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

Abstract

Provided is a molding condition parameter estimation method capable of appropriately adjusting molding condition parameters of a film molding machine.　The molding condition parameter estimation method comprises: acquiring detection data pertaining to the state of a film molding machine, said 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, and thereby estimating molding condition parameters for the film molding machine.

Description

Method for estimating molding condition parameters, computer program, estimation device, and estimation system

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

[0002] Conventionally, molding machines for molding resin materials using molding methods such as injection molding and extrusion molding have been widely used. In molding machines, it is necessary to adjust the settings of various molding parameters so that the molded product meets the required specifications. Adjustment of these molding parameters is 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 assist operators in adjusting molding parameter settings.

[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.

[0004] Japanese Patent Application Laid-Open No. 2017-30152

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

[0006] An object of the present disclosure is to provide a method for estimating molding condition parameters that can appropriately estimate molding condition parameters of a film molding machine.

[0007] A method for estimating molding condition parameters according to one aspect 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 aspect 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 forming 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 forming condition parameters in the film forming machine.

[0009] An estimation device according to one aspect of the present disclosure includes an acquisition unit that acquires detection data regarding the 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 the detection data regarding the state of the film forming machine is input.

[0010] An estimation system according to one aspect of the present disclosure includes a film forming machine, a detector, and an estimation device. The estimation device includes an acquisition unit that acquires detection data regarding the 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.

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

[0012] FIG. 1 is a schematic diagram of an estimation system according to a first embodiment; FIG. 2 is a block diagram showing an example of the configuration of a data collection device; FIG. 3 is a block diagram showing an example of the configuration of an estimation device; FIG. 4 is a diagram showing an example of the contents of information stored in a molding information DB; FIG. 5 is an explanatory diagram showing an overview of an estimation model; FIG. 6 is a flowchart showing an example of a processing procedure executed by an estimation device; and FIG. 7 is a flowchart showing an example of a processing procedure executed by an estimation device according to a second embodiment.

[0013] The present disclosure will be described in detail with reference to the drawings illustrating embodiments thereof. (First Embodiment) Fig. 1 is a schematic diagram of an estimation system 100 according to a first embodiment. The estimation system 100 includes a film forming machine 1, a plurality of detectors 2, and an estimation device 4 as main devices. The estimation device 4 is communicably connected to a data collection 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 collection device 3, but a plurality of data collection 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 collection device 3. Each data collection device 3 and one or more film forming machines 1 connected to the data collection 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, or a quantum computer. 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 each film forming machine 1 so that the quality of the film formed by each film forming machine 1 meets the target quality. The estimation device 4 according to the first embodiment adjusts multiple molding condition parameters set in the film forming machine 1 so that the film thickness (film thickness) 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 communication and display functions, such as a personal computer, a tablet terminal, or a smartphone. 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 that organization. The user of the film forming machine 1 may also 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 the 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. The resin raw material introduced into the hopper is discharged from the feeder into the cylinder 112 at a constant flow rate. The screw 113 is rotatably inserted into the bore of the cylinder 112 and transports the resin raw material discharged from the raw material supply section 111 in the extrusion direction (to the right in FIG. 1 ), where it is melted and kneaded. The screw 113 rotates at a predetermined rotation speed by a driving force output from a drive unit 115, for example, including a motor and a reducer (not shown). The extruder 11 extrudes the molten resin raw material into a film form through 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 are rotated 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 control unit (not shown) for cooling the molten material, and is axially 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 together with the second roll 121b, forms the film-like molten material into a film (sheet) while cooling it in a short time. The casting device 12 controls the film thickness within a predetermined range, thereby obtaining an unstretched film. The temperature of the first roll 121a can be controlled by any suitable method, including, but not limited to, a heat medium such as air, water, or oil, or 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. The unstretched film transported from the casting device 12 is inserted between the tension rolls 131 and stretched in the MD direction. The MD direction is the direction along the film transport direction and is also referred to as the longitudinal direction. The tension rolls 131 rotate at a predetermined rotation speed by a driving force output from a driving device 132 including, for example, a motor and a reducer (not shown).

[0022] The multiple 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 by the same method as that for the cast roll 121 described above. The film is heated to a predetermined temperature range within 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 between the cooling rolls. The stretch ratio in the MD direction can be adjusted by the speed ratio of the tension rolls 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 (width direction of the film) that intersects with the conveying 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), which heats the film to a predetermined temperature range at which the film can be stretched, and stretches the film transversely.

[0025] The TD stretching device 14 includes an oven 141 equipped with a heating device such as a hot air blower, and the oven 141 is provided with a traveling mechanism including a pair of left and right rails 142 and multiple links 143 arranged on the rails 142. The rails 142 are arranged so as to widen in the TD direction toward the downstream direction in the MD direction. The multiple links 143 form an endless chain. The endless chain made up of the multiple 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 gripping device, is attached to each of the multiple 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, i.e., 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 drive devices so that there is no phase difference between the clips 144, i.e., so that the conveyance 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, while gripping the film, are transported together with the links 143 in the film feed direction, traveling on rails 142 to transport the film in the film feed direction. Both sides of the film in the oven are heated by air blown from hot air blowers installed 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 TD width of the rails 142. The oven 141 is divided into, for example, multiple zones in the transport direction, and the set temperature or air volume can be adjusted for each zone. The zones include a preheating zone, a stretching zone, and a heat setting zone.

[0028] The clips 144 release 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 abnormality occurs in the device 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 external devices via wire or wirelessly, a display unit such as a liquid crystal display or an organic electroluminescence (EL) display that displays various images to the user, an operation unit that accepts operations from the user, and the like.

[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 collection 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 operating data indicating the operating state of the film forming machine 1 to the data collection device 3 through the communication unit. The data collection device 3 transmits the accepted operating data to the estimation device 4. Note that the operating data may also be transmitted to the estimation device 4 directly from devices such as motors and inverters in the film forming machine 1 without going through the collection device 3.

[0032] The film forming machine 1 is set with forming condition parameters that define the forming conditions, and operates in accordance with the forming condition parameters. Examples of molding condition parameters include the feeder supply amount in the extruder 11, the rotation speed or rotational speed of the screw 113, the rotation speed or rotational speed of the motor of the drive device 115, and the lip opening of the die 114, the rotation speed or rotational speed of the cast roll 121 in the casting device 12, the rotation speed or rotational speed of the motor of the drive device 122, and the film discharge amount, the rotation speed or rotational speed of the tension roll 131 in the MD stretching device 13, the rotation speed or rotational speed of the motor of the drive device 132, the stretching ratio, and the film discharge amount, as well as 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 nozzles of the hot air blowing device near the entrance and exit), the stretching angle, the stretching ratio, and the film discharge amount. Instead of the film discharge rate, the film speed, film width, and film thickness may be set. The optimal molding condition parameters change as needed 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 motor rotation speed, motor torque, and motor current, the rotation speed of the casting roll 121 in the casting device 12, the rotation speed of the motor, motor torque, and motor current, the rotation speed of the tension roll 131 in the MD stretching device 13, the motor rotation speed, motor torque, and 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> Detector 2 is a sensor that detects, in a time series manner, physical quantities that indicate the state of the film forming machine 1. The state of the film forming machine 1 detected by 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. Detector 2 is connected to data collecting device 3 by wire or wirelessly, and outputs the detection data obtained by detection to data collecting device 3 directly or indirectly.

[0035] The estimation system 100 includes a plurality of detectors 2 that detect various types of 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 not shown.

[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 method for the forming condition parameters of the film forming machine 1. 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 the detector 2 include an acceleration sensor, a speed sensor, a displacement sensor, an Acoustic Emission (AE) sensor, a viscometer, a gel counter, a strain sensor, a crystal orientation sensor, an anemometer, an air volume meter, 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 scale, a length measuring sensor, a photosensor (camera), a flow meter, a voltmeter, a timer, and a wattmeter.

[0038] Examples of the detection data detected by the detector 2 include acceleration, speed, displacement, viscosity, number of gels, strain, degree of crystalline 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, the detection data detected by the detector 2 is not limited to sensor values ​​directly detected by the 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 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 rotational 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 said 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 rails 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 clips 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 for 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 the oven 141 (particularly the air blowing nozzles of the hot air blowing device near the entrance and exit), an anemometer that detects the wind speed at the entrance and exit of the oven 141 (particularly the air blowing nozzles of the hot air blowing device near the entrance and exit), a pressure gauge that detects the pressure at the entrance and exit of the oven 141 (particularly the air blowing nozzles 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 the TD stretching device 14), a gel counter that detects the number of gels in the film (particularly the film at the most downstream of the 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] The detection data relating to the winder 15 and the detector 2 for the detection data include, for example, a surface potential sensor that detects the amount of charge on the film.

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

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

[0046] 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 programmable logic controller (PLC).

[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), an FPGA (Field-Programmable Gate Array), an internal storage device such as a ROM (Read Only Memory) and a RAM (Random Access Memory), an I / O terminal, etc. The control unit 31 executes a control program stored in a storage unit 32 (described later) to collect forming information including detection data and operation data and transmit 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 thereof.

[0048] The storage unit 32 includes a nonvolatile 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 for 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 local area network (LAN). 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, or the like. 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 signals 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 as needed.

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

[0052] 3 is a block diagram showing an example 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 multiple computers, may be realized by multiple virtual machines provided in a single server, or may be realized using a cloud server.

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

[0054] The storage unit 42 includes a nonvolatile memory such as a hard disk, flash memory, or 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 in this embodiment stores a program 4P for causing a computer to execute processing related to estimation of molding condition parameters of the film molding machine 1, as well as a molding information DB (Database) 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. The estimation model 422 is expected to be used as a program module constituting 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 may be, for example, a magnetic disk, an optical disk, or a semiconductor memory. Alternatively, the computer program may be downloaded from an external server connected to a communications 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 on multiple computers interconnected by a communications 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 to and from the data collection device 3 and the terminal device 5 via the communication unit 43.

[0057] <Molding Information DB 421> Figure 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 that link information such as equipment ID, date and time, operating data, and detection data, using, for example, a data ID as a key. The equipment ID represents the equipment identifier of the film molding machine 1. The date and time represent the year, month, date and time when the operating data or detection data stored as a record was obtained. The operating data includes the multiple types of operating data described above. The detection data includes the multiple types of detection data described above. Each time the estimation device 4 receives operating data and detection data from the data collection device 3, it stores the data in chronological order in the molding information DB 421. The contents of the molding information DB 421 are updated as needed.

[0058] 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 the action value Q(s, a) of an action a in the input state s, and an intermediate layer that extracts the features of the state s.

[0061] The state s input to the estimation model 422 includes detection data and operation data obtained from the film forming machine 1. The detection data and operation data include the multiple types of data described above. 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 portion 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] 5, 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 produced and actual quality data (e.g., an actual measured film thickness) of the formed film. 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 predetermined preprocessing, such as deriving features based on the detection data and driving data.

[0064] The output layer of the estimation model 422 has nodes corresponding to multiple actions a, and each node outputs an action value Q(s, a) for the corresponding action a. Action a corresponds to a changed value of a molding condition parameter. The changed value may be the value of the molding condition parameter after the change, or may be the amount of change in the molding condition parameter. In the following, the changed 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 multiple types of molding condition parameters.

[0065] The estimation device 4 can derive the change value of the molding condition parameter according to the molding information by selecting the change value with the maximum action value based on the action value output from the estimation model 422.

[0066] The estimation device 4 generates an estimation model 422 through deep reinforcement learning using predetermined data. In reinforcement learning, the shaped information is defined as a "state," the changed value is defined as an "action," a required "reward" is calculated, and a Q value or a Q function (action value function) value is learned.

[0067] The update formula for the action value function is expressed by the following formula (1): Q(s, a) ← Q(s, a) + α(R + γmaxQ(s_next, a_next) - Q(s, a)) ... (1) where, s: state 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): maximum value of action value Q for the action that can be taken in the next state

[0068] The estimation device 4 performs machine learning on the estimation model 422 by optimizing the parameters of the estimation model 422, for example, by backpropagation, so that the TD error, i.e., the second term on the right-hand side of the above formula (1), approaches zero. 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 operating data. The quality data is obtained by measuring the film thickness of the film molded in the operating 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, but may also be predicted values ​​obtained using, for example, a machine learning model or a predetermined simulation model that predicts quality data according to the molding information.

[0070] The estimation device 4 performs reinforcement learning based on a 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, for example, the target quality included in the operation data stored in the molding information DB 421 and the quality data included in the detection data. The estimation device 4 calculates the reward so that the smaller the difference between the film thickness, which is the target quality, and the film thickness, which is the quality data, the larger the value. If the difference between the target quality and the quality data is large, a zero reward 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 molding 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 molding information acquired from multiple 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 as long as it can estimate the change values ​​of the molding condition parameters based on the detection data and the operation data. The estimation model 422 may be configured to adjust the molding condition parameters using a map that associates state quantities with action values. The estimation model 422 may be a model trained by supervised learning using training data that associates the change values ​​of the molding condition parameters that are correct values ​​with the molding information. The estimation model 422 may be a model based on other learning algorithms, such as 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, or a random forest.

[0075] The estimation model 422 can accurately estimate molding condition parameters by taking into account a variety of characteristics through input of a wide variety of detection data and operational data. For example, inputting mechanical vibrations indicated by acceleration is considered to be able to take into account the deterioration state of equipment components. Inputting resin viscosity is considered to be able to take into account changes in resin properties, which is considered to lead to improved feedback of feeder supply rates and kneading conditions in the extruder 11, among other things. Inputting gel count is considered to be able to take into account quality abnormalities due to the presence or absence of unmixed portions of the resin raw material or the presence or absence of foreign matter. Inputting clip strain is considered to be able to take into account the deterioration state of the equipment, which is considered to lead to improved feedback of parameters that particularly contribute to reducing product defects. Inputting crystalline orientation is considered to be able to take into account quality abnormalities. Inputting the air speed or air volume in the oven 141 is considered to be able to take into account the environment within the oven 141, which is considered to lead to improved feedback of conditions within the oven 141, among other things. Inputting the temperatures of each detection location and detection target is considered to be able to take into account a variety of temperature environments. Inputting film charge is considered to be able to take into account film winding defects and quality abnormalities around the winder 15. By inputting the synchronization error indicated by the phase difference between the left and right clips, it is thought that it is possible to take into account the sliding load and quality changes due to equipment load and deterioration. By inputting the line tension, it is thought that it is possible to take into account the wear state of the rail. By inputting the motor torque, rotation number, and rotation speed of each detection point and detection target, it is thought that 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, 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 molding machine 1. The detection data may include quality information, and the operation data may include target quality. The control unit 41 may acquire multiple pieces of detection data and operation data together.

[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 a 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 changed values ​​for 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 acquired 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 then 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 to control the operation of the film forming machine 1 in accordance with the accepted changed molding condition parameters. The film forming machine 1 operates in accordance with the control signal based on the changed molding condition parameters.

[0081] In step S14, the control unit 41 may automatically transmit the acquired change values ​​of the molding condition parameters or adjustment instructions corresponding to the change values ​​of the molding condition parameters to the control device 16 via the data collection device 3, etc. The control device 16 outputs a control signal corresponding to the change values ​​of the molding condition parameters or adjustment instructions 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, it is possible that such an abnormality can be detected early.

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

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

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

[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.

[0087] The estimation device 4 may evaluate the accuracy of the estimation model 422 by an evaluation method such as cross-validation, and if the evaluation index satisfies a predetermined standard, may update the estimation model 422. The estimation device 4 may also receive a request from a service provider regarding whether or not the estimation model 422 needs to be updated. This allows the estimation accuracy of molding condition parameters to be improved through the operation of this system.

[0088] According to this embodiment, it is possible to accurately estimate the molding condition parameters of the film forming machine 1 based on a variety of detection data obtained by a detector 2 that detects the state of the film forming machine 1. In conventional film forming machines, the detection data from 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 molding condition parameters. In this embodiment, by installing a 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 this data 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 becomes possible.

[0090] Use of the estimation model 422 makes it possible to easily and accurately estimate the molding 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 molding condition parameters.

[0091] The film forming machine 1 includes many devices and components in a single line, and each device and component operates in conjunction with one another. Therefore, deterioration or minor malfunctions in some of the devices or components located upstream or downstream of the film forming machine 1 can result in a decrease in the quality of the molded product. Continuing operation with a partially deteriorated device increases the load on the entire device, accelerating overall deterioration. Furthermore, the 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 in quality. Using the estimation model 422, multiple parameters can be simultaneously optimized, taking into account a wide variety of molding information according to the status of each device and component in the film forming machine 1. By appropriately adjusting the parameters to track 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 store a huge amount of data on the cloud and provide the estimation results of the molding condition parameters based on the huge amount of stored 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 components common to the first embodiment will be assigned the same reference numerals and detailed descriptions thereof will be omitted.

[0095] The estimation device 4 of the second embodiment stores multiple types of estimation models 422 generated by different algorithms in the storage unit 42. Prior to estimating molding condition parameters according to molding information, the estimation device 4 determines the estimation model 422 to be used to estimate the molding condition parameters based on the type of 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 results of each estimation model 422 for test data of 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 of 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 molding machine 1 used by the user by fine-tuning a previously generated, trained estimation model 422.

[0097] The estimation device 4 may also select 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 depending on the type of 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 of the detection data and operation data. For example, estimation is performed using the estimation model 422 using a plurality of preset detection data and operation data, and the contribution 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 is equal to or greater than a predetermined value, or a predetermined number of detection data and operation data in descending order of contribution.

[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 operating data used as input information, in association with the equipment ID of the film forming machine 1 to which the estimation model 422, detection data, and operating 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 the detection data during continuous operation, molding information including operating data, and the equipment ID of the film molding machine 1 in association with each other, 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 identifies the estimation model 422 corresponding to the acquired device ID based on the correspondence relationship between the film forming machine 1 and the estimation model 422 to be applied, which is stored in advance.

[0102] Thereafter, the control unit 41 executes the same processing as in the first embodiment to estimate the molding condition parameters using the selected estimation model 422. When molding condition parameters to be used for each type of film forming machine 1 are set, 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, which 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 that correspond to the type of film forming machine 1 owned by the user. The configuration of each film forming machine 1 often varies greatly. 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 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 equivalents thereto. The sequences shown in each embodiment are not limited, and within the scope of no contradiction, each processing step may be executed in a different order, or multiple processes may be executed in parallel. The entity that performs each process is not limited, and within the scope of no contradiction, the process of each device may be executed by another device.

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

[0106] The following supplementary notes are further disclosed with respect to the above embodiments. (Supplementary Note 1) A method for estimating molding condition parameters in a film forming machine, comprising: acquiring detection data relating to the state of a film forming machine detected by a detector; and inputting the acquired detection data into an estimation model that estimates molding condition parameters for the film forming machine when the detection data relating to the state of the film forming machine is input; thereby estimating molding condition parameters for the film forming machine. (Supplementary Note 2) A method for estimating molding condition parameters as described in Supplementary Note 1, comprising: acquiring the detection data during continuous operation of the film forming machine; and estimating the molding condition parameters based on the detection data while the continuous operation is continued. (Supplementary Note 3) A method for estimating molding condition parameters as described in Supplementary Note 1 or Supplementary Note 2, comprising: acquiring quality data indicating the quality of a film formed by the film forming machine; and inputting the acquired detection data and quality data into the estimation model that estimates molding condition parameters for the film forming machine when the detection data and quality data for the film forming machine are input; thereby estimating the molding condition parameters. (Supplementary Note 4) The method for estimating molding condition parameters according to any one of Supplementary Note 1 to Supplementary Note 3, wherein the estimated molding condition parameters are transmitted to the film forming machine. (Supplementary Note 5) The method for estimating molding condition parameters according to any one of Supplementary Note 1 to Supplementary Note 4, wherein information indicating the estimated molding condition parameters is transmitted to a user of the film forming machine. (Appendix 6) The method for estimating molding condition parameters according to any one of Appendices 1 to 5, wherein the film forming machine comprises an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding machine, and the molding condition parameters include at least one of a feeder supply amount in the extruder, a rotation speed in the extruder, a die lip opening in the extruder, a rotation speed in the casting device, a rotation speed in the casting 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.(Supplementary Note 7) The method for estimating molding condition parameters according to any one of Supplementary Note 1 to Supplementary Note 6, wherein the film forming machine includes an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, and 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. (Appendix 8) The method for estimating molding condition parameters according to any one of Appendices 1 to 7, wherein the film forming machine comprises an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding device, and 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, air 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 crystalline orientation in the TD stretching device, number of gels in the film in the TD stretching device, and amount of electrostatic charge on the film in the winding device. (Appendix 9) The method for estimating molding condition parameters according to any one of Appendices 1 to 8, wherein detection data selected from a plurality of types of preset detection data is input to the estimation model. (Supplementary Note 10) The method for estimating molding condition parameters according to any one of Supplementary Note 1 to Supplementary Note 9, wherein a plurality of types of the estimation models generated by different algorithms are prepared, and the estimation model to be used is selected depending on the type of the film molding machine.

[0107] 100 Estimation system 1 Film forming machine 11 Extruder 12 Casting device 13 MD stretching device 14 TD stretching device 15 Winder 16 Control device 2 Detector 3 Data collection device 31 Control unit 32 Memory unit 33 Communication unit 34 Data input unit 4 Estimation device 41 Control unit 42 Memory unit 43 Communication unit 4A Recording medium 4P Program 421 Forming information DB 422 Estimation model 5 Terminal device

Claims

1. A method for estimating molding condition parameters, comprising: acquiring detection data relating to the state of a film forming machine detected by a detector; and 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, thereby estimating molding condition parameters in the film forming machine.

2. The method for estimating molding condition parameters according to claim 1, further comprising the steps of: acquiring the detection data during continuous operation of the film forming machine; and estimating the molding condition parameters based on the detection data while the continuous operation is continued.

3. A method for estimating molding condition parameters as described in claim 1 or claim 2, comprising: acquiring quality data indicating the quality of the film formed by the film forming machine; and estimating the molding condition parameters by inputting the acquired detection data and quality data into the estimation model which estimates molding condition parameters in the film forming machine when the detection data and quality data of the film forming machine are input.

4. The method for estimating a molding condition parameter according to any one of claims 1 to 3, wherein the estimated molding condition parameter is transmitted to the film molding machine.

5. The method for estimating a molding condition parameter according to any one of claims 1 to 4, further comprising transmitting information indicating the estimated molding condition parameter to a user of the film forming machine.

6. The method for estimating molding condition parameters according to any one of claims 1 to 5, wherein the film forming machine is equipped with an extruder, a casting device, an MD stretching device, a TD stretching device and a winding machine, and the molding condition parameters include at least one of the feeder supply amount in the extruder, the rotation speed in the extruder, the rotation speed in the extruder, the lip opening of a die in the extruder, the rotation speed in the casting device, the rotation speed in the casting device, the rotation speed in the MD stretching device, the rotation speed in the MD stretching device, the line speed in the TD stretching device, the temperature in the TD stretching device, the pressure in the TD stretching device, the air volume in the TD stretching device, and the air speed in the TD stretching device.

7. A method for estimating molding condition parameters according to any one of claims 1 to 6, wherein the film forming machine is equipped with an extruder, a casting device, an MD stretching device, a TD stretching device and a winding device, and 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.

8. The method for estimating molding condition parameters according to any one of claims 1 to 7, wherein the film forming machine is equipped with an extruder, a casting device, an MD stretching device, a TD stretching device, and a winding machine, and the detection data includes at least one of the following: 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 a film gripper 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 winding machine.

9. A molding condition parameter estimation method according to any one of claims 1 to 8, wherein detection data selected from a plurality of types of preset detection data is input to the estimation model.

10. A method for estimating molding condition parameters according to any one of claims 1 to 9, comprising preparing a plurality of types of estimation models generated by different algorithms, and selecting an estimation model to be used depending on the type of the film molding machine.

11. A computer program for causing a computer to execute a process of acquiring detection data relating to the state of a film forming machine detected by a detector, and estimating 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.

12. An estimation device comprising: an acquisition unit that acquires detection data relating to the 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.

13. An estimation system comprising a film forming machine, a detector, and an estimation device, wherein the estimation device comprises: an acquisition unit that acquires detection data relating 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 relating to the state of the film forming machine is input.

Images