JP2021172015A - Molding system, abnormality predicting device, abnormality predicting method, program and learned model - Google Patents

Abstract

To predict burning of a molded product.SOLUTION: A molding system comprises: a molding device; and an abnormality predicting device for predicting abnormality of a molded product molded by the molding device. The molding device comprises: a plurality of molds which is assembled for forming a cavity, and a gas vent being a discharge passage of a gas in the cavity; an injection part for filling a molten molding material in the cavity; and a first sensor for detecting a pressure or a temperature of the molding material in the cavity. The abnormality predicting device has: a data acquisition part which acquires a prescribed first time point as a start point of filling of the molding material, and a second time point when rising of the pressure or temperature is detected by the first sensor, as an end point of filling of the molding material, for acquiring filling time which is required since filling of the molding material in the cavity is started, until the filling is completed; and an abnormality predicting part for acquiring prediction information for predicting burning of the molded product, on the basis of the filling time.SELECTED DRAWING: Figure 1

Description

The present invention relates to molding systems, anomaly predictors, anomaly prediction methods, programs and trained models.

An injection molding apparatus is known 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 "burning (resin burning)". When burning occurs, it not only spoils the appearance of the molded product (for example, whitening or carbonizing), but may also affect the performance of the molded product. For this reason, we are currently visually inspecting molded products.

The cause of burning of the molded product is the blockage of the gas vent. The gas vent is a discharge path for discharging the air in the cavity to the outside of the mold when the cavity is filled with the molded material in a molten state. When the gas vent is blocked by a foreign substance or the like and the cavity is filled with the molding material in a state where the air in the cavity cannot be discharged, the air is compressed by the molding material in the cavity. Then, the temperature of the air rises due to the compression (adiabatic compression), and the portion of the molding material that is in contact with the air is heated, so that the molded product is burnt.

In Patent Document 1, as a countermeasure against molding defects (gas residual defects) caused by gas remaining in the cavity, molding is performed so that the forward position of the screw is retracted by a predetermined amount when a gas residual defect is confirmed by a quality check. Change the condition settings. As described above, Patent Document 1 discloses a technique for changing the molding conditions so that the gas discharge capacity is superior to the filling amount of the resin in the cavity.

JP-A-2015-24568

In the technique of Patent Document 1, the molding conditions are changed based on the quality check of the molded product. However, since the quality check is performed by a visual inspection of the worker or the like, the accuracy of the quality check largely depends on the skill level of the worker. In addition, since man-hours are required for workers, the burden of inspection is large. Therefore, it is required to automatically inspect the burnt of the molded product regardless of the skill level of the operator.

Therefore, an object of the present invention is to provide a molding system, an abnormality prediction device, an abnormality prediction method, a program, and a trained model for predicting burning of a molded product.

(1) The molding system according to the present invention is a molding system including a molding apparatus and an abnormality predicting apparatus for predicting an abnormality of a molded product molded by the molding apparatus, and the molding apparatus is combined. A plurality of molds forming a cavity and a gas vent serving as a gas discharge path in the cavity, an injection portion for filling the cavity with a molded material in a molten state, and a pressure of the molding material in the cavity. Alternatively, the abnormality predictor has a first sensor for detecting the temperature, and the abnormality predictor has a predetermined first time point as a starting point for filling the molding material, and the first sensor detects a rise in pressure or temperature. Based on the data acquisition unit that acquires the filling time required from the start to the completion of filling of the molding material in the cavity by acquiring the time point as the end point of filling of the molding material, and the filling time. , A molding system comprising an abnormality prediction unit for acquiring prediction information for predicting burning of the molded product.

As a result of diligent research, the inventors have found that the pressure rise of the molding material in the cavity when the gas vent is closed is slower than the rise when the pressure is normal (when the gas vent is not closed). That is, focusing on the length of the "filling time" from the predetermined first time point such as when the filling of the molding material into the cavity is started to the second time point when the pressure rise of the molding material is detected, the gas vent It was discovered that the blockage and, by extension, the burning of the molded product caused by the blockage can be predicted. Therefore, the molding system according to the present invention acquires prediction information for predicting the burning of the molded product based on the filling time required from the start to the completion of filling of the molding material in the cavity. This makes it possible to predict the burning of the molded product from the information acquired by the first sensor provided in the molding apparatus.

(2) Preferably, the injection portion includes a cylinder having a nozzle that stores the molding material and connects to the mold, and a screw that is inserted into the cylinder and pushes the molding material toward the mold. The molding apparatus further has a second sensor for detecting the pressure received by the cylinder or the screw from the molding material, or the amount of movement of the screw in the mold direction. , The time when the rise of pressure or movement amount is detected by the second sensor.

With this configuration, it is possible to obtain a certain certain time point indicating the start of filling regardless of whether the gas vent is closed or not, so that it is possible to more accurately predict the burning of the molded product.

(3) Preferably, the plurality of molds have flow paths that open to the injection portion side and the cavity side, respectively, and the molding apparatus has the cavity in the flow path rather than the first sensor. It is provided near the gate, which is the opening on the side, and further has a second sensor for detecting the pressure or temperature of the molding material in the cavity, and at the first time point, the pressure or temperature is measured by the second sensor. This is the time when the rise is detected.

With this configuration, it is possible to obtain a certain certain time point indicating the start of filling regardless of whether the gas vent is closed or not, so that it is possible to more accurately predict the burning of the molded product.

(4) Preferably, the first sensor is provided in a weld region of the cavity where the molding material joins. With this configuration, the first sensor is arranged in the cavity at the position where the molding material is last filled, so that it is possible to more accurately detect the lengthening of the filling time due to the blockage of the gas vent.

(5) Preferably, the abnormality prediction unit acquires the prediction information by inputting the filling time into a learned model in which the correlation between the filling time and the burning of the molded product is machine-learned. The explanatory variables of the trained model include the filling time, and the objective variables of the trained model include the presence or absence of burning of the molded product, the size of burning, or the color of burning. With this configuration, it is possible to more accurately predict the burning of the molded product even when the molding conditions vary.

(6) Preferably, the abnormality predicting unit is filled with the filling obtained at the time of molding of the molded product to be predicted, rather than the reference time generated based on the filling time obtained at the time of normal molding of the molded product. When the time becomes long, the prediction information indicating that the molded product is burnt is acquired. With this configuration, it is possible to easily predict the burning of the molded product by comparing the filling time with the reference time.

(7) The abnormality prediction device according to the present invention is an abnormality prediction device that predicts an abnormality of a molded product molded by a molding device, and the molding devices are combined to form a cavity and a gas in the cavity. A plurality of molds forming a gas vent serving as an discharge path, an injection portion for filling the cavity with a molded material in a molten state, a first sensor for detecting the pressure or temperature of the molded material in the cavity, and a first sensor. The abnormality predictor has a predetermined first time point as a starting point for filling the molding material, and a second time point when the rise in pressure or temperature is detected by the first sensor as the ending point for filling the molding material. In order to predict the burning of the molded product based on the data acquisition unit for acquiring the filling time required from the start to the completion of filling of the molding material in the cavity and the filling time. It is an abnormality prediction device including an abnormality prediction unit for acquiring the prediction information of the above.

The abnormality prediction device according to the present invention acquires prediction information for predicting burning of a molded product based on the filling time required from the start to the completion of filling of the molding material in the cavity. This makes it possible to predict the burning of the molded product from the information acquired by the first sensor provided in the molding apparatus.

(8) The abnormality prediction method according to the present invention includes a mold clamping step of forming a cavity and a gas vent serving as a gas discharge path in the cavity by combining a plurality of molds, and molding of a molten state into the cavity. An abnormality prediction method for predicting an abnormality of a molded product formed by a molding method comprising a filling step of filling a material, wherein a predetermined first time point is set as a starting point of filling of the molding material, and the said in the cavity. By acquiring the second time point at which the rise in pressure or temperature is detected by the first sensor that detects the pressure or temperature of the molding material as the end point of filling the molding material, the filling of the molding material is started in the cavity. An abnormality prediction method including a data acquisition step for acquiring the filling time required from the time of completion to completion, and an abnormality prediction step for acquiring prediction information for predicting the burning of the molded product based on the filling time. Is.

The abnormality prediction method according to the present invention acquires prediction information for predicting burning of a molded product based on the filling time required from the start to the completion of filling of the molding material in the cavity. This makes it possible to predict the burning of the molded product from the information acquired by the first sensor provided in the molding apparatus.

(9) In the program according to the present invention, a molding step of forming a cavity and a gas vent serving as a gas discharge path in the cavity by combining a plurality of molds, and a molding material in a molten state in the cavity are applied. A program for predicting an abnormality of a molded product formed by a molding method comprising a filling step of filling, wherein a predetermined first time point is set as a starting point of filling of the molding material, and the molding material in the cavity is formed. By acquiring the second time point at which the rise of pressure or temperature is detected by the first sensor that detects the pressure or temperature of the molding material as the end point of filling of the molding material, the filling of the molding material into the cavity is started. A program that causes a computer device to execute a data acquisition step of acquiring a filling time required to complete and an abnormality prediction step of acquiring prediction information for predicting burning of a molded product based on the filling time. Is.

The program according to the present invention acquires prediction information for predicting the burning of a molded product based on the filling time required from the start to the completion of filling of the molding material in the cavity. This makes it possible to predict the burning of the molded product from the information acquired by the first sensor provided in the molding apparatus.

(10) The learned model according to the present invention has a mold clamping step of forming a cavity and a gas vent serving as a gas discharge path in the cavity by combining a plurality of molds, and molding of a molten state into the cavity. It is a learned model for predicting an abnormality of a molded product formed by a molding method including a filling step of filling a material, and an explanatory variable has a predetermined first time point as a starting point of filling of the molding material. , The filling of the molding material in the cavity is set at the second time point when the rise of pressure or temperature is detected by the first sensor that detects the pressure or temperature of the molding material in the cavity as the end point of filling of the molding material. The objective variable is a trained model that includes the filling time required from the start to the completion of, and the objective variable includes the presence or absence of burn, the size of the burn, or the color of the burn.

In the learned model according to the present invention, the explanatory variable is the filling time required from the start to the completion of filling the molding material in the cavity, and the objective variable is the burnt state of the molded product (presence or absence of burning of the molded product, Since the size or color is used, if the filling time is input to the trained model, the predicted burnt state of the molded product is output. Thereby, the burnt state of the molded product can be predicted based on the filling time acquired by the first sensor.

According to the present invention, it is possible to predict the burning of the molded product.

It is a block diagram which shows typically the molding system which concerns on 1st Embodiment. It is explanatory drawing which conceptually shows the molding apparatus which concerns on FIG. It is explanatory drawing which conceptually shows the molding apparatus which concerns on FIG. It is sectional drawing of the mold part which shows typically the state at the end of a filling process. It is sectional drawing which shows the mold part cut by the cutting line of the arrow V of FIG. This is an example of a graph showing time series data of pressure. It is explanatory drawing explaining the rising behavior of the pressure of the molding material in a cavity in a state where a gas vent is closed. It is a block diagram which shows the functional structure of the learning apparatus which concerns on 1st Embodiment. It is sectional drawing of the mold part which concerns on the modification of 1st Embodiment. It is a block diagram which shows the functional structure of the abnormality prediction apparatus which concerns on 1st Embodiment. It is a block diagram which shows typically the molding system which concerns on 2nd Embodiment. It is a block diagram which shows the functional structure of the abnormality prediction apparatus which concerns on 2nd Embodiment.

<First Embodiment>
Hereinafter, the first embodiment of the present invention will be described with reference to the drawings.

<Overall configuration of molding system>
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.

In the present embodiment, 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.

<Outline configuration of molding equipment>
2 and 3 are explanatory views conceptually showing the molding apparatus 20. In FIGS. 2 and 3, the portion shown as a cross section is hatched. 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, and 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 fixing plate 231, a movable platen 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.

Here, in the molding apparatus 20, 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, and 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 platen 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 from the ball screw 234 to the support plate 235. The mold clamping force sensor 236 outputs a pressure detection signal 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 portion 24 has a plurality of molds 241 and 242. The mold 241 is fixed to the movable platen 232. When the ball screw 234 moves in the axial direction, 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.

As shown in FIG. 3, when the mold 241 is moved to the other side in the axial direction by the ball screw 234 and the mold 241 comes into contact with the mold 242 (that is, when a plurality of molds 241 and 242 are combined), the mold Cavity C1 (space) is formed between the molds 241 and 242. Further, a gas vent G1 is formed between the molds 241 and 242. The gas vent G1 functions as an discharge path for discharging the air in the cavity C1 to the outside of the molds 241 and 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, a pressure sensor 227a, a movement amount sensor 228, and a heater 229. 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. When the motor 226 is rotated by the operation command of the control unit 271, the ball screw 225 moves in the axial direction. Along with this, the screw 223 also moves in the axial direction. At this time, 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). That is, the pressurization sensor 227 detects the pressure that the screw 223 receives from the molding material L1 (a molten state or a solidified state, or a state in which a molten state and a solidified state are mixed). 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 pressure sensor 227a is installed on the inner peripheral surface of the cylinder 222 and detects the pressure received by the cylinder 222 from the molding material L1 (a molten state or a solidified state, or a state in which a molten state and a solidified state are mixed). The pressure sensor 227a outputs a pressure detection signal to the control unit 271.

The movement amount sensor 228 detects the movement amount of the ball screw 225 in the axial direction. The movement amount sensor 228 outputs a detection signal related to the movement amount to the control unit 271. The movement amount sensor 228 may be installed at another position as long as it can detect the movement amount of the ball screw 225 in the axial direction.

The heater 229 is, for example, a resistance heating heater in which a resistance wire is wound in a coil shape. The heater 229 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 (first sensor) is installed in a region of the molds 241 and 242 facing the cavity C1. More specifically, the pressure sensor 25 is installed in the weld region of the cavity C1 where the molding materials meet. The pressure sensor 25 detects the pressure in the cavity C1. In particular, 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. In this embodiment, one pressure sensor 25 is installed in the mold 242. However, a plurality of pressure sensors 25 may be installed in both the molds 241 and 242, respectively.

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.

<Manufacturing method using molding equipment>
A method of manufacturing a molded product by the molding apparatus 20 will be described with reference to FIGS. 2 to 5 as appropriate. As for the method of manufacturing the molded product by the molding apparatus 20, 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. In this embodiment, the molded product is a resin cage used for rolling bearings. However, this is an example of a molded product, and the molded product molded by the molding apparatus according to the present invention may be a molded product having another shape and use.

First, the pre-process ST1 is executed. In the previous step ST1, 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 229. 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 229, and become the molding material L1 in the molten state. When a predetermined amount of the molding material L1 is stored in the cylinder 222, the previous step ST1 is completed.

Next, when the mold clamping step ST2 is started, in the molding apparatus 20 in the state of FIG. 2, 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. In the state where the mold 241 and the mold 242 are combined in this way, 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. As a result, the cavity C1 is formed between the plurality of molds 241 and 242. With the above, the mold clamping step ST2 is completed.

Here, 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.

Subsequently, when the filling step ST3 is started, the ball screw 225 moves to one side in the axial direction while maintaining the above-mentioned mold clamping force. As a result, 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. Further, FIG. 5 is a cross-sectional view of the mold portion 24 cut by the cutting line of the arrow V in FIG. 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. Since the second opening 243b functions as a gate for supplying the molding material L1 from the flow path 243 into the cavity C1, it is hereinafter referred to as “gate 243b”. In the present embodiment, the pressure sensor 25 is installed so as to face the region of the cavity C1 that is farthest from the gate 243b.

See FIG. In this embodiment, the cavity C1 is formed in an annular shape. The gas vent G1 is formed so as to surround the cavity C1 with a predetermined width in the radial direction from the cavity C1. Further, the gas vent G1 has a flow path G1a leading to the outside of the mold portion 24. As shown in FIG. 4, a slight gap G2 between the mold 241 and the mold 242 is formed between the cavity C1 and the gas vent G1. The gap G2 is a gap to which only air is discharged and the molding material L1 is not discharged, and is formed by properly maintaining the mold clamping force of the mold clamping portion 23. The gap G2 may be provided in the entire area between the cavity C1 and the gas vent G1, or may be provided in a part between the cavity C1 and the gas vent G1.

As shown in FIG. 4, when the molding material L1 (more specifically, the molding material in the molten state) is supplied from the flow path 243 to the cavity C1, the air in the cavity C1 passes through the gap G2 to the gas vent G1. It is discharged. Then, the air discharged to the gas vent G1 is discharged to the outside of the mold portion 24 through the flow path G1a. In this way, when the air in the cavity C1 is discharged from the gas vent G1 and the inside of the cavity C1 is completely filled with the molding material L1, the filling step ST3 is completed. Further, in the filling step ST3, 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.

Subsequently, when the pressure holding step ST4 is started, the screw 223 further pushes the molding material L1 to one side in the axial direction, and the molding material L1 is further ejected from the nozzle 224 of the cylinder 222 into the cavity C1. As a result, a predetermined pressure Pt1 (for example, several tens to several hundreds MPa) is applied to the molding material L1 filled in the cavity C1. Then, by holding this state for a predetermined time, 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 and the pressure sensor 227a.

Subsequently, when the pressure holding release step ST5 is started, 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). When a predetermined time elapses after the holding pressure releasing operation and the pressure of the molding material L1 in the cavity C1 becomes equal to or less than a predetermined value, the holding pressure releasing step ST5 ends. After that, when the mold release step ST6 is started, 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. Then, 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.

FIG. 6 is an example of a graph showing 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. That is, the graph shows time-series data of the applied pressure and the pressure of the molding material L1 (a state including at least one of a molten state and a solidified state) in the cavity C1. FIG. 6A shows an overall image of the graph, and FIG. 6B shows an enlarged region of the graph including the end of the filling step ST3. In FIG. 6, the vertical axis is the pressure P and the horizontal axis is the time t.

In the present embodiment, the origin of time (t = 0) is the time when the control unit 271 commands the motor 226 to move the screw 223 to the other side in the axial direction. That is, it is the time when the injection unit 22 starts supplying the molding material L1 to the mold unit 24 in the filling step ST3. The origin of time (t = 0) is not limited to this, and may be the time when the pressurization sensor 227 or the pressure sensor 227a detects a predetermined pressure, or the movement amount sensor 228 detects a predetermined movement amount. It may be at the time when

Graph line F0 is an example of time series data of pressure acquired by the pressurization sensor 227. The graph line F0 tends to obtain substantially the same graph regardless of whether the gas vent G1 is closed or not, if the molding conditions (molding force, pressurization, holding time, etc.) are the same. The graph line F1 is an example of a graph line acquired when the molded product is molded in a state where the gas vent G1 is not closed (that is, in a normal state). The graph line F2 is an example of a graph line acquired when a molded product is molded in a state where the gas vent G1 is closed (that is, when an abnormality occurs).

See FIG. 6 (a). When observed as a whole image, there is no big difference between the graph line F1 in the normal state and the graph line F2 in the abnormal state. The graph lines F1 and F2 rise after the graph line F0 rises in the filling step ST3, the pressure rises to the maximum pressure Pt1 in the pressure holding step ST4, then the pressure decreases, and the pressure increases in the pressure holding release step ST5. It drops sharply. The holding pressure release step ST5 is started from the time point X2. That is, the holding pressure release operation is executed at the time point X2.

See FIG. 6 (b). When the region including the end of the filling step ST3 is enlarged and shown as shown in FIG. 6B, it can be observed that there is a difference between the graph line F1 in the normal state and the graph line F2 in the abnormal state. Specifically, the graph line F1 starts up earlier than the graph line F2.

For example, assuming that the time point at which the pressure P1 (0.01 × Pt1) becomes 1% of the maximum pressure Pt1 is the time point at which the rise of pressure is detected, the rise of graph line F0 is detected at time point X0 and the graph line F1 The rising edge of the graph line F2 is detected at the time point X1 and the rising edge of the graph line F2 is detected at the time point X1a. The time point X1a is later than the time point X1. Further, the time from the rise of the graph line F0 to the rise of the graph line F2 (X1a-X0) is longer than the time from the rise of the graph line F0 to the rise of the graph line F1 (X1-X0).

The time when the pressure rises may be detected as the time when the pressure detected by the pressure sensor 25 becomes a pressure at a predetermined ratio of the maximum pressure as described above, or a predetermined pressure regardless of the maximum pressure. It may be detected as a time point. Further, it may be detected as a time point when the slope of the pressure becomes equal to or higher than a predetermined value.

FIG. 7 is an explanatory diagram illustrating the rising behavior of the pressure of the molding material L1 in the cavity C1 in a state where the gas vent G1 is closed. In FIG. 7A, the flow path G1a of the gas vent G1 is blocked by the foreign matter Fm1. As shown in FIG. 7A, when the molding material L1 is supplied from the gate 243b to the cavity C1, the molding material L1 moves in the circumferential direction of the annular cavity C1 (the direction indicated by the arrow AR1) in the cavity C1. Will be filled in.

Here, a part of the air in the cavity C1 is discharged to the gas vent G1, but since the flow path G1a connected to the outside of the mold portion 24 is blocked, the air goes out of the mold portion 24. Not discharged. Therefore, as the filling of the molding material L1 into the cavity C1 progresses, the air is compressed by the molding material L1 in the cavity C1, and the air is gradually compressed.

After that, as shown in FIG. 7B, when the filling of the molding material L1 into the cavity C1 proceeds further, the air in the compressed cavity C1 becomes a resistance, and the molding material L1 is filled into the cavity C1. The speed slows down. Therefore, the time point at which the pressure sensor 25 provided in the region away from the gate 243b detects the pressure from the molding material L1 is also delayed. As a result, as shown in FIG. 6B, the rise of the graph line F2 is slower than that of the graph line F1.

Then, after the state shown in FIG. 7B, the cavity C1 is further filled with the molding material L1 so that the molding material L1 flows clockwise in the circumferential direction in the region where the pressure sensor 25 is located and counterclockwise in the circumferential direction. The molding material L1 flowing in the merging with the molding material L1. As a result, a weld is formed at a position facing the pressure sensor 25 in the molded product. The compressed and hot air heats the weld and the molding material around the weld, causing the molded product to burn.

As explained above, as a result of diligent research, the inventors have found that the rise in pressure of the molding material L1 in the cavity C1 when the gas vent is closed is slower than the rise in the normal state (when the gas vent is not closed). discovered. That is, pay attention to the length of the "filling time T1" from the predetermined first time point such as when the filling of the molding material L1 into the cavity C1 is started to the second time point when the pressure rise of the molding material L1 is detected. For example, it was discovered that the blockage of the gas vent and, by extension, the burning of the molded product caused by the blockage can be predicted.

Therefore, in the molding system 10 according to the present embodiment, the learning device 30 generates a trained model Tm1 in which the correlation between the filling time T1 and the burning of the molded product is learned, and the abnormality prediction device 40 generates the trained model Tm1. Based on the filling time T1, the prediction information for predicting the burning of the molded product is acquired. Hereinafter, the learning device 30 and the abnormality prediction device 40 will be described.

<Explanation of learning device>
FIG. 8 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.

Various molding information is stored in the molding information storage unit 33. The molding information is, for example, table-type information in which various types of first information and second information are associated with each other. For example, when the first information is the type of the mold, the second information includes various dimensions of the mold and the volume of the cavity C1. When 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 a filling time described later, a plurality of evaluation values, molding information, and burning 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.

For example, the training data acquisition unit 31 acquires the filling time T1 based on the pressures detected by the pressure sensor 25 and the pressurization sensor 227, respectively. In addition, the training data acquisition unit 31 acquires a plurality of evaluation values. The evaluation value includes, for example, a value related to the temperature detected by the temperature sensor 26 and a value related to the surrounding and internal environment of the molding apparatus 20 detected by another sensor (for example, a humidity sensor) (not shown).

The filling time T1 will be described with reference to FIG. 6 (b). The filling time T1 is the time required from the start to the completion of filling the molding material L1 into the cavity C1. In the present embodiment, the starting point of the filling time T1 is the time point X0 when the pressure rise is detected by the pressurization sensor 227. The end point of the filling time T1 is the time when the pressure rise is detected by the pressure sensor 25.

For example, when a certain molded product is molded and the graph line F1 is acquired, the end point of the filling time T1 is the time point X1, and the filling time T1 is (X1-X0). Further, when another molded product is molded and the graph line F2 is acquired, the end point of the filling time T1 is the time point X1a, and the filling time T1 is (X1a-X0). The filling time T1 is acquired by the training data acquisition unit 31 by calculating based on the time series data of the pressure detected by the pressurization sensor 227 and the pressure sensor 25.

The starting point of the filling time T1 is not limited to the time when the pressure rise is detected by the pressurization sensor 227. Regardless of whether the gas vent G1 is closed or not, any time may be used as long as it indicates a certain time point in the filling step ST3. For example, it may be the time when the rise of the movement amount is detected by the movement amount sensor 228, or the time when the origin t = 0 of the time in FIG. 6 (that is, the time based on the operation command of the control unit 271). May be good. Further, it may be the time when the rise of pressure is detected by the pressure sensor 227a provided on the inner peripheral surface of the cylinder 222. Further, a sensor for detecting the pressure of the molding material L1 in the cavity C1 may be further added, and the sensor may be configured to acquire the start point of the filling time T1.

FIG. 9 is a cross-sectional view of the mold portion 24 according to the modified example. In the modified example of FIG. 9, a pressure sensor 28 (second sensor) is provided in addition to the pressure sensor 25 (first sensor). The pressure sensor 28 is provided at a position close to the gate 243b. That is, the pressure sensor 28 is provided at a position closer to the gate 243b than the pressure sensor 25.

Since the molding material L1 supplied from the gate 243b passes through the pressure sensor 28 at an early stage in the filling step ST3, the molding conditions (molding force, pressurization, holding pressure) regardless of whether the gas vent G1 is closed or not. If the time, etc.) are the same, there is a tendency to obtain substantially the same graph. Therefore, even when the start point of the filling time T1 is the time when the pressure rise is detected by the pressure sensor 28 (for example, the time when the detected pressure of the pressure sensor 28 becomes P1), the inside of the cavity C1 is inserted. The time required from the start to the completion of filling of the molding material L1 can be obtained.

See FIG. 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, burnt information regarding the burnt of the molded product or first information stored in the molded 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 burnt information is, for example, information (for example, a dummy variable) in which the color of the burnt presence / absence of the molded product, the burnt size (for example, the burnt area, the width), and the color is quantified. As for the size of the burn, the area of the burn itself may be quantified, or the degree of the burn may be quantified. For example, the degree of burning is divided into 4 groups of large, medium, small, and none, and dummy variables are assigned as "large = 3", "medium = 2", "small = 1", and "none = 0". By doing so, it may be quantified. The burnt color may also be quantified by dividing it into groups according to the tint and assigning a dummy variable to each group. The operator acquires burn information by actually inspecting the appearance of the molded product molded by the molding apparatus 20, and inputs the burn information to the input unit 50.

The learning calculation unit 32 generates a learned model that models the correlation between the filling time T1 and the burning of the molded product by performing a supervised machine learning calculation based on the training data. In this embodiment, a convolutional neural network (CCN) is used as the machine learning model, but other models may be used. For example, it may be a regression tree model that is a model for grouping data.

Specifically, the filling time T1, a plurality of evaluation values, molding information, mold clamping force and pressure (hereinafter, these information are collectively referred to as "evaluation information") are used as explanatory variables, and burning information is used as an objective variable. By doing so, the correlation between the evaluation information and the burn information is modeled.

The learned model Tm1 generated by the learning calculation unit 32 is stored in the learned model storage unit 34. When new information is input to the learning device 30 and new training data is acquired by the training data acquisition unit 31, 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. Further, 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.

<How to generate a trained model>
Next, a method of generating the trained model Tm1 by the learning device 30 will be described. The method of generating the trained model Tm1 includes a training data acquisition step and a learning calculation step. First, when the training data acquisition process is started, the training data acquisition unit 31 acquires the training data. For example, the filling time T1 is acquired based on the time series data of the pressure detected by the pressurization sensor 227 and the pressure sensor 25. Further, the burnt information is acquired by the worker actually inspecting the molded product for which the filling time T1 has been acquired. Next, when the learning calculation process is started, the learning calculation unit 32 generates the trained model Tm1 based on the training data.

<Explanation of abnormality prediction device>
FIG. 10 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 arithmetic unit executes the data acquisition process and the abnormality prediction process described later based on the program stored in the storage unit.

Similar to the molding information storage unit 33, 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. Here, the evaluation information is the same as the evaluation information acquired by the learning device 30 described above. For example, the data acquisition unit 41 acquires the filling time T1 based on the pressures detected by the pressure sensor 25 and the pressurization sensor 227, respectively.

The abnormality prediction unit 42 inputs the evaluation information acquired by the data acquisition unit 41 into the trained model Tm1 to execute an abnormality prediction process for acquiring prediction information for predicting the burning of the molded product. 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.

For example, when the objective variable is the burn size of the molded product, the prediction information is information including the probability predicted for the burn size of the molded product. More specifically, the prediction information includes, for example, a first probability of 1 mm or less, a second probability of being longer than 1 mm and 5 mm or less, and a third probability of being longer than 5 mm with respect to the burn width of the molded product. As an example, when some evaluation information is input to the trained model Tm1, 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 burning of the molded product. That is, when the objective variable is the presence or absence of burning of the molded product, the prediction information is the fourth probability that the molded product is not burnt and the fifth probability that the molded product is burnt.

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. A predetermined threshold is, for example, the maximum width of burn allowed. For example, when the maximum allowable burning width is 5 mm, it is determined that the molded product is defective (there is a burning defect) when the third probability is the highest among the first to third probabilities described above. ..

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.

Further, when it is determined that the molded product is defective, 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. 7) as necessary.

In this embodiment, 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. In this case, 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.

<Abnormality prediction method using anomaly prediction device>
Next, the abnormality prediction method by the abnormality prediction device 40 will be described. The abnormality prediction method includes a data acquisition step and an abnormality prediction step. When the data acquisition process is started, the data acquisition unit 41 acquires the evaluation information. For example, the filling time T1 is acquired based on the time series data of the pressure detected by the pressure sensor 25 and the pressurization sensor 227, respectively. Next, when the abnormality prediction step is started, the abnormality prediction unit 42 acquires prediction information for predicting the burning of the molded product based on the evaluation information including the filling time T1.

<Action / effect of molding system>
The molding system 10 according to the present embodiment acquires prediction information for predicting the burning of the molded product based on the filling time T1 required from the start to the completion of filling of the molding material L1 in the cavity C1. .. With such a configuration, once the trained model Tm1 is generated, it is not necessary for the worker to inspect the molded product one by one, and various sensors (pressure sensor 25, pressurization sensor 227) provided in the molding apparatus 20 are not required. Etc.), it is possible to predict the burning of the molded product from the information obtained.

Further, the molding system 10 according to the present embodiment acquires prediction information by inputting the filling time T1 into the learned model Tm1 in which the correlation between the filling time T1 and the burning of the molded product is machine-learned. Here, the explanatory variable of the trained model Tm1 includes the filling time T1, and the objective variable of the trained model Tm1 includes the presence or absence of burning of the molded product, the size of burning, or the color of burning. With this configuration, it is possible to more accurately predict the burning of the molded product even when the molding conditions vary.

Further, the pressure sensor 25 according to the present embodiment is provided in the weld region of the cavity C1 where the molding material L1 joins. With this configuration, the pressure sensor 25 is arranged in the cavity C1 at the position where the molding material L1 is finally filled, so that the lengthening of the filling time T1 due to the blockage of the gas vent G1 can be detected more accurately. Can be done.

The filling time T1 also depends on the temperature of the molding material L1. The higher the temperature of the molding material L1, the lower the viscosity of the molding material L1 and the easier it is for the molding material L1 to flow. Therefore, the higher the temperature of the molding material L1, the easier it is for the molding material L1 to be filled in the cavity C1 faster, and the filling time T1 tends to be shorter. By including the evaluation value regarding the temperature of the molding material L1 in the explanatory variable, the molding system 10 according to the present embodiment can be a trained model in which the above correlation is incorporated, and more accurately the molded product can be made. Burning can be predicted.

<Second Embodiment>
The molding system according to the first embodiment has been described above. However, the practice of the present invention is not limited to this, and various modifications can be made. Hereinafter, the molding system 11 according to the second embodiment of the present invention will be described. In the following description, the parts that are not changed from the first embodiment are designated by the same reference numerals, and the description thereof will be omitted.

FIG. 11 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. In the present embodiment, the abnormality prediction device 40a of the molding system 11 acquires prediction information for predicting the burning of the molded product based on the filling time T1 and the predetermined reference time T2. That is, the molding system 11 is different from the molding system 10 according to the first embodiment in that the burnt of the molded product is predicted by comparing the filling time T1 and the reference time T2 without using the trained model Tm1.

FIG. 12 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 time 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 times T2 are stored in the reference time storage unit 46. The reference time T2 is a value generated based on the filling time T1 acquired when the molded product is normally molded. The reference time T2 is, for example, a value obtained by adding a predetermined margin to the average value or the median value of the plurality of filling times T1 obtained during normal molding of the plurality of molded products. The predetermined margin is determined by the maximum width of burning allowed in the molded product and the like.

A plurality of values of the reference time T2 are prepared for each evaluation value or for each molding information. For example, when the evaluation value related to temperature is less than the first value, the first reference time, when the evaluation value is the first value or more and less than the second value, the second reference time, and when the evaluation value is the second value or more, the third reference time. Time may be used. The reference time storage unit 46 stores, for example, table-type information in which evaluation values and molding information are associated with a plurality of reference times.

Based on the evaluation value and molding information acquired by the data acquisition unit 41, the abnormality prediction unit 42a has a reference time T2a corresponding to a plurality of reference times T2 stored in the reference time storage unit 46 (hereinafter, “attention reference”). Time T2a ”) is acquired. Then, the abnormality prediction unit 42a compares the filling time T1 acquired by the data acquisition unit 41 (that is, the filling time T1 acquired at the time of molding the molded product to be predicted) with the attention reference time T2a. , Get forecast information. The prediction information is, for example, the difference or ratio between the filling time T1 and the attention reference time T2a.

As described in FIGS. 6B and 7, when the gas vent G1 is blocked and the molded product is burnt, the filling time T1 tends to be longer than the attention reference time T2a. Therefore, when the filling time T1 is larger than the attention reference time T2a, it is predicted that the molded product to be predicted is burnt. Therefore, the prediction information indicates whether or not the molded product to be predicted is burnt.

The output unit 43a determines the quality of the molded product based on the prediction information acquired by the abnormality prediction unit 42a. For example, when the prediction information is the difference between the filling time T1 and the attention reference time T2a (T1-T2a), the output unit 43a has a defective molded product (with burn defect) when the prediction information is a positive value. Is determined to be.

The output unit 43a 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. 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.

In this embodiment, 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. In this case, 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.

According to the molding system 11 according to the present embodiment, the burning of the molded product can be easily predicted by comparing the filling time T1 and the reference time T2.

<Other variants>
In the above embodiment, the pressure sensor 25 detects the rising point of the pressure of the molding material L1 in the cavity C1 as the end point of the filling time T1. However, the sensor for detecting the end point of the filling time T1 is not limited to the pressure sensor. Any sensor that can detect that the cavity C1 is filled with the molding material L1 may be used, and may be, for example, a temperature sensor. That is, the rising point of the temperature acquired by the temperature sensor may be acquired as the end point of the filling time T1.

Specifically, instead of the pressure sensor 25, a temperature sensor 25a for detecting the temperature of the molding material L1 is installed at the same position as the pressure sensor 25 according to the first embodiment. Since the molding material L1 supplied to the cavity C1 is heated to a temperature equal to or higher than the melting point of the molding material (for example, 100 degrees or higher), the temperature of the molding material L1 is higher than the temperature of the molds 241 and 242. Therefore, when the molding material L1 supplied into the cavity C1 reaches the position of the temperature sensor 25a (that is, when the molding material L1 is filled in the cavity C1), the temperature detected by the temperature sensor 25a is shown in FIG. It stands up in the same manner as the graph line shown in (b).

Even in the case of such a configuration, it is possible to detect an increase in the filling time T1 due to the blockage of the gas vent G1 as in the case of detecting the pressure by the pressure sensor 25 according to the above embodiment. , Burning of the molded product due to the blockage of the gas vent G1 can be predicted.

Further, in the present modification, the training data acquisition unit 31 and the data acquisition unit 41 determine the maximum value of the temperature detected by the temperature sensor 25a during the filling step ST3, the holding pressure step ST4, and the holding pressure releasing step ST5, or a predetermined value. The average value of the temperature during the period (for example, during the pressure holding step ST4) may be acquired as an evaluation value regarding the temperature of the molding material L1.

In the above embodiment, the temperature sensor 26 acquires the evaluation value related to the temperature, and the pressure sensor 25 acquires the end point of the filling time T1. However, in this modification, the temperature sensor 25a acquires the evaluation value and the end point of the filling time T1. You can get both. According to such a configuration, the number of sensors provided in the molding system 10 can be reduced, and the burnt of the molded product can be predicted at a lower cost.

Further, in the modified example of the first embodiment described above, the temperature sensor 28a may be installed instead of the pressure sensor 28. Then, the rising point of the temperature acquired by the temperature sensor 28a may be acquired as the start point of the filling time T1.

In the above embodiments and modifications, the pressure sensor 25 or the temperature sensor 25a functions as the "first sensor" according to the present invention. Further, the pressure sensor 227, the pressure sensor 227a, the movement amount sensor 228, the pressure sensor 28 or the temperature sensor 28a function as the "second sensor" according to the present invention.

The embodiments disclosed as described above are exemplary in all respects and are not restrictive. That is, the molding system of the present invention is not limited to the illustrated form, and may be another form within the scope of the present invention.

10, 11 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 Movement amount sensor 229 Heater 23 Type tightening part 231 Fixed plate 232 Movable plate 233 Tieber 234 Ball Screw 235 Support plate 236 Mold tightening force sensor 237 Motor 24 Mold part 241 Mold 242 Mold 243 Flow path 243a First opening 243b Second opening (gate) 25 Pressure sensor 25a Temperature sensor 26 Temperature sensor 27 Control panel 271 Control unit 272 Communication unit 28 Pressure sensor 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 time storage unit 50 Input unit 60 Display unit C1 Cavity G1 Gas vent G1a Flow path L1 Molding material Fm1 Foreign matter Tm1 Learned model T1 Filling time T2 Reference time

Claims (10)

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.
The molding apparatus is
A plurality of molds that, when combined, form a cavity and a gas vent that serves as a gas discharge path in the cavity.
An injection part that fills the cavity with a molten molding material,
A first sensor that detects the pressure or temperature of the molding material in the cavity,
Have,
The abnormality prediction device is
By acquiring a predetermined first time point as a start point for filling the molding material and a second time point when the rise in pressure or temperature is detected by the first sensor as the end point for filling the molding material, the cavity is filled with the above. A data acquisition unit that acquires the filling time required from the start to the completion of filling of the molding material,
An abnormality prediction unit that acquires prediction information for predicting burning of the molded product based on the filling time, and
Have,
Molding system. The injection part
A cylinder having a nozzle for storing the molding material and connecting to the mold,
It has a screw that is inserted into the cylinder and pushes the molding material toward the mold.
The molding apparatus further includes a second sensor that detects the pressure that the cylinder or the screw receives from the molding material or the amount of movement of the screw in the mold direction.
The first time point is a time point when the rise of pressure or movement amount is detected by the second sensor.
The molding system according to claim 1. The plurality of molds have flow paths that are opened on the injection portion side and the cavity side, respectively.
The molding apparatus is provided at a position closer to the gate which is an opening on the cavity side of the flow path than the first sensor, and further includes a second sensor for detecting the pressure or temperature of the molding material in the cavity. Have and
The first time point is a time point when the rise in pressure or temperature is detected by the second sensor.
The molding system according to claim 1. The first sensor is provided in a weld region of the cavity where the molding material joins.
The molding system according to any one of claims 1 to 3. The abnormality prediction unit acquires the prediction information by inputting the filling time into a trained model in which the correlation between the filling time and the burning of the molded product is machine-learned.
The explanatory variables of the trained model include the filling time.
The objective variables of the trained model include whether or not the molded product is burnt, the size of the burn, or the color of the burn.
The molding system according to any one of claims 1 to 4. When the abnormality predicting unit has a longer filling time obtained during molding of the molded product to be predicted than a reference time generated based on the filling time obtained during normal molding of the molded product. , Acquire the prediction information indicating that the molded product is burnt.
The molding system according to any one of claims 1 to 4. An abnormality prediction device that predicts abnormalities in a molded product molded by a molding device.
The molding apparatus is
A plurality of molds that, when combined, form a cavity and a gas vent that serves as a gas discharge path in the cavity.
An injection part that fills the cavity with a molten molding material,
A first sensor that detects the pressure or temperature of the molding material in the cavity,
Have,
The abnormality prediction device is
By acquiring a predetermined first time point as a start point for filling the molding material and a second time point when the rise in pressure or temperature is detected by the first sensor as the end point for filling the molding material, the cavity is filled with the above. A data acquisition unit that acquires the filling time required from the start to the completion of filling of the molding material,
An abnormality prediction unit that acquires prediction information for predicting burning of the molded product based on the filling time, and
An abnormality predictor. A molding method including a molding step of forming a cavity and a gas vent serving as a gas discharge path in the cavity by combining a plurality of molds, and a filling step of filling the cavity with a molten molding material. It is an abnormality prediction method for predicting an abnormality of a molded product formed by
A predetermined first time point is set as a starting point for filling the molding material, and a second time point when a rise in pressure or temperature is detected by a first sensor that detects the pressure or temperature of the molding material in the cavity is filled with the molding material. A data acquisition step of acquiring the filling time required from the start to the completion of filling of the molding material in the cavity by acquiring as the end point of the above.
An abnormality prediction step of acquiring prediction information for predicting burning of the molded product based on the filling time, and
An abnormality prediction method. A molding method including a molding step of forming a cavity and a gas vent serving as a gas discharge path in the cavity by combining a plurality of molds, and a filling step of filling the cavity with a molten molding material. It is a program for predicting the abnormality of the molded product formed by
A predetermined first time point is set as a starting point for filling the molding material, and a second time point when a rise in pressure or temperature is detected by a first sensor that detects the pressure or temperature of the molding material in the cavity is filled with the molding material. A data acquisition step of acquiring the filling time required from the start to the completion of filling of the molding material in the cavity by acquiring as the end point of the above.
An abnormality prediction step of acquiring prediction information for predicting burning of the molded product based on the filling time, and
A program that causes a computer device to execute. A molding method including a molding step of forming a cavity and a gas vent serving as a gas discharge path in the cavity by combining a plurality of molds, and a filling step of filling the cavity with a molten molding material. A trained model for predicting anomalies in the part formed by
As the explanatory variable, the predetermined first time point is set as the starting point of filling of the molding material, and the second time point when the rise of pressure or temperature is detected by the first sensor that detects the pressure or temperature of the molding material in the cavity is the said. The filling time required from the start to the completion of filling of the molding material in the cavity, which is the end point of filling of the molding material, is included.
The objective variable includes whether or not the molded product is burnt, the size of the burn, or the color of the burn.
Trained model.

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