization unit may be provided in the management device.

[0164] For example, in the above-described embodiment, the first optimization unit and the second optimization unit in the production line management system may perform optimization using a learning model. This learning model may be constructed based on past performance or empirical rules. For example, if the production plan indicates that production of a first type of substrate will be completed with only a few minutes remaining, the learning model may be trained to continue production of the first type of substrate without optimization. As another example, if the production plan indicates that production of a second type of substrate will be completed with only a few hours remaining and then stopped for a few minutes, the learning model may be trained to continue production of the second type of substrate after optimization, and to replace or perform maintenance on operational units with restricted use after the stoppage.

[0165] For example, the steps in the production line management method may be executed by a computer (computer system). The present disclosure can be realized as a program for causing a computer to execute the steps included in the production line management method. Furthermore, the present disclosure can be realized as a non-transitory computer-readable recording medium, such as a CD-ROM, on which the program is recorded.

[0166] For example, when the present disclosure is realized as a program (software), each step is performed by running the program using hardware resources such as a computer's CPU, memory, input / output circuits, etc. In other words, each step is performed by the CPU acquiring data from memory or input / output circuits, etc., performing calculations on the data, and outputting the calculation results to memory or input / output circuits, etc.

[0167] Furthermore, each of the components included in the production line management system 1 of the above embodiment may be realized as a dedicated or general-purpose circuit.

[0168] Furthermore, each of the components included in the production line management system 1 of the above embodiment may be realized as an LSI (Large Scale Integration) which is an integrated circuit (IC).

[0169] Furthermore, the integrated circuit is not limited to an LSI, but may be realized by a dedicated circuit or a general-purpose processor. A programmable FPGA (Field Programmable Gate Array) or a reconfigurable processor, in which the connections and settings of circuit cells within the LSI can be reconfigured, may also be used.

[0170] Furthermore, if an integrated circuit technology that can replace LSI emerges due to advances in semiconductor technology or other derivative technologies, that technology may naturally be used to integrate each component included in the production line management system 1 into an integrated circuit.

[0171] In addition, this disclosure also includes forms obtained by making various modifications to the embodiments that a person skilled in the art would think of, and forms realized by arbitrarily combining the components and functions of each embodiment within the scope of the present disclosure. [Industrial Applicability]

[0172] The present disclosure can be used, for example, in managing a production floor. [Explanation of symbols]

[0173] 1 Production line management system 4 production lines 5a Work Unit 22 Second status monitoring unit (status monitoring unit) 23 Second Countermeasure Decision-Making Department (Instruction Decision-Making Department) 24 Second optimization section (production takt time estimation section) 25 Countermeasures and Mediation Department 26 Instruction output unit 30 Storage section M4, M5 component mounting equipment (production equipment)

Claims

1. A production line management system that manages a production line equipped with a plurality of production devices that produce products, a status monitoring unit that monitors the status of the production device; a production takt time estimation unit that estimates a production takt time of the production line when the production device executes each of a plurality of instructions extracted corresponding to the state of the production device; an instruction determination unit that determines an instruction to be executed from among the plurality of instructions based on the plurality of production takt times estimated by the production takt time estimation unit; an instruction output unit that outputs the instruction determined by the instruction determination unit, the instruction determination unit sets priorities corresponding to the plurality of instructions from the plurality of production takt times estimated by the production takt time estimation unit, and determines priority instructions, which are instructions to be executed from among the plurality of instructions, based on the set priorities; the state monitoring unit monitors use restrictions or release of use restrictions on work units included in at least a portion of the production equipment; a storage unit that stores production data for producing the product before the priority instruction is executed, When the state monitoring unit detects the release of the usage restriction of the production device after the priority instruction for the usage restriction of the production device is executed, the instruction determination unit determines the change instruction to the production data stored in the storage unit as the priority instruction. Production line management system.

2. The production system further includes a countermeasure arbitration unit that arbitrates the execution of the priority instruction based on the availability of resources including at least one of the production devices and the task units for executing the priority instruction. The production line management system according to claim 1 .

3. The instruction determination unit sets the priorities corresponding to the instructions in the order of shortest estimated production takt times. The production line management system according to claim 1 or 2.

4. The countermeasure arbitration unit outputs the priority instruction to be arbitrated to the instruction output unit so that the priority instruction is executed by at least one of the production devices that executes the priority instruction after production of the product being produced in at least one of the production devices that executes the priority instruction is completed. The production line management system according to claim 2 .

5. The instructions include a first instruction to change at least a part of the work of producing the product by the production device, and a second instruction to change at least a part of the work of producing the product by the production line across the plurality of production devices. The production line management system according to any one of claims 1 to 4.

6. The instruction determination unit determines the second instruction as the priority instruction when the production takt time after the second instruction is executed is improved compared to the production takt time after the first instruction is executed. The production line management system according to claim 5 .

7. the production device is a component mounting device that mounts components on a substrate, the working unit includes a suction nozzle that picks up a component, and a component supply device that supplies the component to the suction nozzle, The state monitoring unit monitors the use restriction or the release of the use restriction of the task unit. The production line management system according to claim 1 .

8. A production line management method for managing a production line equipped with a plurality of production devices that produce products, comprising: monitor the status of the production equipment; a production takt time of the production line when the production device executes each of a plurality of instructions extracted in accordance with the state of the production device; determining an instruction to be executed from among the plurality of instructions based on the plurality of estimated production takt times; Output the determined instructions, setting priorities corresponding to the plurality of instructions from the estimated plurality of production takt times, and determining priority instructions, which are instructions to be executed from among the plurality of instructions, based on the set priorities; monitors the use restriction or the release of the use restriction of an operational unit included in at least a part of the production equipment; determining, when the release of the usage restriction of the production device is detected after the priority instruction for the usage restriction of the production device is executed, an instruction to change the production data for producing the product before the priority instruction is executed as the priority instruction. Production line management methods.

9. A production line management system including a production line equipped with a plurality of production devices that produce products, and a management device that manages the production line, The production line comprises: a first optimization unit that optimizes mounting data corresponding to the respective states of the plurality of production devices and estimates a first production takt time when the plurality of optimized mounting data are executed; a first instruction determination unit that determines first mounting data from among the plurality of mounting data for the production device based on the plurality of first production takt times; The management device a second optimization unit that optimizes mounting data corresponding to the state of the production line and estimates a second production takt time when the optimized mounting data is executed; a second instruction determination unit that determines second mounting data from among the plurality of mounting data for the production line based on the plurality of second production takt times; an instruction output unit that outputs the second mounting data determined by the second instruction determination unit; a status monitoring unit that monitors the status of the production line, the second optimization unit compares the first production takt time with the second production takt time, the second instruction determination unit determines implementation data according to the set priority by setting a priority based on the result of the comparison by the second optimization unit; the second instruction determination unit sets priorities corresponding to the plurality of instructions based on the second production takt time estimated by the second optimization unit, and determines priority instructions that are instructions to be executed based on the set priorities; the state monitoring unit monitors use restrictions or release of use restrictions on work units included in at least a portion of the production equipment; a storage unit that stores production data for producing the product before the priority instruction is executed, When the state monitoring unit detects the release of the usage restriction of the production device after the priority instruction for the usage restriction of the production device is executed, the second instruction determination unit determines the change instruction for the production data stored in the storage unit as the priority instruction. Production line management system.

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