WO2021106225A1 - Dispositif de surveillance d'anomalie, procédé de surveillance d'anomalie et programme de surveillance d'anomalie - Google Patents

Dispositif de surveillance d'anomalie, procédé de surveillance d'anomalie et programme de surveillance d'anomalie Download PDF

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Publication number
WO2021106225A1
WO2021106225A1 PCT/JP2019/046921 JP2019046921W WO2021106225A1 WO 2021106225 A1 WO2021106225 A1 WO 2021106225A1 JP 2019046921 W JP2019046921 W JP 2019046921W WO 2021106225 A1 WO2021106225 A1 WO 2021106225A1
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WIPO (PCT)
Prior art keywords
abnormality monitoring
mold
captured image
divided portion
learning
Prior art date
Application number
PCT/JP2019/046921
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English (en)
Japanese (ja)
Inventor
藤井　浩
泰彦 原
慎一 槇原
Original Assignee
菱洋エレクトロ株式会社
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 菱洋エレクトロ株式会社 filed Critical 菱洋エレクトロ株式会社
Priority to JP2020515276A priority Critical patent/JP6722836B1/ja
Priority to PCT/JP2019/046921 priority patent/WO2021106225A1/fr
Publication of WO2021106225A1 publication Critical patent/WO2021106225A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
    • B29C45/17—Component parts, details or accessories; Auxiliary operations
    • B29C45/76—Measuring, controlling or regulating
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C45/00—Injection moulding, i.e. forcing the required volume of moulding material through a nozzle into a closed mould; Apparatus therefor
    • B29C45/17—Component parts, details or accessories; Auxiliary operations
    • B29C45/84—Safety devices

Definitions

  • the present invention relates to an abnormality monitoring device, an abnormality monitoring method, and an abnormality monitoring program.
  • Japanese Patent No. 5220948 stores a plurality of reference image data to be used as a reference for normal operation in a predetermined step of an injection molding cycle based on a video signal, and a video signal in a sequentially repeated injection molding cycle.
  • the detected image data is acquired based on the above, the detected image data is compared with the reference image data of 1, and it is determined that the detected image data is dissimilar to the reference image data, the detected image data is stored.
  • an injection molding machine monitoring device that monitors an abnormal operation of the injection molding machine main body by comparing with the reference image data that has not yet been compared with the detected reference image data among the plurality of reference image data.
  • the present invention provides an abnormality monitoring device, an abnormality monitoring method, and an abnormality monitoring method capable of monitoring the abnormal state of the monitored target without causing complicated initial setting work by the user even when the monitored target is new.
  • the purpose is to provide an anomaly monitoring program.
  • the abnormality monitoring device of one aspect of the present invention includes a learned model generated by machine learning a learning image representing a divided portion of a mold, and an captured image acquired by imaging the divided portion with a camera. Based on the above, it has a control unit that determines whether or not the divided portion is in an abnormal state, and when it is determined that the divided portion is in an abnormal state, outputs a stop instruction for stopping the mold clamping. ..
  • a learned model generated by machine learning a learning image representing a divided portion of a mold is prepared, and the divided portion is imaged by a camera to obtain an captured image.
  • the divided unit is in an abnormal state based on the captured image and the learned model.
  • a stop instruction for stopping the mold clamping is output.
  • the abnormality monitoring program of one aspect of the present invention uses a machine learning image showing the divided portion of the mold in response to input of control information indicating the end of the extraction process of the molded product formed by mold clamping.
  • a program for determining whether or not the divided portion is in an abnormal state based on the trained model generated by learning and the captured image acquired by imaging the divided portion with a camera, and the divided portion. It consists of a program that outputs a stop instruction for stopping the mold clamping when it is determined that the unit is in an abnormal state.
  • an abnormality monitoring device capable of monitoring an abnormal state of a monitored object without causing complicated initial setting work by the user even when the monitored object is new.
  • Anomaly monitoring programs can be provided.
  • FIG. 1 is a block diagram showing an example of the configuration of the injection molding machine 1.
  • the injection molding machine 1 has a molding machine main body 2 and an abnormality monitoring device 3.
  • the molding machine main body 2 and the abnormality monitoring device 3 are connected to each other by a signal line.
  • the molding machine main body 2 has an injection mechanism 10, a mold clamping mechanism 20, an instruction input unit 30, a control unit 40, and a storage unit 50.
  • the injection mechanism 10 injects the molten resin material into the mold clamping mechanism 20.
  • the injection mechanism 10 includes a hopper 11, an injection cylinder 12, and an injection drive unit 13.
  • the hopper 11 is connected to one end of the injection cylinder 12.
  • the hopper 11 can charge the resin material, accommodates the charged resin material, and supplies the resin material to the injection cylinder 12.
  • the injection cylinder 12 has a nozzle 12a provided at the other end opposite to one, a heater provided at the outer peripheral portion, and a screw rotatably provided around the axis at the inner peripheral portion.
  • the injection drive unit 13 has a drive device such as a motor, for example. Under the control of the control unit 40, the injection drive unit 13 rotates the screw to convey the resin material to the other, heats the heater to melt the resin material, and ejects the melted resin material from the nozzle 12a.
  • a drive device such as a motor
  • the mold clamping mechanism 20 molds the resin material injected from the injection mechanism 10 by the mold clamping.
  • the mold clamping mechanism 20 includes a fixed plate 21, a movable plate 22, a fixed side mold 23, a movable side mold 24, a spool 25, a mold clamping drive unit 26, an ejector pin 27, and a take-out drive unit 28.
  • the fixing plate 21 is provided on the other side of the injection cylinder 12.
  • a nozzle connecting portion 21a for connecting the nozzle 12a is provided on one side of the fixing plate 21.
  • the movable board 22 is provided on the other side of the fixed board 21.
  • the movable platen 22 is guided by a tie bar 22a provided between the movable platen 22 and the fixed platen 21, and can move to one or the other.
  • the fixed side mold 23 and the movable side mold 24 are provided between the fixed plate 21 and the movable plate 22.
  • the fixed-side mold 23 has a fixed-side mounting plate 23a for attaching to the fixed plate 21 on one side, and is attached to a portion on the other side of the fixed plate 21. Further, the fixed-side mold 23 has a guide bush 23b, a fixed-side split portion 23c, and a cavity 23d on the other side.
  • the movable side mold 24 is arranged so as to face the fixed side mold 23.
  • the movable side mold 24 has a movable side mounting plate 24a for attaching to the movable board 22 on the other side, and is attached to a portion on one side of the movable board 22.
  • a resin material is poured on one side between the guide pin 24b guided by the guide bush 23b, the movable side dividing portion 24c that presses against the fixed side dividing portion 23c, and the cavity 23d. It has a core 24d that forms a space for it.
  • the spool 25 communicates the nozzle connecting portion 21a with the cavity 23d so that the resin material can flow.
  • the mold clamping drive unit 26 has, for example, a drive device such as a flood control, and opens and closes the movable side mold 24 with respect to the fixed side mold 23. More specifically, the mold clamping drive unit 26 moves the movable platen 22 to one side under the control of the control unit 40, presses the movable side mold 24 against the fixed side mold 23, and molds. After mold clamping, the mold clamping drive unit 26 moves the movable platen 22 to the other side and pulls the movable side mold 24 away from the fixed side mold 23.
  • a drive device such as a flood control
  • the ejector pin 27 is provided so as to penetrate the movable platen 22, the movable mold 24, and the inner peripheral portion of the core 24d.
  • the take-out drive unit 28 has, for example, a drive device such as a motor.
  • the take-out drive unit 28 projects the ejector pin 27 from the core 24d so that the molded product Mo can be taken out under the control of the control unit 40. It is pulled back and buried in the core 24d.
  • the instruction input unit 30 has various buttons, display panels, and other operating tools, and can input various instructions to the molding machine main body 2.
  • the instruction input unit 30 outputs various input instructions to the control unit 40.
  • the control unit 40 has a processor such as a CPU that executes various processes.
  • the function of the control unit 40 is realized by the processor executing various programs stored in the storage unit 50.
  • the control unit 40 controls various operations of the molding machine main body 2. More specifically, the control unit 40 performs an injection process for driving the injection mechanism 10 based on an instruction input from the instruction input unit 30, performs a mold clamping process for driving the mold clamping mechanism 20, and drives the product to be taken out. The take-out process for driving the unit 28 is performed. When the take-out process is completed, the control unit 40 outputs the control information Ci indicating the end of the take-out process. When the stop instruction St is input, the control unit 40 performs a stop process of stopping the drive of the mold clamping mechanism 20 until the drive restart instruction is input.
  • the storage unit 50 has storage elements such as ROM, RAM, HDD, and SSD, and stores various information and programs for controlling various operations of the molding machine main body 2.
  • the abnormality monitoring device 3 monitors whether or not the molding machine main body 2 is in an abnormal state.
  • the abnormality monitoring device 3 has a camera 60 and a monitoring device main body 70.
  • the camera 60 images the movable side split portion 24c and outputs the captured image Im to the monitoring device main body 70.
  • the camera 60 is held by the arm 61.
  • FIG. 2 is an explanatory diagram for explaining an example of the mounting state of the camera 60.
  • the arm 61 has a mounting tool 62 having a camera 60 attached to one end and a magnet at the other end opposite to one end.
  • the arm 61 is attached to the fixing plate 21 or the like by the attractive force of the magnet of the attachment 62, and holds the camera 60.
  • the monitoring device main body 70 has a control unit 71 and a storage unit 72.
  • the control unit 71 has a processor such as a CPU and a GPU that executes various processes.
  • the function of the control unit 71 is realized by the processor reading and executing various information stored in the storage unit 72.
  • the control unit 71 controls various operations of the abnormality monitoring device 3, and also executes a program of the abnormality determination unit Pj in response to the input of the control information Ci.
  • the storage unit 72 has storage elements such as ROM, RAM, HDD, and SSD, and stores various programs and various data, as well as the program of the abnormality determination unit Pj and the trained model Md.
  • Abnormality determination unit Pj performs abnormality determination processing.
  • the abnormality determination process determines whether or not the mold clamping mechanism 20 is in an abnormal state based on the learned model Md and the captured image Im, and when it is determined that the mold clamping mechanism 20 is in an abnormal state, the mold clamping mechanism 20 is in an abnormal state.
  • a stop instruction St for stopping 20 is output.
  • the abnormality determination unit Pj calculates an evaluation value by inputting the captured image Im into the trained model Md, and when the evaluation value is within a predetermined range, the mold clamping mechanism 20 is in an abnormal state. Is determined.
  • FIG. 3 is an explanatory diagram for explaining the movable side divided portion 24c and the molded product Mo.
  • FIG. 4 is an explanatory diagram for explaining the extraction process.
  • FIG. 5 is an explanatory diagram for explaining an example of an abnormal state of the mold clamping mechanism 20.
  • the trained model Md machine-learns teacher data based on techniques such as deep learning and neural networks so that the calculation result of the evaluation value falls within a predetermined range when the mold clamping mechanism 20 is in an abnormal state. Is generated by.
  • the learning image showing the movable side divided portion 24c having no molded product Mo has a correlation with the normal state of the mold clamping mechanism 20 after the extraction process.
  • a learning image showing the movable side divided portion 24c having no molded product Mo is used as an indicator of the normal state of the mold clamping mechanism 20.
  • the molded product Mo is left on the movable side split portion 24c due to catching or the like.
  • the learning image of the movable side split portion 24c having the molded product Mo has a correlation with the abnormal state of the mold clamping mechanism 20 after the extraction process.
  • a learning image showing the movable side split portion 24c having the molded product Mo is used as an indicator of the abnormal state of the mold clamping mechanism 20.
  • the learning image is created based on the captured image Im obtained by performing an injection molding process at the development stage and capturing the movable side split portion 24c with the camera 60. More specifically, the captured image Im representing the movable side divided portion 24c having the molded product Mo is acquired by imaging the movable side divided portion 24c after molding and before taking out the molded product Mo. The captured image Im showing the movable side divided portion 24c having no molded product Mo is acquired by imaging the movable side divided portion 24c after taking out the molded product Mo and before the next mold clamping.
  • the captured image Im is created by imaging the movable side divided portion 24c from various angles such as upward, lateral, and downward. Further, the captured image Im may be inflated by horizontal inversion, vertical inversion, rotation, enlargement, reduction, brightness change, or the like.
  • a part of the captured image Im is used for evaluation of the trained model Md.
  • the abnormality monitoring device 3 monitors whether or not the mold clamping mechanism 20 of the molding machine main body 2 to be monitored is in an abnormal state.
  • the monitoring target is a mold provided in the mold clamping mechanism 20 and a split portion of the mold. More specifically, the monitoring targets are the movable side mold 24 and the movable side dividing portion 24c.
  • the control unit 71 is based on the learned model Md generated by machine learning the learning image representing the divided portion of the mold and the captured image Im acquired by imaging the divided portion with the camera 60. , It is determined whether or not the divided portion is in an abnormal state, and when it is determined that the divided portion is in an abnormal state, a stop instruction St for stopping the mold clamping is output.
  • the control unit 71 calculates the evaluation value by inputting the captured image Im into the trained model Md, and determines that the division unit is in an abnormal state when the evaluation value is within a predetermined range.
  • the trained model Md is obtained by machine learning a learning image showing a divided portion having no molded product Mo showing a normal state and a learning image showing a divided portion having a molded product Mo showing an abnormal state. Will be generated.
  • a learned model Md generated by machine learning a learning image showing a divided portion of a mold is prepared, and the divided portion is imaged by a camera 60 to acquire an captured image Im.
  • the divided unit is in an abnormal state based on the captured image Im and the learned model Md.
  • a stop instruction St for stopping the mold clamping is output.
  • the abnormality monitoring program machine-learns a learning image showing the divided portion of the mold in response to the input of the control information Ci indicating the end of the extraction process of the molded product Mo formed by the mold clamping.
  • a program for determining whether or not the divided portion is in an abnormal state and a program for determining whether the divided portion is abnormal It consists of a program that outputs a stop instruction St for stopping the mold clamping when it is determined that the state is in the state.
  • FIG. 6 is a flowchart showing an example of the flow of the injection molding process.
  • the control unit 40 outputs an instruction for closing the movable mold 24.
  • the mold clamping drive unit 26 moves the movable mold 24 to one side in response to an instruction, presses the movable mold 24 against the fixed mold 23, and clamps the mold.
  • the control unit 40 outputs an instruction for injecting the resin material.
  • the injection drive unit 13 injects the resin material from the nozzle 12a into the mold clamping mechanism 20 in response to an instruction.
  • the resin material flows through the spool 25, flows into the space formed between the cavity 23d and the core 24d, and is molded to form a molded product Mo.
  • the control unit 40 Open the movable mold 24 (S3).
  • the control unit 40 outputs an instruction for opening the movable mold 24.
  • the mold clamping drive unit 26 moves the movable side mold 24 to the other side and pulls the movable side mold 24 away from the fixed side mold 23 in response to an instruction.
  • the molded product Mo appears on the movable side divided portion 24c.
  • the control unit 40 takes out the molded product Mo (S4).
  • the control unit 40 outputs an instruction for taking out the molded product Mo.
  • the take-out drive unit 28 projects the ejector pin 27 from the core 24d in response to an instruction, and then pulls back the ejector pin 27 and buries it in the core 24d.
  • the control unit 40 outputs the control information Ci indicating the end of the retrieval process.
  • the control unit 71 inputs the captured image Im input from the camera 60 into the trained model Md according to the control information Ci, and calculates an evaluation value.
  • the control unit 71 determines that the evaluation value is within the predetermined range, the process proceeds to S6 (S6: YES).
  • the evaluation value is not within the predetermined range, the process returns to S1 (S6: NO).
  • the control unit 71 outputs a stop instruction St for stopping the mold clamping mechanism 20.
  • the control unit 40 stops the mold clamping mechanism 20 in response to the stop instruction St.
  • the treatments S1 to S8 are injection molding treatments.
  • the processing of S1 and S3 is a mold clamping process.
  • the process of S2 is an injection process.
  • the process of S4 is a take-out process.
  • the processing of S5 and S6 is an abnormality determination processing.
  • the processing of S7 and S8 is a stop processing.
  • the abnormality monitoring device 3 is complicated by the user even when the movable side mold 24 to be monitored is new, such as when the molding machine main body 2 is newly installed or when the movable side mold 24 is replaced.
  • the evaluation value is calculated by inputting the captured image Im into the trained model Md without any initial setting work, and it is determined whether or not the mold clamping mechanism 20 is in an abnormal state according to the evaluation value. Can be done.
  • the abnormality monitoring device 3 can monitor the abnormal state of the monitoring target without causing complicated initial setting work by the user even when the monitoring target is new.
  • the trained model Md is not additionally trained, but the trained model Md may be configured to be additionally trained.
  • FIG. 7 is a flowchart showing an example of the flow of the additional learning process.
  • the additional learning process starts according to a predetermined schedule.
  • the captured image Im is stored in the storage unit 72 (S11).
  • the control unit 71 stores the imaged image Im representing each of the movable side divided portion 24c having the molded product Mo and the movable side divided portion 24c having no molded product Mo in the storage unit 72.
  • the captured image Im showing the movable side divided portion 24c having the molded product Mo is acquired by imaging the movable side divided portion 24c after molding and before taking out the molded product Mo.
  • the captured image Im showing the movable side divided portion 24c having no molded product Mo is acquired by imaging the movable side divided portion 24c after taking out the molded product Mo and before the next mold clamping.
  • the control unit 71 determines whether or not to start machine learning according to the schedule read from the storage unit 72. Machine learning is preferably performed at a time when the drive of the injection molding machine 1 is stopped, such as at night. When starting machine learning, the process proceeds to S13 (S12: YES). When machine learning is not started, the process returns to S11 (S12: NO).
  • the captured image Im is read from the storage unit 72 (S13).
  • the control unit 71 performs machine learning based on the read captured image Im, and updates the trained model Md.
  • the captured image Im of the movable side split portion 24c having no molded product Mo is used as an indicator of the normal state of the mold clamping mechanism 20.
  • the captured image Im of the movable side divided portion 24c having the molded product Mo is used as an indicator of the abnormal state of the mold clamping mechanism 20.
  • the storage unit 72 stores the captured image Im
  • the control unit 71 updates the learned model Md by machine learning the captured image Im.
  • the abnormality monitoring device 3 can perform additional learning processing of the trained model Md based on the captured image Im captured by the camera 60, and improve the accuracy of determining the abnormal state of the mold clamping mechanism 20. Can be done.
  • the abnormality monitoring device 3 is not connected to the server 4, but the abnormality monitoring device 3 may be configured to be connected to the server 4 (two-dot chain line in FIG. 1).
  • the control unit 71 is connected to the server 4 by wired communication or wireless communication via a network such as the Internet or LAN.
  • the control unit 71 can output the captured image Im to the server 4.
  • the server 4 performs additional learning processing based on the captured image Im input from the monitoring device main body 70, and outputs the result of the additional learning processing to the monitoring device main body 70.
  • control unit 71 updates the trained model Md based on the result of the additional learning process input from the server 4.
  • the server 4 can receive the input of the captured image Im from the plurality of abnormality monitoring devices 3 and perform the additional learning process, and can perform the additional learning process based on more captured images Im. is there.
  • the server 4 outputs a keep-alive signal to the control unit 71 at a predetermined timing.
  • the control unit 71 receives the keep-alive signal
  • the control unit 71 outputs a response signal to the server 4.
  • the server 4 warns the user that the abnormality monitoring device 3 is in the stopped state by a notification means such as an e-mail.
  • the server 4 outputs a signal to the control unit 71 at a predetermined timing, and outputs a warning when the response signal cannot be received from the control unit 71 within the predetermined time.
  • the abnormality monitoring device 3 can more reliably determine whether or not the mold clamping mechanism 20 is in an abnormal state.
  • the learning image showing the movable mold 24 having the molded product Mo and the learning image showing the movable mold 24 having the molded product Mo are machine-learned. Generated by, but not limited to.
  • the movable side split portion 24c made of metal appears, while in the abnormal state, the molded product Mo made of resin appears on the movable side split portion 24c.
  • the learning image of the movable side split portion 24c showing the metal has a correlation with the normal state of the mold clamping mechanism 20. Further, the learning image showing both the metal and the resin or the resin has a correlation with the abnormal state of the mold clamping mechanism 20.
  • the trained model Md is a machine learning image showing a metal as showing the normal state of the mold clamping mechanism 20 and a learning image showing both metal and resin or resin as showing an abnormal state. It is generated by learning.
  • the abnormality monitoring device 3 can determine that the mold clamping mechanism 20 is in an abnormal state when the resin appears in the captured image Im indicating after the extraction process.
  • the trained model Md has a first trained model, a second trained model, and a third trained model.
  • the first trained model is generated based on a learning image created by changing the type of lighting that illuminates the movable side dividing portion 24c and the type of the camera 60 and taking an image.
  • the second learned model is generated based on the learning image created by changing the type of the movable side dividing portion 24c and taking an image.
  • the third trained model is generated based on the learning image created by changing the color of the molded product Mo and taking an image.
  • the control unit 71 has a first evaluation value calculated based on the first trained model and the captured image Im, a second evaluation value calculated based on the second trained model and the captured image Im, and a third learning. It is determined whether or not the divided portion of the mold clamping mechanism 20 is in an abnormal state according to the completed model and the third evaluation value calculated based on the captured image Im.
  • control unit 71 weights each of the first evaluation value, the second evaluation value, and the third evaluation value by a predetermined weighting coefficient, and totals the weighting results to calculate the evaluation value. , When the evaluation value is within the predetermined range, it is determined that the state is abnormal.
  • the abnormality monitoring device 3 can determine whether or not the mold clamping mechanism 20 of more types is in an abnormal state with higher accuracy.
  • the camera 60 monitors the movable side split portion 24c, but the present invention is not limited to this.
  • the molded product Mo appears in the fixed-side divided portion 23c instead of the movable-side divided portion 24c after molding and before taking out the molded product Mo, the fixed-side divided portion 23c may be monitored.
  • the abnormality monitoring device 3 captures the movable side split portion 24c with the camera 60, but when the molded product Mo is formed on the fixed side mold 23, the fixed side split portion 23c is captured by the camera. An image may be taken by 60 to determine whether or not the mold clamping mechanism 20 is in an abnormal state. Further, the abnormality monitoring device 3 may image both the movable side dividing portion 24c and the fixed side dividing portion 23c to determine whether or not the mold clamping mechanism 20 is in an abnormal state.
  • the camera 60 is located above the fixed plate 21 by the arm 61, but the position of the camera 60 is not limited to this.
  • the camera 60 may be located on the side or lower part of the fixing plate 21 by the arm 61. Further, the camera 60 may be located on the side of the movable mold 24 as long as the core 24d can be imaged. Further, the camera 60 may be attached to other than the fixed plate 21.
  • the storage unit 72 stores the abnormality determination unit Pj and the learned model Md, but these may be stored in the storage unit 50 or the server 4. In that case, the function of the control unit 71 may be realized by the control unit 40 or the server 4.
  • the determination of the start of the additional learning process (S12) was performed by reading the predetermined schedule from the storage unit 72, but the determination of the start of the additional learning process was performed by the instruction input unit 30. Alternatively, it may be performed by a user's instruction input via an instruction unit (not shown) connected to the control unit 71.
  • Each step of each procedure in the present embodiment may be executed at the same time by changing the execution order, or may be executed in a different order for each execution, as long as the property is not contrary to the property. Further, all or a part of each step of each procedure in the present embodiment may be realized by hardware.
  • the present invention is not limited to the above-described embodiment, and various modifications, modifications, and the like can be made without changing the gist of the present invention.

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  • Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Mechanical Engineering (AREA)
  • Injection Moulding Of Plastics Or The Like (AREA)
  • Moulds For Moulding Plastics Or The Like (AREA)

Abstract

L'invention concerne un dispositif de surveillance (3) d'anomalie qui comprend une unité de commande (71) qui, sur la base d'une image capturée Im qui est obtenue par imagerie d'une partie de division d'un moule par une caméra (60) et un modèle appris Md qui est généré par réalisation d'un apprentissage automatique d'images d'apprentissage montrant la partie de division, détermine si oui ou non la partie de division est dans un état anormal, et qui délivre une instruction d'arrêt St servant à arrêter la fermeture du moule lorsqu'il est déterminé que la partie de division est dans un état anormal.
PCT/JP2019/046921 2019-11-29 2019-11-29 Dispositif de surveillance d'anomalie, procédé de surveillance d'anomalie et programme de surveillance d'anomalie WO2021106225A1 (fr)

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JP2020515276A JP6722836B1 (ja) 2019-11-29 2019-11-29 異常監視装置、異常監視方法、及び、異常監視プログラム
PCT/JP2019/046921 WO2021106225A1 (fr) 2019-11-29 2019-11-29 Dispositif de surveillance d'anomalie, procédé de surveillance d'anomalie et programme de surveillance d'anomalie

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PCT/JP2019/046921 WO2021106225A1 (fr) 2019-11-29 2019-11-29 Dispositif de surveillance d'anomalie, procédé de surveillance d'anomalie et programme de surveillance d'anomalie

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WO2022162806A1 (fr) * 2021-01-27 2022-08-04 菱洋エレクトロ株式会社 Dispositif de surveillance d'anomalie, procédé de surveillance d'anomalie et programme de surveillance d'anomalie
JP2022167220A (ja) * 2021-04-22 2022-11-04 ウシオライティング株式会社 金型監視装置及び金型監視方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005125561A (ja) * 2003-10-22 2005-05-19 Sumitomo Heavy Ind Ltd 金型監視装置、方法及びプログラム
JP2019168973A (ja) * 2018-03-23 2019-10-03 ファナック株式会社 駆動装置及び機械学習装置

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005125561A (ja) * 2003-10-22 2005-05-19 Sumitomo Heavy Ind Ltd 金型監視装置、方法及びプログラム
JP2019168973A (ja) * 2018-03-23 2019-10-03 ファナック株式会社 駆動装置及び機械学習装置

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