Quality prediction model creation system, quality prediction system, quality prediction model creation method, and quality prediction method

Abstract

To reduce cost for predicting the quality characteristics of a long product.SOLUTION: A quality prediction model creation system includes: a first logging data acquisition unit 301 that acquires known first logging data detected and stored at every predetermined interval by a plurality of detectors when a long product is manufactured; a first arithmetic data calculation unit 305 that calculates first arithmetic data indicating a result of performing at least one of the arithmetic operations using the first logging data acquired by the first logging data acquisition unit 301; and a model creation unit 304 that creates a quality prediction model associated with the quality characteristics of the long product based on the arithmetic data calculated by the first arithmetic data calculation unit 305.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a quality prediction model creation system, a quality prediction system, a quality prediction model creation method, and a quality prediction method. [Background technology]

[0002] Japanese Patent Application Laid-Open No. 2022-87429 (Patent Document 1) describes a technology that utilizes artificial intelligence to estimate the physical property values ​​of composite materials with high accuracy. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2022-87429 Summary of the Invention [Problem to be solved by the invention]

[0004] For long products, although it is possible to evaluate the quality characteristics of the end portions using destructive testing or the like, it is not possible to evaluate the quality characteristics over the entire length. For this reason, it is not possible to determine whether the intermediate portion of a long product actually meets the required quality characteristics. Therefore, it is desirable to inspect the quality characteristics of long products over their entire length. To determine the quality characteristics over the entire length of a long product, it is possible to install various sensors at many locations during the manufacturing process of the long product and perform simulations using the detected sensor values.

[0005] Furthermore, to improve the prediction accuracy of the simulation, it is necessary to collect many sensor values. However, when the types and sizes of long products vary, it may be difficult to obtain a sufficient number of sensor values ​​for each type of long product. In such cases, it becomes impossible to create a highly accurate quality prediction model to be used in the simulation, and as a result, the accuracy of the prediction of quality characteristics also decreases.

[0006] In the manufacturing process, the prediction accuracy of the simulation can be improved by placing many sensors and acquiring many sensor values ​​from the many sensors. However, placing many sensors increases costs, and acquiring many sensor values ​​requires high-performance simulation software, which also increases costs. [Means for solving the problem]

[0007] In one embodiment, the quality prediction model creation system includes a first logging data acquisition unit that acquires known first logging data that is detected and stored by multiple detectors at predetermined intervals during the manufacture of long products; a first calculation data calculation unit that calculates first calculation data that indicates the result of performing at least one of four arithmetic operations using the first logging data acquired by the first logging data acquisition unit; and a model creation unit that creates a quality prediction model associated with the quality characteristics of the long product based on the first calculation data calculated by the first calculation data calculation unit.

[0008] In one embodiment, the quality prediction system includes a quality prediction model memory unit that stores a quality prediction model associated with the quality characteristics of a long item created based on first calculation data indicating the result of performing at least one of the four arithmetic operations using known first logging data of the long item; a second logging data acquisition unit that acquires second logging data during the production of the long item to be predicted; a second calculation data calculation unit that calculates second calculation data using the second logging data to perform one of the four arithmetic operations; and a prediction unit that predicts the quality characteristics of the long item to be predicted by substituting the second logging data acquired by the second logging data acquisition unit and the second calculation data into the quality prediction model.

[0009] In one embodiment, a quality prediction model creation method acquires known first logging data detected and stored by multiple detectors at predetermined intervals during the manufacture of long products, calculates first calculation data indicating the result of performing at least one of the four arithmetic operations using the acquired first logging data, and creates a quality prediction model associated with the quality characteristics of the long product based on the first calculation data.

[0010] In one embodiment, a quality prediction method acquires known first logging data detected and stored by multiple detectors at predetermined intervals during the manufacture of long products, calculates first calculation data indicating the result of performing at least one of the four arithmetic operations from the acquired first logging data, creates a quality prediction model associated with the quality characteristics of the long product based on the first calculation data, acquires second logging data during the manufacture of the long product to be predicted, calculates second calculation data indicating the result of performing at least one of the four arithmetic operations using the second logging data, and substitutes the second logging data and the second calculation data into the quality prediction model to predict the quality characteristics of the long product to be predicted. [Effects of the Invention]

[0011] According to one embodiment, the cost of predicting the quality characteristics of long products can be reduced. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of a configuration of a manufacturing system including a quality prediction system. [Figure 2] FIG. 1 is a schematic diagram illustrating an example of a configuration of a manufacturing system including a quality prediction system. [Figure 3] FIG. 1 illustrates an example of a hardware configuration of a quality prediction system. [Figure 4] FIG. 1 is a diagram illustrating an example of functional blocks of a quality prediction system. [Figure 5] 10 is a flowchart illustrating an example of a quality prediction model creation process. [Figure 6] FIG. 10 is a diagram showing an example of first logging data. [Figure 7] FIG. 10 is a diagram illustrating an example of calculation result data showing a calculation result of an average value of logging data. [Figure 8] 1 is a flowchart illustrating an example of a process for implementing a full-length quality prediction technique. [Figure 9] FIG. 1 is a diagram for explaining a full-length quality prediction technique. [Figure 10] FIG. 10 is a diagram showing an example of second logging data. [Figure 11] FIG. 10 is a diagram for explaining an outline of a simulation. [Figure 12] FIG. 10 is a diagram showing an example of the relationship between the predicted tensile strength of a heat-resistant insulated electric wire and the actually measured tensile strength in this example. [Figure 13] FIG. 10 is a diagram showing an example of the relationship between predicted tensile elongation and actually measured tensile elongation in this example. [Figure 14] FIG. 10 is a diagram showing an example of the relationship between the predicted tensile strength of a heat-resistant insulated electric wire and the actually measured tensile strength in a comparative example. [Figure 15] FIG. 10 is a diagram showing an example of the relationship between predicted tensile elongation and actually measured tensile elongation in a comparative example. [Figure 16] FIG. 10 is a diagram showing an example of the influence of each factor on the predicted value of tensile strength in this embodiment. [Figure 17] FIG. 10 is a diagram showing an example of the influence of each factor on the predicted value of tensile strength in this embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] In all the drawings for explaining the embodiments, the same components are generally designated by the same reference numerals, and repeated explanations thereof will be omitted. In addition, hatching may be used even in plan views to make the drawings easier to understand.

[0014] The technical idea of ​​this embodiment is to create a quality prediction model and use the model to predict the quality characteristics of a long product over its entire length. For example, if the long product is an electric wire coated with a heat-resistant insulating material, CAE and AI / machine learning are used to predict the quality characteristics such as tensile strength and tensile elongation at each position on the electric wire.

[0015] The technology of this embodiment uses machine learning to create a quality prediction model associated with quality characteristics. The quality prediction model uses data that can be measured and logged during the production of long products (e.g., extruder screw rotation speed, cylinder temperature, resin pressure, outer diameter, etc.), data that can be calculated by numerical simulation (e.g., average resin temperature in the cylinder, maximum resin pressure, torque, magnitude of strain, etc.), data that can be logged, and data that can be calculated by simulation to perform at least one of the four arithmetic operations. Furthermore, the technology of this embodiment makes it possible to predict the quality characteristics of long products over their entire length using the created quality prediction model and real-time data during production. Using the calculated data calculated by the four arithmetic operations as new feature quantities makes it possible to improve the prediction accuracy of the quality characteristics of long products.

[0016] <Description of Related Art> Let's take the case of a long product, such as a heat-resistant insulated wire. Standards have been established for the quality characteristics of heat-resistant insulated wire, such as insulation resistance, withstand voltage, tensile strength of the insulator and sheath, heat resistance, and oil resistance, and the test methods are also specified in detail in JIS C3005, Test Methods for Rubber and Plastic Insulated Wires.

[0017] As stated in 4.16 of the same, the mechanical properties (tensile strength, tensile elongation) of the insulator and sheath of long cables such as rubber wires are to be measured by taking three or more dumbbell-shaped or tubular test pieces from the finished product and measuring them at a specified tensile speed (25 to 500 mm / min). For this reason, three or more pieces are cut out from the end of a finished long cable, and the tensile strength and tensile elongation are calculated using a tensile tester, which are then used as the quality characteristics of that long cable.

[0018] With related technology, quality characteristics can only be evaluated by destructive testing of the ends of long products, making it impossible to determine whether the middle part of a long product truly meets the standards. Also, unlike outer diameter, eccentricity, and capacitance, there are some quality characteristics for which it is difficult to constantly log the actual measured values ​​of quality characteristics of long products during manufacturing using a detector or other device.

[0019] <Manufacturing system configuration> 1 and 2 are schematic diagrams showing an example of the configuration of a manufacturing system 1 including a quality prediction system 100. FIG. 1 is a schematic diagram showing an example of the configuration of an extruder 10 of the manufacturing system 1 as viewed from the Y direction. FIG. 2 is a schematic diagram showing an example of the configuration of the extruder 10 of the manufacturing system 1 as viewed from the X direction in the head 31 of FIG. 1. In this embodiment, a case will be described in which the quality prediction system 100 includes a quality prediction model creation system, but the quality prediction system 100 and the quality prediction model creation system may be separate systems.

[0020] 1 and 2, the X direction, Y direction, and Z direction are defined. The X direction, Y direction, and Z direction are perpendicular to each other, but may intersect at an angle other than a perpendicular angle.

[0021] The manufacturing system 1 includes an extruder 10 and a quality prediction system 100. The extruder 10 has a single screw (not shown). In this embodiment, the extruder 10 will be described in the case of predicting the quality characteristics of a heat-resistant insulated electric wire EW as an example of a long product. Note that the long product is not limited to an electric wire such as the heat-resistant insulated electric wire EW. For example, the long product may be a pipe, a sheet, or a film. The quality prediction system 100 is, for example, a computer system. Details of the quality prediction system 100 will be described later.

[0022] As shown in Fig. 1, extruder 10 produces heat-resistant insulated electric wire EW by coating core wire CW with resin, for example, a heat-resistant insulating material. As shown in Fig. 1, extruder 10 has drawing device 20, head 31, die 32, water tank 40 which is a cooling device, and take-up device 50. The drawing device 20, head 31, die 32, water tank 40, and take-up device 50 form an extrusion line. As shown in Fig. 2, extruder 10 has cylinder 33 connected to head 31 and material input section 34.

[0023] The drawing device 20 accommodates the core wire CW wound thereon. The drawing device 20 rotates in the direction of the arrow AR1 shown in the figure, thereby drawing out the core wire CW from the drawing device 20. The core wire CW drawn in the direction of the arrow AR2 shown in the figure passes through the head 31 and the die 32. The heat-resistant insulated electric wire EW that has passed through the head 31 and the die 32 passes through the water tank 40 and is taken up by the take-up device 50. The heat-resistant insulated electric wire EW is wound and accommodated in the take-up device 50. The arrow AR3 shown in the figure indicates the direction in which the heat-resistant insulated electric wire EW is wound.

[0024] The head 31 is connected to a cylinder 33. A heat-resistant insulating material is supplied to the head 31 from the cylinder 33. The heat-resistant insulating material supplied to the head 31 is attached to the core wire CW drawn out from the drawing device 20. The die 32 has a hole (not shown) of a predetermined size. The core wire CW with the heat-resistant insulating material attached passes through the hole of the die 32. As a result, a heat-resistant insulating material of a thickness corresponding to the size of the hole is formed on the core wire CW, and a heat-resistant insulated electric wire EW is produced.

[0025] The heat-resistant insulated wire EW that has passed through the die 32 passes through the water tank 40. In the water tank 40, the heat-resistant insulated wire EW is cooled to a predetermined temperature. The heat-resistant insulated wire EW cooled in this manner is taken up by the take-up device 50. In the take-up device 50, the heat-resistant insulated wire EW is wound at a take-up speed (hereinafter also referred to as "linear speed") and stored in the take-up device 50. Information indicating the take-up speed and a take-up current value that indicates the value of a current supplied to the take-up device 50 is transmitted to the quality prediction system 100 at predetermined intervals, for example, every one second.

[0026] As shown in FIG. 2, a head 31 is connected to one end of a cylinder 33, and a material feeder 34 is connected to the top of the other end. Heat-resistant insulating material is fed into the material feeder 34. As indicated by the dashed arrow AR4 in the figure, the heat-resistant insulating material is fed into the cylinder 33 from the material feeder 34. The cylinder 33 has a single-axis screw. The cylinder 33 is configured to rotate the screw at a predetermined rotation speed depending on the amount of current supplied. As the screw rotates, the heat-resistant insulating material fed from the material feeder 34 is gradually extruded toward the head 31, as indicated by the dashed arrow AR5 in the figure. The extruded heat-resistant insulating material is attached to the core wire CW within the head 31 and is extruded from the die 32 to the outside as part of the heat-resistant insulated wire EW. Information indicating the screw rotation speed, acceleration, current value, etc., is transmitted to the quality prediction system 100 at predetermined intervals, for example, every one second.

[0027] Here, the screw has, for example, three roles. The first role is to push the heat-resistant insulating material supplied from the material input section 34 toward the head 31 so that it reaches the head 31. At this time, residual heat is applied to the heat-resistant insulating material. The second role is to change the heat-resistant insulating material from a solid state to a molten state. The third role is to stably extrude a constant amount of heat-resistant insulating material from the die 32.

[0028] A core wire temperature sensor S11, which is one of the detectors, is provided between the drawing device 20 and the head 31. The core wire temperature sensor S11 detects the temperature of the core wire CW between the drawing device 20 and the head 31. The temperature detected by the core wire temperature sensor S11 is transmitted to the quality prediction system 100 at predetermined intervals, for example, at intervals of one second.

[0029] As shown in FIG. 1, the head 31 is provided with a head temperature sensor S12 and a die temperature sensor S13. The head temperature sensor S12 and the die temperature sensor S13 are each a detector. The head temperature sensor S12 is a sensor that detects the head temperature of the head 31. The die temperature sensor S13 is a sensor that detects the die temperature of the die 32. The temperature detected by the die temperature sensor S13 is substantially the same as the temperature of the heat-resistant insulating material extruded from the head 31. In other words, the die temperature sensor S13 is also a sensor (first temperature sensor) that detects the temperature (first temperature) of the heat-resistant insulating material extruded from the head 31. In addition, a neck temperature sensor S14, which is one of the detectors, is provided near the joint between the cylinder 33 and the head 31 (see FIG. 2). The neck temperature sensor S14 is a sensor that detects the neck temperature near the joint between the cylinder 33 and the head 31. The head temperature, die temperature, and neck temperature detected by the head temperature sensor S12, die temperature sensor S13, and neck temperature sensor S14, respectively, are transmitted to the quality prediction system 100 at predetermined intervals, for example, at intervals of one second.

[0030] A water tank temperature sensor S15 (second temperature sensor), which is one of the detectors, is provided in water tank 40. Water tank temperature sensor S15 is a sensor that detects the water temperature (second temperature) of water tank 40. The temperature detected by water tank temperature sensor S15 is transmitted to quality prediction system 100 at predetermined intervals, for example, at one-second intervals.

[0031] An outer diameter measuring sensor S16, which is one of the detectors, is provided between the water tank 40 and the take-up device 50. The outer diameter measuring sensor S16 is a sensor that detects the size of the outer diameter of the heat-resistant insulated electric wire EW after it has been cooled in the water tank 40. The size of the outer diameter detected by the outer diameter measuring sensor S16 is transmitted to the quality prediction system 100 at predetermined intervals, for example, at intervals of one second.

[0032] As shown in FIG. 2, a resin temperature sensor S17 and a resin pressure sensor S18 (first pressure sensor) are provided near the connection portion of the cylinder 33 with the head 31. The resin temperature sensor S17 and the resin pressure sensor S18 are each detectors. The resin temperature sensor S17 is a sensor that detects the resin temperature of the heat-resistant insulating material supplied to the head 31. The resin pressure sensor S18 is a sensor that detects the resin pressure value (also referred to as the tip resin pressure value: first pressure value) of the heat-resistant insulating material supplied to the head 31. The resin temperature and resin pressure values ​​detected by the resin temperature sensor S17 and the resin pressure sensor S18 are each transmitted to the quality prediction system 100 at predetermined intervals, for example, at one-second intervals.

[0033] Five temperature sensors S21 to S25 are provided on the cylinder 33 along the longitudinal direction from the material input section 34 side. Each of the five temperature sensors S21 to S25 is a detector. This allows the temperature of the cylinder 33 to be detected at multiple positions along the longitudinal direction of the cylinder 33. The number of temperature sensors is not limited to five and may be, for example, three, four, or six or more. The temperatures detected by the five temperature sensors S21 to S25 are transmitted to the quality prediction system 100 at predetermined intervals, for example, at one-second intervals. The temperatures detected by the five temperature sensors S21 to S25 are also referred to as the first to fifth cylinder temperatures, respectively. As described above, the cylinder 33 has three roles. By providing multiple temperature sensors S21 to S25, the quality prediction system 100 can detect changes in the temperature of the cylinder 33 during each role.

[0034] An air temperature sensor S31 (third temperature sensor) and a humidity sensor S32 are arranged around the extruder 10, for example, around the cylinder 33. The air temperature sensor S31 and the humidity sensor S32 are each detectors. The temperature (third temperature) detected by the air temperature sensor S31 and the humidity detected by the humidity sensor S32 are each transmitted to the quality prediction system 100 at predetermined intervals, for example, at one-second intervals.

[0035] <Hardware configuration> Next, a hardware configuration of the quality prediction system 100 in this embodiment will be described. Fig. 3 is a diagram showing an example of the hardware configuration of the quality prediction system 100 in this embodiment. Note that the configuration shown in Fig. 3 merely shows an example of the hardware configuration of the quality prediction system 100, and the hardware configuration of the quality prediction system 100 is not limited to the configuration shown in Fig. 1 and may be other configurations.

[0036] 3, a quality prediction system 100 includes a CPU (Central Processing Unit) 101 that executes a program. This CPU 101 is electrically connected to, for example, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, and a hard disk drive 112 via a bus 113, and is configured to control these hardware devices.

[0037] The CPU 101 is also connected to input devices and output devices via a bus 113. Examples of input devices include a keyboard 105, a mouse 106, a communication port 107, and a scanner 111. Examples of output devices include a display 104, a communication port 107, and a printer 110. The CPU 101 may also be connected to, for example, a removable disk device 108 and a CD / DVD-ROM device 109. The communication port 107 is connected to, for example, a cylinder 33, a drawing device 20, a take-up device 50, a core wire temperature sensor S11, a head temperature sensor S12, a die temperature sensor S13, a neck temperature sensor S14, a water tank temperature sensor S15, an outer diameter measurement sensor S16, a resin temperature sensor S17, a resin pressure sensor S18, temperature sensors S21 to S25, an air temperature sensor S31, and a humidity sensor S32.

[0038] The quality prediction system 100 may be connected to, for example, a network. For example, when the quality prediction system 100 is connected to other external devices via a network, a communication port 107 constituting a part of the quality prediction system 100 is connected to a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

[0039] The RAM 103 is an example of a volatile memory, and the storage media of the ROM 102, the removable disk device 108, the CD / DVD-ROM device 109, and the hard disk device 112 are examples of a non-volatile memory. These volatile memories and non-volatile memories constitute the storage device of the quality prediction system 100.

[0040] The hard disk drive 112 stores, for example, an operating system (OS) 201, a program group 202, and a file group 203. The programs included in the program group 202 are executed by the CPU 101 using the operating system 201. The RAM 103 also temporarily stores at least some of the programs of the operating system 201 and application programs that the CPU 101 executes, as well as various data required for processing by the CPU 101.

[0041] A BIOS (Basic Input Output System) program is stored in the ROM 102, and a boot program is stored in the hard disk drive 112. When the quality prediction system 100 is started up, the BIOS program stored in the ROM 102 and the boot program stored in the hard disk drive 112 are executed, and the operating system 201 is started up by the BIOS program and the boot program.

[0042] The program group 202 stores programs that realize the functions of the quality prediction system 100, and these programs are read and executed by the CPU 101. The file group 203 stores information, data, signal values, variable values, and parameters indicating the results of processing by the CPU 101 as factors of files. The program group 202 includes, for example, a quality prediction model creation program 202A and a quality prediction program 202B, which will be described later.

[0043] The file group 203 includes logging data 203A, simulation data 203B, and a quality prediction model 203C. The logging data 203A is manufacturing data when the heat-resistant insulated wire EW is manufactured. The simulation data 203B is simulation data showing the results of a simulation performed using the logging data. The quality prediction model 203C is a model for predicting the quality characteristics of the heat-resistant insulated wire EW. Details of the logging data 203A, the simulation data 203B, and the quality prediction model 203C will be described later.

[0044] The files are stored in a storage medium such as the hard disk drive 112 or memory. The information, data, signal values, variable values, and parameters stored in the storage medium such as the hard disk drive 112 or memory are read into the main memory or cache memory by the CPU 101 and used for the operations of the CPU 101, such as extraction, search, reference, comparison, calculation, processing, editing, output, printing, and display. For example, during the operations of the CPU 101 described above, the information, data, signal values, variable values, and parameters are temporarily stored in the main memory, registers, cache memory, buffer memory, etc.

[0045] The functions of the quality prediction system 100 may be realized by firmware stored in ROM 102, or may be realized by software alone, hardware alone such as elements, devices, boards, and wiring, a combination of software and hardware, or even a combination of firmware and firmware. The firmware and software are stored as programs in a storage medium such as a hard disk drive 112, a removable disk drive 108, or a CD / DVD-ROM drive 109. The programs are read and executed by the CPU 101. For example, the programs cause a computer to function as the quality prediction system 100.

[0046] Thus, the quality prediction system 100 is a computer equipped with a CPU 101 as a processing device, a hard disk drive 112 and memory as storage devices, a keyboard 105, a mouse 106, and a communication port 107 as input devices, and a display 104, a printer 110, and the communication port 107 as output devices. The functions of the quality prediction system 100 are realized by using the processing device, the storage device, the input device, and the output device.

[0047] <Function block> FIG. 4 is a diagram illustrating an example of functional blocks of the quality prediction system 100. As shown in FIG. 4, the quality prediction system 100 includes a first logging data acquisition unit 301, an average value calculation unit 302, a first simulation unit 303, a model creation unit 304, a first calculation data calculation unit 305, a data merging unit 306, a quality prediction model storage unit 307, a second logging data acquisition unit 308, a second simulation unit 309, a second calculation data calculation unit 310, and a prediction unit 311. Details of the functions realized by the first logging data acquisition unit 301, the average value calculation unit 302, the first simulation unit 303, the model creation unit 304, the first calculation data calculation unit 305, the data merging unit 306, the quality prediction model storage unit 307, the second logging data acquisition unit 308, the second simulation unit 309, the second calculation data calculation unit 310, and the prediction unit 311 will be described later.

[0048] <Creating a quality prediction model> Next, a quality prediction model creation process will be described, which creates a quality prediction model 203C for predicting the quality characteristics of the heat-resistant insulated electric wire EW produced by the extruder 10. Fig. 5 is a flowchart showing an example of the quality prediction model creation process. This process is realized by reading out a quality prediction model creation program 202A stored in the hard disk drive 112 and executing it with the CPU 101.

[0049] First, in step ST101, the CPU 101 acquires first logging data. The processing of step ST101 realizes the function of the first logging data acquisition unit 301 of the quality prediction system 100. The first logging data acquisition unit 301 acquires, from the hard disk drive 112, known first logging data detected and stored at predetermined intervals by multiple devices and sensors during the manufacture of the heat-resistant insulated electric wire EW. In other words, the acquired first logging data is known manufacturing data for each serial number of the same product during the manufacture of the heat-resistant insulated electric wire EW. Here, the multiple devices and sensors include, for example, the cylinder 33, the drawing device 20, the take-up device 50, the core wire temperature sensor S11, the head temperature sensor S12, the die temperature sensor S13, the neck temperature sensor S14, the water tank temperature sensor S15, the outer diameter measurement sensor S16, the resin temperature sensor S17, the resin pressure sensor S18, the temperature sensors S21 to S25, the air temperature sensor S31, and the humidity sensor S32.

[0050] FIG. 6 is a diagram showing an example of the first logging data 203A1. The first logging data 203A1 is data included in the logging data 203A. FIG. 6 shows the first logging data 203A1 for the heat-resistant insulated electric wire EW with production number AAA. Time is associated with factors such as the screw rotation speed (rpm), cylinder temperature (°C), linear velocity (m / s), etc. Note that, although not shown, the cylinder temperature factor includes the temperatures acquired by the temperature sensors S21 to S25. In this embodiment, the first logging data 203A1 is stored as part of the logging data 203A of the file group 203 at a predetermined interval of one second. Therefore, the first logging data 203A1 is acquired from the file group 203. In the figure, an example is shown in which the first logging data 203A1 includes three factors, but the first logging data 203A1 corresponds to time the logging data of a specific factor among the logging data of all factors that the quality prediction system 100 can acquire.

[0051] To create the quality prediction model 203C, for example, it is desirable that the CPU 101 acquires several tens or more pieces of known first logging data 203A1 for the same product. Furthermore, it is desirable that the number of factors acquired in the first logging data 203A1 be large. Of the factors acquired from the logging data 203A, it is desirable that the CPU 101 acquire as much logging data as possible of measurable factors, such as the screw rotation speed, cylinder temperature, and linear velocity, as shown in FIG. 6 , as the first logging data 203A1. Based on the settings, the CPU 101 can select any factor from all the factors included in the logging data 203A as the target for creating the quality prediction model 203C. The created quality prediction model 203C is stored in the hard disk drive 112.

[0052] Next, in step ST102, the CPU 101 calculates an average value. The processing of step ST102 realizes the function of the average value calculation unit 302 of the quality prediction system 100. The average value calculation unit 302 calculates an average value of the first logging data 203A1 for each factor from the first logging data 203A1 acquired by the first logging data acquisition unit 301 in step ST101. In this embodiment, if the logging data 203A includes data on the setup time and adjustment time before manufacturing the heat-resistant insulated electric wire EW, the CPU 101 performs processing to exclude the setup time and adjustment time from the first logging data 203A1. In this way, the CPU 101 can calculate the average value of the first logging data 203A1 for each factor from the start to the end of manufacturing the heat-resistant insulated electric wire EW.

[0053] 7 is a diagram showing an example of calculation result data D10 showing the calculation results of the average value of the first logging data 203A1. As shown in Fig. 7, the average screw rotation speed, average cylinder temperature, average linear velocity, etc. are calculated for each production number.

[0054] Next, the CPU 101 executes a simulation in step ST103. The processing of step ST103 realizes the function of the first simulation unit 303 of the quality prediction system 100. The first simulation unit 303 simulates the quality characteristics of the heat-resistant insulated electric wire EW using the average value of the first logging data 203A1. The CPU 101 calculates a feature quantity (described later) using the average value of the first logging data 203A1 calculated in step ST102 as a condition for CAE (Computer Aided Engineering) analysis. The feature quantity can also be considered a quality characteristic of the heat-resistant insulated electric wire EW obtained from the simulation data. It is desirable to use a large number of production numbers in the CAE analysis to improve the accuracy of correlation with the quality characteristics of the heat-resistant insulated electric wire EW. On the other hand, if the number of production numbers is large, the time required for the CAE analysis may become enormous. For this reason, it is desirable to set the number of production numbers so that the calculation load used in the CAE analysis does not exceed the limit of the CPU 101 while increasing the number of production numbers. The simulation result in step ST103 is defined as first simulation data. The first simulation data is stored in the hard disk drive 112 as simulation data 203B.

[0055] The aforementioned feature quantities are, for example, data that cannot be measured directly, such as the strain and residence time of the heat-resistant insulating material in the extruder 10, or data that would require a great deal of effort to measure. Additionally, the feature quantities may also include the cylinder temperatures (first to fifth cylinder temperatures) of the cylinder 33, the temperature of the heat-resistant insulating material in the cylinder 33, and the resin pressure value. While these can be measured directly, only values ​​near the detectors can be detected. In other words, the "average" temperature of the heat-resistant insulating material in the extruder 10 and the "maximum" resin pressure (maximum resin pressure value) of the heat-resistant insulating material in the cylinder can be derived by simulation. Therefore, it is desirable for the CPU 101 to set factors whose values ​​can only be detected near the detectors as feature quantities and include them in the simulation.

[0056] Next, in step ST104, the CPU 101 calculates first calculation data. The processing of step ST104 realizes the function of the first calculation data calculation unit 305 of the quality prediction system 100. The first calculation data calculation unit 305 calculates calculation data indicating the result of performing at least one of the four arithmetic operations using the average value of the first logging data acquired by the first logging data acquisition unit 301 and the first simulation data by the first simulation unit 303. The four arithmetic operations are division, multiplication, subtraction, and addition. It can be arbitrarily specified how the four arithmetic operations are performed on which factors of the first logging data and the first simulation data.

[0057] The four arithmetic operations include the following: (1) subtraction, which calculates the difference between the die temperature obtained from the die temperature sensor S13 (which is substantially the same temperature as the heat-resistant insulating material extruded from the head 31) and the water temperature obtained from the water bath temperature sensor S15; and (2) subtraction, which calculates the difference between the die temperature obtained from the die temperature sensor S13 (which is substantially the same temperature as the heat-resistant insulating material extruded from the head 31) and the temperature obtained from the air temperature sensor S31. (3) The four arithmetic operations also include, for example, division, which divides the screw rotation speed obtained from the extruder 10 by the screw torque, and division, which divides the maximum resin pressure value of the heat-resistant insulating material in the cylinder calculated by the first simulation unit 303 by the tip resin pressure value obtained from the resin pressure sensor S18. Specific examples of the four arithmetic operations will be described later.

[0058] Next, in step ST105, CPU 101 merges the result data measured in the quality inspection with the first simulation data. The processing of step ST105 realizes the function of the data merging unit 306 of the quality prediction system 100. The data merging unit 306 acquires quality result data of a quality test performed on the end of the heat-resistant insulated electric wire EW, and merges the quality result data with the first simulation data. The quality inspection is, for example, a destructive inspection. The quality inspection targets factors that can only be evaluated by destructive testing, such as tensile strength, tensile elongation, flame retardancy, and oil resistance. If there is a factor among these factors that can determine the validity of the first simulation data, CPU 101 preferably simulates the factor and merges it with the quality result data.

[0059] Next, in step ST106, the CPU 101 creates a quality prediction model 203C. The processing of step ST106 is realized by the function of the model creation unit 304 of the quality prediction system 100. The model creation unit 304 creates a quality prediction model 203C associated with the quality characteristics of the heat-resistant insulated electric wire EW based on the average value of the first logging data 203A1, first simulation data indicating the results of the simulation, and first calculation data indicating the results of arithmetic operations. The model creation unit 304 creates the quality prediction model 203C through machine learning, using quality characteristic data indicating the quality characteristics of the heat-resistant insulated electric wire EW as a response variable and the first logging data, first simulation data, and first calculation data of the heat-resistant insulated electric wire EW as explanatory variables.

[0060] Multivariate analysis is used to create the quality prediction model 203C. There are many methods for performing multivariate analysis. For example, the CPU 101 can perform multiple regression analysis using the "sklearn.Linear model.Linear Regression" class of the Python machine learning library "scikit-learn," which allows for relatively easy calculation of the regression coefficients of each data. The CPU 101 may also use a library such as "SHAP" to interpret the quality prediction model 203C, determine the contribution rate, and examine whether the quality prediction model 203C is appropriate. In this way, the quality prediction model 203C is created using machine learning. The quality prediction model 203C created in this way is stored in the hard disk drive 112 by the function of the quality prediction model storage unit 307.

[0061] <Full length quality prediction> Next, a full-length quality prediction technique for predicting the quality characteristics of the heat-resistant insulated electric wire EW over its entire length using the created quality prediction model 203C will be described. Fig. 8 is a flowchart showing an example of processing for implementing the full-length quality prediction technique. This processing is implemented by reading out the quality prediction program 202B stored in the hard disk drive 112 and executing it with the CPU 101. Fig. 9 is a diagram for explaining the full-length quality prediction technique.

[0062] 8, the CPU 101 acquires second logging data in step ST201. The processing of step ST201 realizes the function of the second logging data acquisition unit 308 of the quality prediction system 100. For example, during the manufacture of the heat-resistant insulated electric wire EW, the second logging data acquisition unit 308 acquires second logging data 203A2 (see FIG. 10) during the manufacture of the heat-resistant insulated electric wire EW that is the prediction target.

[0063] FIG. 10 is a diagram showing an example of the second logging data 203A2. FIG. 10 shows the second logging data 203A2 for the heat-resistant insulated electric wire EW with production number FFF. Time is associated with factors such as the screw rotation speed (rpm), cylinder temperature (°C), linear velocity (m / s), etc. Note that the factor of the cylinder temperature, although not shown, includes the temperatures acquired by the temperature sensors S21 to S25. In this embodiment, during the manufacture of the heat-resistant insulated electric wire EW with production number FFF, the second logging data 203A2 is stored in the file group 203 as part of the logging data 203A at predetermined intervals of one second. Therefore, the second logging data 203A2 is acquired from the file group 203. In the figure, an example is shown in which the second logging data 203A2 includes three factors, but the second logging data 203A2 is data on any factor from among all logging data on factors that the quality prediction system 100 can acquire, and is, for example, logging data on the same factors as the first logging data 203A1 used to create the quality prediction model 203C.

[0064] Next, in step ST202, the CPU 101 executes continuous simulation at regular time intervals. The processing of step ST202 realizes the function of the second simulation unit 309 of the quality prediction system 100. The second simulation unit 309 executes a simulation from the second logging data 203A2 acquired by the second logging data acquisition unit 308 and the acquired second logging data 203A2 to create second simulation data.

[0065] Although the second logging data 203A2 used for the simulation differs from the example shown in FIG. 10, as shown in FIGS. 9(a) and 9(b), the CPU 101 acquires the cylinder temperature C, the screw rotation speed N, the tip resin pressure P detected by the resin pressure sensor S18, and the air temperature Ta detected by the air temperature sensor S31 as the second logging data 203A2, which are actual measurements. The CPU 101 then extracts the second logging data 203A2 acquired at regular intervals and performs continuous simulations using the extracted second logging data 203A2. The feature quantities to be simulated are the magnitude of strain γ, the material average temperature τ of the heat-resistant insulating material, and the maximum resin pressure MP of the heat-resistant insulating material in the cylinder 33.

[0066] In this embodiment, the second logging data 203A2 is often collected at intervals of, for example, one second, generally at intervals of one second or less. Therefore, the amount of second logging data 203A2 is enormous. Therefore, if a simulation is performed using all of the second logging data 203A2 as analysis conditions, the computational load on the CPU 101 becomes enormous. Considering the computational load on the CPU 101, it is desirable for the CPU 101 to thin out the second logging data 203A2 at regular intervals, for example, every 10 seconds or every 60 seconds, and extract the second logging data 203A2 to be used in the simulation from all of the second logging data 203A2. Furthermore, because the second simulation data is calculated at regular intervals, the second simulation data, which is the result of the simulation, is treated as continuous data rather than as individual data.

[0067] Next, in step ST203, the CPU 101 calculates second calculation data. The processing of step ST203 realizes the function of the second calculation data calculation unit 310 of the quality prediction system 100. The second calculation data calculation unit 310 calculates second calculation data indicating the result of performing at least one of the four arithmetic operations using the second logging data. Here, the four arithmetic operations calculated by the second calculation data calculation unit 310 are the same as the four arithmetic operations used when calculating the first calculation data.

[0068] 9(c), one of the four arithmetic operations is, for example, division of the maximum resin pressure MP by the tip resin pressure P. Although not shown, other operations such as division of the screw rotation speed by the magnitude of the screw torque, subtraction of the difference between the die temperature acquired from the die temperature sensor S13 (which is substantially the same temperature as the heat-resistant insulating material extruded from the head 31) and the water temperature acquired from the water bath temperature sensor S15, and subtraction of the difference between the die temperature acquired from the die temperature sensor S13 (which is substantially the same temperature as the heat-resistant insulating material extruded from the head 31) and the temperature acquired from the air temperature sensor S31 are also performed.

[0069] Next, in step ST204, the CPU 101 assigns the second logging data 203A2, the second simulation data, and the second calculation data to the quality prediction model 203C. The processing of step ST203 realizes the function of the prediction unit 311 of the quality prediction system 100. The prediction unit 311 assigns the second logging data 203A2, the second simulation data created by the second simulation unit 309, and the second calculation data calculated by the second calculation data calculation unit 310 to the quality prediction model 203C, and predicts the quality characteristics of the heat-resistant insulated electric wire EW to be predicted.

[0070] For example, as shown in FIG. 9(d), the CPU 101 substitutes the second logging data 203A2, the second simulation data, and the second calculation data into the quality prediction model 203C for machine learning. While advanced AI can be used for machine learning, the CPU 101 can perform multiple regression analysis relatively easily by using the aforementioned Python "scikit-learn" library. Multiple regression analysis determines the regression coefficients a1, a2, ..., a101 and the intercept b for each variable. FIG. 9(d) shows an example of determining the relationship between tensile strength (predicted value) and tensile strength (actual value). In this example, the predicted values ​​and actual values ​​are concentrated near the line L1, and therefore the CPU 101 determines that the quality prediction model 203C is valid.

[0071] Next, in step ST205, the CPU 101 substitutes the data obtained in step ST204 into the logging data 203A stored in the file group 203. That is, if the quality prediction model 203C is valid, the CPU 101 substitutes the obtained data, for example, the tensile strength (predicted value) shown in FIG. 9(d), into the original logging data 203A. As a result, as shown in FIG. 9(e), the CPU 101 can store the tensile strength magnitude obtained near the end of the production time, that is, the tensile strength magnitude obtained as an actual measurement only at a few points on the terminals of the heat-resistant insulated electric wire EW, as the logging data 203A in the file group 203, in place of the tensile strength magnitude from the start to the end of production. This allows the quality prediction system 100 to predict fluctuations in the value of the quality characteristic for a set objective variable, such as tensile strength, over the entire length of the heat-resistant insulated electric wire EW.

[0072] Furthermore, the CPU 101 may calculate the objective variable, i.e., the predicted value of the quality characteristic, in near real time while the heat-resistant insulated wire EW is being manufactured. By displaying the result on a display device such as the display 104, the operator can constantly monitor fluctuations in the predicted value of the quality characteristic of the heat-resistant insulated wire EW. By further applying this system, for example, if the predicted value of the quality characteristic tends to exceed the appropriate range during the manufacture of the heat-resistant insulated wire EW, the CPU 101 can change the manufacturing parameters so that the predicted value of the quality characteristic tends to move toward the appropriate range. In other words, the CPU 101 can optimally control the manufacturing parameters.

[0073] Additionally, when there are few detectors installed and only a few values ​​can be calculated by simulating the quality characteristics over the entire length of the voltage-resistant insulating material EW, the quality prediction system 100 can easily increase the number of explanatory variables by combining factors from the logging data 203A and factors from the simulation data. This allows the quality prediction system 100 to improve the prediction accuracy of the quality characteristics over the entire length of the voltage-resistant insulating material EW.

[0074] <Example> Next, as an example, a more detailed description will be given of a process for predicting the overall length quality of the heat-resistant insulated electric wire EW manufactured by the manufacturing system 1. In this example, the quality characteristics to be predicted are the tensile strength and tensile elongation specified in JIS C3005.

[0075] First, the logging data used in this embodiment will be described. The quality prediction system 100 constantly stores 13 values ​​at 1-second intervals: the screw rotation speed of the screw in the cylinder 33, the current value for rotating the screw, the screw acceleration, the cylinder temperatures at five different positions (first to fifth cylinder temperatures), the neck temperature, the head temperature, the die temperature, the resin temperature, and the tip resin pressure value. These 13 pieces of data are stored in a file group 203 of the hard disk drive 112 as logging data 203A.

[0076] Furthermore, a total of seven values, namely, the air temperature of the extruder 10, the humidity of the extruder 10, the core temperature of the core wire CW, the water temperature of the water tank 40, the outer diameter of the heat-resistant insulated electric wire EW, the take-up speed of the take-up device 50, and the take-up current value, are constantly stored at one-second intervals in the quality prediction system 100. These seven pieces of data are stored in a file group 203 of the hard disk drive 112 as logging data 203A. As described above, in this embodiment, the CPU 101 uses data on 20 factors as the first logging data 203A1.

[0077] Next, the CPU 101 executes a simulation, as shown in FIG. 11. FIG. 11 is a diagram for explaining an outline of the simulation. As shown in FIG. 11, the CPU 101 executes the simulation using analysis conditions including screw structure information D1 indicating the structure of the screw in the cylinder 33, the screw rotation speed D2, the resin pressure value D3, the cylinder temperatures D4 of the temperature sensors S21 to S25, and the air temperature D5. The simulation software used in this embodiment is SingleScrewSimulator by HASL. Furthermore, in this embodiment, the simulation is executed for seven factors: "discharge volume," "maximum resin pressure," "driving force," "residence time," "average temperature," "strain," and "torque." The "discharge volume," "maximum resin pressure," "driving force," "residence time," "average temperature," "strain," and "torque" are characteristic quantities of the heat-resistant insulating member in the cylinder 33 and the screw. The CPU 101 stores the seven first simulation data D11, which are the results of the simulation execution, namely, "discharge volume," "maximum resin pressure," "driving force," "residence time," "average temperature," "strain," and "torque," as simulation data 203B in, for example, a file group 203 of the hard disk device 112.

[0078] Furthermore, in this embodiment, first calculation data indicating the results of performing two of the four arithmetic operations using the average value of the acquired first logging data is added as an explanatory variable.

[0079] The first of the four arithmetic operations is subtraction. In this embodiment, for example, two explanatory variables are added: a subtraction that calculates the difference between the resin temperature of the heat-resistant insulating material extruded from the head 31 and the water temperature of the water tank 40, and a subtraction that calculates the difference between the resin temperature of the heat-resistant insulating material extruded from the head 31 and the air temperature. Specifically, two factors, "resin temperature - water temperature" and "resin temperature - air temperature," are added as explanatory variables. It is thought that the characteristics of the heat-resistant insulating material (resin) can change due to rapid or gradual cooling. For this reason, one of the four arithmetic operations is specified to include subtraction of logging data related to the temperature of the heat-resistant insulating material.

[0080] The second arithmetic operation is division. In this embodiment, for example, the following 72 factors are added by dividing each factor. Each factor is acquired by the first logging data acquisition unit 301 and calculated by the first simulation unit 303.

[0081] The explanatory variables added by division are specifically "torque / retention time", "torque / screw rotation speed", "torque / maximum resin pressure", "torque / average temperature", "torque / strain", "torque / humidity", "torque / resin pressure", "torque / resin temperature", "retention time / torque", "retention time / screw rotation speed", "retention time / maximum resin pressure", "retention time / average temperature", "retention time / strain", "retention time / humidity", "retention time / resin pressure", "retention time / resin temperature", "screw rotation speed / torque", "screw rotation speed / retention time", "screw rotation speed / maximum resin pressure", "screw rotation speed / average temperature", "screw rotation speed / strain", "screw rotation speed / humidity", "screw rotation speed / resin pressure", "screw rotation speed / resin temperature", "maximum resin pressure / torque", "maximum resin pressure / retention time", "maximum resin pressure / screw rotation speed", "maximum resin pressure / average temperature", "maximum resin pressure / strain", "maximum resin pressure / humidity", "maximum resin pressure / resin pressure", "maximum resin pressure / resin temperature", "average temperature / torque", "Average Temperature / Residence Time", "Average Temperature / Screw Speed", "Average Temperature / Maximum Resin Pressure", "Average Temperature / Strain", "Average Temperature / Humidity", "Average Temperature / Resin Pressure", "Average Temperature / Resin Temperature", "Strain / Torque", "Strain / Residence Time", "Strain / Screw Speed", "Strain / Maximum Resin Pressure", "Strain / Average Temperature", "Strain / Humidity", "Strain / Resin Pressure", "Strain / Resin Temperature", "Humidity / Torque", "Humidity / Residence Time", "Humidity / Screw Speed", "Humidity / Maximum Resin Pressure", "Humidity / Average Temperature ", "Humidity / Strain", "Humidity / Resin Pressure", "Humidity / Resin Temperature", "Resin Pressure / Torque", "Resin Pressure / Residence Time", "Resin Pressure / Screw Rotation Speed", "Resin Pressure / Maximum Resin Pressure", "Resin Pressure / Temperature", "Resin Pressure / Strain", "Resin Pressure / Humidity", "Resin Pressure / Resin Temperature", "Resin Temperature / Torque", "Resin Temperature / Residence Time", "Resin Temperature / Screw Rotation Speed", "Resin Temperature / Maximum Resin Pressure", "Resin Temperature / Temperature", "Resin Temperature / Strain", "Resin Temperature / Humidity", and "Resin Temperature / Resin Pressure".

[0082] The "average temperature" is the average temperature inside the cylinder, and more specifically, is the average of the temperatures acquired by the temperature sensors S21 to S25. The "temperature" is the temperature detected by the air temperature sensor S31. There is a certain degree of correlation between the "screw rotation speed" and the "torque," and these are considered to be effective as new factors. In this embodiment, the pressure-resistant insulating member EW is a resin, and its physical properties are affected by the moisture content and temperature. For this reason, "resin pressure / humidity," "resin temperature / resin pressure," and the like are also considered to be effective as new factors.

[0083] In this embodiment, subtraction and division are described as examples of arithmetic operations using logging data, but the present invention is not limited to this. Any of the four arithmetic operations may be added to the explanatory variables. In other words, the quality prediction system 100 can perform any of subtraction, division, addition, and multiplication on any combination of factors from the first logging data 203A1 and factors from the first simulation data.

[0084] As described above, the CPU 101 creates a quality prediction model 203C for calculating the objective variables, tensile strength and tensile elongation, using a total of 101 variables, consisting of 20 pieces of first logging data 203A1, 7 pieces of first simulation data, and 74 pieces of first calculation data, as explanatory variables.

[0085] In this example, a predetermined number of production numbers are prepared as a learning model, and are analyzed by performing "multiple regression analysis" using the "sklearn.linear_model.LinearRecression" class of the Python machine learning library "scikitlearn" to create a quality prediction model 203C. The created quality prediction model 203C is then used to predict the tensile strength and tensile elongation over the entire length of the heat-resistant insulated electric wire EW.

[0086] <Relationship between predicted values ​​and actual measured values ​​in this embodiment> Fig. 12 is a diagram showing an example of the relationship between the tensile strength of the heat-resistant insulated wire EW predicted using 101 factors in this embodiment and the tensile strength of the actual measured value. Fig. 13 is a diagram showing an example of the relationship between the tensile elongation predicted using 101 factors in this embodiment and the tensile elongation of the actual measured value. In Figs. 12 and 13, the horizontal axis represents the predicted value and the vertical axis represents the actual measured value. The actual measured value can be measured, for example, by cutting out an end of the manufactured heat-resistant insulated wire EW and conducting a tensile test on the cut out end. As shown in Fig. 12, the coefficient of determination R 2 As shown in Figure 13, the coefficient of determination for tensile elongation, R 2 is 0.596.

[0087] <Relationship between predicted and measured values ​​in comparative examples> Next, a comparative example will be described. In the comparative example, a quality prediction model was created using 20 factors of the first logging data 203A1 and 7 factors of the first simulation data, and the tensile strength and tensile elongation of the heat-resistant insulated electric wire EW were predicted using the created quality prediction model.

[0088] Fig. 14 is a graph showing an example of the relationship between the tensile strength of a heat-resistant insulated electric wire EW predicted using 27 factors and the tensile strength of the actual measured value. Fig. 15 is a graph showing an example of the relationship between the tensile elongation predicted using 27 factors and the tensile elongation of the actual measured value. In Figs. 14 and 15, the horizontal axis represents the predicted value and the vertical axis represents the actual measured value. The actual measured value is, for example, the same as the result of the tensile test described in this example. As shown in Fig. 14, the coefficient of determination R 2 As shown in Figure 15, the coefficient of determination for tensile elongation, R 2 is 0.436.

[0089] <Comparison between this example and comparative example> As shown in Figs. 14 and 15, in the comparative example, the coefficient of determination R 2 were 0.505 and 0.436, respectively, whereas in this example, the coefficients of determination R 2are 0.696 and 0.596, respectively. That is, the coefficient of determination R in this example is 1.3 to 1.4 times higher than that in the comparative example. 2 Therefore, it can be seen that the prediction accuracy is improved in this example in which new feature quantities are created between factors, compared to the comparative example.

[0090] <Influence on quality characteristic prediction> Next, we will explain the influence of each factor on the prediction of the quality characteristics of heat-resistant insulated electric wire EW, in this example, tensile strength and tensile elongation over the entire length. In this example, the influence is calculated using the "SHAP" module in "python."

[0091] <Impact of this Example> FIG. 16 is a diagram showing an example of the influence of each factor on the predicted value of tensile strength in this example. FIG. 17 is a diagram showing an example of the influence of each factor on the predicted value of tensile elongation in this example. In FIGS. 16 and 17, the factors are arranged according to their influence. Note that in FIGS. 16 and 17, the top 20 factors by influence are shown in descending order of influence. Factors surrounded by squares in the diagram indicate that they are calculated by one of the four arithmetic operations.

[0092] As shown in Fig. 16, the 20 factors with the greatest influence on the prediction of tensile strength include seven factors calculated by division: "Maximum resin pressure / resin pressure," "Maximum resin pressure / humidity," "Torque / strain," "Resin pressure / humidity," "Temperature / resin pressure," "Screw rotation speed / strain," and "Humidity / screw rotation speed," as well as one factor calculated by subtraction: "Resin temperature - Water temperature."

[0093] As shown in Fig. 17, the 20 factors with the greatest influence on predicting tensile elongation include 11 factors calculated by division: "screw speed / torque," "temperature / screw speed," "resin temperature / temperature," "maximum resin pressure / strain," "strain / maximum resin pressure," "torque / screw speed," "residence time / strain," "resin pressure / residence time," "maximum resin pressure / torque," "maximum resin pressure / residence time," and "residence time / maximum resin pressure," as well as one factor calculated by subtraction: "resin temperature - air temperature."

[0094] 16 and 17 show that the factors calculated by division and subtraction are ranked among the most influential factors. Therefore, by using, as a new feature, a factor that combines the factors included in the first logging data and the first simulation data, in addition to the first logging data and the first simulation data, the quality prediction system 100 can significantly improve the accuracy of the quality prediction model 203C.

[0095] Furthermore, the quality prediction system 100 is not limited to predicting the tensile strength and tensile elongation of long products, but can also predict the quality characteristics of long products, such as flame retardancy and oil resistance, which could only be evaluated by destructive testing of terminals.

[0096] In the above description, the quality prediction system 100 uses the known first logging data 203A1 and first simulation data based on the known first logging data 203A1 to calculate the first calculation data using the first calculation data calculation unit 305, and creates the quality prediction model 203C based on the first logging data 203A1, the first simulation data, and the first calculation data. The quality prediction system 100 also predicts the quality characteristics of the long product using the second logging data 203A2 during production, the second simulation data based on the second logging data 203A2 during production, the second calculation data calculated by the second calculation data calculation unit 310, and the quality prediction model 203C. However, the quality prediction system 100's quality prediction model creation and quality prediction process are not limited to this. For example, the quality prediction system 100 may have the first calculation data calculation unit 305 calculate the first calculation data based only on the known first logging data 203A1, and create a quality prediction model based on the first logging data 203A1 and the first calculation data. Alternatively, the quality prediction system 100 may predict the quality characteristics of a long product using a quality prediction model created based on the second logging data 203A2 during production, the second calculation data calculated from the second logging data 203A2, the known first logging data 203A1, and the first calculation data calculated by the first calculation data calculation unit 305. This reduces the processing load on the quality prediction system 100. While the first logging data 203A1 and the like have been described as using average values ​​of data acquired at predetermined intervals, the present invention is not limited to this. For example, multiple data may be acquired from data acquired over time, and these multiple data may be used as the first logging data.

[0097] The invention made by the inventor has been specifically described above based on the embodiments thereof, but it goes without saying that the present invention is not limited to the above-described embodiments and can be modified in various ways without departing from the spirit of the invention. [Explanation of symbols]

[0098] 1. Manufacturing System 10. Extruder 20 Pull-out device 100 Quality Prediction System 101 CPU 112 Hard disk drive 202 Programs 202A Quality Prediction Model Creation Program 202B Quality Prediction Program 203A Logging data 203B Simulation Data 203C Quality Prediction Model 301 First logging data acquisition unit 302 Average value calculation unit 303 Simulation Section 1 304 Model Creation Department 305 First calculation data calculation unit 306 Data Merge Section 307 Quality prediction model memory section 308 Second logging data acquisition unit 309 Second Simulation Section 310 Second calculation data calculation unit 311 Prediction Department CW core wire EW Heat-resistant insulated wire

Claims

1. a first logging data acquisition unit that acquires known first logging data detected and stored by a plurality of detectors at predetermined intervals during the manufacture of the long product; a first calculation data calculation unit that calculates first calculation data indicating a result of performing at least one of four arithmetic operations using the first logging data acquired by the first logging data acquisition unit; and a model creation unit that creates a quality prediction model associated with quality characteristics of the long product based on the first calculation data calculated by the first calculation data calculation unit; A quality prediction model creation system comprising:

2. 2. The quality prediction model creation system according to claim 1, an average value calculation unit that calculates an average value of each of the first logging data acquired by the first logging data acquisition unit; the model creation unit creates the quality prediction model based on the first calculation data as well as an average value of the known first logging data. Quality prediction model creation system.

3. 3. The quality prediction model creation system according to claim 2, a first simulation unit that simulates quality characteristics of the long product using an average value of the first logging data; the first calculation data calculation unit calculates the first calculation data by performing at least one of the four arithmetic operations using the average value of the known first logging data and first simulation data indicating a result of the simulation by the first simulation unit; the model creation unit creates the quality prediction model based on the first calculation data, the average value of the known first logging data, and the first simulation data. Quality prediction model creation system.

4. 4. The quality prediction model creation system according to claim 3, the model creation unit creates the quality prediction model by machine learning using quality characteristic data indicating the quality characteristics of the long product as a target variable and the first logging data, the first calculation data, and the first simulation data of the long product as explanatory variables. Quality prediction model creation system.

5. 5. The quality prediction model creation system according to claim 4, the long product is manufactured by an extruder having a cylinder including a screw and a head through which a core wire passes, extruding a heat-resistant insulating material from the inside of the cylinder into the head, coating the core wire with the heat-resistant insulating material in the head, and then cooling the heat-resistant insulating material coating the core wire with a cooling device; The quality prediction model creation system includes: obtaining a first temperature of the heat-resistant insulating member extruded from the head from a first temperature sensor; obtaining a second temperature in the cooling device from a second temperature sensor; one of the four arithmetic operations is subtraction to calculate a difference between the first temperature and the second temperature; Quality prediction model creation system.

6. 5. The quality prediction model creation system according to claim 4, the long product is manufactured by an extruder having a cylinder including a screw and a head through which a core wire passes, extruding a heat-resistant insulating material from the inside of the cylinder into the head, and coating the core wire with the heat-resistant insulating material within the head; The quality prediction model creation system includes: obtaining a first temperature of the heat-resistant insulating member extruded from the head from a first temperature sensor; acquiring a third temperature around the cylinder from a third temperature sensor; one of the four arithmetic operations is an operation for calculating a difference between the first temperature and the third temperature; Quality prediction model creation system.

7. 5. The quality prediction model creation system according to claim 4, the long product is manufactured by an extruder having a cylinder including a screw and a head through which a core wire passes, extruding a heat-resistant insulating material from the inside of the cylinder into the head, and coating the core wire with the heat-resistant insulating material within the head; The quality prediction model creation system includes: Obtaining the rotation speed of the screw and the magnitude of the torque of the screw from the extruder; One of the four arithmetic operations is division of the number of rotations of the screw by the magnitude of the torque of the screw. Quality prediction model creation system.

8. 5. The quality prediction model creation system according to claim 4, the long product is manufactured by an extruder having a cylinder including a screw and a head through which a core wire passes, extruding a heat-resistant insulating material from the inside of the cylinder into the head, and coating the core wire with the heat-resistant insulating material within the head; The quality prediction model creation system includes: calculating a maximum resin pressure value of the heat-resistant insulating member in the cylinder by the first simulation unit; obtaining a first pressure value of the heat-resistant insulating member supplied to the head from a first pressure sensor; one of the four arithmetic operations is division of the maximum resin pressure value by a first pressure value; Quality prediction model creation system.

9. 5. The quality prediction model creation system according to claim 4, The long product is an electric wire having a core wire covered with a heat-resistant insulating material, The quality characteristics include the tensile strength or tensile elongation of the electric wire. Quality prediction model creation system.

10. a quality prediction model storage unit that stores a quality prediction model associated with quality characteristics of the long product, the quality prediction model being created based on first calculation data that indicates a result of performing at least one of four arithmetic operations using known first logging data of the long product; and a second logging data acquisition unit that acquires second logging data during the manufacturing of the long product that is the prediction target; a second calculation data calculation unit that calculates second calculation data by performing one of the four arithmetic operations using the second logging data; a prediction unit that predicts the quality characteristics of the long product to be predicted by substituting the second logging data acquired by the second logging data acquisition unit and the second calculation data into the quality prediction model; and Equipped with a quality prediction system.

11. Acquire known first logging data detected and stored at predetermined intervals by a plurality of detectors during the manufacture of the long product; calculating first operation data indicating a result of performing at least one of four arithmetic operations using the acquired first logging data; creating a quality prediction model associated with the quality characteristics of the long product based on the first calculation data; How to create a quality prediction model.

12. Acquire known first logging data detected and stored at predetermined intervals by a plurality of detectors during the manufacture of the long product; calculating first operation data indicating a result of performing at least one of four arithmetic operations from the acquired first logging data; creating a quality prediction model associated with quality characteristics of the long product based on the first calculation data; acquiring second logging data during the manufacturing of the long product to be predicted; calculating second operation data indicating a result of performing at least one of the four arithmetic operations using the second logging data; predicting the quality characteristics of the long product by substituting the second logging data and the second calculation data into the quality prediction model; Quality prediction methods.

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