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WO2021206017A1 - Système de moulage, dispositif de prédiction d'irrégularité, procédé de prédiction d'irrégularité, programme et modèle entraîné - Google Patents

Système de moulage, dispositif de prédiction d'irrégularité, procédé de prédiction d'irrégularité, programme et modèle entraîné Download PDF

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Publication number
WO2021206017A1
WO2021206017A1 PCT/JP2021/014318 JP2021014318W WO2021206017A1 WO 2021206017 A1 WO2021206017 A1 WO 2021206017A1 JP 2021014318 W JP2021014318 W JP 2021014318W WO 2021206017 A1 WO2021206017 A1 WO 2021206017A1
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WIPO (PCT)
Prior art keywords
pressure
molding
molding material
evaluation value
molded product
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PCT/JP2021/014318
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English (en)
Japanese (ja)
Inventor
智也 足立
紀行 馬場
慎太郎 辻
勇佐 大久保
幸治 木村
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JTEKT Corp
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JTEKT Corp
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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
    • 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

Definitions

  • This disclosure relates to molding systems, anomaly predictors, anomaly prediction methods, programs and trained models.
  • An injection molding apparatus in which a melt obtained by melting a molding material is supplied to a cavity formed between a plurality of molds to mold a molded product.
  • Examples of defective molded products include burrs. The occurrence of burrs not only spoils the appearance of the molded product, but may also affect the performance of the molded product. For this reason, we are currently visually inspecting molded products.
  • Patent Document 1 in order for the operator to judge the suitability of the current injection conditions with respect to the occurrence of burrs, a curve showing the time-dependent change of the pressure in the mold detected by the pressure sensor and the pressure as a guide for the burrs to blow are described.
  • a technique for drawing a burr blowing limit curve on a display unit is disclosed.
  • the burr blowing limit curve is a curve that can be drawn by acquiring various values by experiments.
  • the burr blowing limit curve according to Patent Document 1 is a different curve depending on the molding conditions. Molding conditions include the shape of the mold, the type / viscosity / temperature of the molding material, the pressure applied to the molding material (melt), the molding environment (room temperature, humidity), and the like. Therefore, in order to determine the occurrence of burrs based on the technique according to Patent Document 1, it is necessary to prepare a burr blowing limit curve for each molding condition. In addition, if the actual molding conditions deviate from the conditions that form the basis of the burr blowing limit curve, the actual pressure will be lower than the burr blowing limit curve even though burrs have occurred, and the occurrence of burrs will be accurate. It may not be possible to judge.
  • an object of the present disclosure is to provide a molding system, an abnormality prediction device, an abnormality prediction method, a program, and a trained model for more accurately predicting the state of burrs.
  • the present disclosure is a molding system including a molding device and an abnormality predicting device for predicting an abnormality of a molded product molded by the molding device, and the molding device combines a plurality of molds.
  • a mold clamping portion for tightening the plurality of molds, a filling operation for filling a cavity formed between the plurality of molds by tightening the plurality of molds with a molten molding material, and the above-mentioned
  • the injection portion that performs the pressure holding operation for holding the pressure of the molding material filled in the cavity and the pressure holding release operation for releasing the pressure holding of the molding material, and the pressure of the molding material in the cavity.
  • It has a pressure sensor to detect, and the abnormality predictor determines an evaluation value regarding a change in pressure of the molding material after the pressure holding release operation based on the time series data of the pressure detected by the pressure sensor.
  • a molding system having a data acquisition unit to be acquired and an abnormality prediction unit for acquiring prediction information for predicting the burr state of the molded product based on the evaluation value.
  • the inventors monotonically decreased the pressure in the cavity after the pressure release operation during normal times (when burrs did not occur), while the pressure increased or decreased momentarily when burrs occurred.
  • the degree of decrease becomes smaller (the pressure gradient becomes larger for a moment). That is, it was discovered that the state of burrs can be predicted by paying attention to the change in the pressure of the molding material after the pressure holding release operation. Therefore, the molding system according to the present disclosure acquires prediction information for predicting the state of burrs based on the change in the pressure of the molding material after the pressure holding release operation. With such a configuration, it is not necessary to prepare a reference curve for each of various molding conditions, and it is possible to predict the state of burrs from one pressure information to be predicted. This makes it possible to predict the state of burrs more accurately.
  • the present disclosure is the molding system described in (1) above, and the abnormality prediction unit uses a trained model in which the correlation between the evaluation value and the burr state of the molded product is machine-learned.
  • the prediction information is acquired, the explanatory variable of the trained model includes the evaluation value, and the objective variable of the trained model is the burr state of the molded product.
  • the burr state may provide a molding system including the presence or absence of the burr or the size of the burr.
  • the burr state can be predicted more accurately even if the molding conditions vary.
  • the present disclosure is the molding system according to (2) above, wherein the molding apparatus further includes a temperature sensor for detecting the temperature of the molding material, and the explanatory variables of the trained model are: Molding that includes a second evaluation value related to the temperature detected by the temperature sensor, and the abnormality prediction unit acquires the prediction information by inputting the evaluation value and the second evaluation value into the trained model.
  • the molding apparatus further includes a temperature sensor for detecting the temperature of the molding material
  • the explanatory variables of the trained model are: Molding that includes a second evaluation value related to the temperature detected by the temperature sensor, and the abnormality prediction unit acquires the prediction information by inputting the evaluation value and the second evaluation value into the trained model.
  • a system may be provided.
  • the change in the pressure of the molding material after the pressure holding release operation also depends on the temperature of the molding material.
  • the higher the temperature of the molding material the lower the viscosity of the molding material and the easier it is for the molding material to flow. Therefore, the higher the temperature of the molding material, the more likely it is that the pressure change at the time of burr generation will occur sharply within a short period of time.
  • the present disclosure is the molding system according to (1) above, and the abnormality prediction unit is more than a reference value generated based on the evaluation value acquired at the time of normal molding of the molded product.
  • the present disclosure is the molding system according to any one of (1) to (4) above, and the data acquisition unit is a pressure obtained based on the time series data after the pressure holding release operation.
  • the evaluation value is acquired based on the second time-series data of the inclination of, and the evaluation value is the maximum value of the inclination in the second time-series data or the inclination of 0 in the second time-series data.
  • the present disclosure is the molding system according to any one of (1) to (4) above, and the data acquisition unit maximizes the pressure at the minimum of the time series data after the pressure holding release operation.
  • a molding system may be provided that acquires, as the evaluation value, the difference from the pressure at the above, or the rate of change from the pressure at the minimum to the pressure at the maximum. With this configuration, the evaluation value can be easily obtained from the time series data.
  • the present disclosure is an abnormality prediction device for predicting an abnormality in a molded product molded by a molding device, wherein the molding device is a mold for tightening the plurality of molds in a state where a plurality of molds are combined.
  • the filling operation of filling the molded material in a molten state into the tightening portion and the cavity formed between the plurality of molds by tightening the plurality of molds, and the pressure of the molding material filled in the cavity. It has an injection unit that performs a holding pressure holding operation and a pressure holding release operation that releases the pressure holding of the molding material, and a pressure sensor that detects the pressure of the molding material in the cavity.
  • the abnormality prediction device includes a data acquisition unit that acquires an evaluation value regarding a change in pressure of the molding material after the pressure holding release operation based on the time-series data of the pressure detected by the pressure sensor, and the evaluation value. Based on this, the present invention provides an abnormality prediction device including an abnormality prediction unit for acquiring prediction information for predicting a burr state of the molded product.
  • the abnormality prediction device acquires prediction information for predicting the state of burrs based on the change in the pressure of the molding material after the pressure holding release operation. With such a configuration, it is not necessary to prepare a reference curve for each of various molding conditions, and it is possible to predict the state of burrs from one pressure information to be predicted. This makes it possible to predict the state of burrs more accurately.
  • a mold clamping step of tightening the plurality of molds and a state of being melted into a cavity formed between the plurality of molds by the mold clamping step Molding by a molding method including a filling step of filling the molding material, a pressure holding step of holding the pressure of the molding material filled in the cavity, and a pressure holding release step of releasing the pressure holding of the molding material.
  • It is an abnormality prediction method for predicting an abnormality of a molded product to be formed, and is after the pressure holding release step based on time-series data of pressure detected by a pressure sensor that detects the pressure of the molding material in the cavity. It includes a data acquisition step of acquiring an evaluation value regarding a change in pressure of the molding material, and an abnormality prediction step of acquiring prediction information for predicting a burr state of the molded product based on the evaluation value.
  • An abnormality prediction method is provided.
  • the abnormality prediction method acquires prediction information for predicting the state of burrs based on the change in the pressure of the molding material after the pressure holding release operation. With such a configuration, it is not necessary to prepare a reference curve for each of various molding conditions, and it is possible to predict the state of burrs from one pressure information to be predicted. This makes it possible to predict the state of burrs more accurately.
  • a mold clamping step of tightening the plurality of molds and a state of being melted into a cavity formed between the plurality of molds by the mold clamping step Molding by a molding method including a filling step of filling the molding material, a pressure holding step of holding the pressure of the molding material filled in the cavity, and a pressure holding release step of releasing the pressure holding of the molding material. It is a program for predicting an abnormality of a molded product to be formed, and is after the pressure holding release step based on time-series data of pressure detected by a pressure sensor that detects the pressure of the molding material in the cavity.
  • a computer device includes a data acquisition process for acquiring an evaluation value regarding a change in pressure of the molding material, and an abnormality prediction process for acquiring prediction information for predicting the burr state of the molded product based on the evaluation value. Provide a program to be executed by.
  • a mold clamping step of tightening the plurality of molds and a state of being melted into a cavity formed between the plurality of molds by the mold clamping step In a state where a plurality of molds are combined, a mold clamping step of tightening the plurality of molds and a state of being melted into a cavity formed between the plurality of molds by the mold clamping step.
  • Molding by a molding method including a filling step of filling the molding material, a pressure holding step of holding the pressure of the molding material filled in the cavity, and a pressure holding release step of releasing the pressure holding of the molding material.
  • It is a trained model for predicting the abnormality of the molded product to be made, and the explanatory variables are acquired based on the time-series data of the pressure detected by the pressure sensor that detects the pressure of the molding material in the cavity.
  • the objective function is the burr state of the molded product, and the burr state is the presence or absence of the burr or the burr.
  • the explanatory function is the evaluation value regarding the change in the pressure of the molding material after the pressure release operation and the objective function is the burr state of the molded product
  • the evaluation value is input to the trained model.
  • the predicted burr state is output.
  • the state of burrs can be predicted based on the change in the pressure of the molding material after the pressure holding release operation.
  • the state of burrs can be predicted more accurately.
  • FIG. 1 is a block diagram schematically showing a molding system according to the first embodiment.
  • FIG. 2 is an explanatory diagram conceptually showing the molding apparatus according to FIG. 1.
  • FIG. 3 is an explanatory diagram conceptually showing the molding apparatus according to FIG. 1.
  • FIG. 4 is a cross-sectional view of a mold portion schematically showing a state at the end of the filling process.
  • 5 (a) and 5 (b) are examples of graphs showing time-series data of pressure and pressure gradient.
  • FIG. 6 is a cross-sectional view of a mold portion schematically showing a state at the end of a filling process when burrs are generated in a molded product.
  • FIG. 1 is a block diagram schematically showing a molding system according to the first embodiment.
  • FIG. 2 is an explanatory diagram conceptually showing the molding apparatus according to FIG. 1.
  • FIG. 3 is an explanatory diagram conceptually showing the molding apparatus according to FIG. 1.
  • FIG. 4 is a cross-sectional view of
  • FIG. 7 is a cross-sectional view schematically showing a state of the pressure holding release step when burrs are generated in the molded product.
  • 8 (a) and 8 (b) are examples of graphs showing time-series data of pressure and pressure gradient when burrs are generated in the molded product.
  • FIG. 9 is a block diagram showing a functional configuration of the learning device according to the first embodiment.
  • 10 (a) and 10 (b) are explanatory views for explaining the evaluation values according to the first embodiment.
  • FIG. 11 is a block diagram showing a functional configuration of the abnormality prediction device according to the first embodiment.
  • FIG. 12 is a block diagram schematically showing the molding system according to the second embodiment.
  • FIG. 13 is a block diagram showing a functional configuration of the abnormality prediction device according to the second embodiment.
  • FIG. 14 is a graph schematically explaining the comparison between the evaluation value and the attention reference value according to the second embodiment.
  • FIG. 1 is a block diagram schematically showing a molding system 10 according to the first embodiment.
  • the molding system 10 includes a plurality of molding devices 20, a learning device 30, an abnormality prediction device 40, an input unit 50, and a display unit 60.
  • the molding device 20, the learning device 30, the abnormality prediction device 40, the input unit 50, and the display unit 60 are each provided so as to be able to communicate wirelessly or by wire.
  • the learning device 30 and the abnormality prediction device 40 are composed of an information processing device (computer device) having a calculation unit (for example, CPU, GPU, etc.) and a storage unit (for example, HDD, SSD, etc.).
  • the learning device 30 and the abnormality prediction device 40 may be configured by the same information processing device or may be configured by separate information processing devices.
  • a plurality of molding devices 20 are connected to one learning device 30 and one abnormality prediction device 40, and the learning device 30 and the abnormality prediction device 40 are used for various data transmitted from the plurality of molding devices 20. Based on this, learning and abnormality prediction are performed.
  • the molding device 20 may have a one-to-one correspondence with the learning device 30 and the abnormality prediction device 40. That is, in the molding system 10, the molding device 20 may be one, or a plurality of learning devices 30 and abnormality prediction devices 40 may be provided.
  • the input unit 50 is, for example, a keyboard or a mouse, and receives various inputs from the operator.
  • the display unit 60 is, for example, a display or a speaker, and displays various information in the molding system 10.
  • the input unit 50 and the display unit 60 may be integrated, for example, a touch panel. Further, the input unit 50 and the display unit 60 may be provided as a portable terminal device so as to be movable to a place away from the molding device 20, the learning device 30, and the abnormality prediction device 40.
  • FIGS. 2 and 3 are explanatory views conceptually showing the molding apparatus 20.
  • the molding apparatus 20 includes a bed 21, an injection portion 22, a mold clamping portion 23, a mold portion 24, a pressure sensor 25, a temperature sensor 26, and a control panel 27.
  • FIG. 2 shows a molding apparatus 20 in a state where the mold portion 24 is open
  • FIG. 3 shows a molding apparatus 20 in a state where the mold portion 24 is combined.
  • the molding apparatus 20 is an apparatus for performing mold-fastening injection molding.
  • the control panel 27 has a control unit 271 and a communication unit 272.
  • the control unit 271 is electrically connected to each drive unit (motor 237, etc.) of the molding apparatus 20 and outputs an operation command to each drive unit. Further, the control unit 271 is electrically connected to each sensor (pressure sensor 25 or the like) of the molding apparatus 20, and the signal detected by each sensor is input to the control unit 271.
  • the control unit 271 is composed of an information processing device having a calculation unit (for example, CPU, GPU, etc.) and a storage unit (for example, HDD, SSD, etc.).
  • the communication unit 272 communicates with each other unit (learning device 30, etc.) of the molding system 10.
  • the communication unit 272 transmits, for example, the signal detected by each of the sensors to the learning device 30 or the abnormality prediction device 40. Further, the communication unit 272 receives the determination information and the prediction information described later from the abnormality prediction device 40.
  • the mold clamping portion 23 includes a fixed plate 231, a movable plate 232, a tie bar 233, a ball screw 234, a support plate 235, a mold clamping force sensor 236, and a motor 237.
  • the fixing plate 231 and the supporting plate 235 are fixed to the bed 21.
  • the support board 235 supports the ball screw 234.
  • the ball screw 234 is connected to the motor 237. When the motor 237 is rotated by the operation command of the control unit 271, the ball screw 234 moves.
  • a movable platen 232 is fixed to the end of the ball screw 234 opposite to the end connected to the motor 237.
  • the direction in which the ball screw 234 moves is referred to as an "axial direction".
  • the side where the motor 237 is located with respect to the ball screw 234 is referred to as the "one side” in the axial direction
  • the side where the movable platen 232 is located with respect to the ball screw 234 is referred to as the “other side” in the axial direction.
  • the movable board 232 moves in the axial direction as the ball screw 234 moves.
  • the movable platen 232 is formed with a through hole 232a penetrating in the axial direction.
  • the end of the tie bar 233 on one side in the axial direction is fixed to the support plate 235, and the end on the other side in the axial direction is fixed to the fixing plate 231.
  • the tie bar 233 is inserted into the through hole 232a of the movable platen 232. As a result, the tie bar 233 guides the axial movement of the movable platen 232.
  • the mold clamping force sensor 236 detects the pressure (reaction force of the mold clamping force) applied to the support plate 235 from the ball screw 234.
  • the mold clamping force sensor 236 outputs a detection signal regarding pressure to the control unit 271.
  • the mold clamping force sensor 236 may be installed at another position as long as it can detect the mold clamping force in the mold portion 24 described later.
  • the fixing plate 231 is formed with a through hole 231a having a diameter widened on the other side in the axial direction. A cylinder 222, which will be described later, is inserted into the through hole 231a.
  • the mold unit 24 has a plurality of molds 241 and 242.
  • the mold 241 is fixed to the movable platen 232.
  • the mold 241 moves in the axial direction together with the movable platen 232. That is, the mold 241 is a movable mold.
  • the mold 242 is fixed to the fixing plate 231. That is, the mold 242 is a fixed mold.
  • a flow path 243 is formed in the mold 242.
  • the injection unit 22 includes a hopper 221, a cylinder 222, a screw 223, a ball screw 225, a motor 226, a pressurization sensor 227, and a heater 228.
  • the hopper 221 is connected to the cylinder 222 and supplies the molding material into the cylinder 222.
  • the cylinder 222 is a member having a hollow cylindrical shape extending in the axial direction. The diameter of one end of the cylinder 222 on one side in the axial direction becomes narrower as it approaches the end on one side in the radial direction, and a nozzle 224 is provided at the end.
  • the nozzle 224 is connected to the flow path 243 of the mold 242.
  • the screw 223 is inserted into the cylinder 222 from the end on the other side in the axial direction of the cylinder 222.
  • a ball screw 225 is connected to the other side of the screw 223 in the axial direction, and a motor 226 is connected to the other side of the ball screw 225 in the axial direction.
  • the motor 226 is rotated by the operation command of the control unit 271
  • the ball screw 225 moves in the axial direction.
  • the screw 223 also moves in the axial direction.
  • the screw 223 rotates in the circumferential direction with the axial direction as the central axis.
  • the pressurization sensor 227 detects the pressure applied from the ball screw 225 to the motor 226 (the reaction force of the pushing force of the screw 223).
  • the pressurization sensor 227 outputs a detection signal related to pressure to the control unit 271.
  • the pressurization sensor 227 may be installed at another position as long as it can detect the pushing force of the screw 223.
  • the heater 228 is, for example, a resistance heating heater in which a resistance wire is wound in a coil shape.
  • the heater 228 heats the inside of the cylinder 222 by the resistance heat when a current is passed through the resistance wire by the operation command of the control unit 271.
  • the pressure sensor 25 is installed in the region of the molds 241 and 242 facing the cavity C1.
  • the pressure sensor 25 detects the pressure in the cavity C1.
  • the pressure sensor 25 detects the pressure of the molding material (melted state or solidified state, or a state in which the molten state and the solidified state are mixed) supplied into the cavity C1.
  • the pressure sensor 25 outputs a detection signal related to pressure to the control unit 271.
  • a plurality of pressure sensors 25 are installed on both the molds 241 and 242, respectively. However, only one pressure sensor 25 may be installed in one of the molds 241 and 242.
  • the temperature sensor 26 is built in the mold 241 and detects the temperature of the mold 241.
  • the temperature sensor 26 outputs a detection signal related to temperature to the control unit 271.
  • the temperature sensor 26 may be installed in a region of the mold 241 facing the cavity C1 or may be installed in the mold 242. Further, the temperature sensor 26 may be installed in the cylinder 222. That is, the temperature sensor 26 may be able to directly or indirectly detect the temperature of the molding material supplied into the cavity C1.
  • a method for manufacturing a molded product by the molding apparatus 20 will be described with reference to FIGS. 2 to 5 (b) as appropriate.
  • the pre-process ST1, the mold clamping process ST2, the filling process ST3, the pressure holding process ST4, the pressure holding release process ST5, and the mold release process ST6 are performed in this order. Will be executed.
  • the molded product is a resin cage used for rolling bearings.
  • this is an example of a molded product, and the molded product molded by the molding apparatus according to the present disclosure may be a molded product having another shape and use.
  • the pre-process ST1 is executed.
  • the screw 223 is rotated by the motor 226, and the pellets of the molding material are supplied from the hopper 221 into the cylinder 222 while the inside of the cylinder 222 is heated by the heater 228.
  • the pellets of the molding material are melted in the cylinder 222 by the frictional heat accompanying the rotation of the screw 223 and the heating by the heater 228 to become the molding material L1 in the molten state.
  • the previous step ST1 is completed.
  • the ball screw 234 is moved to the other side in the axial direction by the operation command of the control unit 271, and the mold is as shown in FIG.
  • the 241 is brought into contact with the mold 242.
  • the ball screw 234 further presses the mold 241 against the mold 242 toward the other side in the axial direction by a predetermined mold tightening force. That is, the plurality of molds 241 and 242 are tightened.
  • the cavity C1 is formed between the plurality of molds 241 and 242.
  • the mold clamping force is one of the molding conditions, and is determined according to other molding conditions such as the shape of the molds 241 and 242.
  • the mold clamping force is detected by the mold clamping force sensor 236.
  • the ball screw 225 moves to one side in the axial direction while maintaining the above-mentioned mold clamping force.
  • the screw 223 pushes the molding material L1 to one side in the axial direction, and the molding material L1 is ejected from the nozzle 224 of the cylinder 222 to the cavity C1 via the flow path 243 of the mold 242 (filling operation).
  • FIG. 4 is a cross-sectional view of the mold portion 24 schematically showing the state at the end of the filling step ST3.
  • the flow path 243 has a first opening 243a that opens on the nozzle 224 side and a second opening 243b that opens on the cavity C1 side.
  • the cavity C1 is formed in an annular shape.
  • the molding material L1 more specifically, the molding material in a molten state
  • the filling step is performed.
  • ST3 ends.
  • the molten molding material L1 is supplied into the cavity C1 while gradually solidifying from the vicinity of the surface of the molds 241 and 242.
  • the screw 223 further pushes the molding material L1 to one side in the axial direction, and the molding material L1 is further injected from the nozzle 224 of the cylinder 222 into the cavity C1.
  • a predetermined pressure Pt1 for example, several tens to several hundreds of MPa
  • the screw 223 continues to apply a predetermined pressure to the molding material L1 for a predetermined time (for example, several seconds) (pressure holding operation).
  • the pressure (pressurization) at which the screw 223 pushes the molding material L1 into the cavity C1 is detected by the pressurization sensor 227.
  • the screw 223 moves to the other side in the axial direction to release the pressure holding of the molding material L1 (pressure holding release operation).
  • the holding pressure releasing step ST5 ends.
  • the mold release step ST6 the mold portion 24 is cooled, so that the molding material L1 in the cavity C1 is completely solidified, and a molded product is formed.
  • the ball screw 234 moves to one side in the axial direction, and the mold 241 separates from the mold 242, so that the molded product is taken out.
  • the cooling of the mold portion 24 may be started at the same time as the pressure holding release step ST5.
  • 5 (a) and 5 (b) show the time-series data of the pressure detected by the pressurization sensor 227 and the pressure sensor 25 in the filling step ST3, the pressure holding step ST4, and the pressure holding release step ST5, and the pressure.
  • This is an example of a graph showing time-series data of inclination.
  • 5 (a) and 5 (b) show graphs obtained when the molded product is molded without the occurrence of burrs.
  • the vertical axis is the pressure P and the horizontal axis is the time t.
  • the graph line F1 shown by the broken line is the time series data of the pressure detected by the pressurization sensor 227, and represents the pressure at which the screw 223 pushes the molding material L1 into the cavity C1.
  • the graph line F2 shown by the solid line is the time series data of the pressure detected by the pressure sensor 25, and represents the pressure of the molding material L1 (the state including at least one of the molten state and the solidified state) in the cavity C1. ..
  • the pressurization rises from 0 to the pressure Pt1 in the filling step ST3, is held at the pressure Pt1 for a predetermined time in the pressure holding step ST4, and is held in the pressure Pt1 in the holding pressure releasing step ST5.
  • the pressure drops from Pt1.
  • the holding pressure release step ST5 is started from the time point X1. That is, the holding pressure release operation is executed at the time point X1.
  • the pressure of the molding material L1 in the cavity C1 decreases monotonically after the time point X1.
  • FIG. 5B shows the graph line F2a of the slope of the pressure obtained by time-differentiating the graph line F2 after the time point X1. That is, the graph line F2a is a graph showing the change in the pressure of the molding material L1 in the cavity C1. After the time point X1, the graph line F2 decreases monotonically, so that the graph line F2a becomes a value smaller than 0.
  • FIG. 6 is a cross-sectional view of the mold portion 24 schematically showing the state at the end of the filling step ST3 when burrs are generated in the molded product.
  • the foreign matter Fm1 is sandwiched between the molds 241 and the molds 242, as shown in an enlarged manner at the bottom. Therefore, an unintended gap C2 is formed between the mold 241 and the mold 242 in addition to the cavity C1. Further, the volume of the cavity C1 is also increased by the width W1 at which the gap C2 is formed as compared with the example of FIG. Then, in the filling step ST3, the molding material L1 is filled in the cavity C1 and the gap C2.
  • FIG. 7 is a cross-sectional view schematically showing the state of the pressure holding release step ST5 when burrs are generated in the molded product.
  • FIG. 7 shows the same area as the enlarged view of FIG.
  • the pressure holding step ST4 when the predetermined pressure Pt1 is held by the molding material L1, the molding material L1 is exposed to the surfaces (for example, the mold 241) exposed to the cavities C1 and the gaps C2 of the molds 241 and 242.
  • the surface 241a is pressed.
  • the force with which the molding material L1 pushes the exposed surface 241a is larger by the amount of the gap C2 than when burrs are not generated.
  • the pressure holding release step ST5 When the holding of the pressure Pt1 of the molding material L1 is released by the pressure holding release step ST5, the force with which the molding material L1 pushes the exposed surface 241a or the like weakens, and the mold clamping force of the mold clamping portion 23 becomes larger than the pressing force. By becoming stronger, the exposed surface 241a moves in the direction indicated by the arrow AR1 in FIG. In FIG. 7, the position of the exposed surface 241a during the pressure holding step ST4 is indicated by a two-dot chain line, and the position of the exposed surface 241a after the pressure holding release step ST5 is indicated by a solid line. That is, the pressure holding release operation slightly tightens the mold 241 and reduces the volume of the cavity C1.
  • the volume of the gap C2 is also reduced.
  • the density of the molding material L1 in the region C1a near the exposed surface 241a becomes higher than that before the pressure holding release operation.
  • the pressure of the molding material L1 detected by the pressure sensor 25 increases as the density of the molding material L1 in the region C1a increases.
  • the graph line F3 shown by the solid line is the time series data of the pressure detected by the pressure sensor 25, and represents the pressure of the molding material L1 in the cavity C1.
  • the pressure of the molding material L1 detected by the pressure sensor 25 rises only momentarily after the pressure holding release operation (after the time point X1). That is, when burrs occur, the graph line F3 after the holding pressure release operation shows a tendency of non-monotonic decrease.
  • FIG. 8B shows the graph line F3a of the slope of the pressure obtained by time-differentiating the graph line F3 after the time point X1. That is, the graph line F3a is a graph showing the change in the pressure of the molding material L1 in the cavity C1. Since the graph line F3 increases for a moment and then decreases monotonically after the time point X1, the graph line F3a has a region having a value larger than 0.
  • the degree of decrease in the pressure of the molding material L1 in the cavity C1 after the pressure holding release operation is the degree of increase in the density of the molding material L1 in the cavity C1. If it is larger than, the pressure increase shown by the arrow AR2 in FIG. 8A may not occur.
  • the time-series data of the pressure when burrs occur after the pressure holding release operation includes an increase in the density of the molding material L1 in the region C1a, and FIG. Compared with the time-series data of the pressure shown in, there is a part where the pressure decrease becomes slow for a moment. That is, after the pressure holding release operation, the slope of the pressure when burrs are generated becomes a larger value than the slope of the pressure when burrs are not generated.
  • the inventors have found that the pressure of the molding material L1 in the cavity C1 after the pressure holding release operation decreases monotonically under normal conditions (when burrs do not occur), while it becomes monotonous. It was discovered that when burrs occur, the pressure of the molding material L1 increases, does not decrease, or decreases for a moment (the pressure gradient increases for a moment). That is, it was discovered that the state of burrs can be predicted by paying attention to the change in the pressure of the molding material L1 after the pressure holding release operation.
  • the learned model Tm1 is trained in the learning device 30 to learn the correlation between the pressure change of the molding material L1 in the cavity C1 after the pressure holding release operation and the burr state.
  • the generation and the abnormality prediction device 40 acquire the prediction information for predicting the state of burrs based on the trained model Tm1 and the change in the pressure of the molding material L1 after the pressure holding release operation.
  • the learning device 30 and the abnormality prediction device 40 will be described.
  • FIG. 9 is a block diagram showing a functional configuration of the learning device 30 according to the present embodiment.
  • the learning device 30 includes a training data acquisition unit 31, a learning calculation unit 32, a molding information storage unit 33, and a learned model storage unit 34. Each of these units is realized by a computer device having a calculation unit such as a CPU and a storage unit such as an HDD.
  • the molding information is, for example, table-type information in which various types of first information and second information are associated with each other.
  • first information is the type of the mold
  • second information includes various dimensions of the mold and the volume of the cavity C1.
  • the first information is the type or lot number of the molding material
  • the second information includes the physical properties (viscosity, moisture content, etc.) of the molding material.
  • the training data acquisition unit 31 acquires information on the training data from each unit of the molding system 10.
  • the training data includes an evaluation value described later, a second evaluation value, a third evaluation value, molding information, and burr information.
  • the training data also includes the pressures (molding force and pressurization) detected by the mold clamping force sensor 236 and the pressurization sensor 227, respectively.
  • the training data acquisition unit 31 acquires an evaluation value regarding a change in pressure after the pressurization release operation based on the pressure detected by the pressure sensor 25 and the pressurization sensor 227, respectively. In addition, the training data acquisition unit 31 acquires a second evaluation value regarding the temperature detected by the temperature sensor 26. In addition, the training data acquisition unit 31 acquires a third evaluation value regarding the surrounding and internal environment of the molding apparatus 20 detected by another sensor (for example, a humidity sensor) (not shown).
  • another sensor for example, a humidity sensor
  • FIG. 10A is a graph showing time-series data of the slope of pressure.
  • the vertical axis of FIG. 10A is the slope of pressure (dP / dt), and the horizontal axis is time t.
  • a plurality of pressure time series data are acquired.
  • the time-series data of the plurality of pressures acquired by the plurality of pressure sensors 25 are acquired, and a plurality of evaluation values are acquired respectively.
  • the time series data of the pressure obtained by one pressure sensor 25 among the plurality of pressure sensors 25 will be focused on.
  • the time series data of the inclination of the average pressure and one evaluation value are acquired. May be good.
  • FIG. 10A shows an example of time-series data of the slope of the pressure acquired when three molded products are molded.
  • the graph line F31 solid line
  • the graph line F32 two-dot chain line
  • the graph line F33 broken line
  • It is a graph which enlarges and shows the region where the slope becomes 0 or more in the slope of pressure (after X1).
  • the pressure gradient (dP / dt) at the time point t is, for example, the amount of change in pressure dP from the time point t to the time point (t + dt) in the time series data of pressure divided by the amount of change in time dt. Demanded by.
  • the evaluation value is, for example, the maximum value of the pressure gradient.
  • the evaluation value is dP1.
  • the evaluation value becomes dP2 or dP3.
  • the evaluation value is acquired by the training data acquisition unit 31 by calculating based on the pressure time series data.
  • the evaluation value is not limited to the slope of pressure (dP / dt), and may be the amount of change in pressure dP. Further, as shown in FIG. 10A, the evaluation value may be the full width at half maximum W1, W2, W3 of the graph lines F31, F32, and F33, respectively.
  • the full width at half maximum W1 is the width of the graph line F31 at the time when the slope of the pressure becomes half of the maximum value dP1 (dP1 / 2). That is, the evaluation value may be the width of the region where the slope of the pressure is 0 or more.
  • the evaluation value may be a pressure difference between two predetermined points in the time series data of pressure, or a rate of change in pressure between the predetermined two points. May be good.
  • FIG. 10B is a graph showing an enlarged area indicated by the arrow AR2 in the graph line F3 of FIG. 8A. As shown in FIG. 10B, when burrs occur in the molded product, the graph line F3 after the pressure holding release operation (time point X1) has a minimum (time point Xa, pressure P1) and a maximum (time point Xb, pressure). P2) appears.
  • the evaluation value may be the difference (P2-P1) between the pressure P1 at the minimum and the pressure P2 at the maximum.
  • the evaluation value is the rate of change in pressure ((P2-P1) / (Xb-Xa)) obtained by dividing the difference (P2-P1) by the time difference (Xb-Xa) between the minimum and the maximum. You may. That is, the evaluation value may be a value indicating a change in pressure after the pressure holding release operation.
  • the training data acquisition unit 31 acquires information input by the operator to the input unit 50.
  • the information input by the operator is, for example, burr information regarding the burr state of the molded product or first information stored in the molding information storage unit 33.
  • the training data acquisition unit 31 acquires the second information corresponding to the first information from the molding information storage unit 33 based on the first information input to the input unit 50. For example, when a lot number of a molding material is input to the input unit 50, the training data acquisition unit 31 acquires information on the physical properties of the molding material corresponding to the lot number from the molding information storage unit 33.
  • the burr information is, for example, information (for example, a dummy variable) that quantifies the presence or absence of burrs and the size of burrs (for example, the length and width of burrs).
  • the size of the burr the length of the burr itself may be quantified, or the degree of the size of the burr may be quantified.
  • the operator acquires burr information by actually inspecting the appearance of the molded product molded by the molding apparatus 20, and inputs the burr information to the input unit 50.
  • the learning calculation unit 32 generates a learned model that models the correlation between the evaluation value and the burr state of the molded product by performing a supervised machine learning calculation based on the training data.
  • a convolutional neural network (CCN) is used as the machine learning model, but other models may be used.
  • CCN convolutional neural network
  • it may be a regression tree model that is a model for grouping data.
  • the evaluation information includes the evaluation value (value related to the change in pressure after the holding pressure release operation), the second evaluation value (value related to temperature), the third evaluation value (value related to humidity, etc.), and molding information (value related to humidity, etc.). Includes molding material viscosity, etc.), mold clamping force and pressurization.
  • the learned model Tm1 generated by the learning calculation unit 32 is stored in the learned model storage unit 34.
  • the trained model Tm1 stored in the trained model storage unit 34 responds to the content of the training data. Will be updated as appropriate.
  • the learned model Tm1 is transmitted from the learning device 30 to the abnormality prediction device 40 described later, and is also stored in the learned model storage unit 45 of the abnormality prediction device 40.
  • the trained model Tm1 of the present embodiment may be stored and provided in an arbitrary storage medium such as a non-temporary computer-readable medium.
  • the method of generating the trained model Tm1 includes a training data acquisition step and a learning calculation step.
  • the training data acquisition unit 31 acquires the training data. For example, based on the time series data of the pressure detected by the pressure sensor 25 that detects the pressure of the molding material L1 in the cavity C1, the evaluation value regarding the change in the pressure of the molding material L1 after the pressure holding release step is acquired. In addition, the operator actually inspects the molded product for which the evaluation value has been acquired to acquire burr information.
  • the learning calculation unit 32 generates the trained model Tm1 based on the training data.
  • FIG. 11 is a block diagram showing a functional configuration of the abnormality prediction device 40 according to the present embodiment.
  • the abnormality prediction device 40 includes a data acquisition unit 41, an abnormality prediction unit 42, an output unit 43, a molding information storage unit 44, and a learned model storage unit 45.
  • Each of these units is realized by a computer device having a calculation unit such as a CPU and a storage unit such as an HDD.
  • the calculation unit executes the data acquisition process and the abnormality prediction process, which will be described later, based on the program stored in the storage unit.
  • the program of the present embodiment may be stored and provided on an arbitrary storage medium such as a non-transitory computer-readable medium.
  • the molding information storage unit 44 stores table-type molding information in which various first information and the second information are associated with each other.
  • the learned model Tm1 generated by the learning device 30 is stored in the learned model storage unit 45.
  • the molding information storage unit 44 and the learned model storage unit 45 may be realized by the same storage area as the molding information storage unit 33 and the learned model storage unit 34 of the learning device 30 among the computer devices, or may be different storage. It may be realized by the area. That is, the learning device 30 and the abnormality prediction device 40 may be configured to share the same molding information storage unit 33 and the learned model storage unit 34, or the learning device 30 and the abnormality prediction device 40 may be independently molded. It may be configured to have information storage units 33, 44 and trained model storage units 34, 45.
  • the data acquisition unit 41 executes a data acquisition process for acquiring evaluation information for performing abnormality prediction from each unit of the molding system 10.
  • the evaluation information is the same as the evaluation information acquired by the learning device 30 described above. That is, the evaluation information includes the evaluation value (value related to the change in pressure after the pressurization release operation), the second evaluation value (value related to temperature), the third evaluation value (value related to humidity, etc.), and the molding information (viscosity of the molding material). Etc.), including mold clamping force and pressurization.
  • the data acquisition unit 41 acquires an evaluation value regarding a change in pressure after the pressure holding release operation based on the pressure detected by the pressure sensor 25 and the pressurization sensor 227, respectively.
  • the evaluation value is the same value as the evaluation value acquired by the training data acquisition unit 31 of the learning device 30 described above. That is, the evaluation value is a value related to the change in pressure of the molding material L1 after the pressure holding release operation.
  • the abnormality prediction unit 42 executes an abnormality prediction process for acquiring prediction information for predicting the burr state of the molded product by inputting the evaluation information acquired by the data acquisition unit 41 into the trained model Tm1. .. In the abnormality prediction process, the information corresponding to the objective variable used when the trained model Tm1 is generated is output as the prediction information.
  • the prediction information is information including the probability of being predicted about the size of the burr of the molded product. More specifically, the prediction information includes, for example, the first probability that the burr length of the molded product is 1 mm or less, the second probability that it is longer than 1 mm and 5 mm or less, and the third probability that it is longer than 5 mm. ..
  • the trained model Tm1 outputs prediction information having a first probability of 10%, a second probability of 10%, and a third probability of 80%.
  • the prediction information may be information on the probability of being classified according to the presence or absence of burrs. That is, when the objective variable is the presence or absence of burrs on the molded product, the prediction information is the fourth probability without burrs and the fifth probability with burrs.
  • the output unit 43 determines the quality of the molded product based on the prediction information acquired by the abnormality prediction unit 42. For example, the output unit 43 determines the quality based on a predetermined threshold value and the above prediction information.
  • the predetermined threshold is, for example, the maximum length of burrs allowed. For example, when the maximum allowable length of burrs is 5 mm, it is determined that the molded product is defective (with burrs) when the third probability is the highest among the first to third probabilities described above. do.
  • the output unit 43 outputs the determination information regarding the quality of the molded product and the prediction information to the display unit 60 and the control unit 271. Judgment information and prediction information are displayed on the display unit 60. In particular, when it is determined that the molded product is defective, the determination information may be displayed in a highlighted color such as red on the display of the display unit 60, and an alert may be issued on the speaker.
  • the operation command of the control unit 271 causes the molding apparatus 20 that has molded the molded product determined to be defective to be stopped in a state where the mold portion 24 is open. It may be configured. In this case, the operator inspects the mold unit 24 based on the alert or the like by the display unit 60, and removes the foreign matter Fm1 (FIG. 6) as necessary.
  • the prediction information obtained by the abnormality prediction unit 42 may be displayed as it is on the display unit 60 without providing the output unit 43.
  • the operator may determine the quality of the molded product and the state of the molding apparatus 20 based on the prediction information displayed on the display unit 60.
  • the abnormality prediction method includes a data acquisition step and an abnormality prediction step.
  • the data acquisition unit 41 acquires the evaluation information. For example, based on the time series data of the pressure detected by the pressure sensor 25 that detects the pressure of the molding material L1 in the cavity C1, the evaluation value regarding the change in the pressure of the molding material L1 after the pressure holding release step is acquired.
  • the abnormality prediction unit 42 acquires prediction information for predicting the burr state of the molded product based on the evaluation information including the evaluation value.
  • the molding system 10 acquires prediction information for predicting the state of burrs based on the change in the pressure of the molding material L1 after the pressure holding release operation. With such a configuration, it is not necessary to prepare a reference curve for each of various molding conditions, and it is possible to predict the state of burrs from one pressure information to be predicted. This makes it possible to predict the state of burrs more accurately.
  • the molding system 10 acquires prediction information by inputting the evaluation value into the trained model Tm1 in which the correlation between the evaluation value and the burr state of the molded product is machine-learned.
  • the explanatory variable of the trained model Tm1 includes an evaluation value
  • the objective variable of the trained model Tm1 is the state of burrs (for example, the presence or absence of burrs or the size of burrs) of the molded product. With this configuration, the burr state can be predicted more accurately even when the molding conditions vary.
  • the change in the pressure of the molding material L1 after the pressure holding release operation also depends on the temperature of the molding material L1.
  • the molding system 10 according to the present embodiment can be a trained model in which the above correlation is incorporated, and more accurately burrs. The state of can be predicted.
  • FIG. 12 is a block diagram schematically showing the molding system 11 according to the second embodiment.
  • the molding system 11 includes a plurality of molding devices 20, an abnormality prediction device 40a, an input unit 50, and a display unit 60.
  • the abnormality prediction device 40a of the molding system 11 acquires prediction information for predicting the burr state of the molded product based on the evaluation value and a predetermined reference value. That is, the molding system 11 is different from the molding system 10 according to the first embodiment in that the state of burrs is predicted by comparing the evaluation value and the reference value without using the trained model Tm1.
  • FIG. 13 is a block diagram showing a functional configuration of the abnormality prediction device 40a according to the present embodiment.
  • the abnormality prediction device 40a includes a data acquisition unit 41, an abnormality prediction unit 42a, an output unit 43a, a molding information storage unit 44, and a reference value storage unit 46. Each of these units is realized by a computer device having a calculation unit such as a CPU and a storage unit such as an HDD.
  • the data acquisition unit 41 acquires evaluation information in the same manner as the data acquisition unit 41 according to the first embodiment.
  • a plurality of reference values are stored in the reference value storage unit 46.
  • the reference value is a value generated based on the evaluation value obtained when the molded product is normally molded.
  • the reference value is, for example, a value obtained by adding a predetermined margin to the average value or the median value of a plurality of evaluation values obtained during normal molding of a plurality of molded products.
  • the predetermined margin is determined by the maximum length of burrs allowed in the molded product and the like.
  • a plurality of values are prepared for each of the second evaluation values, for each of the third evaluation values, or for each molding information.
  • the first reference value when the second evaluation value (value related to temperature) is less than the first value, the first reference value is used, when the second evaluation value is equal to or more than the first value and less than the second value, the second reference value and the second evaluation value are the second.
  • the third reference value may be used.
  • the reference value storage unit 46 stores, for example, table-type information in which the second evaluation value, the third evaluation value, and the molding information are associated with a plurality of reference values.
  • the abnormality prediction unit 42a has a reference value (corresponding from a plurality of reference values stored in the reference value storage unit 46, based on the second evaluation value, the third evaluation value, and the molding information acquired by the data acquisition unit 41. Hereinafter, it is referred to as “attention reference value”). Then, the abnormality prediction unit 42a compares the evaluation value acquired by the data acquisition unit 41 (that is, the evaluation value acquired at the time of molding the molded product to be predicted) with the attention reference value to predict the prediction information. To get.
  • the prediction information is, for example, the difference or ratio between the evaluation value and the attention reference value.
  • the evaluation value for example, the slope of pressure
  • the evaluation value tends to be larger than the reference value of interest. Therefore, when the evaluation value is larger than the attention reference value, it is predicted that burrs are generated in the molded product to be predicted. Therefore, the prediction information indicates whether or not burrs are generated in the molded product to be predicted.
  • FIG. 14 is a graph schematically explaining the comparison between the evaluation value and the attention reference value in the abnormality prediction unit 42a.
  • the vertical axis is the slope of pressure (dP / dt), and the horizontal axis is time t.
  • FIG. 14 also shows the attention reference value Th1.
  • the graph lines F34 and F35 are time-series data of the pressure gradient acquired by the data acquisition unit 41 based on the time-series data of the pressure obtained by the pressure sensor 25 when the molded product to be predicted is molded, respectively. be.
  • the evaluation values are dP4 and dP5 in the example of FIG.
  • the evaluation value dP4 is larger than the attention reference value Th1
  • the evaluation value dP5 is smaller than the attention reference value Th1
  • no burrs are generated in the molded product from which the graph line F35 is acquired (or even if burrs are generated, they are within the permissible range. Yes) is expected.
  • the output unit 43a determines whether or not the burr state of the molded product is good or bad based on the prediction information acquired by the abnormality prediction unit 42a. For example, when the prediction information is the difference between the evaluation value and the attention reference value (evaluation value-attention reference value), the output unit 43a has a poor burr state of the molded product when the prediction information is a positive value. Judged as (abnormal).
  • the output unit 43a outputs the determination information regarding the quality of the burr of the molded product and the prediction information to the display unit 60 and the control unit 271.
  • the display unit 60 and the control unit 271 perform the same operations as in the first embodiment based on the determination information and the prediction information.
  • the prediction information obtained by the abnormality prediction unit 42a may be displayed as it is on the display unit 60 without providing the output unit 43a.
  • the operator may determine the quality of the molded product and the state of the molding apparatus 20 based on the prediction information displayed on the display unit 60.
  • the state of burrs can be easily predicted by comparing the reference value and the evaluation value.
  • Molding system 20 Molding equipment 21 Bed 22 Injection part 221 Hopper 222 Cylinder 223 Screw 224 Nozzle 225 Ball screw 226 Motor 227 Pressure sensor 228 Heater 23 Type tightening part 231 Fixed plate 232 Movable plate 233 Tie bar 234 Ball screw 235 Support plate 236 Mold tightening force sensor 237 Motor 24 Mold part 241 Mold 241a Exposed surface 242 Mold 243 Flow path 25 Pressure sensor 26 Temperature sensor 27 Control panel 271 Control unit 272 Communication unit 30 Learning device 31 Training data acquisition unit 32 Learning calculation unit 33 Molding information storage unit 34 Learned model storage unit 40, 40a Abnormality prediction device 41 Data acquisition unit 42, 42a Abnormality prediction unit 43, 43a Output unit 44 Molding information storage unit 45 Learned model storage unit 46 Reference value storage unit 50 Input Part 60 Display part C1 Cavity C1a Area C2 Gap L1 Molding material Fm1 Foreign matter

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Abstract

L'invention concerne un système de moulage (10) qui comprend un dispositif de moulage (20) et un dispositif de prédiction d'irrégularité (40). Le dispositif de prédiction d'irrégularité (40) est conçu pour acquérir une valeur d'évaluation concernant une variation de pression d'une matière de moulage après une opération de relâchement de maintien de pression du dispositif de moulage (20) sur la base de données de série chronologique de la pression de la matière de moulage dans une cavité d'une matrice du dispositif de moulage (20) et, sur la base de la valeur d'évaluation, acquérir des informations de prédiction pour prédire la présence de bavures sur un produit moulé.
PCT/JP2021/014318 2020-04-07 2021-04-02 Système de moulage, dispositif de prédiction d'irrégularité, procédé de prédiction d'irrégularité, programme et modèle entraîné Ceased WO2021206017A1 (fr)

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