Judgment device, judgment system, judgment method, and program

Abstract

To determine the condition of materials and equipment with greater accuracy based on torque. [Solution] The determination device comprises an estimation unit that estimates information regarding the torque generated when a predetermined material is extruded from a predetermined device based on predetermined information; an acquisition unit that acquires measurement results regarding the torque generated when the predetermined material is extruded from the predetermined device; and a determination unit that determines the state of the material or the predetermined device based on the estimation results by the estimation unit and the measurement results acquired by the acquisition unit.

Description

Technical Field

[0001] The present invention relates to a determination device, a determination system, a determination method, and a program.

Background Art

[0002] Conventionally, there has been a device that processes and conveys materials by extruding a predetermined material using a rotating mechanism such as a screw. For example, an extruder melts a material such as a polymer introduced into a cylinder (barrel) and extrudes it from a die at a discharge port for molding. In such a device, as one method for determining whether the state of the material in the cylinder and the state of the device itself are normal, there is a method of estimating the force (torque) applied to the rotating shaft of the screw and making a determination based on the estimated torque. For example, Patent Document 1 describes a data processing system that determines the presence or absence of an abnormality in a fluid pressure drive valve from the estimated operating torque.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, the value of the torque applied to the actual rotating shaft generally varies depending on the type of material and device, and may further vary depending on the surrounding environment such as temperature and humidity. Therefore, even if the torque is estimated simply based on the type of material and device, variations are likely to occur in the estimation results. As a result, conventionally, there has been a problem that it is difficult to accurately determine the state of the material and the device based on the torque.

[0005] The present invention has been made in view of the above circumstances, and aims to provide a determination device, determination system, determination method, and program that can determine the state of materials and equipment with greater accuracy based on torque. [Means for solving the problem]

[0006] The present invention encompasses the following [1] to

[11] . [1] A determination device comprising: an estimation unit that estimates information relating to the torque generated when a predetermined material is extruded from a predetermined device based on predetermined information; an acquisition unit that acquires measurement results of the information relating to the torque generated when the predetermined material is extruded from the predetermined device; and a determination unit that determines the state of the material or the predetermined device based on the estimation results by the estimation unit and the measurement results acquired by the acquisition unit. [2] The determination device according to [1], wherein the estimation unit estimates using a learning model obtained in advance by performing a learning process using training data which includes the torque generated when the predetermined material is pushed out from the predetermined equipment, information about the predetermined material or information about the predetermined equipment, and the predetermined information. [3] The determination device according to [1] or [2], wherein the predetermined information is a characteristic quantity indicating the state of the predetermined material, or a characteristic quantity indicating the state of the predetermined equipment. [4] The determination device according to [3], wherein the characteristic quantity indicating the state of the predetermined material is at least one characteristic quantity from the temperature, density, viscosity, chromaticity, transparency, and metal content of the predetermined material. [5] The determination device according to [3], wherein the characteristic quantity indicating the state of the predetermined equipment is at least one characteristic quantity from the temperature, vibration amount, internal pressure, and generated sound volume of the predetermined equipment. [6] The determination device according to [1] or [2], wherein the predetermined information is information indicating the processing steps of the predetermined material. [7] The determination device described in [1] or [2], wherein the predetermined information is information indicating the surrounding environment of the predetermined equipment. [8] The determination device according to [7], wherein the information indicating the surrounding environment of the predetermined equipment is information indicating at least one of temperature, humidity, and atmospheric pressure. [9] A determination system comprising an extruder for extruding a predetermined material from a predetermined device, and a determination device, wherein the determination device includes an estimation unit for estimating information relating to the torque generated when the predetermined material is extruded from the predetermined device based on predetermined information, an acquisition unit for acquiring measurement results of the information relating to the torque generated when the predetermined material is extruded from the predetermined device, and a determination unit for determining the state of the material or the predetermined device based on the estimation results by the estimation unit and the measurement results acquired by the acquisition unit.

[10] A computer-based determination method comprising: an estimation step of estimating information relating to the torque generated when a predetermined material is extruded from a predetermined device based on predetermined information; an acquisition step of acquiring measurement results relating to the torque generated when the predetermined material is extruded from the predetermined device; and a determination step of determining the state of the material or the predetermined device based on the estimation results in the estimation step and the measurement results acquired in the acquisition step.

[11] A program for causing a computer to perform an estimation step of estimating information relating to the torque generated when a predetermined material is extruded from a predetermined device, based on predetermined information; an acquisition step of acquiring measurement results relating to the torque generated when the predetermined material is extruded from the predetermined device; and a determination step of determining the state of the material or the predetermined device based on the estimation results in the estimation step and the measurement results acquired in the acquisition step. [Effects of the Invention]

[0007] According to the present invention, the state of materials and equipment can be determined with greater accuracy based on torque. [Brief explanation of the drawing]

[0008] [Figure 1] This is an overall configuration diagram of the determination system 1 in one embodiment of the present invention. [Figure 2] This is a block diagram showing the functional configuration of the determination device 10 in one embodiment of the present invention. [Figure 3] This is a flowchart showing the operation of the determination device 10 during the learning process in one embodiment of the present invention. [Figure 4] This is a flowchart showing the operation of the determination device 10 during the determination process in one embodiment of the present invention. [Figure 5] This is an overall configuration diagram of the determination system 1a in a modified example of one embodiment of the present invention. [Modes for carrying out the invention]

[0009] The following describes in detail the determination device, determination system, determination method, and program in embodiments of the present invention with reference to the drawings.

[0010] [Overall structure of the judgment system] Figure 1 is an overall configuration diagram of a determination system 1 in one embodiment of the present invention. As shown in Figure 1, the determination system 1 comprises a determination device 10, a temperature sensor 15, a torque sensor 16, and an extruder 20. The extruder 20 also includes a hopper 21, a heating cylinder 22 (barrel), and a motor 23. A screw 24 is housed inside the heating cylinder 22, which is a cylindrical steel material. The screw 24 is fixed to the rotation shaft of the motor 23 and rotates inside the heating cylinder 22.

[0011] The material to be molded is fed into the hopper 21. This material is, for example, granular (pellet-shaped) polymer (synthetic resin, etc.). An opening 25 is provided at the point where the heating cylinder 22 connects to the hopper 21. This allows the material fed into the hopper 21 to be further fed into the heating cylinder 22.

[0012] Inside the heating cylinder 22, the input material is heated and melted by the heat from a heater (not shown) and the shear heat generated by the rotation of the screw 24. The melted material is conveyed toward the discharge port 26 by the rotation of the screw 24. The discharge port 26 is equipped with a mold (die) having a desired shape, and the material is formed into the desired shape by being extruded from the discharge port 26.

[0013] The temperature sensor 15 is a sensor that measures the temperature of the material conveyed inside the heating cylinder 22. The temperature sensor 15 is attached to, for example, the heating cylinder 22. Note that the temperature sensor 15 may measure the temperature of the heating cylinder 22 instead of the temperature of the material. The temperature sensor 15 is communicatively connected to the determination device 10. The temperature sensor 15 outputs the measured temperature value to the determination device 10.

[0014] The torque sensor 16 is a sensor that measures the load (torque) applied to the rotating shaft of the motor 23. The torque sensor 16 is attached to, for example, the motor 23. The torque sensor 16 is communicatively connected to the determination device 10. The torque sensor 16 outputs the measured torque value to the determination device 10.

[0015] The determination device 10 is an information processing device such as a general-purpose computer. The determination device 10 acquires the temperature value of the material conveyed inside the heating cylinder 22 from the temperature sensor 15 and acquires the torque value applied to the rotating shaft of the motor 23 from the torque sensor 16. The determination device 10 uses the acquired temperature value and torque value to determine whether the state of the material inside the heating cylinder 22 and the state of the extruder 20 are normal. Thus, the determination device 10 is an information processing device for detecting, for example, abnormalities occurring in the material inside the heating cylinder 22 and abnormalities occurring in the extruder 20. Hereinafter, the functional configuration of the determination device 10 will be described in more detail.

[0016] [Functional Configuration of the Determination Device 10] The determination device 10 in this embodiment has a function of performing machine learning. By performing machine learning, the determination device 10 can estimate the torque generated on the rotating shaft of the motor 23 based on information such as the temperature of the material in the heating cylinder 22. The determination device 10 compares the value of the torque estimated by machine learning with the value of the torque actually generated on the rotating shaft. Based on the comparison result, the determination device 10 determines whether the state of the material in the heating cylinder 22 and the state of the extruder 20 are normal.

[0017] FIG. 2 is a block diagram showing the functional configuration of the determination device 10 in an embodiment of the present invention. As shown in FIG. 2, the determination device 10 includes a learning unit 11, a torque estimation unit 12, and a state determination unit 13.

[0018] The learning unit 11 includes an equipment / material information acquisition unit 111, a temperature information acquisition unit 112, a torque information acquisition unit 113, a learning model generation unit 114, and a learning model storage unit 115.

[0019] The equipment / material information acquisition unit 111 acquires equipment information and material information. The equipment / material information acquisition unit 111 outputs the acquired equipment information and material information to the learning model generation unit 114. The equipment information here is information regarding the determination device 10 used. For example, the equipment information is information indicating the type of the determination device 10, information indicating the characteristics of the determination device 10, and information indicating the specifications of the determination device 10, etc. Also, the material information here is information regarding the material used. For example, the material information is information indicating the type of the material and information indicating the characteristics of the material, etc. The equipment information and the material information are information that can affect the estimation result of the torque.

[0020] The temperature information acquisition unit 112 acquires temperature information. The temperature information acquisition unit 112 outputs the acquired temperature information to the learning model generation unit 114. The temperature information here is information indicating the value of the temperature of the material in the heating cylinder 22.

[0021] The torque information acquisition unit 113 acquires torque information. The torque information acquisition unit 113 outputs the acquired torque information to the learning model generation unit 114. Here, torque information refers to information indicating the value of the torque generated on the rotation axis of the motor 23.

[0022] The above-mentioned equipment / material information acquisition unit 111, temperature information acquisition unit 112, and torque information acquisition unit 113 are functional units that acquire training data used in machine learning. The equipment / material information acquisition unit 111 and temperature information acquisition unit 112 acquire equipment information, material information, and temperature information, which are explanatory variables for machine learning. The torque information acquisition unit 113 acquires torque information, which is the correct label for machine learning.

[0023] In other words, the determination device 10 in this embodiment performs supervised machine learning using the learning unit 11. However, it is not limited to this configuration, and other configurations such as unsupervised machine learning may be used, as long as they generate a learning model that can estimate torque information based on equipment information, material information, and temperature information.

[0024] The equipment / material information acquisition unit 111, the temperature information acquisition unit 112, and the torque information acquisition unit 113 are communication interfaces that receive equipment / material information, temperature information, and torque information, respectively, from an external device (not shown) that is connected to the determination device 10. In other words, the learning unit 11 acquires training data from an external device (not shown). However, the configuration is not limited to this, and the learning unit 11 may be configured to acquire training data that has been pre-stored in a storage device (not shown) provided by the determination device 10, for example. Alternatively, the learning unit 11 may be configured to acquire training data that has been manually entered by a user using an input interface such as a keyboard (not shown) or touch panel (not shown) provided by the determination device 10, for example.

[0025] The learning model generation unit 114 acquires equipment information / material information, temperature information, and torque information output from the equipment / material information acquisition unit 111, the temperature information acquisition unit 112, and the torque information acquisition unit 113, respectively. The learning model generation unit 114 uses the acquired equipment information / material information, temperature information, and torque information as training data to perform machine learning and generate a learning model. The learning model generation unit 114 stores the generated, trained learning model in the learning model storage unit 115. The learning model generation unit 114 is configured using a processor such as a CPU (Central Processing Unit).

[0026] The learning model generation unit 114 generates a learning model by performing machine learning using, for example, backpropagation, which is one of the learning methods using neural networks. In this case, the learning model is a neural network model in which the parameters (weights) are updated to minimize the value of the loss function by inputting training data. However, the learning method used by the learning model generation unit 114 is not limited to this, and any known learning method can be used.

[0027] The learning model storage unit 115 stores the learned model generated by the learning model generation unit 114. The learning model storage unit 115 is configured to include, for example, semiconductor memory such as RAM (Random Access Memory) and EEPROM (Electrically Erasable Programmable Read-Only Memory), flash memory such as SSD (Solid State Drive), magnetic disks such as HDD (Hard Disk Drive), optical disks, or any combination of these storage media.

[0028] The learning unit 11 described above is a functional unit for generating a learning model by performing machine learning in advance, and is mainly used during the learning process. In contrast, the torque estimation unit 12 and state determination unit 13 described below are functional units for estimating the torque applied to the rotation axis of the actual motor 23 using the learned learning model, and for determining the state of the material and equipment based on the estimation result, and are used during the determination process.

[0029] As shown in Figure 2, the torque estimation unit 12 is composed of an equipment / material information acquisition unit 121, a temperature information acquisition unit 122, an estimation unit 123, and an estimation result storage unit 124.

[0030] The equipment / material information acquisition unit 121 acquires equipment information and material information. The equipment / material information acquisition unit 121 outputs the acquired equipment information and material information to the estimation unit 123. As mentioned above, equipment information refers to information about the judgment device 10 used. For example, equipment information includes information indicating the type of judgment device 10, information indicating the characteristics of the judgment device 10, and information indicating the specifications of the judgment device 10. Material information refers to information about the materials used. For example, material information includes information indicating the type of material and information indicating the characteristics of the material. Equipment information and material information are information that can affect the torque estimation result. The equipment / material information acquisition unit 121 is a communication interface that receives equipment information and material information from, for example, an external device (not shown) that is connected to the judgment device 10.

[0031] The temperature information acquisition unit 122 acquires temperature information from the temperature sensor 15, indicating the temperature value of the material inside the heating cylinder 22. The temperature information acquisition unit 122 outputs the acquired temperature information to the estimation unit 123.

[0032] The estimation unit 123 acquires equipment information, material information, and temperature information output from the equipment / material information acquisition unit 121 and the temperature information acquisition unit 122, respectively. The estimation unit 123 also acquires a trained model stored in the trained model storage unit 115. The estimation unit 123 performs estimation by inputting the acquired equipment information, material information, and temperature information into the trained model and obtains the estimated torque value of the motor 23's rotation axis (hereinafter referred to as "estimated torque information"). The estimation unit 123 stores the obtained estimated torque information in the estimation result storage unit 124. The estimation unit 123 is configured using a processor such as a CPU.

[0033] The estimation result storage unit 124 stores the estimated torque information output from the estimation unit 123. The estimation result storage unit 124 is configured to include, for example, semiconductor memory such as RAM and EEPROM, flash memory such as SSD, magnetic disk such as HDD, optical disk, or any combination of these storage media.

[0034] As shown in Figure 2, the state determination unit 13 is composed of a torque information acquisition unit 131, a determination unit 132, a determination logic storage unit 133, and a determination result output unit 134.

[0035] The torque information acquisition unit 131 acquires torque information from the torque sensor 16, which indicates the torque value related to the rotation axis of the motor 23. The torque information acquisition unit 131 outputs the acquired torque information to the determination unit 132.

[0036] The determination unit 132 acquires torque information output from the torque information acquisition unit 131. The determination unit 132 also acquires estimated torque information stored in the estimated result storage unit 124. The determination unit 132 compares the torque value based on the torque information (actual torque value) with the torque value based on the estimated torque information (estimated torque value). Based on the comparison result and the determination logic stored in the determination logic storage unit 133, the determination unit 132 determines the state of the material in the heating cylinder 22 and the state of the extruder 20. The determination unit 132 outputs information indicating the determination result to the determination result output unit 134. The determination unit 132 is configured using a processor such as a CPU.

[0037] The judgment logic storage unit 133 stores judgment logic in advance. The judgment logic referred to here is, for example, a predetermined judgment rule for determining the state of the material in the heating cylinder 22 and the state of the extruder 20 according to the difference between the torque value based on torque information (measured value) and the torque value based on estimated torque information (estimated value). For example, the judgment logic storage unit 133 stores judgment logic that determines that at least one of the states of the material in the heating cylinder 22 and the state of the extruder 20 is abnormal if the difference between the measured value and the estimated value is greater than a predetermined value.

[0038] The determination logic storage unit 133 is configured to include, for example, semiconductor memory such as RAM and EEPROM, flash memory such as SSD, magnetic disk such as HDD, optical disk, or any combination of these storage media.

[0039] The judgment result output unit 134 acquires information indicating the judgment result output from the judgment unit 132. The judgment result output unit 134 outputs the acquired information indicating the judgment result to an external device (not shown) that is communicated with the judgment device 10. The judgment result output unit 134 is, for example, a communication interface that transmits information indicating the judgment result to an external device (not shown). The judgment result output unit 134 may also be configured to display the information indicating the judgment result on a display device (not shown) provided by the judgment device 10. In this case, the display device may include, for example, a liquid crystal display (LCD), an organic EL (Electro-Luminescence) display, or a CRT (Cathode Ray Tube) display.

[0040] [Operation during the learning process] The following describes an example of the operation of the determination device 10 during the learning process (operation of the learning unit 11). Figure 3 is a flowchart showing the operation of the determination device 10 during the learning process in one embodiment of the present invention.

[0041] The equipment / material information acquisition unit 111 acquires equipment information and material information (step S101). The temperature information acquisition unit 112 acquires temperature information (step S102). The torque information acquisition unit 113 acquires torque information (step S103).

[0042] The learning model generation unit 114 acquires equipment information / material information, temperature information, and torque information output from the equipment / material information acquisition unit 111, temperature information acquisition unit 112, and torque information acquisition unit 113, respectively. The learning model generation unit 114 uses the acquired equipment information / material information, temperature information, and torque information as training data to perform machine learning and generate a learning model (step S104). The learning model generation unit 114 stores the generated, trained learning model in the learning model storage unit 115 (step S105).

[0043] This completes the operation of the determination device 10 during the learning process (operation of the learning unit 11) as shown in the flowchart of Figure 3.

[0044] [Actions during the judgment process] The following describes an example of the operation of the determination device 10 during the determination process (operation of the torque estimation unit 12 and the state determination unit 13). Figure 4 is a flowchart showing the operation of the determination device 10 during the determination process in one embodiment of the present invention.

[0045] The equipment and material information acquisition unit 121 acquires equipment information and material information (step S201). The temperature information acquisition unit 122 acquires temperature information from the temperature sensor 15, indicating the temperature value of the material inside the heating cylinder 22 (step S202).

[0046] The estimation unit 123 acquires equipment information, material information, and temperature information output from the equipment / material information acquisition unit 121 and the temperature information acquisition unit 122, respectively. The estimation unit 123 also acquires a trained model stored in the trained model storage unit 115 (step S203). The estimation unit 123 performs estimation by inputting the acquired equipment information, material information, and temperature information into the trained model and obtains the estimated torque value of the motor 23's rotation shaft (estimated torque information) as an estimation result (step S204). The estimation unit 123 stores the obtained estimated torque information in the estimation result storage unit 124.

[0047] The torque information acquisition unit 131 acquires torque information from the torque sensor 16, indicating the torque value related to the rotation axis of the motor 23 (step S205). The determination unit 132 acquires the torque information output from the torque information acquisition unit 131. The determination unit 132 also acquires the estimated torque information stored in the estimated result storage unit 124. The determination unit 132 compares the torque value based on the torque information (actual torque value) with the torque value based on the estimated torque information (estimated torque value) (step S206). Based on the comparison result and the determination logic stored in the determination logic storage unit 133, the determination unit 132 determines the state of the material in the heating cylinder 22 and the state of the extruder 20 (step S207).

[0048] The judgment result output unit 134 acquires information indicating the judgment result output from the judgment unit 132. The judgment result output unit 134 outputs the acquired information indicating the judgment result to an external device (not shown) that is connected to the judgment device 10 via communication (step S208).

[0049] This concludes the operation of the determination device 10 during the determination process as shown in the flowchart of Figure 4 (operation of the torque estimation unit 12 and the state determination unit 13).

[0050] As described above, the determination device 10 in one embodiment of the present invention estimates information regarding the torque generated when a predetermined material is extruded from the extruder 20, based on the temperature of the material in the heating cylinder 22, which is obtained from the temperature sensor 15. The determination device 10 also obtains measurement results regarding the torque generated when the predetermined material is extruded from the extruder 20 from the torque sensor 16. Then, the determination device 10 determines the state of the material in the heating cylinder 22 or the state of the extruder 20 based on the estimated torque value and the measured torque value.

[0051] By having such a configuration, the determination device 10 in one embodiment of the present invention can determine the state of the material in the heating cylinder 22 and the extruder 20 with greater accuracy based on torque.

[0052] (modified version) In the embodiment described above, as an example, the learning unit 11, torque estimation unit 12, and state determination unit 13 are all provided in a single device (determination device 10), but the invention is not limited to this. For example, the learning unit 11 and the torque estimation unit 12 and state determination unit 13 may be provided in two separate devices.

[0053] Figure 5 is an overall configuration diagram of a determination system 1a in a modified example of one embodiment of the present invention. As shown in Figure 5, the determination system 1a comprises a learning device 10a, a determination device 10b, a temperature sensor 15, a torque sensor 16, and an extruder 20. The learning device 10a includes the learning unit 11 described above. The determination device 10b includes the torque estimation unit 12 and the state determination unit 13 described above. The determination device 10b estimates the torque related to the rotation shaft of the motor 23 using a learned model generated by the learning device 10a, and determines the state of the material in the heating cylinder 22 and the state of the extruder 20 by comparing the estimated torque with the actually measured torque.

[0054] For example, the learning unit 11, the torque estimation unit 12, and the state determination unit 13 may be provided in three separate devices, or the learning unit 11, the torque estimation unit 12, and the state determination unit 13 may be provided in two separate devices.

[0055] In the embodiments described above, the device whose state is determined by the determination device 10 is an extruder 20, but the invention is not limited to this. The present invention can be applied to any device that has a rotating mechanism, such as a motor 23, and is capable of determining the state (of a material or device, etc.) based on the torque applied to the rotating shaft.

[0056] In the above-described embodiment, the determination device 10 is configured to perform machine learning and state determination based on the temperature of the material in the heating cylinder 22, but it is not limited to this configuration. For example, the determination device 10 may be configured to perform machine learning and state determination based on a feature quantity indicating the state of a predetermined material, or a feature quantity indicating the state of a predetermined device, instead of the temperature of the material.

[0057] In this case, the characteristic quantity indicating the state of a given material is, for example, at least one of the following characteristics of the given material: density, viscosity, chromaticity, transparency, and metal content. Similarly, the characteristic quantity indicating the state of a given device is, for example, at least one of the following characteristics of the given device: temperature, vibration level, internal pressure, and generated sound volume.

[0058] Furthermore, for example, the determination device 10 may be configured to perform machine learning and state determination based on information indicating a predetermined material processing step, instead of the material's temperature.

[0059] Furthermore, for example, the determination device 10 may be configured to perform machine learning and state determination based on information indicating the surrounding environment of a predetermined device, instead of the temperature of the material. In this case, the information indicating the surrounding environment of the predetermined device is, for example, information indicating at least one of temperature, humidity, and atmospheric pressure.

[0060] According to the embodiment described above, the determination device comprises an estimation unit, an acquisition unit, and a determination unit. For example, the determination device is the determination device 10 in the embodiment, the estimation unit is the estimation unit 123 in the embodiment, the acquisition unit is the torque information acquisition unit 113 and 131 in the embodiment, and the determination unit is the determination unit 132 in the embodiment. The estimation unit estimates information regarding the torque generated when a predetermined material is extruded from a predetermined device, based on predetermined information. For example, the predetermined device is the extruder 20 in the embodiment, the information regarding torque is torque information indicating the torque value measured by the torque sensor 16 in the embodiment, and the predetermined information is temperature information indicating the temperature value measured by the temperature sensor 15 in the embodiment. The acquisition unit acquires the measurement results regarding the torque generated when a predetermined material is extruded from a predetermined device. The determination unit determines the state of the material or the predetermined device based on the estimation result by the estimation unit and the measurement result acquired by the acquisition unit. For example, the estimation result by the estimation unit is the estimated torque information (estimated value) in the embodiment, and the measurement result acquired by the acquisition unit is the torque information (measured value) in the embodiment.

[0061] In the above-described determination device, the estimation unit performs estimation using a learning model obtained in advance by performing a learning process using training data that includes the torque generated when a predetermined material is pushed out from a predetermined device, information about the predetermined material or information about the predetermined device, and predetermined information.

[0062] In the above-described determination device, the predetermined information may be a feature quantity indicating the state of a predetermined material, or a feature quantity indicating the state of a predetermined device. The feature quantity indicating the state of a predetermined material may be at least one of the following: temperature, density, viscosity, chromaticity, transparency, and metal content of the predetermined material. The feature quantity indicating the state of a predetermined device may be at least one of the following: temperature, vibration amount, internal pressure, and generated sound volume of the predetermined device.

[0063] In addition, in the above-mentioned determination device, the predetermined information may be information indicating the processing steps of a predetermined material.

[0064] In the above-described determination device, the specified information may be information indicating the surrounding environment of the specified device. The information indicating the surrounding environment of the specified device may be information indicating at least one of temperature, humidity, and atmospheric pressure.

[0065] Furthermore, according to the embodiment described above, the determination system includes an extruder that extrudes a predetermined material from a predetermined device, and the determination device described above. For example, the determination system is determination system 1 in the embodiment, the extruder is extruder 20 in the embodiment, and the determination device is determination device 10 in the embodiment.

[0066] Some or all of the configuration of the determination device 10 in each of the embodiments described above may be implemented using a computer. In that case, the program for implementing this function may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be loaded into a computer system and executed. Here, "computer system" includes hardware such as an OS and peripheral devices. Furthermore, "computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs, CD-ROMs, and storage devices such as hard disks built into a computer system. Moreover, "computer-readable recording medium" may also include those that dynamically hold programs for a short period of time, such as communication lines used when transmitting programs via networks such as the Internet or communication lines such as telephone lines, and those that hold programs for a certain period of time, such as volatile memory inside a computer system that acts as a server or client in such cases. Furthermore, the above program may be for implementing some of the functions described above, or it may be a program that can implement the above functions in combination with a program already recorded in the computer system, or it may be implemented using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0067] While embodiments of this invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments and includes designs and the like that do not depart from the spirit of this invention. [Explanation of symbols]

[0068] 1. Judgment System 10 Judgment device 11. Learning Department 12 Torque Estimation Unit 13 State determination unit 15. Temperature sensor 16 Torque sensor 20 Extruders 21 Hopper 22 Heating cylinder 23 Motor 24 Screws 25 Opening 26 Outlet 111 Equipment / Material Information Acquisition Department 112 Temperature information acquisition section 113 Torque Information Acquisition Unit 114 Learning Model Generation Unit 115 Learning Model Memory Unit 121 Equipment / Material Information Acquisition Department 122 Temperature information acquisition section 123 Estimation Department 124 Estimation result storage unit 131 Torque Information Acquisition Unit 132 Judgment section 133 Judgment Logic Memory Unit 134. Output section for judgment result

Claims

1. An estimation unit that estimates information regarding the torque generated when a predetermined material is extruded from a predetermined device, based on predetermined information, An acquisition unit that acquires measurement results of information regarding the torque generated when the predetermined material is extruded from the predetermined equipment, A determination unit that determines the state of the material or the predetermined equipment based on the estimation result by the estimation unit and the measurement result obtained by the acquisition unit, A determination device equipped with the following features.

2. The estimation unit performs the estimation using a learning model obtained in advance by performing a learning process using training data that includes the torque generated when the predetermined material is pushed out from the predetermined equipment, information about the predetermined material or information about the predetermined equipment, and the predetermined information. The determination device according to claim 1.

3. The predetermined information is a feature quantity indicating the state of the predetermined material, or a feature quantity indicating the state of the predetermined equipment. The determination device according to claim 1 or 2.

4. The characteristic quantity indicating the state of the predetermined material is at least one of the following characteristics of the predetermined material: temperature, density, viscosity, color, transparency, and metal content. The determination device according to claim 3.

5. The characteristic quantity indicating the state of the predetermined device is at least one of the following characteristics: temperature, vibration amount, internal pressure, and generated sound volume of the predetermined device. The determination device according to claim 3.

6. The predetermined information is information indicating the processing steps of the predetermined material. The determination device according to claim 1 or 2.

7. The aforementioned predetermined information is information indicating the surrounding environment of the aforementioned predetermined device. The determination device according to claim 1 or 2.

8. The information indicating the surrounding environment of the specified device is information indicating at least one of the following: temperature, humidity, and atmospheric pressure. The determination device according to claim 7.

9. A determination system comprising an extruder that extrudes a predetermined material from a predetermined device, and a determination device, The determination device is An estimation unit that estimates information regarding the torque generated when a predetermined material is extruded from a predetermined device, based on predetermined information, An acquisition unit that acquires measurement results of information regarding the torque generated when the predetermined material is extruded from the predetermined equipment, A determination unit that determines the state of the material or the predetermined equipment based on the estimation result by the estimation unit and the measurement result obtained by the acquisition unit, A judgment system equipped with the following features.

10. A computer-based determination method, An estimation step in which information regarding the torque generated when a predetermined material is extruded from a predetermined device is estimated based on predetermined information, An acquisition step of obtaining measurement results related to the torque generated when the predetermined material is extruded from the predetermined equipment, A determination step in which the state of the material or the predetermined equipment is determined based on the estimation result in the estimation step and the measurement result obtained in the acquisition step, A method for determining the thumbnail.

11. On the computer, An estimation step in which information regarding the torque generated when a predetermined material is extruded from a predetermined device is estimated based on predetermined information, An acquisition step of obtaining measurement results related to the torque generated when the predetermined material is extruded from the predetermined equipment, A determination step in which the state of the material or the predetermined equipment is determined based on the estimation result in the estimation step and the measurement result obtained in the acquisition step, A program to execute.

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