WO2022158227A1 - Système de gestion d'atelier, procédé de détermination d'effet de travail et programme de détermination d'effet de travail - Google Patents

Système de gestion d'atelier, procédé de détermination d'effet de travail et programme de détermination d'effet de travail Download PDF

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Publication number
WO2022158227A1
WO2022158227A1 PCT/JP2021/047309 JP2021047309W WO2022158227A1 WO 2022158227 A1 WO2022158227 A1 WO 2022158227A1 JP 2021047309 W JP2021047309 W JP 2021047309W WO 2022158227 A1 WO2022158227 A1 WO 2022158227A1
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WIPO (PCT)
Prior art keywords
priority
countermeasure
instruction
state
production
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PCT/JP2021/047309
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English (en)
Japanese (ja)
Inventor
道明 馬渡
義明 粟田
憲一郎 石本
憲 末継
利彦 永冶
裕起 竹原
Original Assignee
パナソニックＩｐマネジメント株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Application filed by パナソニックＩｐマネジメント株式会社 filed Critical パナソニックＩｐマネジメント株式会社
Priority to JP2022577057A priority Critical patent/JPWO2022158227A1/ja
Priority to DE112021006864.4T priority patent/DE112021006864T5/de
Priority to CN202180090584.4A priority patent/CN116710864A/zh
Publication of WO2022158227A1 publication Critical patent/WO2022158227A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32194—Quality prediction

Definitions

  • the present disclosure relates to a production floor management system that manages the state of a production floor equipped with production equipment that produces products, a work effect determination method, and a work effect determination program in the system.
  • Patent Document 1 A technique for determining method priority has been disclosed (for example, Patent Document 1).
  • the present disclosure provides a production floor management system and the like that can improve the accuracy of determining whether work instructions are correct.
  • a production floor management system is a production floor management system that manages the state of a production floor that includes production equipment that produces a product, the system comprising: a state monitoring unit that monitors the state of the production floor; a countermeasure determination unit that determines a countermeasure corresponding to a first priority instruction to be executed from among the plurality of countermeasures based on respective priorities of the plurality of countermeasures extracted corresponding to the state; An instruction output unit that outputs an instruction, and an effect determination unit that determines an effect of the executed first priority instruction based on the states before and after the output first priority instruction is executed.
  • FIG. 1 is a diagram showing a mounting line to which a production floor management system according to an embodiment is applied.
  • FIG. 2 is a configuration diagram showing an example of the production floor management system according to the embodiment.
  • FIG. 3 is a diagram illustrating an example of a monitoring target of a state monitoring unit according to the embodiment;
  • FIG. 4 is a diagram schematically showing the flow of operations of the production floor management system according to the embodiment.
  • 5 is a flowchart illustrating an example of the operation of the state monitoring unit according to the embodiment;
  • FIG. 6 is a flowchart illustrating an example of an operation of a countermeasure determination unit according to the embodiment;
  • FIG. 7 is a table showing an example of a countermeasure candidate list according to the embodiment.
  • FIG. 8 is a table showing an example of a priority countermeasure list according to the embodiment
  • 9 is a flowchart illustrating an example of the operation of a countermeasure mediation unit according to the embodiment
  • FIG. FIG. 10 is a table showing an example of a resource management table according to the embodiment.
  • 11 is a flowchart illustrating an example of operations of an instruction output unit, an effect determination unit, and an update unit according to the embodiment
  • FIG. 12 is a table showing an example of an updated countermeasure candidate list according to the embodiment.
  • FIG. 13 is a flow chart showing an example of a work effect determination method according to another embodiment.
  • a production floor management system of the present disclosure is a production floor management system that manages the state of a production floor that includes production equipment that produces products, and includes a state monitoring unit that monitors the state of the production floor, and a state monitoring unit that corresponds to the state.
  • a countermeasure determination unit for determining a countermeasure corresponding to a first priority instruction to be executed from among the plurality of countermeasures based on respective priorities of the plurality of countermeasures extracted by the above-mentioned method; and outputting the first priority instruction.
  • the effect of the first priority instruction can be determined based on whether the state of the production floor before and after the execution of the first priority instruction has changed, and if so, how it has changed. , it is possible to improve the accuracy of determining whether or not the work instruction is correct.
  • the instruction output unit determines the priority of the first priority instruction and subsequent measures from among the plurality of countermeasures. 2 It is not necessary to output the priority instruction.
  • the state on the production floor depends on the second priority instruction. As a result, it becomes difficult to correctly determine the effect of the first priority instruction. Therefore, by not outputting the second priority instruction until the effect of the first priority instruction is determined, it is possible to further improve the accuracy of determining whether or not the work instruction is correct.
  • the effect determination unit improves the state after the execution of the first priority instruction by a predetermined amount or more with respect to the state before the execution of the first priority instruction, due to the execution of the first priority instruction.
  • the instruction output unit may not output the second priority instruction before execution, which is extracted in relation to the state corresponding to the first priority instruction.
  • the conditions on the production floor tend to improve due to the execution of the first priority instruction, the conditions on the production floor can be improved without executing the second priority instruction. Therefore, in this case, by not outputting the second priority instruction, the trouble of executing the second priority instruction can be saved.
  • the effect determination unit improves the state after the execution of the first priority instruction by a predetermined amount or more with respect to the state before the execution of the first priority instruction, due to the execution of the first priority instruction. If it is determined that there is no tendency, the countermeasure determination unit determines the second priority instruction to be executed based on the priority of each of the plurality of countermeasures excluding the first priority instruction, and the instruction output unit may output the second priority indication.
  • an additional second priority indication can be output to try to improve the conditions on the production floor.
  • the countermeasure determination unit performs the second countermeasure to be executed based on the priority of each of the plurality of countermeasures excluding the first priority instruction.
  • a priority instruction may be determined, and the instruction output unit may output the second priority instruction.
  • the first priority instruction After the first priority instruction is executed, it will take some time to determine its effect. Therefore, by additionally outputting the second priority instruction when the effect cannot be determined for a predetermined period of time, it is possible to further improve the accuracy of determining whether or not the work instruction is correct.
  • the countermeasure determination unit determines the first A priority may be updated to determine the priority for one priority indication.
  • the priority associated with can be updated.
  • the production floor management system may further include a learning model for updating the priorities, and the countermeasure determination unit may update the priorities based on the learning model.
  • a work effect determination method of the present disclosure is a work effect determination method in a production floor management system that manages the state of a production floor equipped with production equipment that produces products, and monitors the state of the production floor, determining a measure corresponding to a first priority instruction to be executed from among the plurality of measures based on the priority of each of the plurality of measures extracted correspondingly; outputting the first priority instruction; determining the effect of the executed first priority instruction based on the states before and after the execution of the first priority instruction.
  • the work effect determination program of the present disclosure is a work effect determination program that causes a computer to execute the work effect determination method described above.
  • FIG. 1 An embodiment will be described below with reference to FIGS. 1 to 12.
  • FIG. 1 An embodiment will be described below with reference to FIGS. 1 to 12.
  • FIG. 1 is a diagram showing a mounting line 4 to which the production floor management system 1 according to the embodiment is applied.
  • the mounting line 4 is equipped with a plurality of production devices that produce products.
  • the mounting line 4 has a function of mounting a component (electronic component) on a substrate to produce a product (for example, a mounting substrate), and has a function of supplying, delivering, and retrieving the substrate to be mounted. have.
  • the substrate supply device M1, the substrate transfer device M2, the printer M3, the mounting devices M4 and M5, the reflow device M6, and the substrate recovery device M7 are arranged in this order. connected in series. Each device from the substrate supply device M1 to the substrate recovery device M7 is connected to the management device 5 via the communication network 2.
  • FIG. 1 the substrate supply device M1, the substrate transfer device M2, the printer M3, the mounting devices M4 and M5, the reflow device M6, and the substrate recovery device M7 are arranged in this order. connected in series.
  • Each device from the substrate supply device M1 to the substrate recovery device M7 is connected to the management device 5 via the communication network 2.
  • the solder printing device M3, the component mounting devices M4 and M5, and the reflow device M6 perform component mounting work for mounting components on the board conveyed along the mounting line 4. That is, the substrate supplied by the substrate supply device M1 is carried into the printer M3 via the substrate transfer device M2.
  • the printer M3 performs a solder printing operation of screen-printing solder for joining components to the board that has been carried in.
  • the solder-printed boards are sequentially delivered to the mounting devices M4 and M5.
  • the mounting apparatuses M4 and M5 perform a component mounting operation for mounting components on the substrate after solder printing.
  • the component mounting apparatuses M4 and M5 are equipped with a base, a board transfer section, a component supply device, a mounting head, and the like.
  • a substrate is arranged on the base.
  • the substrate transfer section can transfer a substrate transferred from an upstream device to a downstream device.
  • the component supply device can supply components to the mounting head.
  • a component supply device is provided with a plurality of tape feeders for supplying components to the mounting head.
  • the mounting head can pick up the component from the tape feeder by suction, move it above the board, and mount the component at the mounting position on the board.
  • the mounting head is equipped with suction nozzles that suction and hold components and that can move up and down individually. Component mounting work is performed by such component mounting apparatuses M4 and M5.
  • the board after component mounting is carried into the reflow device M6 and heated according to a predetermined heating profile.
  • the solder for joining components printed on the heated substrate is melted and solidified.
  • soldering the component to the board in this manner a mounting board having the component mounted on the board is completed.
  • the completed mounting substrate is recovered by the substrate recovery device M7.
  • FIG. 2 is a configuration diagram showing an example of the production floor management system 1 according to the embodiment.
  • the production floor management system 1 is a system that manages the status of production floors equipped with production equipment that produces products.
  • the production floor includes, for example, the mounting line 4, inventory warehouses, preparation areas and maintenance areas.
  • production devices such as the mounting devices M4 and M5 and the printing device M3 and inspection devices are arranged. Materials such as parts, solder, and screen masks are stored in the inventory warehouse.
  • preparation area preparation of equipment elements such as carriages, feeders, nozzles and heads is carried out.
  • maintenance area maintenance of the equipment elements, jigs, and the like is performed.
  • the jig mentioned here is for adjusting the feeder, nozzle, head, etc., and may also be for adjusting the head moving mechanism and the transport mechanism of the equipment.
  • the production floor management system 1 is a computer placed on the production floor.
  • the functions of the production floor management system 1 may be provided in the management device 5 .
  • the production floor management system 1 may be a computer provided in one housing, or may be divided into two or more housings and implemented by two or more computers.
  • the production floor management system 1 may not be arranged on the production floor, and may be a computer such as a server provided outside the production floor. Note that workers are not limited to humans, and include robots, work mechanisms, and automated guided vehicles that perform the above-described work.
  • the production floor management system 1 is a system that outputs instructions to workers or production equipment on the production floor according to the state of the production floor.
  • the production floor management system 1 includes an acquisition unit 10, a state monitoring unit 20, a countermeasure determination unit 30, a countermeasure mediation unit 40, an instruction output unit 50, an effect determination unit 60, an update unit 70, learning models 23, 24, 33 and 34, Also, a resource database 41 is provided.
  • the production floor management system 1 is implemented by a computer including a processor, memory and the like.
  • the acquisition unit 10, the state monitoring unit 20, the countermeasure determination unit 30, the countermeasure mediation unit 40, the instruction output unit 50, the effect determination unit 60, and the update unit 70 are implemented by the processor operating according to the program stored in the memory. .
  • the state monitoring unit 20 may be provided independently as a computer, or may be provided in the production apparatus.
  • the production floor management system 1 may include a plurality of state monitoring units 20 .
  • the learning models 23, 24, 33 and 34 and the resource database 41 are stored in memory.
  • the memory in which the programs, learning models 23, 24, 33 and 34, and resource database 41 are stored may be the same memory or different memories.
  • the acquisition unit 10 acquires information for monitoring the state on the production floor. For example, the acquisition unit 10 acquires information indicating the production status of the production floor. Specifically, the acquisition unit 10 acquires the results of the production process of the production equipment (specifically, productivity, quality, or the presence or absence of defects, etc.) as information indicating the production status of the production floor. The result of the production process may be a sensing history record by a sensor, or may be data input by a person. Further, for example, the acquisition unit 10 acquires information indicating the state of production resources managed on the production floor and used for production. Production resources are, for example, production equipment, facility elements, workers, materials, or jigs.
  • information indicating the state of production resources may be sensing data from a camera, sensor, or the like, or may be data input by a person.
  • the acquisition unit 10 acquires event information that has changed on the production floor. More specifically, the acquisition unit 10 detects when the production apparatus stops, when the feeder, nozzle, parts, or board attached to the production apparatus is replaced, when the worker who performs the work is replaced, or when the operation data of the production apparatus is changed. Acquire information indicating that the
  • the state monitoring unit 20 monitors the state of the production floor through the information acquired by the acquisition unit 10 .
  • the state monitoring unit 20 detects a predetermined state based on a first condition for detecting a predetermined state (for example, a first state or a second state described later) among the states on the production floor.
  • the first condition is a learning model associated with a detection threshold corresponding to a given state. Note that the first condition may be a set detection threshold instead of the learning model.
  • the state monitoring section 20 has a first state monitoring section 21 and a second state monitoring section 22 .
  • the first state monitoring section 21 and the second state monitoring section 22 each perform the operation of the state monitoring section 20 described above.
  • the first state monitoring unit 21 monitors the first state on the production floor.
  • the first state is the production state of the production device, and the first state monitoring unit 21 monitors the result of the production process as the production state of the production device.
  • the first state monitoring section 21 detects the first predetermined state based on the learning model 23 .
  • the learning model 23 is a learning model for detecting a first predetermined state that occurs on the production floor corresponding to the production state of the production equipment.
  • the learning model 23 is also a learning model (that is, the first condition) associated with the detection threshold corresponding to the production state of the production equipment.
  • the first state monitoring unit 21 monitors, within a predetermined period, at least information on production errors occurring in the production equipment, information on the amount of products produced by the production equipment, and quality information on the products produced by the production equipment. A first predetermined condition corresponding to a production index for one is detected.
  • the second state monitoring unit 22 monitors a second state different from the first state on the production floor.
  • the second state is the state of the production resource
  • the second state monitoring section 22 monitors the state of the production resource.
  • the second state monitoring section 22 detects the second predetermined state based on the learning model 24 .
  • Learning model 24 is a learning model for detecting a second predetermined state occurring on the production floor corresponding to the state of production resources.
  • Learning model 24 is also a learning model (ie, first condition) associated with a detection threshold corresponding to the state of the production resource.
  • the second state monitoring unit 22 detects a second predetermined state corresponding to an operating index related to the operating state of production equipment included in the production resource.
  • the second state monitoring unit 22 detects a second predetermined state corresponding to the work index related to the work performed by the worker included in the production resource.
  • FIG. 3 is a diagram showing an example of objects monitored by the state monitoring unit 20 according to the embodiment.
  • the production apparatus is assumed to be a mounting apparatus, and the production process is assumed to be a mounting process.
  • the state monitoring unit 20 (specifically, the first state monitoring unit 21) monitors the result of the mounting process including processes such as suction, recognition, and mounting as the production state. For example, the first state monitoring unit 21, within a predetermined period, at least information on mounting errors occurring in the mounting apparatus, information on the amount of mounted components mounted by the mounting apparatus, and information on the quality of the mounted components mounted by the mounting apparatus.
  • a first predetermined state corresponding to one production index is detected.
  • the state monitoring unit 20 monitors, as the state of production resources, elements (production resources) related to the mounting process such as boards, parts, workers, heads, nozzles, and feeders. Monitor status.
  • the second state monitoring unit 22 detects a second predetermined state (for example, deterioration of mounting devices, nozzles, and feeders, etc.) corresponding to operation indicators relating to the operating states of mounting devices included in production resources.
  • the second state monitoring unit 22 detects a second predetermined state corresponding to a work index (for example, a worker's work error, etc.) related to work performed on a mounting apparatus by a worker included in the production resource.
  • a second predetermined state corresponding to a measurement index, a time index related to the expiration date and time of the solder or parts and the date and time of actual measurement may be detected.
  • the countermeasure determination unit 30 determines a countermeasure to be executed from among a plurality of countermeasures extracted corresponding to the state of the production floor monitored by the state monitor unit 20.
  • the countermeasure to be executed out of the plurality of countermeasures corresponds to a first priority instruction, a second priority instruction, etc., which will be described later.
  • the countermeasure determination unit 30 determines a countermeasure to be executed based on a second condition for determining a countermeasure (for example, a first countermeasure or a second countermeasure to be described later) corresponding to a predetermined state.
  • the second condition is a learning model associated with a plurality of measures corresponding to a predetermined state and priority, including a priority set for each of the plurality of measures to be extracted.
  • the countermeasure determining unit 30 selects the first countermeasure to be executed from among the plurality of countermeasures based on the priority of each of the plurality of countermeasures extracted corresponding to the state of the production floor monitored by the state monitoring unit 20. Determine priority instructions.
  • the countermeasure determination unit 30 determines a countermeasure (that is, the first priority instruction) to be executed from among the plurality of countermeasures based on the priority of each of the plurality of countermeasures extracted corresponding to the predetermined state.
  • the countermeasure determination unit 30 uses operation information of production equipment acquired on the production floor, worker information working on the production floor, and material information used for the product. analyse. In the analysis, there are cases where countermeasures can be determined based only on the tendency of the predetermined state, and cases where the countermeasure cannot be determined based only on the tendency of the predetermined state.
  • the countermeasure determination unit 30 obtains operation information of production equipment acquired on the production floor for each production resource based on event information, information on workers working on the production floor, Also, by analyzing material information used in the product, executing a predetermined countermeasure and further analyzing trends before and after the execution of the predetermined countermeasure, it is possible to determine a countermeasure corresponding to the predetermined state. Also, the analysis may be performed using an analysis unit independent of the countermeasure determination unit 30 . Further, for example, the countermeasure determination unit 30 may change the state of the production floor after the execution of the first priority instruction to the state of the production floor before the execution of the first priority instruction, which is caused by the execution of the first priority instruction.
  • the countermeasure determination unit 30 updates the priority based on a learning model for updating the priority.
  • the second condition may be a data table containing priorities set for each of a plurality of countermeasures instead of the learning model.
  • the countermeasure determination unit 30 has a first countermeasure determination unit 31 and a second countermeasure determination unit 32 .
  • the first countermeasure determination unit 31 and the second countermeasure determination unit 32 each perform the operation of the countermeasure determination unit 30 described above.
  • the first countermeasure determining unit 31 determines a first countermeasure corresponding to the first state monitored by the first state monitoring unit 21. For example, the first countermeasure determining unit 31 executes a first countermeasure (that is, a first priority instruction) from among the plurality of countermeasures based on the respective priorities of the plurality of countermeasures extracted corresponding to the first state. to decide. For example, the first countermeasure determination unit 31 determines first countermeasures including countermeasures for improving the production index. Specifically, the first countermeasure determining unit 31 determines a first countermeasure for improving MTTR (Mean Time To Recovery).
  • MTTR is an index indicating the maintainability of a system or equipment, and the shorter the MTTR, the higher the maintainability.
  • the first countermeasure determination unit 31 determines the first countermeasure based on the learning model 33 .
  • the learning model 33 is a learning model for determining the first countermeasure corresponding to the first state, and specifically, a learning model for updating the priority.
  • the learning model 33 is also a learning model (that is, the second condition) associated with multiple countermeasures and priorities corresponding to the predetermined state.
  • the second countermeasure determining unit 32 determines a second countermeasure corresponding to the second state monitored by the second state monitoring unit 22. For example, the second countermeasure determination unit 32 executes a second countermeasure (that is, a first priority instruction) from among the plurality of countermeasures based on the respective priorities of the plurality of countermeasures extracted corresponding to the first state. to decide. For example, the second countermeasure determination unit 32 determines countermeasures (maintenance/replacement) for production equipment and facility elements corresponding to the operation index, or second countermeasures including work availability and work training for workers corresponding to the work index. to decide. Specifically, the second countermeasure determining unit 32 determines a second countermeasure for improving MTBF (Mean Time Between Failures).
  • MTBF Mobile Time Between Failures
  • the second countermeasure determination unit 32 determines the second countermeasure based on the learning model 34 .
  • the learning model 34 is a learning model for determining a second countermeasure corresponding to the second state, and more specifically, a learning model for updating the priority.
  • the learning model 34 is also a learning model (that is, the second condition) associated with multiple countermeasures and priorities corresponding to the predetermined state.
  • the second countermeasure determined here may be completed by notifying the second countermeasure to a maintenance plan creation device or worker management device provided separately from the production floor management system 1, or the second countermeasure may be completed. 2 It may be completed after receiving the execution result of countermeasures.
  • the countermeasure arbitration unit 40 arbitrates between the first countermeasure and the second countermeasure.
  • the reason why mediation between the first measure and the second measure is necessary will be explained in detail with reference to FIG.
  • the result of the mounting process regarding picking is associated with the board, component, worker, head, nozzle and feeder
  • the result of the mounting process regarding recognition is associated with the component, head and nozzle.
  • the first countermeasure includes countermeasures for substrates, parts, workers, heads, nozzles, or feeders, depending on the results of the production process.
  • the second countermeasures also include countermeasures for substrates, parts, workers, heads, nozzles, or feeders corresponding to the state of production resources.
  • the production resource targeted by the first measure and the production resource targeted by the second measure may overlap. If the production resources targeted by the first measure and the production resources targeted by the second measure overlap, it is difficult to implement both the first measure and the second measure at the same time. It is necessary to mediate with two countermeasures.
  • the countermeasure arbitration unit 40 selects the first countermeasure and the second countermeasure based on the problem level set in the first state corresponding to the first countermeasure and the problem level set in the second state corresponding to the second countermeasure. conduct mediation with Also, for example, the countermeasure arbitration unit 40 arbitrates between the first countermeasure and the second countermeasure based on the presence or absence of production resources required for the first countermeasure or the second countermeasure. The presence or absence of production resources is managed by the resource database 41 .
  • the instruction output unit 50 outputs the first priority instruction. Specifically, the instruction output unit 50 outputs the first countermeasure or the second countermeasure, which is the first priority instruction, according to the arbitration by the countermeasure arbitration unit 40 . For example, until the effect determination unit 60, which will be described later, determines the effect of the executed first priority instruction, the instruction output unit 50 determines the second priority instruction, which is determined from among the plurality of countermeasures, and has the priority after the first priority instruction. Do not output priority instructions. Further, for example, when the first priority instruction is executed, the effect determination unit 60 changes the state of the production floor after execution of the first priority instruction to the state of the production floor before execution of the first priority instruction.
  • the instruction output unit 50 does not output the second priority instruction before execution, which is extracted in relation to the state on the production floor corresponding to the first priority instruction. Further, for example, when the first priority instruction is executed, the effect determination unit 60 changes the state of the production floor after execution of the first priority instruction to the state of the production floor before execution of the first priority instruction. If it is determined that there is no improvement tendency as described above, the countermeasure determination unit 30 determines the second priority instruction to be executed based on the priority of each of the plurality of countermeasures excluding the first priority instruction, and the instruction output unit 50 outputs the second priority indication.
  • the countermeasure determination unit 30 determines the second priority to be executed based on the priority of each of the plurality of countermeasures excluding the first priority instruction. After determining the instruction, the instruction output unit 50 outputs the second priority instruction.
  • the instruction output unit 50 may output an instruction to a control unit of a production apparatus or a mobile terminal possessed by a worker, or through a production management apparatus that manages the production apparatus or a worker management apparatus that manages workers. Instructions may be output to production equipment and workers.
  • the effect determination unit 60 determines the effect of the executed countermeasure (instruction) based on the state of the production floor before and after the determined countermeasure (in other words, the output first priority instruction) was executed. Specifically, the effect determination unit 60 determines whether the first countermeasure or the second countermeasure corresponding to the output instruction is executed based on at least one of the first state and the second state before and after the first countermeasure or the second countermeasure is executed. Determine the effect of the countermeasure or the second countermeasure.
  • the update unit 70 updates the first and second conditions, that is, the learning models 23, 24, 33 and 34, based on the determined effects.
  • the details of the state monitoring unit 20, the countermeasure determination unit 30, the countermeasure mediation unit 40, the instruction output unit 50, the effect determination unit 60, and the update unit 70 will be described later.
  • FIG. 4 is a diagram schematically showing the flow of operations of the production floor management system 1 according to the embodiment.
  • the production floor management system 1 performs problem discovery by the state monitoring unit 20, determination of countermeasure policies by the countermeasure determination unit 30, arbitration by the countermeasure arbitration unit 40, and execution of countermeasures by the instruction output unit 50. These can be applied to so-called OODA loops, where problem finding corresponds to 'Observe', policy decision to 'Orient', mediation to 'Decide', and countermeasure execution to 'Action'. corresponds to
  • the monitoring of the first state by the first state monitoring unit 21 and the determination of the first countermeasure by the first countermeasure determining unit 31 are for improving the MTTR as described above.
  • a cycle of problem discovery by the unit 21, determination of a countermeasure policy by the first countermeasure determination unit 31, arbitration by the countermeasure arbitration unit 40, and countermeasure execution by the instruction output unit 50 is defined as an MTTR cycle.
  • the monitoring of the second state by the second state monitoring unit 22 and the determination of the second countermeasure by the second countermeasure determining unit 32 are for improving the MTBF as described above.
  • a cycle of problem discovery by the unit 22, determination of a countermeasure policy by the second countermeasure determination unit 32, arbitration by the countermeasure arbitration unit 40, and countermeasure execution by the instruction output unit 50 is defined as an MTBF cycle.
  • the monitoring of the first state by the first state monitoring unit 21 and the determination of the first countermeasure by the first countermeasure determining unit 31, the monitoring of the second state by the second state monitoring unit 22 and the second countermeasure determining unit 32 The determination of the second countermeasure by is carried out in parallel. For example, process guarantees can be achieved by the OODA loop from both MTBF and MTTR.
  • each process of the OODA loop, problem finding, policy decision, arbitration, and countermeasure execution maintains independence and is executed in parallel to minimize the waiting time between processes, enabling real-time control. can be realized. Details will be described later, but it is possible to implement countermeasures while changing the order of priority for the conditions on the production floor that change over time.
  • the first state is the production state of the production equipment and the second state is the production resource state
  • the present invention is not limited to this.
  • the first state may be the production state of the production device
  • the second state may be the production state of the production device different from the first state.
  • the first state may be the state of the production resource
  • the second state may be the state of the production resource different from the first state.
  • FIG. 4 shows an example in which the MTBF cycle and the MTTR cycle are performed in parallel, a plurality of MTBF cycles may be performed in parallel, or a plurality of MTTR cycles may be performed in parallel.
  • the countermeasure arbitration unit 40 may prioritize the first countermeasure over the second countermeasure.
  • the degree of problem which will be described below, the operation of the production equipment can be maintained by giving priority to the first countermeasure.
  • the phrase "there is no difference in the degree of problem" as used herein includes that the degree of problem is the same and that the difference in the degree of problem is within a predetermined difference.
  • FIG. 5 the details of the state monitoring unit 20, the countermeasure determination unit 30, the countermeasure mediation unit 40, the instruction output unit 50, the effect determination unit 60, and the update unit 70 will be described with reference to FIGS. 5 to 12.
  • FIG. 5 the details of the state monitoring unit 20, the countermeasure determination unit 30, the countermeasure mediation unit 40, the instruction output unit 50, the effect determination unit 60, and the update unit 70 will be described with reference to FIGS. 5 to 12.
  • FIG. 5 is a flow chart showing an example of the operation of the state monitoring section 20 according to the embodiment.
  • FIG. 5 shows problem finding (Observe) processing in the OODA loop.
  • the state monitoring unit 20 acquires monitoring data (step S11). Specifically, the first state monitoring unit 21 obtains monitoring data regarding the production state of the production equipment, and the second state monitoring unit 22 obtains monitoring data regarding the state of the production resource.
  • the state monitoring unit 20 reads the learning model (step S12). Specifically, the first state monitoring unit 21 reads the learning model 23 and the second state monitoring unit 22 reads the learning model 24 .
  • the learning model 23 is a learning model for detecting a first predetermined state that occurs on the production floor corresponding to the production state of the production equipment, and is associated with a detection threshold corresponding to the production state of the production equipment.
  • the learning model 23 is input with acquired monitoring data, and the acquired monitoring data indicates production error information occurring in a production apparatus, production volume information of a product produced by the production apparatus, and It is learned to output a first predetermined state (for example, productivity, quality, or scrap) corresponding to a production index related to at least one piece of quality information of a product produced by the production apparatus.
  • a first predetermined state for example, productivity, quality, or scrap
  • productivity is increased and quality is decreased.
  • the learning policy is set to emphasize quality, learning is performed so that the detection threshold corresponding to productivity is loosened (decrease in productivity is less likely to be detected) based on the above effect. . Thereby, a detection threshold that balances productivity and quality is learned.
  • the learning model 24 is a learning model for detecting a second predetermined state that occurs on the production floor corresponding to the state of the production resource, and is associated with a detection threshold corresponding to the state of the production resource.
  • the learning model 24 is input with acquired monitoring data, so that the operating index related to the operating state of the production equipment included in the production resource indicated by the acquired monitoring data, or the worker included in the production resource is learned to output a second predetermined state (eg, deterioration of a production device or facility element, or an error in an operator's work, etc.) corresponding to a work index related to the work performed by the .
  • a second predetermined state eg, deterioration of a production device or facility element, or an error in an operator's work, etc.
  • the learning method (update method) of the learning model 24 will be described with a specific example.
  • a decrease in the nozzle flow rate is detected based on the detection threshold corresponding to the nozzle flow rate associated with the learning model 24, and a countermeasure to perform nozzle maintenance within one week is output.
  • the adsorption error rate worsens before maintenance is performed (before one week has passed) after countermeasures are output, and the output countermeasures are not effective.
  • the decrease in the flow rate of the nozzle was detected too late. learning is done in such a way that As a result, the detection threshold is learned so that maintenance can be performed at the optimum time.
  • detection threshold corresponding to the flow rate of the nozzle is just an example, and other detection thresholds may be set for the deviation of the tape stop position of the feeder, the deviation of the pickup position of the component sucked by the nozzle, or the deviation of the transfer position of the board. can also apply the above learning.
  • the state monitoring unit 20 determines whether or not a problem has been detected in the first predetermined state or the second predetermined state (step S13). For example, the first state monitoring unit 21 determines whether or not it is detected that the productivity is declining, the quality is declining, or the number of defective products is increasing. For example, the second state monitoring unit 22 determines whether or not it has detected that the production equipment is deteriorating, that the equipment elements are deteriorating, or that there is an error in the worker's work.
  • step S13 If no problem is detected (No in step S13), the process from step S11 is repeated until a problem is detected.
  • step S13 If a problem is detected (Yes in step S13), a countermeasure policy for the detected problem is determined.
  • FIG. 6 is a flow chart showing an example of the operation of the countermeasure determination unit 30 according to the embodiment.
  • FIG. 6 shows the processing of policy determination (Orient) in the OODA loop.
  • the countermeasure determining unit 30 analyzes the production resources (for example, production resources that can be the cause) for the problem detected by the state monitoring unit 20 (step S21). For example, when the first state monitoring unit 21 detects a problem that productivity is declining in the mounting process of the mounting apparatus, the production resources that can cause the problem are substrates, parts, workers, heads, nozzles, and so on. and feeders (see FIG. 3). Also, for example, when the second state monitoring unit 22 detects a problem of nozzle deterioration, it analyzes that the nozzle is the production resource that can cause the problem.
  • the production resources for example, production resources that can be the cause the problem.
  • the countermeasure determination unit 30 reads the learning model (step S22). Specifically, the first countermeasure determination unit 31 reads the learning model 33 and the second countermeasure determination unit 32 reads the learning model 34 .
  • the learning model 33 is a learning model for determining the first countermeasure corresponding to the production state of the production equipment. Specifically, learning for updating the priority for determining the priority of the first countermeasure. is a model. For example, the learning model 33 is trained such that when a detected problem is input, countermeasures for the detected problem are output as candidates. If multiple candidates are output for the detected problem, multiple countermeasures are extracted corresponding to the multiple candidates. A learning method of the learning model 33 will be described later.
  • the learning model 34 is a learning model for determining the second countermeasure corresponding to the state of production resources, and specifically, a learning model for updating the priority for determining the priority of the second countermeasure. is.
  • the learning model 34 is trained such that when a detected problem is input, countermeasures for the detected problem are output as candidates. If multiple candidates are output for the detected problem, multiple countermeasures are extracted corresponding to the multiple candidates. A learning method of the learning model 34 will be described later.
  • the countermeasure determination unit 30 analyzes the priority for determining the priority of each of the multiple countermeasures (step S23). For example, when the learning models 33 and 34 output countermeasures for the detected problem as candidates, they are output in association with the priority of the countermeasures. Thereby, the countermeasure determination unit 30 can grasp the priority of each countermeasure.
  • the countermeasure determination unit 30 creates a countermeasure candidate list (step S24).
  • the countermeasure candidate list will be described with reference to FIG.
  • FIG. 7 is a table showing an example of a countermeasure candidate list according to the embodiment.
  • the learning model 33 outputs countermeasure candidates such as pickup position teaching, feeder replacement, and nozzle replacement, and also outputs a priority probability as the priority of each countermeasure candidate.
  • the countermeasure determination unit 30 can create a countermeasure candidate list as shown in FIG. In the countermeasure candidate list shown in FIG. 7, the pick-up position teaching has a priority probability of 60% as a countermeasure candidate, and has the highest priority among the countermeasure candidates for the problem of deterioration of the pick-up error rate. Note that if there are countermeasure candidates with the same priority probability, it may be determined based on past results (countermeasure success count, transition of priority probability, etc.).
  • the countermeasure determination unit 30 registers the countermeasure candidate with the highest priority in the created countermeasure candidate list in the priority countermeasure list as a countermeasure against the detected problem (step S25). For example, when the countermeasure candidate list shown in FIG. 7 is created, the countermeasure of suction position teaching is registered in the priority countermeasure list for the problem of deterioration of the suction error rate. For example, a plurality of countermeasures extracted corresponding to the state on the production floor when the problem of deterioration of the pick-up error rate is detected are pick-up position teaching, feeder replacement, and nozzle replacement.
  • the countermeasure determination unit 30 registers the countermeasure of suction position teaching in the priority countermeasure list, it is a first priority instruction to be executed from among the plurality of countermeasures based on the order of priority (priority) of each of the plurality of countermeasures. , means that the adsorption position teaching is determined.
  • acquisition of monitoring data is repeated at predetermined time intervals, and various problems can be detected. For example, one problem may be detected before the remedy for another problem is completed. For example, multiple problems with a first predetermined condition (e.g., productivity, quality, or work errors, etc.) may be detected. In this way, each time a problem is detected, a countermeasure candidate list for that problem is created, and the countermeasure with the highest priority for each countermeasure candidate list is added to the priority countermeasure list.
  • FIG. 8 shows an example of the priority countermeasure list.
  • FIG. 8 is a table showing an example of a priority countermeasure list according to the embodiment.
  • a countermeasure of suction position teaching is registered in the priority countermeasure list for the problem of deterioration of the suction error rate described above.
  • the countermeasure of cleaning is registered in the priority countermeasure list for the problem of feeder sliding failure.
  • the countermeasure of replacing the conveyor belt is registered in the priority countermeasure list for the problem of wear of the conveyor belt.
  • a problem level is set in advance as an index indicating the seriousness of the problem for the first predetermined state and the second predetermined state.
  • the problem of deterioration of the suction error rate can be said to be the problem of the first state (the production state of the production apparatus) when the problem is detected, so the problem level is set to the problem of deterioration of the suction error rate. can be said to be the degree of problem set in the first state when the problem is detected.
  • the problem of feeder sliding failure can be said to be a problem in the second state (the state of the production resource) when the problem is detected, so the problem level is set to the problem of feeder sliding failure. can be said to be the degree of problem set in the second state when the problem is detected.
  • FIG. 9 is a flow chart showing an example of the operation of the countermeasure arbitration unit 40 according to the embodiment.
  • FIG. 9 shows arbitration (Decide) processing in the OODA loop.
  • the countermeasure arbitration unit 40 reads the prioritized countermeasure list (step S31) and selects countermeasures with high priority (that is, degree of problem) (step S32). For example, when the read priority measure list is the list shown in FIG. 8, the measure arbitration unit 40 selects suction position teaching as a measure with a high priority.
  • the countermeasure arbitration unit 40 reads resource data (resource management table) from the resource database 41 (step S33).
  • the resource management table will be explained using FIG.
  • FIG. 10 is a table showing an example of a resource management table according to the embodiment.
  • the presence or absence of each production resource specifically, whether each production resource is currently taking some measures, or whether each production resource is currently taking any measures It is managed whether or not In FIG. 10, the presence or absence of production resources is indicated by locking on and off.
  • the resource management table shown in FIG. 10 manages the presence or absence of heads, nozzles, feeders, and workers A and B as production resources.
  • the head and feeder are currently under some kind of countermeasure and the lock is on.
  • the nozzle is currently unlocked as no countermeasures are being taken. Workers A and B are currently unlocked because they are not taking any countermeasures.
  • the lock referred to here may include either real space or virtual space, or both.
  • prohibiting removal from a production device until the lock is turned off for a feeder whose resources are locked means locking in the real space. Also, prohibiting the use of a feeder in which a resource is locked when creating or updating a production plan means locking in virtual space.
  • the countermeasure arbitration unit 40 determines whether or not the production resource is locked for the selected countermeasure (step S34). For example, it is assumed that the countermeasure mediation unit 40 selects suction position teaching. Also, for example, it is assumed that the suction position teaching is a countermeasure that the worker A takes. In this case, the countermeasure arbitration unit 40 confirms the lock state of worker A in the read resource management table.
  • step S34 If the production resource for the selected countermeasure is locked (Yes in step S34), the countermeasure arbitration unit 40 selects the countermeasure with the next highest priority (step S35), and performs the processing from step S33 again.
  • the countermeasure arbitration unit 40 locks the production resource for the selected countermeasure (step S36).
  • step S37 it is determined whether or not the production resources for all the measures included in the priority measure list are locked. If the production resources for all countermeasures are not locked (No in step S37), the process from step S32 is performed again except for the countermeasure selected this time. If the production resources for all the measures are locked (Yes in step S37), the selected high-priority measures among the measures in the prioritized measure list whose production resources were not locked are executed. will be
  • FIG. 11 is a flow chart showing an example of operations of the instruction output unit 50, the effect determination unit 60, and the update unit 70 according to the embodiment.
  • FIG. 11 shows countermeasure execution (Action) in the OODA loop and processing after the countermeasure is executed.
  • a specific example 2 is a case where the countermeasure selected by the countermeasure arbitration unit 40 for the feeder sliding failure problem is cleaning, and the locked production resource is the feeder and the worker B.
  • the instruction output unit 50 executes a countermeasure for the production resource locked by the countermeasure arbitration unit 40 (step S41).
  • the instruction output unit 50 outputs a first countermeasure (for example, a first priority instruction) that causes the worker A to perform the pickup position teaching.
  • the instruction output unit 50 outputs a second countermeasure (for example, a first priority instruction) to have worker B clean the feeder. In this way, instructions corresponding to countermeasures are output and executed. Execution of instructions may be performed manually by an operator or the like, or may be performed automatically by a production device or the like.
  • the effect determination unit 60 acquires monitoring data (step S42). Specifically, the effect determination unit 60 acquires monitoring data regarding the production status of production equipment or monitoring data regarding the status of production resources.
  • the reason why the effect determination unit 60 acquires the monitoring data is to confirm the change in the state on the production floor due to the execution of the instruction according to the countermeasure, that is, to determine the effect of the instruction according to the executed countermeasure. is.
  • the effect determination unit 60 acquires monitoring data about the result of the mounting process regarding suction. That is, the effect determination unit 60 monitors the tendency of the error information related to the monitored nozzle as monitoring data.
  • the effect determination unit 60 acquires monitoring data about the state of the feeder.
  • the effect determination unit 60 monitors the tendency of the error information related to the monitored feeder as monitoring data. For example, the effect determination section 60 detects the first predetermined state or the second predetermined state using the learning model 23 or 24 in the same way as the state monitoring section 20 does.
  • the effect determination unit 60 determines whether or not a problem has been detected in the first predetermined state or the second predetermined state (step S43). When a problem is detected, it can be determined that there is no effect of the instructions according to the measures taken, or that the effects of the instructions according to the measures taken have not yet appeared, and the problem is detected. If not, it can be determined that the instruction was effective in accordance with the countermeasures taken.
  • the updating unit 70 updates the learning model 33 or 34 based on the determined effect (step S44). For example, in the case of Concrete Example 1, when the problem is no longer detected, the updating unit 70 determines that the suction position teaching was effective against the problem, and the priority of the suction position teaching is increased.
  • the learning model 33 is updated as follows. For example, in the case of Specific Example 2, when the problem is no longer detected, the updating unit 70 determines that cleaning was effective against the problem, and sets the learning model so that cleaning has a higher priority. Update 34.
  • the effect determination unit 60 unlocks the production resource that was locked when executing the instruction of this time (step S45). For example, in the case of the specific example 1, the locked worker A is unlocked. For example, in the case of the specific example 2, the locked operator B and the feeder are unlocked.
  • the effect determination unit 60 deletes the measures that have been executed according to this instruction from the prioritized measures list (step S46). For example, in the case of specific example 1, the pickup position teaching is deleted from the priority countermeasure list. For example, in the case of specific example 2, cleaning is deleted from the priority measure list.
  • the effect determination unit 60 deletes the countermeasure candidate list for the problems that are no longer detected by this instruction (step S47). For example, in the case of Specific Example 1, the problem of deterioration of the suction error rate is no longer detected, and countermeasures for this problem are no longer necessary, so the countermeasure candidate list shown in FIG. 7 is deleted. Deletion of the countermeasure candidate list means that the effect determination unit 60, due to the execution of the first priority instruction (instruction to teach the pickup position), has reduced the state (suction error rate) before execution of the instruction to teach the pickup position.
  • the instruction output unit 50 When it is determined that the suction error rate after execution of the suction position teaching instruction is improved by a predetermined amount or more, the instruction output unit 50 outputs the extracted suction error rate corresponding to the suction position teaching instruction. , means that the second priority instruction (instruction to replace the feeder or nozzle) before execution is not output.
  • the effect determination unit 60 may also determine whether a problem presented in the priority countermeasure list continues in addition to the problem for which countermeasures have been taken. The effect determination unit 60 may delete the prioritized countermeasure list for the problem for which no problem is detected other than the problem for which countermeasures have been taken. Some of the problems detected are relevant and efficient countermeasures can be taken against such problems.
  • step S48 determines whether or not timeout has occurred. In other words, the effect determination unit 60 determines whether or not the effect cannot be determined for a predetermined period of time. Since it may take some time for the effect to appear after the instruction is executed, the process in step S48 is performed. For the predetermined period, for example, the time required for harvesting the effect is set for each instruction. In addition, such as measures added to the production plan and maintenance plan, by creating a plan that does not take immediate action, the production resource is unlocked and the effect judgment is executed. may be done after
  • step S48 If the timeout has not occurred (No in step S48), the processes in steps S42, S43, and S48 are repeated until the problem is no longer detected or the timeout occurs.
  • the update unit 70 updates the learning model 33 or 34 based on the determined effect (step S49). For example, in the case of Concrete Example 1, if a timeout occurs while a problem is being detected, the updating unit 70 determines that the suction position teaching was not effective against the problem, and the priority of the suction position teaching is set to Update the learning model 33 so that it becomes lower. For example, in the case of Concrete Example 2, if a timeout occurs while a problem is detected, the updating unit 70 determines that cleaning was not effective against the problem, and lowers the priority of cleaning. Update the learning model 34. In addition, when a problem is detected but there is an improvement in the trend of the monitoring data (there is an improvement trend below a predetermined level), the updating unit 70 updates the learning model 34 so that the degree of priority decrease is small. update.
  • the effect determination unit 60 unlocks the production resources that were locked when executing the current instruction (step S50). For example, in the case of specific example 1, the locked worker A is unlocked. For example, in the case of the specific example 2, the locked operator B and the feeder are unlocked.
  • the effect determination unit 60 deletes the measures that have been executed according to this instruction from the prioritized measures list (step S51). For example, in the case of specific example 1, the pickup position teaching is deleted from the priority countermeasure list. For example, in the case of specific example 2, cleaning is deleted from the priority measure list.
  • the effect determination unit 60 updates the countermeasure candidate list for problems that remain detected even after the current instruction is executed (step S52).
  • updating of the countermeasure candidate list in the case of specific example 1 will be described with reference to FIG. 12 .
  • FIG. 12 is a table showing an example of the updated countermeasure candidate list according to the embodiment.
  • the effect determination unit 60 may analyze the priority and update the priority in the countermeasure candidate list. For example, in the countermeasure candidate list shown in FIG. 7, the ratio of priority (priority probability) between feeder replacement and nozzle exchange is 3:1, but in the countermeasure candidate list shown in FIG. I know it's changing.
  • the effect determination unit 60 registers the highest-priority measure candidate in the created measure candidate list in the priority measure list as a measure against the detected problem (step S53). For example, when the countermeasure candidate list shown in FIG. 12 is created, a countermeasure of exchanging nozzles is registered in the priority countermeasure list for the problem of deterioration of the suction error rate. In other words, an instruction to replace the nozzle can be output in response to the problem that the suction error rate continues to deteriorate even after the suction position teaching is performed.
  • step S50 the operations after step S50 are not executed until the effect determination unit 60 determines the effect of the executed first priority instruction (suction position teaching). , feeder exchange and nozzle exchange), and the second priority instruction (feeder exchange or nozzle exchange) after the pickup position teach instruction is not output. This is because the instruction to replace the nozzle is output after it is determined that the suction position teaching is ineffective.
  • the effect determination unit 60 determines that the state (suction error rate) before execution of the suction position teaching is compared with the state (suction error rate) after the execution of the suction position teaching due to the execution of the first priority instruction (suction position teaching).
  • the countermeasure determination unit 30 determines the first countermeasure to be executed based on the priority of each of the plurality of countermeasures (feeder replacement and nozzle replacement) excluding the suction position teaching. This means that the 2nd priority instruction (nozzle replacement) is determined, and the instruction output unit 50 outputs the nozzle replacement instruction. This is because instructions to replace nozzles are output with priorities after the suction position teaching, which has the highest priority.
  • the countermeasure determination unit 30 selects a plurality of countermeasures (feeder exchange instruction and nozzle exchange instruction) excluding the first priority instruction (suction position teaching). ), the second priority instruction (nozzle replacement) to be executed is determined, and the instruction output unit 50 outputs the nozzle replacement instruction. This is because, after the time-out, an instruction to replace the nozzle with the highest priority among the plurality of countermeasures excluding the suction position teaching is output.
  • step S13 after a problem is detected by the state monitoring unit 20 and a high-priority countermeasure is registered in the priority countermeasure list from the countermeasure candidate list for the problem, the registered countermeasure may have a low priority.
  • the registered countermeasures may not be executed easily because the production resources for the registered countermeasures have already been locked.
  • the previously executed countermeasures for other problems may solve the problems corresponding to the unexecuted countermeasures.
  • it may be deleted from the priority countermeasure list and the countermeasure candidate list may also be deleted.
  • the effect of the first priority instruction can be determined based on whether or not the state of the production floor before and after the first priority instruction has changed, and if so, how it has changed. It is possible to improve the accuracy of determining whether or not the work instruction is correct.
  • the production floor management system 1 is provided with the updating unit 70 and the learning models 23, 34, 33 and 34, but it does not have to be provided.
  • the first state is the production state of the production equipment and the second state is the production resource state, but the present invention is not limited to this.
  • the first state and the second state are not particularly limited as long as they are different states on the production floor.
  • the production floor management system 1 includes the acquisition unit 10, the state monitoring unit 20, the countermeasure determination unit 30, the countermeasure mediation unit 40, the instruction output unit 50, the effect determination unit 60, and the update unit 70. , some of which may be included in the production equipment.
  • a production device may include the acquisition unit 10 , the state monitoring unit 20 , and the countermeasure determination unit 30 .
  • instructions according to countermeasures may consist of multiple instructions.
  • instructions corresponding to countermeasures may be output to a plurality of production apparatuses or mobile terminals possessed by a plurality of workers.
  • the present disclosure can be realized not only as the production floor management system 1, but also as a work effect determination method including steps (processes) performed by each component constituting the production floor management system 1.
  • FIG. 13 is a flow chart showing an example of a work effect determination method according to another embodiment.
  • the work effect determination method is a work effect determination method in a production floor management system that manages the state of a production floor equipped with production equipment that produces a product.
  • a measure corresponding to the first priority instruction to be executed is determined from among the plurality of measures based on the order of priority of each of the corresponding extracted measures (step S2), and the first priority instruction is output (step S3) includes judging the effect of the executed first priority instruction based on the states before and after the output first priority instruction is executed (step S4).
  • the steps in the work effect determination method may be executed by a computer (computer system).
  • the present disclosure can be implemented as a program for causing a computer to execute the steps included in the work effect determination method.
  • the present disclosure can be implemented as a non-temporary computer-readable recording medium such as a CD-ROM recording the program.
  • each step is executed by executing the program using hardware resources such as the CPU, memory, and input/output circuits of the computer. . That is, each step is executed by the CPU acquiring data from a memory, an input/output circuit, or the like, performing an operation, or outputting the operation result to the memory, an input/output circuit, or the like.
  • each component included in the production floor management system 1 of the above embodiment may be realized as a dedicated or general-purpose circuit.
  • each component included in the production floor management system 1 of the above embodiment may be implemented as an LSI (Large Scale Integration), which is an integrated circuit (IC).
  • LSI Large Scale Integration
  • IC integrated circuit
  • the integrated circuit is not limited to an LSI, and may be realized by a dedicated circuit or a general-purpose processor.
  • a programmable FPGA (Field Programmable Gate Array) or a reconfigurable processor capable of reconfiguring connections and settings of circuit cells inside the LSI may be used.
  • the present disclosure can be used, for example, for managing production floors.

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Abstract

La présente invention concerne un système de gestion d'atelier (1) qui gère l'état d'un atelier pourvu de dispositifs de production qui produisent des produits, ledit système de gestion d'atelier (1) comprenant : une unité de surveillance d'état (20) qui surveille l'état sur l'atelier ; une unité de détermination de politique (30) qui, sur la base du rang de priorité de chacune d'une pluralité de politiques extraites en correspondance avec l'état mentionné ci-dessus, détermine une politique parmi une pluralité de politiques qui correspondent à une première instruction de priorité à exécuter ; une unité de délivrance en sortie d'instruction (50) qui délivre en sortie la première instruction de priorité ; et une unité de détermination d'effet (60) qui détermine l'effet de la première instruction de priorité exécutée sur la base de l'état avant et après l'exécution de la première instruction de priorité délivrée.
PCT/JP2021/047309 2021-01-19 2021-12-21 Système de gestion d'atelier, procédé de détermination d'effet de travail et programme de détermination d'effet de travail WO2022158227A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10283028A (ja) * 1997-04-07 1998-10-23 Toyota Motor Corp 設備情報伝達装置
JP2004005602A (ja) * 2003-04-21 2004-01-08 Matsushita Electric Ind Co Ltd 実装基板生産システムおよびメンテナンス指示システム
JP2012145997A (ja) * 2011-01-07 2012-08-02 Fuji Mach Mfg Co Ltd 生産システム
JP2013041448A (ja) * 2011-08-17 2013-02-28 Hitachi Ltd 異常検知・診断方法、および異常検知・診断システム
WO2018142604A1 (fr) * 2017-02-06 2018-08-09 株式会社Ｆｕｊｉ Dispositif de gestion de travail
JP2020177547A (ja) * 2019-04-22 2020-10-29 株式会社ジェイテクト 作業支援システム

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH10283028A (ja) * 1997-04-07 1998-10-23 Toyota Motor Corp 設備情報伝達装置
JP2004005602A (ja) * 2003-04-21 2004-01-08 Matsushita Electric Ind Co Ltd 実装基板生産システムおよびメンテナンス指示システム
JP2012145997A (ja) * 2011-01-07 2012-08-02 Fuji Mach Mfg Co Ltd 生産システム
JP2013041448A (ja) * 2011-08-17 2013-02-28 Hitachi Ltd 異常検知・診断方法、および異常検知・診断システム
WO2018142604A1 (fr) * 2017-02-06 2018-08-09 株式会社Ｆｕｊｉ Dispositif de gestion de travail
JP2020177547A (ja) * 2019-04-22 2020-10-29 株式会社ジェイテクト 作業支援システム

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