Molding condition parameter estimation method, computer program, estimation device, and estimation system

Abstract

To provide a molding condition parameter estimation method or the like, capable of appropriately adjusting molding condition parameters of a film molding machine.SOLUTION: A molding condition parameter estimation method comprises: acquiring detection data pertaining to the state of a film molding machine, the data being detected by a detector; and inputting the acquired detection data into an estimation model that estimates, upon input of the detection data pertaining to the state of the film molding machine, molding condition parameters for the film molding machine, thereby estimating molding condition parameters for the film molding machine.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a molding condition parameter estimation method, a computer program, an estimation device, and an estimation system. [Background technology]

[0002] Conventionally, molding machines for molding resin materials by molding methods such as injection molding and extrusion molding have been widely used. In the molding machine, it is necessary to adjust the setting values ​​of various molding condition items in the molding machine so that the molded body meets the required specifications. These molding condition adjustments are performed based on the operator's experience, and repeated trial and error is required to obtain appropriate molding conditions. Therefore, a technology has been proposed to support the operator in adjusting the molding condition parameters.

[0003] For example, Patent Document 1 discloses an injection molding machine system that can appropriately adjust molding condition parameters of an injection molding machine using a machine learning device that learns through reinforcement learning. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2017-30152 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology described in Patent Document 1 is a technology related to an injection molding machine, and is not a technology aimed at estimating the molding condition parameters of a film molding machine that forms a film by extrusion molding. Since the equipment configurations of an injection molding machine and a film molding machine are different, it is difficult to directly apply the technology related to an injection molding machine to a film molding machine. A technology that can estimate the molding condition parameters of a film molding machine is desired.

[0006] An object of the present disclosure is to provide a molding condition parameter estimation method and the like that can appropriately estimate the molding condition parameters of a film molding machine. [Means for solving the problem]

[0007] A method for estimating molding condition parameters according to one embodiment of the present disclosure acquires detection data regarding the state of a film forming machine detected by a detector, and estimates molding condition parameters in the film forming machine by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when detection data regarding the state of the film forming machine is input.

[0008] A computer program according to one embodiment of the present disclosure acquires detection data regarding the state of a film forming machine detected by a detector, and inputs the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when detection data regarding the state of the film forming machine is input, thereby causing a computer to execute a process of estimating molding condition parameters in the film forming machine.

[0009] An estimation device according to one embodiment of the present disclosure includes an acquisition unit that acquires detection data relating to a state of a film forming machine detected by a detector, and an estimation unit that estimates molding condition parameters in the film forming machine by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when detection data relating to the state of the film forming machine is input.

[0010] An estimation system according to one embodiment of the present disclosure includes a film forming machine, a detector, and an estimation device, wherein the estimation device includes an acquisition unit that acquires detection data regarding a state of the film forming machine detected by the detector, and an estimation unit that estimates molding condition parameters in the film forming machine by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when detection data regarding the state of the film forming machine is input. Effect of the Invention

[0011] According to the present disclosure, it is possible to appropriately estimate the molding condition parameters of a film molding machine. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a schematic diagram of an estimation system according to a first embodiment. [Diagram 2] FIG. 2 is a block diagram showing an example of the configuration of a data collection device. [Diagram 3] FIG. 2 is a block diagram showing a configuration example of an estimation device. [Figure 4] 11 is a diagram showing an example of the contents of information stored in a molding information DB. FIG. [Diagram 5] FIG. 2 is an explanatory diagram showing an overview of an estimation model. [Figure 6] 13 is a flowchart illustrating an example of a processing procedure executed by the estimation device. [Figure 7] 10 is a flowchart illustrating an example of a processing procedure executed by the estimation device of the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The present disclosure will be specifically described with reference to the drawings showing the embodiments thereof. (First embodiment) 1 is a schematic diagram of an estimation system 100 according to a first embodiment. The estimation system 100 mainly includes a film forming machine 1, a plurality of detectors 2, and an estimation device 4. The estimation device 4 is communicably connected to a data collecting device 3 and a terminal device 5 via a network N1 such as the Internet.

[0014] 1 shows one film forming machine 1 and one data collecting device 3, but a plurality of data collecting devices 3 (not shown) are connected to the estimation device 4 via a network N1. One or more film forming machines 1 are connected to the data collecting device 3. Each data collecting device 3 and the one or more film forming machines 1 connected to the data collecting device 3 are installed, for example, in the same factory.

[0015] The estimation device 4 is a device capable of various information processing and information transmission and reception, such as a server computer, a personal computer, a quantum computer, etc. The estimation device 4 collects molding information for each of the multiple film forming machines 1. Based on the collected molding information, the estimation device 4 estimates molding condition parameters for the film forming machines 1 so that the quality of the film formed by each film forming machine 1 satisfies a target quality. The estimation device 4 according to the present embodiment 1 adjusts multiple molding condition parameters set in the film forming machines 1 so that the thickness (film thickness) of the film falls within a predetermined range. The estimation device 4 transmits the estimated results of the molding condition parameters to the terminal device 5.

[0016] The terminal device 5 is an information processing terminal having a communication function and a display function, and is, for example, a personal computer, a tablet terminal, a smartphone, etc. The terminal device 5 may be installed in the same factory as the film forming machine 1. The terminal device 5 may be portable. The terminal device 5 is a terminal used by a user of the film forming machine 1. The user of the film forming machine 1 includes an organization such as a corporation that owns the film forming machine 1, and an operator belonging to the organization. The user of the film forming machine 1 may be a service provider such as a sales representative or maintenance manager related to the film forming machine 1. The number of terminal devices 5 may be two or more.

[0017] <Film forming machine 1> The film forming machine 1 forms a thin resin film by extrusion molding. The film forming machine 1 includes an extruder 11, a casting device 12, an MD stretching device 13, a TD stretching device 14, a winder 15, and a control device 16.

[0018] The extruder 11 is, for example, a single-screw extruder or a twin-screw extruder, and includes a raw material supply section 111 into which a resin raw material is introduced, a cylinder 112, a screw 113, and a die 114. The raw material supply section 111 includes a hopper for storing the resin raw material and a feeder for introducing the resin raw material, and discharges the resin raw material introduced into the hopper from the feeder into the cylinder 112 at a constant flow rate. The screw 113 is rotatably inserted into the hole of the cylinder 112, and conveys the resin raw material discharged from the raw material supply section 111 in the extrusion direction (rightward in FIG. 1), melting and kneading the resin raw material. The screw 113 rotates at a predetermined rotation speed by a driving force output from a driving device 115 including, for example, a motor and a reducer (not shown). The extruder 11 extrudes the molten resin raw material into a film shape from a narrow gap (lip) at the tip of the die 114. The die 114 is, for example, a T-die.

[0019] The casting device 12 includes multiple casting rolls 121 for cooling and shaping the high-temperature molten material extruded from the die 114. The casting rolls 121 rotate at a predetermined rotation speed by a driving force output from a driving device 122 including, for example, a motor and a reducer (not shown). The multiple casting rolls 121 include a first roll 121a and a second roll 121b.

[0020] The first roll 121a is, for example, a metal roll having a temperature adjustment unit (not shown) for cooling the molten material, and is supported below the die 114. The first roll 121a sandwiches the film-like molten material extruded from the die 114 between itself and the second roll 121b, and forms the film-like molten material into a film (sheet) shape while cooling it in a short time together with the second roll 121b. The casting device 12 controls the film thickness within a predetermined range. This results in an unstretched film. The temperature adjustment method for the first roll 121a is not particularly limited, but may include, for example, a method using a heat medium such as air, water, or oil, or a method using an electric heater or dielectric heating. In the example shown in FIG. 1, the multiple casting rolls 121 further include a roll for cooling or transporting the molten material.

[0021] The MD stretching device 13 includes multiple tension rolls 131, and the unstretched film transported from the casting device 12 is inserted between the tension rolls 131 to stretch it in the MD direction. The MD direction is the direction along the film transport direction, and is also called the longitudinal direction. The tension rolls 131 rotate at a predetermined number of revolutions by a driving force output from a driving device 132 including, for example, a motor and a reducer (not shown).

[0022] The tension rolls 131 include a heating roll having a temperature adjustment unit for heating the film, and a cooling roll having a temperature adjustment unit for cooling the film. The temperature of the tension rolls 131 can be adjusted in the same manner as the above-mentioned cast roll 121. The film is heated to a predetermined temperature range in which it can be stretched while in contact with the heating roll, and then stretched in the MD direction by utilizing the difference in rotation speed of each cooling roll. The stretch ratio in the MD direction can be adjusted by the speed ratio of each tension roll 131. Note that FIG. 1 is merely an example, and the number of tension rolls 131 is not limited to the example shown in FIG. 1.

[0023] The TD stretching device 14 stretches in the TD direction the film that has been longitudinally stretched by the MD stretching device 13. The TD direction is a direction intersecting the film transport direction (the width direction of the film), and is also called the transverse direction.

[0024] The TD stretching device 14 is, for example, a tenter stretching device, and has a heating device such as a hot air blower (not shown), and heats the film to a predetermined temperature range at which the film can be stretched, and stretches the film laterally.

[0025] The TD stretching device 14 includes an oven 141 having a heating device such as a hot air blower, and a traveling mechanism including a pair of left and right rails 142 and a plurality of links 143 arranged on the rails 142 is provided inside the oven 141. The rails 142 are arranged so as to expand in the TD direction toward the downstream direction in the MD direction. The plurality of links 143 form an endless chain. The endless chain formed by the plurality of links 143 travels on the rails 142 at a predetermined speed by a rotational driving force output from a link driving device 145 arranged outside the oven 141.

[0026] A clip 144, which is a film gripper, is attached to each of the links 143. The number of links 143 and the number of clips 144 provided on each of the left and right rails 142 are the same. That is, the TD stretching device 14 includes a pair of left and right links 143 and a pair of left and right clips 144. The pair of links 143 and the pair of clips 144 are arranged on the rails 142 so as to be located at both the left and right ends of the film, that is, so as to be aligned in a row along the TD direction. The pair of left and right clips 144 are adjusted in advance by synchronizing the driving devices so that no phase difference occurs between the clips 144, that is, so that the conveying periods are the same. The TD stretching device 14 may be of a bearing clip type or a slide clip type.

[0027] At the entrance of the TD stretching device 14, the film is gripped at both ends by clips 144. The clips 144 grip the film and are transported together with the links 143 in the film feed direction, and transport the film in the film feed direction by traveling on the rails 142. Both sides of the film in the oven are heated by air blown from hot air blowers provided above and below the traveling mechanism, and the film is stretched in the width direction as the clips 144 gripping both ends of the film move. The stretch ratio in the TD direction can be adjusted by the width of the rails 142 in the TD direction. The oven 141 is divided into a plurality of zones in the transport direction, for example, and the set temperature or the amount of air can be adjusted for each zone. The above zones include a preheating zone, a stretching zone, and a heat setting zone.

[0028] The clip 144 releases the film at the exit of the TD stretching device 14. The film stretched in the width direction is taken up by the winder 15 via a take-up machine (not shown). The TD stretching device 14 is not limited to a clip tenter, and may be, for example, a pin tenter having pins as film gripping devices. The above description of the structure of each part of the TD stretching device 14 is an example, and the configurations of temperature adjustment, air volume adjustment, link 143 of rail 142, etc. are not particularly limited.

[0029] The film forming machine 1 normally operates continuously without stopping, except at specific times such as when an apparatus abnormality occurs or during a preset stop period.

[0030] The control device 16 is a computer that controls the operation of the film forming machine 1. The control device 16 includes a control unit such as a central processing unit (CPU) (not shown), a storage unit such as a read only memory (ROM), a random access memory (RAM), or a hard disk, a communication unit that transmits and receives information to and from an external device via wired or wireless communication, a display unit such as a liquid crystal display or an organic electroluminescence (EL) display that displays various images to the user, and an operation unit that accepts operations from the user.

[0031] The control device 16 accepts settings of molding condition parameters for the film forming machine 1 through an operation unit or a communication unit. The control device 16 also receives detection data detected by the detector 2 from the data collecting device 3 through the communication unit. The control device 16 controls the operation of the film forming machine 1 based on the accepted molding condition parameters and detection data. The control device 16 also transmits operation data indicating the operating state of the film forming machine 1 to the data collecting device 3 through the communication unit. The data collecting device 3 transmits the accepted operation data to the estimation device 4. Note that the operation data may be transmitted to the estimation device 4 directly from devices such as a motor and an inverter in the film forming machine 1 without going through the collection device 3.

[0032] In the film forming machine 1, forming condition parameters that define forming conditions are set, and the film forming machine 1 operates in accordance with the forming condition parameters. Examples of the molding condition parameters include the feeder supply amount in the extruder 11, the number of revolutions or the rotation speed of the screw 113, the number of revolutions or the rotation speed of the motor of the drive device 115, and the lip opening of the die 114, the number of revolutions or the rotation speed of the cast roll 121 in the casting device 12, the number of revolutions or the rotation speed of the motor of the drive device 122, and the discharge amount of the film, the number of revolutions or the rotation speed of the tension roll 131 in the MD stretching device 13, the number of revolutions or the rotation speed of the motor of the drive device 132, the stretching ratio, and the discharge amount of the film, and the line speed of the film conveying line in the TD stretching device 14, the temperature in the oven 141 (particularly for each zone), the pressure in the oven 141 (particularly for each zone), the air volume or wind speed at the entrance and exit of the oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit), the stretching angle, the stretching ratio, and the discharge amount of the film. Note that instead of the discharge amount of the film, the film speed, the film width, and the film thickness may be set. The optimal molding condition parameters change from time to time during operation depending on the environment of the film molding machine 1 and the state of the molded product.

[0033] Examples of the operation data of the film forming machine 1 include the feeder supply amount in the extruder 11, the temperature of the cylinder 112, the rotation speed of the screw 113, the rotation speed of the motor, the motor torque, and the motor current, the rotation speed of the casting roll 121 in the casting device 12, the rotation speed of the motor, the motor torque, and the motor current, the rotation speed of the tension roll 131 in the MD stretching device 13, the motor rotation speed, the motor torque, and the motor current, and the line speed and line tension in the TD stretching device 14. The operation data corresponds to the set value of each item, i.e., the control value by the control device 16.

[0034] <Detector 2> The detector 2 is a sensor that chronologically detects physical quantities that indicate the state of the film forming machine 1. The state of the film forming machine 1 detected by the detector 2 includes the state of the film forming machine 1 itself as well as the state of the film (resin) formed by the film forming machine 1. The detector 2 is connected to the data collecting device 3 by wire or wirelessly, and outputs detection data obtained by detection to the data collecting device 3 directly or indirectly.

[0035] The estimation system 100 includes a plurality of detectors 2 that detect various detection data. Some of the plurality of detectors 2 are connected to a data collecting device 3, and the data collecting device 3 acquires the detection data from the detectors 2. Some of the plurality of detectors 2 are connected to a control device 16, and the data collecting device 3 acquires the detection data from the detectors 2 via the control device 16. Note that in FIG. 1, connection lines between some of the detectors 2 and the data collecting device 3 or the control device 16 are omitted from illustration.

[0036] The detectors 2 include those provided for the purpose of detecting sensor values ​​necessary for controlling the operation of the film forming machine 1, and those provided for the purpose of detecting sensor values ​​necessary for implementing the estimation of the molding condition parameters of the film forming machine 1 by the present estimation method. The detectors 2 may be configured separately from the film forming machine 1, or may be configured to be incorporated into the film forming machine 1.

[0037] Examples of detector 2 include an acceleration sensor, a speed sensor, a displacement sensor, an AE (Acoustic Emission) sensor, a viscometer, a gel counter, a strain sensor, a crystal orientation sensor, anemometer, anemometer, a thermometer, a surface potential sensor, a laser sensor, an infrared sensor, an X-ray sensor, a position sensor, a tension sensor, a load cell, a torque sensor, an encoder, a current sensor, a tachometer, an angle sensor, a pressure gauge, a weight gauge, a length measuring sensor, a photosensor (camera), a flow meter, a voltmeter, a timer, a wattmeter, etc.

[0038] Examples of detection data detected by detector 2 include acceleration, speed, displacement, viscosity, number of gels, strain, degree of crystal orientation, wind speed, air volume, temperature, charge amount, position, phase difference, tension, torque, rotation speed, angle, pressure, weight, length, thickness, image, flow rate, current, voltage, time, force, power consumption, etc. In this specification, detection data detected by detector 2 is not limited to sensor values ​​directly detected by detector 2, but also includes calculated values ​​indirectly calculated from the sensor values.

[0039] Examples of detection data related to the extruder 11 and the detector 2 for the detection data include an acceleration sensor that detects the acceleration (vibration) of the reducer, a tachometer that detects the rotation speed of the screw 113 or the motor, a torque sensor that detects the torque of the motor, a current sensor that detects the current of the motor, a viscometer that detects the resin viscosity (particularly the resin viscosity immediately after discharge from the die 114), a contact thermometer or a non-contact thermographic camera that detects the temperature inside the cylinder 112 or the resin (particularly the resin immediately after discharge from the die 114), a laser displacement sensor that detects the lip opening of the die 114, and a weight scale that detects the feeder supply amount.

[0040] Examples of detection data related to the casting device 12 and the detector 2 for the detection data include, for example, an acceleration sensor that detects the acceleration (vibration) of the bearing of the casting roll 121, a speed sensor that detects the rotational speed of the casting roll 121 or the motor, a tachometer that detects the rotation speed of the casting roll 121 or the motor, a torque sensor that detects the torque of the motor, a current sensor that detects the current of the motor, a thermometer or thermographic camera that detects the temperature of the casting roll 121 or the film, a laser sensor that detects the width of the film, a laser thickness sensor that detects the thickness of the film, and a gel counter that detects the number of gels in the film (particularly the film downstream of the casting roll 121).

[0041] Examples of detection data related to the MD stretching device 13 and the detector 2 for the detection data include an acceleration sensor that detects the acceleration (vibration) of the bearing of the tension roll 131, a tachometer that detects the rotation speed of the tension roll 131 or the motor, a speed sensor that detects the rotation speed of the tension roll 131 or the motor, a torque sensor that detects the torque of the motor, a current sensor that detects the current of the motor, a thermometer or thermographic camera that detects the temperature of the tension roll 131 or the film, a laser sensor that detects the width of the film, and a laser sensor that detects the thickness of the film.

[0042] Examples of the detection data related to the TD stretching device 14 and the detector 2 for the detection data include an acceleration sensor that detects the acceleration (vibration) of the TD stretching device 14 (particularly the rail 142 near the start and end points of the stretching zone in the oven 141), a velocity sensor that detects the velocity (vibration), a displacement sensor that detects the displacement (vibration), an AE sensor that detects elastic waves, a strain sensor that detects the distortion of the clip 144, a laser sensor that detects the phase difference (shift in the passing time of the reference position) between a pair of left and right clips 144, a photo sensor or a photoelectric sensor, a thermometer or a thermography camera that detects the temperature in the oven 141 (particularly for each zone), a pressure gauge that detects the pressure in the oven 141 (particularly for each zone), and a sensor that detects the air volume at the entrance and exit of the oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit). Examples of such sensors include an air flow meter that detects the wind speed at the entrance and exit of oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit), a pressure gauge that detects the pressure at the entrance and exit of oven 141 (particularly the air blowing nozzle of the hot air blowing device near the entrance and exit), a tachometer that detects the fan rotation speed in the hot air blowing device, a speed sensor that detects the line speed of the film transport line, a tension sensor that detects the tension of the film transport line or clip 144, a crystal orientation sensor that detects the degree of crystal orientation of the film (particularly the film at the most downstream of TD stretching device 14), a gel counter that detects the number of gels in the film (particularly the film at the most downstream of TD stretching device 14), a laser sensor that detects the width of the film, and a laser sensor that detects the thickness of the film.

[0043] An example of the detection data related to the winder 15 and the detector 2 for the detection data is a surface potential sensor that detects the amount of charge on the film.

[0044] Detector 2 is provided in an appropriate location on film forming machine 1 or in the vicinity of film forming machine 1 so as to detect the state of the member and film at a desired position in film forming machine 1. Detector 2 preferably has heat resistance, water resistance, oil resistance, etc., depending on the installation position. Note that detector 2 and the contents of the detection data are not limited to the above-mentioned example. In addition, the above-mentioned detection data is not limited to those detected by detector 2 exemplified in combination, and detector 2 is not limited to those for detecting the detection data exemplified in combination.

[0045] The above-mentioned forming condition parameters, operation data, and detection data are examples of parameters that affect the forming of the film by the film forming machine 1, and among them, the forming condition parameters correspond to parameters that can be controlled by the device settings.

[0046] <Data Collection Device 3> 2 is a block diagram showing an example of the configuration of the data collection device 3. The data collection device 3 is a computer, and includes a control unit 31, a storage unit 32, a communication unit 33, and a data input unit 34. The storage unit 32, the communication unit 33, and the data input unit 34 are connected to the control unit 31. The data collection device 3 is, for example, a PLC (Programmable Logic Controller).

[0047] The control unit 31 includes an arithmetic processing circuit such as a CPU (Central Processing Unit), a multi-core CPU, an ASIC (Application Specific Integrated Circuit), and an FPGA (Field-Programmable Gate Array), an internal storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), and an I / O terminal. The control unit 31 executes a control program stored in a storage unit 32 (described later) to collect molding information including detection data and operation data, and transmits the information to the estimation device 4. Each functional unit of the data collection device 3 may be realized by software, hardware, or a combination of these.

[0048] The storage unit 32 includes a non-volatile memory such as a hard disk, an EEPROM (Electrically Erasable Programmable ROM), a flash memory, etc. The storage unit 32 stores a control program for causing a computer to execute a process of collecting molding information.

[0049] The communication unit 33 includes a communication device that realizes communication via the networks N1 and N2. The communication unit 33 is connected to the control device 16 via the network N2, such as a LAN (Local Area Network). The control unit 31 can transmit and receive various information to and from the control device 16 via the communication unit 33. The communication unit 33 is also connected to the estimation device 4 on the cloud, which is the network N1, via a relay device (not shown). The relay device is, for example, a router, a gateway, etc. The control unit 31 can transmit and receive various information to and from the estimation device 4 via the relay device and the communication unit 33.

[0050] The data input unit 34 is an input interface that receives the signal output from the detector 2. The detector 2 is connected to the data input unit 34. The control unit 31 acquires the detection data output from the detector 2 via the data input unit 34 at any time.

[0051] The data collection device 3 and the control device 16 are not limited to being separate devices, and may be configured as one common device.

[0052] <Estimation device 4> 3 is a block diagram showing an example of the configuration of the estimation device 4. The estimation device 4 includes a control unit 41, a storage unit 42, and a communication unit 43. The storage unit 42 and the communication unit 43 are connected to the control unit 41. The estimation device 4 may be configured to perform distributed processing using a plurality of computers, may be realized by a plurality of virtual machines provided in one server, or may be realized by using a cloud server.

[0053] The control unit 41 has an arithmetic processing circuit such as a CPU, a multi-core CPU, an ASIC, an FPGA, etc., an internal storage device such as a ROM, a RAM, etc., an I / O terminal, etc. Each functional unit of the estimation device 4 may be realized by software, hardware, or a combination thereof.

[0054] The storage unit 42 includes a non-volatile memory such as a hard disk, a flash memory, or an SSD (Solid State Drive). The storage unit 42 may be an external storage device connected to the estimation device 4. The storage unit 42 stores various computer programs and data referenced by the control unit 41. The storage unit 42 of this embodiment stores a program 4P for causing a computer to execute processing related to the estimation of the molding condition parameters of the film forming machine 1, and a molding information DB (Data Base) 421 and an estimation model 422 required for executing the program 4P. The molding information DB 421 is a database that stores molding information received from each data collection device 3. The estimation model 422 is a learning model generated by machine learning. It is assumed that the estimation model 422 is used as a program module constituting a part of artificial intelligence software.

[0055] A computer program (computer program product) including the program 4P may be provided by a non-transitory recording medium 4A on which the computer program is readably recorded. The storage unit 42 stores the computer program read from the recording medium 4A by a reading device (not shown). The recording medium 4A is, for example, a magnetic disk, an optical disk, a semiconductor memory, or the like. The computer program may also be downloaded from an external server connected to a communication network and stored in the storage unit 42. The program 4P may be a single computer program or may be composed of multiple computer programs, and may be executed on a single computer or multiple computers interconnected by a communication network.

[0056] The communication unit 43 includes a communication device that realizes communication via the network N1. The control unit 41 can transmit and receive various information between the data collection device 3 and the terminal device 5 via the communication unit 43.

[0057] <Molding information DB421> FIG. 4 is a diagram showing an example of the contents of information stored in the molding information DB 421. The molding information DB 421 stores records in which information such as an equipment ID, date and time, operation data, and detection data is linked to, for example, a data ID as a key. The equipment ID represents an equipment identifier of the film molding machine 1. The date and time represent the year, month, date and time when the operation data or detection data stored as a record was obtained. The operation data includes the above-mentioned multiple types of operation data. The detection data includes the above-mentioned multiple types of detection data. Every time the estimation device 4 receives operation data and detection data from the data collection device 3, it stores them in chronological order in the molding information DB 421. The contents of the molding information DB 421 are updated as needed.

[0058] <Estimated Model 422> 5 is an explanatory diagram showing an overview of the estimation model 422. The estimation model 422 is a learning model that receives molding information including detection data and operation data of the film forming machine 1 as input, and outputs molding condition parameters for the film forming machine 1.

[0059] The estimation model 422 is, for example, a neural network model that learns forming condition parameters according to forming information by deep reinforcement learning and outputs the action values ​​of each of a plurality of actions a when a state s is input.

[0060] The estimation model 422 includes an input layer to which a state s is input, an output layer that outputs an action value Q(s, a) of an action a in the input state s, and an intermediate layer that extracts features of the state s.

[0061] The state s input to the estimation model 422 includes the detection data and operation data obtained from the film forming machine 1. The detection data and operation data include the above-mentioned multiple types of data. The detection data and operation data input to the estimation model 422 may be a combination of appropriate detection data and operation data selected from the detection data and operation data exemplified above. The detection data preferably includes both detection data detected by the detector 2 for state estimation and detection data detected by the detector 2 for operation control. For example, the detection data includes acceleration, pressure, air volume, period difference, strain, rotation number, rotation speed, temperature, pressure, torque, film thickness, etc., and the operation data includes rotation number, rotation speed, temperature, pressure, torque, etc. The molding information may include molding condition parameters set in the film forming machine 1. The detection data and operation data may be time-series data for a part of the entire period from the start of operation to the time of data acquisition or for a predetermined number of detections. The detection data and operation data may be input as image data representing the time-series data.

[0062] The state s may further include at least one of a target quality (e.g., a target film thickness) desired for the film to be manufactured and actual quality data (e.g., an actual measured film thickness) of the formed film, as shown in Fig. 5. The target quality may be included in the operation data and input as part of the operation data. The quality data may be included in the detection data and input as part of the detection data.

[0063] The input information input to the estimation model 422 may be the detection data and driving data themselves, or may be data that has been subjected to a predetermined pre-processing, such as deriving features based on the detection data and driving data.

[0064] The output layer of the estimation model 422 includes nodes corresponding to a plurality of actions a, and each node outputs an action value Q(s, a) for the corresponding action a. The action a corresponds to a change value of the molding condition parameter. The change value may be the value of the molding condition parameter after the change, or may be the amount of change of the molding condition parameter. In the following, the change value is assumed to be the value of the molding condition parameter. A highly valuable action a represents an appropriate molding condition parameter that should be set in the film molding machine 1. The output layer of the estimation model 422 may output an action value Q for a plurality of types of molding condition parameters.

[0065] The estimation device 4 selects the change value with the maximum action value based on the action value output from the estimation model 422, thereby being able to derive the change value of the molding condition parameter according to the molding information.

[0066] The estimation device 4 generates an estimation model 422 by deep reinforcement learning using predetermined data. In the reinforcement learning, the shaping information is the "state", the change value is the "action", the required "reward" is calculated, and the value of the Q value or Q function (action value function) is learned.

[0067] The update equation for the action value function is expressed by the following equation (1). Q(s,a)←Q(s,a)+α(R+γmaxQ(s_next,a_next)-Q(s,a)…(1) however, s:Status a: Action selected in state s α: learning coefficient R: Reward obtained as a result of the action γ: discount rate maxQ(s_next,a_next): The maximum value of the action value Q for the actions that can be taken in the next state

[0068] The estimation device 4 optimizes the parameters of the estimation model 422 by, for example, backpropagation so that the TD error, i.e., the second term on the right side of the above formula (1), approaches zero, thereby machine learning the estimation model 422. The parameters of the estimation model 422 are updated so that the error between the expected value of the reward and the current behavioral evaluation approaches zero.

[0069] The estimation device 4 performs a process of learning the molding condition parameters using the above-mentioned reinforcement learning. Specifically, the estimation device 4 acquires the target quality of the film, actual quality data, and molding information based on the information stored in the molding information DB 421. The target quality is, for example, a value set in advance by an operator. The target quality may be acquired as part of the operation data. The quality data is obtained by measuring the film thickness of the film molded in the operation state indicated by the molding information. The quality data may be acquired as part of the detection data. The quality data is not limited to actual measured values, and may be, for example, predicted values ​​obtained using a machine learning model that predicts quality data according to the molding information or a predetermined simulation model.

[0070] The estimation device 4 performs reinforcement learning based on the reward (score) calculated according to the target quality and quality data, with the molding condition parameters before the change being the state s and the changed value of the molding condition parameters being the action a. The reward is automatically calculated by the estimation device 4 based on the target quality included in the operation data stored in the molding information DB 421 and the quality data included in the detection data, for example. The estimation device 4 calculates the reward so that the smaller the deviation between the film thickness, which is the target quality, and the film thickness, which is the quality data, the larger the value becomes. When the deviation between the target quality and the quality data is large, a reward of zero or a negative reward is given. The calculation formula for calculating the reward is not particularly limited.

[0071] The target quality is not limited to the film thickness, but may be, for example, the degree of crystal orientation at the outlet of the TD stretching device 14, the film thickness distribution, the air permeability, the degree of crystallinity in the casting device 12, the transparency, etc. The target quality may include a plurality of physical properties.

[0072] The estimation device 4 trains the estimation model 422 based on the forming information input to the estimation model 422, the action value Q output when each state variable is input, and the calculated reward. The estimation device 4 may train the estimation model 422 using forming information acquired from a plurality of film forming machines 1. When the training is completed, the estimation device 4 stores definition information related to the trained estimation model 422 in the storage unit 42 as the trained estimation model 422.

[0073] The estimation model 422 is not limited to being generated and learned by the estimation device 4. The estimation model 422 may be a model that has been learned by an external server and transmitted to the estimation device 4 and stored in the storage unit 42. The estimation model 422 may be generated by an external server and learned by the estimation device 4.

[0074] The configuration of the estimation model 422 is not limited, and it is sufficient if it can estimate the change value of the molding condition parameter for the detection data and the operation data. The estimation model 422 may be configured to adjust the molding condition parameter using a map that associates the state quantity with the action value. The estimation model 422 may be a model learned by supervised learning using training data that associates the change value of the molding condition parameter that is the correct value with the molding information. The estimation model 422 may be a model based on other learning algorithms, such as, for example, a recurrent neural network (RNN), a convolution neural network (CNN), a graph neural network (GNN), a transformer, a support vector machine, a decision tree, a logistic regression, a random forest, etc.

[0075] The estimation model 422 can accurately estimate the molding condition parameters by taking into account various characteristics by using a wide variety of detection data and operation data as input elements. For example, it is considered that the deterioration state of the equipment members can be taken into account by inputting the mechanical vibration indicated by the acceleration. It is considered that the change in the properties of the resin can be taken into account by inputting the resin viscosity, which is particularly thought to lead to improved feedback of the feeder supply amount and the kneading conditions in the extruder 11. It is considered that the quality abnormality due to the presence or absence of unmixed parts of the resin raw material and the presence or absence of foreign matter can be taken into account by inputting the clip distortion. It is considered that the deterioration state of the equipment can be taken into account, which is particularly thought to lead to improved feedback of parameters that lead to reduced product defects. It is considered that the quality abnormality can be taken into account by inputting the crystal orientation degree. It is considered that the environment inside the oven 141 can be taken into account by inputting the wind speed or air volume in the oven 141, which is particularly thought to lead to improved feedback of the conditions inside the oven 141. It is considered that various temperature environments can be taken into account by inputting the temperature of each detection point and the detection target. It is considered that the film charge amount can be taken into account. It is believed that by inputting the synchronization error indicated by the phase difference between the left and right clips, it is possible to take into account the equipment load, sliding load due to deterioration, and quality changes. It is believed that by inputting the line tension, it is possible to take into account the wear state of the rail. It is believed that by inputting the motor torque, rotation number, and rotation speed of each detection point and detection target, it is possible to take into account the deterioration state of the drive unit.

[0076] 6 is a flowchart showing an example of a processing procedure executed by the estimation device 4. The control unit 41 of the estimation device 4 executes the following processing in accordance with a program 4P stored in the storage unit 42. The control unit 41 starts the following processing at predetermined or appropriate intervals.

[0077] The control unit 41 of the estimation device 4, by functioning as an acquisition unit, acquires molding information including detection data and operation data during continuous operation through the data collection device 3, and stores the acquired molding information in the molding information DB 421 (step S11). The molding information is associated with the device ID of the film forming machine 1. The detection data may include quality information, and the operation data may include target quality. The control unit 41 may acquire a plurality of detection data and operation data collectively.

[0078] The control unit 41 also functions as an estimation unit, and inputs molding information including the acquired detection data and operation data to the estimation model 422 (step S12). The input to the estimation model 422 may include target quality and quality data. The control unit 41 acquires molding condition parameters output from the estimation model 422 (step S13). In detail, the control unit 41 acquires change values ​​of one or more molding condition parameters by selecting an optimal action based on the output data from the estimation model 422.

[0079] The control unit 41 transmits information indicating the obtained changed values ​​of the molding condition parameters to the user terminal device 5 corresponding to the device ID of the film forming machine 1 (step S14). The control unit 41 ends the series of processes.

[0080] The user checks the changed values ​​of the molding condition parameters through the terminal device 5, and changes the settings of the molding condition parameters as necessary by operating the control device 16. When the control device 16 accepts the settings of the molding condition parameters, it outputs a control signal to the film forming machine 1 for controlling the operation of the film forming machine 1 according to the accepted changed molding condition parameters. The film forming machine 1 operates according to the control signal based on the changed molding condition parameters.

[0081] In step S14, the control unit 41 may automatically transmit the acquired changed values ​​of the molding condition parameters or an adjustment instruction corresponding to the changed values ​​of the molding condition parameters to the control device 16 via the data collecting device 3 etc. The control device 16 outputs a control signal corresponding to the changed values ​​of the molding condition parameters or the adjustment instruction acquired from the estimation device 4 to the film forming machine 1.

[0082] The control unit 41 may determine whether the change value of the acquired molding condition parameter exceeds a predetermined allowable range. If it is determined that the change value of the molding condition parameter exceeds a predetermined allowable range, the control unit 41 may output an alert to the terminal device 5 or the control device 16. If the change value is large, there is a possibility that the molding condition parameter is inappropriate or that the state of the film forming machine 1 is abnormal. With the above configuration, the possibility of such an abnormality can be discovered at an early stage.

[0083] The control unit 41 repeatedly executes the above-mentioned process during continuous operation of the film forming machine 1. By estimating the molding condition parameters while continuing the continuous operation based on the detection data and operation data detected during the continuous operation, feedback control according to the estimation result becomes possible. It is preferable that the control unit 41 executes the above-mentioned process in real time every time new detection data and operation data are detected. For example, by executing the process at relatively short intervals such as every minute or every five minutes, more appropriate operation control becomes possible.

[0084] Although the above describes an example in which the estimation device 4 executes a series of processes, the processing entity of each process is not limited. Each process in the flowchart may be executed, for example, by the control device 16, the data collection device 3, or the terminal device 5. The estimation model 422 may be deployed in the control device 16 or the data collection device 3, and the estimation process of the molding condition parameters may be executed on the control device 16 or the data collection device 3 side.

[0085] The estimation device 4 may transmit detailed information on the molding condition parameters, the detection data, and the operation data to the terminal device 5. The detailed information may be output in response to an estimation of a change value of the molding condition parameters, or in response to a request from the terminal device 5. The estimation device 4 may generate a trend graph representing at least one trend of the molding condition parameters, the detection data, and the operation data in a predetermined period based on information stored in the molding information DB 421 as the detailed information, for example.

[0086] The estimation device 4 may re-learn the estimation model 422 based on the molding information obtained during continuous operation. The estimation device 4 receives, for example, molding information, target quality, and quality data obtained during a predetermined period from the data collection device 3. The estimation device 4 performs re-learning based on the received molding information, target quality, and quality data, and updates the estimation model 422. The data used for re-learning may include molding information obtained from multiple film molding machines 1, etc.

[0087] The estimation device 4 may evaluate the accuracy of the estimation model 422 by an evaluation method such as cross-validation, and when the evaluation index satisfies a predetermined standard, update the estimation model 422. The estimation device 4 may receive a request for updating the estimation model 422 from a service provider. This makes it possible to improve the estimation accuracy of the molding condition parameters through the operation of this system.

[0088] According to this embodiment, the forming condition parameters of the film forming machine 1 can be estimated with high accuracy based on various detection data obtained by the detector 2 that detects the state of the film forming machine 1. In conventional film forming machines, the detection data by the detector 2 is mainly used for operation control, and the state of the film forming machine 1 during operation is not taken into consideration when adjusting the forming condition parameters. In this embodiment, by installing the detector 2 for the purpose of maintaining the film forming machine 1, it is possible to collect more detection data than could be obtained with conventional film forming machines 1 and use it for condition analysis.

[0089] By collecting detection data during continuous operation of the film forming machine 1 and estimating the forming condition parameters based on the collected data, appropriate feedback is possible.

[0090] By using the estimation model 422, it is possible to easily and accurately estimate the forming condition parameters of the film forming machine 1. By using a plurality of uncorrelated and independent detection data representing the state of the film forming machine 1 as input information for the estimation model 422, it is possible to improve the estimation accuracy of the forming condition parameters.

[0091] The film forming machine 1 includes many devices and components in one line, and each device and component operates in conjunction with each other. Therefore, the quality of the molded product decreases due to deterioration or minor malfunctions in some devices or components located upstream and downstream of the film forming machine 1. If the operation continues with a part of the device degraded, the load on the entire device increases, and the overall deterioration accelerates. In addition, many parameters in the film forming machine 1 affect each other. Changing one parameter to approach the target quality may cause other parameters to fluctuate, resulting in a deterioration of the quality. By using the estimation model 422, multiple parameters can be optimized simultaneously, taking into account a wide variety of molding information according to the status of each device and part in the film forming machine 1. By appropriately adjusting the parameters in accordance with the deterioration status of the film forming machine 1, the operating load of the film forming machine 1 can be reduced.

[0092] The estimation device 4 can accumulate a huge amount of data on the cloud and provide the estimation results of the molding condition parameters based on the accumulated huge amount of data to the terminal device 5. The user can clearly recognize the molding condition parameters.

[0093] The estimation device 4 can output the estimation results of the molding condition parameters to the film forming machine 1 via the control device 16. The process from estimation to adjustment of the molding condition parameters can be performed automatically, improving convenience.

[0094] Second embodiment In the second embodiment, a configuration will be described that uses an estimation model 422 or feature amounts corresponding to the film forming machine 1. In the following embodiment, differences from the first embodiment will be mainly described, and configurations common to the first embodiment will be denoted by the same reference numerals and detailed descriptions thereof will be omitted.

[0095] The estimation device 4 of the second embodiment stores a plurality of types of estimation models 422 generated by different algorithms in the storage unit 42. Prior to estimating the molding condition parameters according to the molding information, the estimation device 4 determines the estimation model 422 to be used for estimating the molding condition parameters based on the type of the film forming machine 1 used by the user and the molding information collected from the film forming machine 1. The estimation model 422 can be determined based on the estimation accuracy of each estimation model 422. For example, the accuracy of each estimation model 422 is evaluated by an evaluation method such as cross-validation based on the estimation result of each estimation model 422 for the test data of the molding information in the film forming machine 1. The estimation model 422 is determined by selecting the estimation model 422 with the highest estimation accuracy from all the prepared estimation models 422.

[0096] The estimation device 4 may generate an estimation model 422 specialized for estimating the molding condition parameters of the film forming machine 1 used by the user, by performing fine tuning on a previously generated and trained estimation model 422.

[0097] The estimation device 4 may also select the detection data and operation data to be used as input information to the estimation model 422 from a plurality of preset detection data and operation data according to the type of the film forming machine 1 used by the user. The detection data and operation data to be used as input information can be determined, for example, based on the contribution degree of the detection data and operation data. For example, estimation is performed by the estimation model 422 using a plurality of preset detection data and operation data, and the contribution degree of the input data corresponding to each detection data and operation data is calculated using SHAP (SHapley Additive exPlanation), LIME (Local Interpretable Model-Agnostic Explanations), or the like. The estimation device 4 determines the detection data and operation data to be used as input information by, for example, selecting detection data and operation data whose calculated contribution degree is equal to or greater than a predetermined value, or a predetermined number of detection data and operation data in descending order of contribution degree.

[0098] The estimation device 4 stores in the memory unit 42 the equipment ID of the determined estimation model 422 and the type of detection data and operation data used in the input information in association with the equipment ID of the film forming machine 1 to which the estimation model 422, the detection data, and the operation data are applied.

[0099] FIG. 7 is a flowchart showing an example of a processing procedure executed by the estimation device 4 of the second embodiment.

[0100] The control unit 41 of the estimation device 4 acquires molding information including detection data and operation data during continuous operation in association with the device ID of the film forming machine 1, and stores the acquired molding information in the molding information DB 421 (step S21).

[0101] The control unit 41 selects the estimation model 422 associated with the acquired device ID of the film forming machine 1 (step S22). In step S22, the control unit 41 specifies the estimation model 422 according to the acquired device ID based on the correspondence relationship between the film forming machine 1 and the applied estimation model 422 stored in advance.

[0102] Thereafter, the control unit 41 executes the same process as in the first embodiment to estimate the molding condition parameters using the selected estimation model 422. When the molding condition parameters to be used are set for each type of film forming machine 1, the control unit 41 identifies the molding condition parameters associated with the acquired device ID of the film forming machine 1 based on the correspondence between the film forming machine 1 and the molding condition parameters to be applied that is stored in advance.

[0103] According to this embodiment, the estimation accuracy of the molding condition parameters can be improved by using the estimation model 422, detection data, and operation data according to the type of film forming machine 1 owned by the user. The configuration of the film forming machine 1 often differs greatly from device to device. By setting the type of estimation model 422 and the detection data and operation data to be used according to the type of film forming machine 1, the molding condition parameters can be estimated with high accuracy in accordance with the state of the film forming machine 1.

[0104] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the scope of the claims and the scope equivalent to the claims. The sequences shown in each embodiment are not limited, and the order of each process may be changed and multiple processes may be executed in parallel, as long as there is no contradiction. The subject of each process is not limited, and the process of each device may be executed by another device, as long as there is no contradiction.

[0105] The matters described in each embodiment can be combined with each other. In addition, the independent claims and dependent claims described in the claims can be combined with each other in any and all combinations regardless of the citation format. Furthermore, the claims use a format in which a claim cites two or more other claims (multiple claim format), but this is not limited to this. A multiple claim (multi-multi claim) that cites at least one multiple claim may also be used.

[0106] Regarding the above embodiment, the following supplementary notes are further disclosed. (Appendix 1) Obtaining detection data regarding the state of the film forming machine detected by the detector; The molding condition parameters in the film forming machine are estimated by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when the detection data relating to the state of the film forming machine is input. A method for estimating molding condition parameters. (Appendix 2) acquiring the detection data during continuous operation of the film forming machine; The molding condition parameters are estimated based on the detection data while the continuous operation is continued. A method for estimating molding condition parameters as described in Appendix 1. (Appendix 3) Acquire quality data indicating the quality of the film formed by the film forming machine; The estimation model estimates molding condition parameters in a film forming machine when the detection data and quality data of the film forming machine are input, and the molding condition parameters are estimated by inputting the acquired detection data and quality data into the estimation model. A method for estimating molding condition parameters according to appendix 1 or appendix 2. (Appendix 4) The estimated molding condition parameters are transmitted to the film molding machine. A method for estimating a molding condition parameter according to any one of appendix 1 to appendix 3. (Appendix 5) Transmitting information indicating the estimated molding condition parameters to a user of the film molding machine. A method for estimating a molding condition parameter according to any one of appendix 1 to appendix 4. (Appendix 6) The film forming machine includes an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, The molding condition parameters include at least one of a feeder supply amount in the extruder, a rotation speed in the extruder, a lip opening of a die in the extruder, a rotation speed in a casting device, a rotation speed in a casting device, a rotation speed in a MD stretching device, a rotation speed in a MD stretching device, a line speed in a TD stretching device, a temperature in a TD stretching device, a pressure in a TD stretching device, an air volume in a TD stretching device, and an air speed in a TD stretching device. 6. A method for estimating a molding condition parameter according to any one of appendix 1 to appendix 5. (Appendix 7) The film forming machine includes an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes a physical quantity related to the extruder, a physical quantity related to the casting device, a physical quantity related to the MD stretching device, a physical quantity related to the TD stretching device, or a physical quantity related to the winding device. 7. A method for estimating a molding condition parameter according to any one of claims 1 to 6. (Appendix 8) The film forming machine includes an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes at least one of the acceleration in the extruder, the resin viscosity in the extruder, the acceleration in the casting device, the number of gels in the film in the casting device, the acceleration in the MD stretching device, the acceleration in the TD stretching device, the wind speed in the TD stretching device, the air volume in the TD stretching device, the distortion of the film gripping tools in the TD stretching device, the phase difference between a pair of left and right film gripping tools in the TD stretching device, the degree of crystal orientation in the TD stretching device, the number of gels in the film in the TD stretching device, and the amount of electrostatic charge of the film in the winder. 8. A method for estimating a molding condition parameter according to any one of claims 1 to 7. (Appendix 9) Detection data selected from a plurality of types of detection data set in advance is input to the estimation model. A method for estimating a molding condition parameter according to any one of appendix 1 to appendix 8. (Appendix 10) preparing a plurality of types of the estimation models generated by different algorithms; Select the estimation model to be used depending on the type of the film forming machine. 10. A method for estimating a molding condition parameter according to any one of appendix 1 to appendix 9. [Explanation of symbols]

[0107] 100 Estimation System 1 Film forming machine 11 Extruder 12 Casting equipment 13 MD stretching device 14 TD stretching device 15 Winder 16 Control device 2. Detector 3 Data collection equipment 31 Control Unit 32 Storage section 33 Communications Department 34 Data Entry Section 4 Estimation device 41 Control section 42 Storage section 43 Communications Department 4A Recording Media 4P Program 421 Molding information DB 422 Estimation Model 5 Terminal Equipment

Claims

1. Obtaining detection data regarding the state of the film forming machine detected by the detector; The molding condition parameters in the film forming machine are estimated by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when the detection data relating to the state of the film forming machine is input. A method for estimating molding condition parameters.

2. acquiring the detection data during continuous operation of the film forming machine; The molding condition parameters are estimated based on the detection data while the continuous operation is continued. The molding condition parameter estimation method according to claim 1 .

3. Acquire quality data indicating the quality of the film formed by the film forming machine; The estimation model estimates molding condition parameters in a film forming machine when the detection data and quality data of the film forming machine are input, and the molding condition parameters are estimated by inputting the acquired detection data and quality data into the estimation model. The molding condition parameter estimation method according to claim 1 or 2.

4. The estimated molding condition parameters are transmitted to the film molding machine. The molding condition parameter estimation method according to claim 1 or 2.

5. Transmitting information indicating the estimated molding condition parameters to a user of the film molding machine. The molding condition parameter estimation method according to claim 1 or 2.

6. The film forming machine includes an extruder, a cast device, an MD stretching device, a TD stretching device, and a winding device, The molding condition parameters include at least one of a feeder supply amount in the extruder, a rotation speed in the extruder, a lip opening of a die in the extruder, a rotation speed in the cast device, a rotation speed in the cast device, a rotation speed in the MD stretching device, a rotation speed in the MD stretching device, a line speed in the TD stretching device, a temperature in the TD stretching device, a pressure in the TD stretching device, an air volume in the TD stretching device, and an air speed in the TD stretching device. The molding condition parameter estimation method according to claim 1 or 2.

7. The film forming machine includes an extruder, a cast device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes a physical quantity related to the extruder, a physical quantity related to the casting device, a physical quantity related to the MD stretching device, a physical quantity related to the TD stretching device, or a physical quantity related to the winding device. The molding condition parameter estimation method according to claim 1 or 2.

8. The film forming machine includes an extruder, a cast device, an MD stretching device, a TD stretching device, and a winding device, The detection data includes at least one of acceleration in the extruder, resin viscosity in the extruder, acceleration in the casting device, number of gels in the film in the casting device, acceleration in the MD stretching device, acceleration in the TD stretching device, wind speed in the TD stretching device, air volume in the TD stretching device, distortion of film grippers in the TD stretching device, phase difference between a pair of left and right film grippers in the TD stretching device, degree of crystal orientation in the TD stretching device, number of gels in the film in the TD stretching device, and amount of electrostatic charge of the film in the winder. The molding condition parameter estimation method according to claim 1 or 2.

9. Detection data selected from a plurality of types of detection data set in advance is input to the estimation model. The molding condition parameter estimation method according to claim 1 or 2.

10. Prepare multiple types of estimation models generated by different algorithms, Select the estimation model to be used depending on the type of the film forming machine. The molding condition parameter estimation method according to claim 1 or 2.

11. Obtaining detection data regarding the state of the film forming machine detected by the detector; The molding condition parameters in the film forming machine are estimated by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when the detection data relating to the state of the film forming machine is input. A computer program for causing a computer to execute a process.

12. an acquisition unit that acquires detection data related to a state of the film forming machine detected by the detector; and an estimation unit that estimates molding condition parameters in the film forming machine by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when detection data related to a state of the film forming machine is input. Estimation device.

13. The present invention includes a film forming machine, a detector, and an estimation device, The estimation device includes: an acquisition unit that acquires detection data related to a state of the film forming machine detected by the detector; and an estimation unit that estimates molding condition parameters in the film forming machine by inputting the acquired detection data into an estimation model that estimates molding condition parameters in the film forming machine when detection data related to a state of the film forming machine is input. Estimation system.

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