Information processing apparatus and information processing method

Abstract

To accurately evaluate the physical properties of a kneaded material discharged from a kneader and cooled by a cooler in advance.SOLUTION: An information processing apparatus includes first calculation means for calculating a first physical quantity of a kneaded material inside a kneading machine that kneads a material mixture, based on a setting of a first condition for the kneading machine, second calculation means for calculating a second physical quantity of the kneaded material inside a cooling machine that cools the kneaded material discharged from the kneading machine, based on a setting of a second condition for the cooling machine, and acquisition means for acquiring a physical property of the kneaded material discharged from the cooling machine, using the first physical quantity and the second physical quantity.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and an information processing method. [Background technology]

[0002] Screw extruders are used as devices (kneaders) to melt and knead mixtures of resin and filler materials. Twin-screw extruders can achieve crushing dispersion and distribution dispersion by applying shear force to melt the materials early. There are three main analytical methods using computer simulation for twin-screw extruders.

[0003] The first analysis method is an analysis based on the FAN method (Flow Analysis Network Method). The FAN method is a calculation method that acquires the characteristic values ​​of each screw element, stores them in a database, and performs one-dimensional steady-state calculations in the conveying direction. The FAN method analyzes the entire range from pellet conveyance, melt plasticization, and molten material by having the user input each screw element and its combination, making it possible to perform calculations even when the material is not fully filled (see Non-Patent Document 1). The calculation load using the FAN method is smaller than that of other analysis methods.

[0004] The second analysis method is analysis based on the Finite Element Method (FEM). FEM is a calculation method that performs three-dimensional calculations of the melt for a portion of the screw. FEM does not perform analysis of pellet transport, melt plasticization, or calculations for non-full conditions, but it is possible to perform unsteady calculations of local temperature, pressure, shear stress, etc. through resin flow analysis (see Non-Patent Document 2). The calculation load using FEM is greater than that using the FAN method.

[0005] The third is analysis based on the particle method. The particle method is a calculation method that performs three-dimensional calculations for a portion of the screw, including non-filled states. The particle method differs from FEM in that it can calculate filled states. By combining the particle method with DEM (Discrete Element Method), it is also possible to calculate melt plasticization (see Non-Patent Document 3). The calculation load using the particle method is greater than that using the FAN method.

[0006] However, although the above analysis method can obtain information such as the filling rate, residual solid resin rate, pressure distribution, and resin temperature rise distribution, it is difficult to clarify the dispersion state of the kneaded material and changes in its physical properties.This is because the above analysis method is calculated based on several assumptions, and therefore does not directly analyze complex behavior such as the dispersion state of the kneaded material and changes in its physical properties.

[0007] A technology is known that enables simulation with a side feed function to be performed in an analysis based on the FAN method for a twin-screw extruder, and further calculates indices of mixing and shear force (see Patent Document 1). The index shown in Patent Document 1 is not used to evaluate the dispersion state and changes in physical properties of the kneaded material for all materials, but is merely shown as one index.

[0008] It is known that the dispersion state and changes in physical properties of the kneaded material depend not only on the state of the kneaded material inside the screw extruder, but also on the state of the kneaded material inside a cooler that cools the kneaded material discharged from the screw extruder. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Japanese Patent Application Publication No. 09-029819 [Non-patent literature]

[0010] [Non-Patent Document 1] Hideki Toyama, Masamichi Ishibashi, Shigeki Inoue, "Development of "TEX-FAN", Software for Analyzing Resin Behavior in Twin-Screw Extruders", Japan Steel Works Technical Report, Japan Steel Works, October 2004, No. 55, pp. 32-38 [Non-patent document 2] Toshihisa Kajiwara, "Study on Numerical Simulation of Polymer Liquid Flow and Polymer Processing," Journal of the Japanese Society of Rheology, 1999, Vol. 27, No. 4, pp. 219-226 [Non-patent document 3] Hideki Toyama, "Mixing Simulation of Extrusion Molding Machines," Journal of the Society of Rubber Science and Technology of Japan, 2016, Vol. 89, No. 12, pp. 368-374 Summary of the Invention [Problem to be solved by the invention]

[0011] Conventional computer simulations of screw extruders make it difficult to directly calculate the dispersion state and changes in physical properties of kneaded materials. Furthermore, while conventional analytical methods can analyze screw extruders, it is difficult to analyze the entire process, including the cooler that cools the kneaded material discharged from the screw extruder. The physical properties of the cooled kneaded material also change depending on the cooling method used after the material mixture is melt-kneaded in the screw extruder. Therefore, it is desirable to analyze the entire process, including the cooler, in order to be able to evaluate the physical properties of the cooled kneaded material in advance.

[0012] Therefore, an object of the present invention is to accurately evaluate in advance the physical properties of a kneaded product discharged from a kneader and cooled in a cooler. [Means for solving the problem]

[0013] The information processing device according to the present invention is characterized by comprising a first calculation means for calculating a first physical quantity of a kneaded material inside a kneading machine that kneads a material mixture based on setting of a first condition for the kneading machine, a second calculation means for calculating a second physical quantity of a kneaded material inside a cooler that cools the kneaded material discharged from the kneading machine based on setting of a second condition for the cooler, and an acquisition means for acquiring physical properties of the kneaded material discharged from the cooler using the first physical quantity and the second physical quantity. [Effects of the Invention]

[0014] According to the present invention, the physical properties of the kneaded product discharged from the kneader and cooled in the cooler can be evaluated in advance with high accuracy. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 2 is a functional block diagram of a physical property evaluation device. [Figure 2] FIG. 1 is a schematic diagram of a screw extruder. [Figure 3] FIG. [Figure 4] 1 is a flowchart showing an outline of processing by a physical property evaluation device. [Figure 5] 10 is a flowchart showing an outline of a screw extruder analysis process. [Figure 6] The thermal circuit network for the kneaded material sandwiched between rollers is shown. [Figure 7] The thermal circuit network for the kneaded material sandwiched between the rollers and air is shown. [Figure 8] The thermal circuit network for the kneaded material sandwiched between the rollers and belt is shown. [Figure 9] FIG. 2 is a diagram illustrating a hardware configuration of a physical property evaluation apparatus. [Figure 10] FIG. 2 is a functional block diagram of the physical property estimation device. [Figure 11] 10 is a flowchart showing an outline of processing by the physical property estimation device. [Figure 12] FIG. 1 is a diagram illustrating a flow curve in a flow tester temperature rising method. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described with reference to the drawings. The same components are designated by the same reference numerals, and their description will be omitted or simplified. The present invention is not limited to the embodiments exemplified below. Furthermore, not all of the features described in each embodiment are necessarily essential to solving the problems of the present invention.

[0017] First Embodiment The functional configuration of a physical property evaluation device 1 as an information processing device according to the first embodiment will be described with reference to Fig. 1. The physical property evaluation device 1 is a simulation device for evaluating the physical properties of a kneaded material after cooling.

[0018] Fig. 1 is a functional block diagram of a physical property evaluation device 1. The physical property evaluation device 1 includes a screw extruder analysis input unit 11, a screw extruder analysis calculation unit 12, a screw extruder analysis output unit 13, a cooler analysis input unit 14, a cooler analysis calculation unit 15, a cooler analysis output unit 16, and a physical property evaluation unit 17. Details of the processing of each functional unit will be described later with reference to Fig. 4.

[0019] The screw-type extruder and the cooler can be applied to the first embodiment as long as they are devices that respectively perform the steps of melt-kneading a material mixture and cooling the kneaded mixture. In the step of cooling the kneaded mixture, the kneaded mixture may be cooled naturally in air. A screw-type extruder is an example of a kneader. The kneader may be any device that can melt-knead a material mixture. In the following description, the kneader is a screw-type extruder.

[0020] Fig. 2 is a schematic diagram of a screw extruder. The screw extruder shown in Fig. 2 is a co-rotating intermeshing twin screw extruder. When a material mixture 22 is fed into the screw extruder, the fed material mixture 22 is conveyed by a screw 24 through a cylinder 23. A kneaded product 26 is conveyed to an outlet 25 of the screw extruder.

[0021] In the analysis of a screw-type extruder, the physical property evaluation device 1 performs one-dimensional calculations, treating the material mixture as a single fluid kneaded mass in each section of the screw conveying direction. The physical property evaluation device 1 assumes that the material mixture is a single fluid that does not generate viscous heat until it reaches the melting temperature, and that the material mixture generates viscous heat once it reaches the melting temperature. For example, if the material mixture contains a thermoplastic resin, the melting temperature can be the softening temperature of the thermoplastic resin measured with a flow tester.

[0022] Fig. 3 is a schematic diagram of a cooler. The cooler shown in Fig. 3 is a schematic diagram of a belt drum flaker (Nippon Coke and Engineering Co., Ltd.). The kneaded material discharged from outlet 25 of the screw-type extruder passes between rollers 28a and 28b. Cooling water 27a and 27b circulates inside rollers 28a and 28b. The kneaded material further passes between roller 28a and belt 29, and is discharged from the cooler as kneaded material 30 after cooling.

[0023] In the analysis of the cooler, the physical property evaluation device 1 performs one-dimensional calculations of the temperature of the kneaded material when it is cooled in the conveying direction while being sandwiched between the roller 28a and the belt 29. The physical property evaluation device 1 can calculate the temperature of the kneaded material by a thermal network method.

[0024] An example of a process in which the physical property evaluation device 1 evaluates the physical properties of the kneaded material after cooling will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an outline of the process of the physical property evaluation device 1.

[0025] In step S11, the screw extruder analysis input unit 11 sets input information (first conditions) for the screw extruder. The screw extruder analysis input unit 11 may set input information for the screw extruder based on an instruction from a user, or may set input information based on a preset instruction. The input information may be set as follows.

[0026] Input information for the screw extruder includes the operating conditions of the screw extruder, screw configuration conditions, and conditions of the materials fed into the screw extruder. The operating conditions of the screw extruder include the screw rotation speed, cylinder temperature, and feed rate of the material mixture. The screw configuration conditions include the type of screw element and the combination of elements. The conditions of the materials fed into the screw extruder include the inlet temperature of the material mixture, the melting temperature of the material mixture, the specific heat of the kneaded product, the density of the kneaded product, and the viscosity of the kneaded product.

[0027] In step S11, the chiller analysis input unit 14 sets input information (second condition) for the chiller. The chiller analysis input unit 14 may set the input information for the chiller based on an instruction from a user, or may set preset input information.

[0028] Input information for the cooler includes the operating conditions of the cooler and the conditions of the material fed into the cooler. The operating conditions of the cooler include the water temperature, air temperature, roller gap, roller rotation speed, etc. The conditions of the material fed into the cooler include the specific heat, density, and thermal conductivity of the kneaded material. The initial belt temperature is calculated and set as a function of the roller gap and roller rotation speed identified through experiments.

[0029] In step S12, the screw extruder analysis calculation unit 12 analyzes the screw extruder. The screw extruder analysis processing by the screw extruder analysis calculation unit 12 will be described with reference to Fig. 5. Fig. 5 is a flowchart showing an outline of the screw extruder analysis processing.

[0030] In step S121, the screw extruder analysis calculation unit 12 acquires the characteristic values ​​of each element based on the input information set by the screw extruder analysis input unit 11. The characteristic value information is acquired in advance and stored in a database. Specifically, the settings of the screw extruder operating conditions, such as the screw rotation speed, cylinder temperature, and feed rate of the material mixture, are changed for each element, and computer simulations are performed under various settings. The characteristic value information acquired by the computer simulation is stored in the database.

[0031] The characteristic values ​​include the pressure difference (depending on the rotation speed, feed rate, and viscosity), the volume average value of shear rate (depending on the rotation speed and feed rate), the viscous heat generation amount (depending on the rotation speed, feed rate, and viscosity), the heat transfer coefficient between the cylinder and the kneaded material (depending on the rotation speed, feed rate, and viscosity), etc. However, in the section consisting only of the cylinder at the outlet of a screw-type extruder, the pressure difference depends on the feed rate and viscosity, the volume average value of shear rate depends on the feed rate, the viscous heat generation amount depends on the feed rate and viscosity, and the heat transfer coefficient between the cylinder and the kneaded material depends on the feed rate and viscosity.

[0032] In step S122, the screw extruder analysis calculation unit 12 calculates the viscosity of the kneaded material. The viscosity is measured in advance as a quantity dependent on shear rate and temperature for the kneaded material discharged from the screw extruder under specified operating conditions, and a viscosity equation is set. The viscosity equation can be fitted using the Bird-Carreau law model and the WLF law model. The shear rate of each element is determined based on the volume average value of the shear rate, which is a characteristic value. The initial kneaded material temperature is determined based on the cylinder temperature, which is the input information in step S11. By determining the viscosity, the screw extruder analysis calculation unit 12 can calculate other characteristic values, such as the pressure difference, viscous heat generation, and heat transfer coefficient between the cylinder and the kneaded material.

[0033] In step S123, the screw extruder analysis calculation unit 12 calculates the pressure. The screw extruder analysis calculation unit 12 obtains the pressure difference at each element and at the outlet portion using the pressure characteristic formula (Formula 1).

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[0034] In Equation 1, α' and β' are coefficients determined for each element and outlet section. ΔP is a characteristic value. Once the pressure difference at each element and outlet section is determined, the pressure at each element and outlet section can be calculated.

[0035] In step S124, the screw extruder analysis calculation unit 12 calculates the filling rate and residence time. Equation 3 is a calculation formula for the filling rate and residence time when the pressure is positive. Equation 4 is a calculation formula for the filling rate and residence time when the pressure is negative.

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[0036] In step S125, the screw extruder analysis calculation unit 12 calculates the temperature of the kneaded material at each element and at the outlet using the heat conservation equation (Equation 5). It is assumed that the amount of heat stored at each element and at the outlet is equal to the sum of the amount of heat obtained by heat transfer from the cylinder to the kneaded material and the amount of viscous heat generated by the kneaded material.

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[0037] In step S126, the screw type extruder analysis calculation unit 12 determines whether the residual of the temperature of the kneaded material has converged. The residual of the temperature of the kneaded material at each element and the outlet portion is calculated by comparing the initial temperature of the kneaded material calculated in step S122 with the temperature of the kneaded material calculated in step S125. For example, the screw type extruder analysis calculation unit 12 determines that the residual of the temperature of the kneaded material has converged when the residual is equal to or less than a predetermined threshold, and determines that the residual of the temperature of the kneaded material has not converged when the residual is greater than the predetermined threshold. If the residual of the temperature of the kneaded material has not converged, the process returns to step S122, and steps S122 to S126 are repeated. If the residual of the temperature of the kneaded material has converged, the process shown in FIG. 5 ends.

[0038] In step S13 of FIG. 4, the screw-type extruder analysis output unit 13 calculates a physical quantity (first physical quantity) of the kneaded material inside the screw-type extruder based on the input information (first condition) set for the screw-type extruder in step S11. Specifically, the screw-type extruder analysis output unit 13 calculates the temperature (°C), viscosity (Pa·s), viscous heat generation (J / s), pressure difference (Pa), pressure (Pa), filling rate, residence time (s), shear stress (Pa), and the like of the kneaded material at each position in the screw conveying direction. Additionally, the screw-type extruder analysis output unit 13 calculates various physical quantities that contribute to the physical properties of the kneaded material after cooling, in addition to these physical quantities. Here, the shear stress can be a value obtained by multiplying the viscosity by the shear rate volume average value, or a value previously prepared in a database in step S121.

[0039] In step S14, the cooler analysis calculation unit 15 analyzes the cooler. In the following explanation, the cooler is a model in which a flat plate material of a predetermined size is cooled in three stages.

[0040] The first stage of cooling targets the kneaded material 40 sandwiched between the rollers 28a and 28b in FIG. 3. The cooler analysis calculation unit 15 calculates the physical quantities of the kneaded material 40 using the thermal circuit network shown in FIG. 6. Nodes 41 and 47 are provided inside the water 27a and 27b, respectively. Nodes 42 and 46 are provided at the boundaries between the rollers 28a and 28b, respectively, and the cooled kneaded material 40 (also referred to as the kneaded and cooled material 40). Contact points 43 to 45 are provided inside the kneaded and cooled material 40.

[0041] The heat capacity is taken into account at the junctions 43 and 45. The thermal resistance is the heat conduction inside the rollers 28a and 28b and the heat conduction inside the kneaded and cooled material 40. The thermal conductivity of the rollers 28a and 28b is set in advance. The other input values ​​are set in step S11. The temperature of the kneaded material at the outlet of the screw extruder calculated in step S13 is used as the initial temperature of the junctions 43 and 45.

[0042] The second stage of cooling targets the kneaded material 50 sandwiched between the roller 28a and the air 31 in FIG. 3. The cooler analysis calculation unit 15 calculates the physical quantities of the kneaded material 50 using the thermal circuit network shown in FIG. 7. A node 51 is provided inside the water 27a. A node 52 is provided at the boundary between the roller 28a and the cooled kneaded material 50 (the kneaded and cooled material 50). Contact points 53 to 55 are provided inside the kneaded and cooled material 50. A contact point 56 is provided at the boundary between the kneaded and cooled material 50 and the air 31. A node 57 is provided inside the air 31.

[0043] The heat capacity is taken into consideration at the contact points 53 and 55. The thermal resistances are the heat conduction inside the roller 28a, the heat conduction inside the mixed and cooled material 50, and the natural convection and radiation between the mixed and cooled material 50 and the air 31. The thermal conductivity of the roller 28a, the natural convection heat transfer coefficient between the mixed and cooled material 50 and the air 31, and the emissivity are set in advance. The other input values ​​are set in step S11. The temperature of the mixed and cooled material 40 calculated in the first stage is used as the initial temperature of the contact points 53 and 55.

[0044] The third stage of cooling targets the kneaded material 60 sandwiched between the roller 28a and the belt 29 in FIG. 3. The cooler analysis calculation unit 15 calculates the physical quantities of the kneaded material 60 using the thermal network shown in FIG. 8. A node 61 is provided inside the water 27a. A node 62 is provided at the boundary between the roller 28a and the cooled kneaded material 60 (the kneaded and cooled material 60). Contact points 63 to 65 are provided inside the kneaded and cooled material 60. A contact point 66 is provided at the boundary between the kneaded and cooled material 60 and the belt 29. A contact point 67 is provided inside the belt 29. A contact point 68 is provided at the boundary between the belt 29 and the air 32. A contact point 69 is provided inside the air 32.

[0045] The heat capacity is taken into consideration at the contact points 63, 65, and 67. The thermal resistances are the heat conduction inside the roller 28a, the heat conduction inside the mixed and cooled material 60, the heat conduction inside the belt 29, and the natural convection and radiation between the belt 29 and the air 32. The thermal conductivity of the roller 28a, the thermal conductivity of the belt 29, the natural convection heat transfer coefficient and emissivity between the belt 29 and the air 32, the density of the belt 29, and the specific heat of the belt 29 are set in advance. The other input values ​​are set in step S11. The temperature of the mixed and cooled material 50 calculated in the second stage is used as the initial temperature of the contact points 63 and 65.

[0046] In step S15, the cooler analysis output unit 16 calculates physical quantities (second physical quantities) of the cooled kneaded material (cooled kneaded material) inside the cooler based on the input information (second conditions) set for the cooler in step S11. Specifically, the cooler analysis output unit 16 calculates the temperature (°C), cooling time (s), etc. of the cooled kneaded material at each position in the conveying direction. In addition to these physical quantities, the cooler analysis output unit 16 calculates various physical quantities that contribute to the physical properties of the cooled kneaded material.

[0047] In step S16, the physical property evaluation unit 17 uses the physical quantity of the kneaded material inside the screw extruder calculated in step S13 and the physical quantity of the kneaded material inside the cooler calculated in step S15 to obtain and evaluate the physical properties of the kneaded material after cooling discharged from the cooler.

[0048] An evaluation formula for evaluating the physical properties is prepared in advance. The evaluation formula can be derived by changing the settings of input information for the screw extruder and the cooler and analyzing the relationship between the measured values ​​of the physical properties of the kneaded product after cooling obtained from an experiment and the physical quantities obtained from an analysis of the screw extruder and the cooler with the same settings.

[0049] The physical properties of the cooled mixture discharged from the cooler can be obtained using, for example, multiple regression analysis. The physical quantities used as explanatory variables are selected from the physical quantities of the mixture inside the screw extruder calculated in step S13 and the physical quantities of the mixture inside the cooler calculated in step S15. The physical quantities used as explanatory variables are examined in advance, including the mechanism of the phenomenon, and can be selected taking into account the P value of the explanatory variables. Examples of physical quantities used as explanatory variables include the cooling time from the cooler inlet to the half-melt temperature, the temperature of the mixture at the center of the screw extruder in the conveying direction, and the total amount of viscous heat generated from the mixing element to the outlet. The half-melt temperature is a physical property related to flow properties that can be measured using a flow tester.

[0050] The evaluation formula used in the multiple regression analysis may be corrected using the estimated results of a trained model that has been trained to input physical quantities that are used as explanatory variables and output the physical properties of the kneaded material discharged from the cooler.

[0051] By the process of FIG. 4, the physical properties of the kneaded material can be evaluated using the physical quantities that contribute to the physical properties of the kneaded material after cooling calculated by the screw extruder analysis in step S13 and the physical quantities that contribute to the physical properties of the kneaded material after cooling calculated by the cooler analysis in step S15.

[0052] FIG. 9 is a diagram showing the hardware configuration of the physical property evaluation device 1. The physical property evaluation device 1 is The computer includes a U 101, a RAM 102, a ROM 103, a HDD (Hard Disk Drive) 104, an input device 105, and an output device 106. These components are connected to each other via a bus or the like.

[0053] The CPU 101 is a processor that reads out programs stored in the ROM 103 and the HDD 104 into the RAM 102, executes the programs, and controls each functional unit of the physical property evaluation apparatus 1. Each functional unit of the physical property evaluation apparatus 1 executes various types of arithmetic processing according to instructions from the CPU 101.

[0054] The RAM 102 is a volatile storage medium and functions as a work memory when the CPU 101 executes a program. The ROM 103 is a non-volatile storage medium and stores firmware and the like for operating the physical property evaluation apparatus 1. The HDD 104 is a non-volatile storage medium and stores programs used for analyzing the screw extruder, analyzing the cooler, and processing the physical property evaluation of the kneaded material after cooling.

[0055] The input device 105 is a device for inputting information to the physical property evaluation apparatus 1, and is typically a user interface for a user to operate the physical property evaluation apparatus 1. The input device 105 is, for example, a keyboard, a button, a mouse, or a touch panel.

[0056] The output device 106 is a device for outputting information from the physical property evaluation apparatus 1 to the outside, and is typically a user interface for presenting information to a user. The output device 106 is, for example, a display and a speaker.

[0057] 9 is an example and can be modified as appropriate. For example, the processor that can be installed in the physical property evaluation apparatus 1 is not limited to the CPU 101, but may include a GPU, an ASIC, an FPGA, etc. The physical property evaluation apparatus 1 may be provided with a plurality of these processors, and the plurality of processors may execute processing in a distributed manner. The HDD 104 may be a storage medium such as an optical disk, a magneto-optical disk, or an SSD (Solid State Drive).

[0058] According to the first embodiment, the physical property evaluation device 1 can accurately evaluate the physical properties of the kneaded material after cooling by analyzing the kneaded material in both the screw extruder and the cooler. Therefore, users can reduce the number of prototypes at the production site and save the labor required for trial and error. In addition, the amount of prototypes is reduced, and production losses caused by trial and error are reduced.

[0059] Second Embodiment In the second embodiment, the physical properties of the kneaded material discharged from the cooler are estimated using a trained model that is trained to input the physical quantities of the kneaded material inside the screw extruder and the physical quantities of the kneaded material inside the cooler and output the physical properties of the kneaded material discharged from the cooler. The trained model is a model trained by machine learning using actual measurement values ​​sampled in advance and physical quantities that evaluate the physical properties of the kneaded material after cooling. In other words, the trained model is a model trained using the actual measurement values ​​of the physical properties of the kneaded material discharged from the cooler and the physical quantities calculated based on the settings of the input information for the screw extruder and the cooler when the actual measurement values ​​are obtained.

[0060] The functional configuration of a physical property estimation device 2 serving as an information processing device according to the second embodiment will be described with reference to Fig. 10. The physical property estimation device 2 is a simulation device for estimating the physical properties of the kneaded material after cooling.

[0061] 10 is a functional block diagram of the physical property estimation device 2. The physical property estimation device 2 includes a screw extruder analysis input unit 11, a screw extruder analysis calculation unit 12, a screw extruder analysis output unit 13, and a screw extruder analysis input unit 14. 3, the cooling machine analysis input unit 14, the cooling machine analysis calculation unit 15, the cooling machine analysis output unit 16, the trained model acquisition unit 18, and the physical property estimation unit 19. Explanation of the same functional units as those in the first embodiment will be omitted. Details of the processing of the trained model acquisition unit 18 and the physical property estimation unit 19 will be described later using FIG. 11.

[0062] The second embodiment can be applied to any screw-type extruder and cooler as long as they are devices that respectively include a process for melt-kneading a material mixture and a process for cooling the kneaded product. The screw-type extruder and cooler to which the second embodiment is applied are similar to the screw-type extruder and cooler in the first embodiment described with reference to Figures 2 and 3. The hardware configuration of the physical property estimation device 2 is similar to the hardware configuration of the physical property evaluation device 1 in the first embodiment described with reference to Figure 9.

[0063] An example of processing in which the physical property estimation device 2 estimates the physical properties of the kneaded material after cooling will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an outline of processing in the physical property estimation device 2.

[0064] In step S21, the trained model acquisition unit 18 acquires a trained model. The trained model is prepared in advance. The trained model is a model that has undergone machine learning using measured values ​​of physical properties of the kneaded material after cooling obtained from an experiment by changing the settings of input information for the screw extruder and the cooler, and physical quantities obtained from an analysis of the screw extruder and the cooler with the same settings. The trained model used in the second embodiment may be a machine learning model that has learned the relationship between measured values ​​of physical properties and physical quantities obtained from the analysis.

[0065] In step S22, the screw extruder analysis input unit 11 and the cooler analysis input unit 14 set input information for the screw extruder and the cooler, similarly to step S11 in the first embodiment.

[0066] In step S23, the screw type extruder analysis calculation unit 12 analyzes the screw type extruder, similar to step S12 in the first embodiment. In step S24, the screw type extruder analysis output unit 13 calculates the physical quantities of the kneaded material inside the screw type extruder, similar to step S13 in the first embodiment. In step S25, the cooler analysis calculation unit 15 analyzes the cooler, similar to step S14 in the first embodiment. In step S26, the cooler analysis output unit 16 calculates the physical quantities of the kneaded and cooled material inside the cooler, similar to step S15 in the first embodiment.

[0067] In step S27, the physical property estimation unit 19 estimates the physical properties of the kneaded material after cooling. The physical property estimation unit 19 inputs the physical quantities of the kneaded material inside the screw extruder calculated in step S24 and the physical quantities of the kneaded material inside the cooler calculated in step S26 into the trained model acquired in step S21, and estimates the physical properties of the kneaded material after cooling.

[0068] According to the second embodiment, the physical property estimation device 2 can accurately estimate the physical properties of the kneaded material discharged from the screw extruder and cooled in the cooler using a trained model prepared in advance. This allows the user to reduce the number of prototypes at the production site and the labor required for trial and error. Furthermore, the amount of prototypes is reduced, eliminating production losses caused by trial and error.

[0069] <Measuring method for softening temperature and 1 / 2 melting temperature> The method for measuring the softening temperature and 1 / 2 melting temperature used in the present invention will be explained below. The softening temperature and 1 / 2 melting temperature are measured using a constant load extrusion type capillary rheometer "Flow characteristic evaluation device Flow Tester CFT-500D" (manufactured by Shimadzu Corporation) according to the manual that comes with the device. This device applies a constant load from above the measurement sample using a piston. The measurement sample filled in the cylinder is heated and melted while applying pressure, and the molten measurement sample is extruded from the die at the bottom of the cylinder. At this time, a flow curve showing the relationship between the piston depression amount and temperature is obtained.

[0070] The melting temperature by the 1 / 2 method in the present invention is the "melting temperature by the 1 / 2 method" described in the manual attached to the "Flow Property Evaluation Device Flow Tester CFT-500D." Calculation of the melting temperature by the 1 / 2 method will be explained with reference to Fig. 12. Fig. 12 is a diagram illustrating a flow curve by the flow tester temperature rising method.

[0071] Calculate half the difference between the amount of piston descent Smax when the outflow ends and the amount of piston descent Smin when the outflow starts. The calculated value is X (X = (Smax - Smin) / 2). The melting temperature in the 1 / 2 method is the temperature on the flow curve when the amount of piston descent on the flow curve is the sum of X and Smin. The softening temperature is "Ts" as stated in the accompanying manual.

[0072] The measurement sample is prepared by compressing approximately 1.0 g of toner at 25°C using a tablet press (e.g., NT-100H, manufactured by NPA Systems Co., Ltd.) at approximately 10 MPa for approximately 60 seconds to form a cylindrical sample with a diameter of approximately 8 mm.

[0073] The measurement conditions for the CFT-500D are as follows: Test mode: Temperature rising method Starting temperature: 50(℃) Achieved temperature: 200(℃) Measurement interval: 1.0 (℃) Heating rate: 4.0 (℃ / min) Piston cross-sectional area: 1.000 (cm 2 ) Test load (piston load): 10.0 (kgf) (0.9807 (MPa)) Preheat time: 300 seconds Die hole diameter: 1.0 (mm) Die length: 1.0 (mm) [Example]

[0074] The present invention will be described in more detail below using examples and comparative examples. The present invention is not limited to the following examples as long as the gist of the present invention is not exceeded. [Example]

[0075] The physical properties of the kneaded product after cooling were evaluated using a simulation device (physical property evaluation device 1) according to the present invention. In Example 1, a co-rotating intermeshing twin screw extruder shown in Figure 2 was used.

[0076] The operating conditions for the screw extruder were a screw rotation speed of 200 rpm, a cylinder temperature of 140°C, and a feed rate of the material mixture of 10 kg / h. The screw configuration was such that a mixing element with a kneading and dispersion mechanism was located in the center. The material mixture was toner (a mixture of resin, wax, and pigment powder particles). The material conditions were a material mixture inlet temperature of 50°C, a material mixture melting temperature of 70°C, a specific heat of the kneaded product of 2059 J / (kg·K), and a density of 1243 kg / m 3 The viscosity formula of the kneaded material is I chose 6.

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[0077] F(γdot) is expressed by Equation 7.

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[0078] H(T) is expressed by Equation 8.

number

[0079] In Example 1, η0 is 2148 (Pa·s), λ is 14.02 (s), n is 0.394, and η ∞ is 35.66 (Pa·s), C1 is 10, C2 is 200, T α 413(K) and did.

[0080] In Example 1, the cooler shown in Figure 3 was used. The operating conditions of the cooler were a water temperature of 13°C, an air temperature of 25°C, a roller gap of 2 mm, and a roller rotation speed of 13 Hz. The material conditions were a specific heat of the kneaded and cooled product of 2059 J / (kg K) and a density of 1243 kg / m 3 ), the thermal conductivity of the cooled mixture is 0.5 (W / (m ·K)).

[0081] Analysis of the screw extruder revealed that the temperature of the kneaded material at the center of the screw extruder's conveying direction and the total amount of viscous heat generated from the mixing element to the outlet were calculated as physical quantities that contribute to a certain physical property A of the kneaded material after cooling. The temperature of the kneaded material was 140.21°C, and the total amount of viscous heat generated was 25,387 J / kg.

[0082] Analysis of the cooler revealed that the cooling time required for the cooled mixture to reach the half-melting temperature from the cooler inlet was calculated as a physical quantity that contributes to a certain physical property A of the cooled mixture. The cooling time was 2.7 seconds. The half-melting temperature is a physical property related to fluidity that can be measured with a flow tester.

[0083] These physical quantities were substituted into Equation 9 to evaluate a certain physical property A of the kneaded product after cooling, and the physical property A was calculated to be 0.01945. Evaluation value of physical property A = β0 + β1 × x1 + β2 × x2 + β3 × x3… (Equation 9) Here, β0 is -9.1586E-03, β1 is 4.1865E-03, β2 is 1.2092E-04, and β3 is 1.8377E-08. x1 is the cooling time (s) from the cooler inlet to the half-method melt temperature, x2 is the temperature of the kneaded material at the center position in the conveying direction of the screw extruder (°C), and x3 is the total amount of viscous heat generated from the mixing element to the outlet (J / kg).

[0084] Equation 9 for evaluating the physical property A was derived in advance by multiple regression analysis of the measured values ​​of the physical properties of the kneaded product after cooling, obtained by changing the settings of the input information for the screw extruder and the cooler, and the physical quantities obtained from the screw extruder analysis and the cooler analysis using the same settings, respectively.

[0085] The measured value of the actual sample was 0.01876, which is approximately consistent with the evaluated value by the simulation device (physical property evaluation device 1). In this way, the physical property evaluation device 1 was able to accurately evaluate the physical properties of the kneaded material after cooling by analyzing the screw-type extruder and the cooler together. [Example]

[0086] Using a simulation device (physical property estimation device 2) according to the present invention, estimation of the physical properties of a kneaded material after cooling was carried out. First, a trained model was prepared by machine learning using a plurality of measured physical property values ​​of the kneaded material after cooling obtained by changing the input information settings of the screw extruder and the cooler, and physical quantities obtained from screw extruder analysis and cooler analysis with the same settings. Specifically, the trained model was prepared as a ridge regression model.

[0087] Next, a screw extruder analysis and a cooler analysis were carried out. In Example 2, as in Example 1, a co-rotating intermeshing twin screw extruder shown in FIG. 2 was used. The operating conditions of the screw extruder, the screw configuration, and the material conditions of the material fed into the screw extruder were the same as in Example 1. Furthermore, in Example 2, as in Example 1, a cooler shown in FIG. 3 was used. The operating conditions and material conditions, which are input information for the cooler, were set the same as in Example 1.

[0088] Analysis of a screw extruder allowed the calculation of approximately 80 physical quantities of the kneaded material at the position of the mixing element as physical quantities that contribute to a certain physical property A of the kneaded material after cooling. For example, residence time (s), average filling rate, average shear stress (Pa), time integral of shear stress (Pa·s), and maximum shear stress (Pa) were calculated. Also calculated were average temperature (°C), time integral of temperature (°C·s), maximum temperature (°C), and total viscous heat generation (J / kg).

[0089] Analysis of the cooler resulted in the calculation of approximately 10 physical quantities of the cooled kneaded material that contribute to a certain physical property A of the cooled kneaded material. For example, the cooling time (s) from the cooler inlet to the half-method melting temperature and the average cooling rate (°C / s) were calculated, as were the cooling time (s) from the cooler inlet to the softening temperature and the average cooling rate (°C / s). Here, the half-method melting temperature and softening temperature are physical properties related to fluidity that can be measured with a flow tester.

[0090] These physical quantities were input into the machine learning model, and the physical property A of the kneaded material after cooling was estimated to be 0.01908. The measured value of the actual sample was 0.01876, which is approximately consistent with the value estimated by the simulation device (physical property estimation device 2). In this way, the physical property estimation device 2 was able to accurately evaluate the physical properties of the cooled kneaded material by analyzing the screw extruder and the cooler together using a trained model prepared in advance.

[0091] As a comparative example to Example 1, an example will be described in which the physical properties of the kneaded material after cooling were evaluated only based on the physical quantities of the kneaded material inside the screw extruder, as seen in the prior art. Equation 10 was derived to evaluate a certain physical property A of the kneaded material after cooling by multiple regression analysis of multiple measured values ​​of physical properties of the kneaded material after cooling, obtained by changing the settings of input information for the screw extruder and cooler, and physical quantities obtained from screw extruder analysis using the same settings for each. The explanatory variables in Equation 10 were the same as the explanatory variables obtained from the screw extruder analysis in Example 1. Evaluation value of physical property A = β0 + β1 × x1 + β2 × x2… (Equation 10) Here, β0 is -7.2751E-03, β1 is 1.6643E-04, and β2 is - x1 is the temperature (°C) of the kneaded material at the center position in the conveying direction of the screw extruder, and x2 is the total amount of viscous heat generated from the mixing element to the outlet (J / kg).

[0092] Analysis of the screw extruder revealed that the temperature of the kneaded material at the center position in the conveying direction of the screw extruder and the total amount of viscous heat generated from the mixing element to the outlet were calculated as physical quantities contributing to a certain physical property A of the kneaded material after cooling. The temperature of the kneaded material was 140.21 (°C), and the total amount of viscous heat generated was 25,387 (J / kg). These physical quantities were substituted into Equation 10, and the physical property A was calculated to be 0.01554. The accuracy of the evaluation results in the comparative example was lower than that in Example 1.

[0093] The various controls described above may or may not be performed by a single piece of hardware (e.g., a processor or circuit). The entire device may be controlled by multiple pieces of hardware (e.g., multiple processors, multiple circuits, or a combination of one or more processors and one or more circuits) sharing the processing.

[0094] The above processor is a processor in a broad sense, and includes general-purpose processors and dedicated processors. General-purpose processors include, for example, CPUs (Central Processing Units), MPUs (Micro Processing Units), and DSPs (Digital Signal Processors). Dedicated processors include, for example, GPUs (Graphics Processing Units), ASICs (Application Specific Integrated Circuits), and PLDs (Programmable Logic Devices). Programmable logic devices include, for example, FPGAs (Field Programmable Gate Arrays) and CPLDs (Complex Programmable Logic Devices).

[0095] Although the embodiments of the present invention have been described in detail, the present invention is not limited to these specific embodiments, and various forms within the scope of the gist of the present invention are also included in the present invention. Furthermore, each of the above-described embodiments merely represents one embodiment of the present invention, and each embodiment can be combined as appropriate.

[0096] <Other embodiments> The present invention can also be realized by supplying a program that realizes one or more of the functions of the above-described embodiments to a system or device via a network or a storage medium, and having one or more processors in the computer of the system or device read and execute the program, or by a circuit that realizes one or more of the functions.

[0097] The disclosure of this embodiment includes the following configuration, method, program, and medium. (Configuration 1) a first calculation means for calculating a first physical quantity of a kneaded material inside a kneader based on a first condition set for the kneader that kneads the material mixture; a second calculation means for calculating a second physical quantity of the kneaded material inside a cooler that cools the kneaded material discharged from the kneader based on setting of a second condition for the cooler; an acquisition means for acquiring physical properties of the kneaded material discharged from the cooler using the first physical quantity and the second physical quantity; An information processing device comprising: (Configuration 2) The acquisition means estimates the physical properties of the kneaded material discharged from the cooler using a trained model that has been trained to input the first physical quantity and the second physical quantity and output the physical properties of the kneaded material discharged from the cooler. 2. The information processing device according to configuration 1, (Configuration 3) The trained model is a model trained using an actual measurement value of a physical property of the kneaded material discharged from the cooler, the first physical quantity calculated based on the setting of the first condition when the actual measurement value is obtained, and the second physical quantity calculated based on the setting of the second condition when the actual measurement value is obtained. 3. The information processing device according to configuration 2. (Configuration 4) The acquiring means acquires the physical properties of the kneaded material discharged from the cooler by multiple regression analysis using the first physical quantity and the second physical quantity as explanatory variables. 3. The information processing device according to configuration 1 or 2. (Configuration 5) The evaluation formula used in the multiple regression analysis is corrected using an estimation result by a trained model that is trained to input the physical quantities that are the explanatory variables and output the physical properties of the kneaded material discharged from the cooler. 5. The information processing device according to configuration 4. (Configuration 6) The first condition includes at least one of a cylinder temperature, a feed amount of the material mixture, an inlet temperature of the material mixture, a melting temperature of the material mixture, a specific heat of the kneaded product, a density of the kneaded product, and a viscosity formula of the kneaded product. 6. The information processing device according to any one of configurations 1 to 5. (Configuration 7) The kneader is a screw extruder, The first condition includes at least one of the screw rotation speed, the type of screw element, and the combination of the elements. 7. The information processing device according to configuration 6. (Configuration 8) The information processing device according to any one of configurations 1 to 7, wherein the second condition includes at least one of the conditions of water temperature, air temperature, gap between rollers, roller rotation speed, specific heat of the kneaded material, density of the kneaded material, and thermal conductivity of the kneaded material. (Configuration 9) The first physical quantity includes at least one of the temperature of the kneaded material, the viscosity of the kneaded material, the viscous heat generation amount of the kneaded material, the pressure difference, the pressure, the filling rate, the residence time, and the shear stress. 9. The information processing device according to any one of configurations 1 to 8. (Configuration 10) The second physical quantity includes at least one of the temperature of the kneaded material and the cooling time. 10. The information processing device according to any one of configurations 1 to 9. (Method 1) a first calculation step of calculating a first physical quantity of a kneaded material inside a kneader based on setting of a first condition for the kneader that kneads the material mixture; a second calculation step of calculating a second physical quantity of the kneaded material inside the cooler based on setting of a second condition for the cooler that cools the kneaded material discharged from the kneader; an acquiring step of acquiring physical properties of the kneaded material discharged from the cooler using the first physical quantity and the second physical quantity; An information processing method comprising: (Method 2) In the obtaining step, the physical properties of the kneaded material discharged from the cooler are estimated using a trained model that has been trained to input the first physical quantity and the second physical quantity and output the physical properties of the kneaded material discharged from the cooler. 12. The information processing method according to configuration 11. (program) 13. A program for causing a computer to execute the steps of the information processing method according to claim 11 or 12. (medium) 13. A computer-readable storage medium storing a program for causing a computer to execute each step of the information processing method according to claim 11 or 12. [Explanation of symbols]

[0098] 1: Physical property evaluation device (information processing device), 11: Screw type extruder analysis input unit, 12: Screw type extruder analysis calculation unit, 13: Screw type extruder analysis output unit, 14: Cooling machine analysis input unit, 15: Cooling machine analysis calculation unit, 16: Cooling machine analysis output unit, 101: CPU

Claims

1. a first calculation means for calculating a first physical quantity of a kneaded material inside a kneader based on a first condition set for the kneader that kneads the material mixture; a second calculation means for calculating a second physical quantity of the kneaded material inside a cooler that cools the kneaded material discharged from the kneader based on setting of a second condition for the cooler; an acquisition means for acquiring physical properties of the kneaded material discharged from the cooler using the first physical quantity and the second physical quantity; An information processing device comprising:

2. The acquisition means estimates the physical properties of the kneaded material discharged from the cooler using a trained model that has been trained to input the first physical quantity and the second physical quantity and output the physical properties of the kneaded material discharged from the cooler.

2. The information processing apparatus according to claim 1, wherein:

3. The trained model is a model trained using an actual measurement value of a physical property of the kneaded material discharged from the cooler, the first physical quantity calculated based on the setting of the first condition when the actual measurement value is obtained, and the second physical quantity calculated based on the setting of the second condition when the actual measurement value is obtained.

3. The information processing apparatus according to claim 2, wherein:

4. The acquiring means acquires the physical properties of the kneaded material discharged from the cooler by multiple regression analysis using the first physical quantity and the second physical quantity as explanatory variables.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

5. The evaluation formula used in the multiple regression analysis is corrected using an estimation result by a trained model that is trained to input the physical quantities that are the explanatory variables and output the physical properties of the kneaded material discharged from the cooler.

5. The information processing apparatus according to claim 4,

6. The first condition includes at least one of a cylinder temperature, a feed amount of the material mixture, an inlet temperature of the material mixture, a melting temperature of the material mixture, a specific heat of the kneaded product, a density of the kneaded product, and a viscosity formula of the kneaded product.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

7. The kneader is a screw extruder, The first condition includes at least one of the screw rotation speed, the type of screw element, and the combination of the elements.

7. The information processing apparatus according to claim 6,

8. 3. The information processing device according to claim 1, wherein the second condition includes at least one of water temperature, air temperature, roller gap, roller rotation speed, specific heat of the kneaded material, density of the kneaded material, and thermal conductivity of the kneaded material.

9. The first physical quantity includes at least one of the temperature of the kneaded material, the viscosity of the kneaded material, the viscous heat generation amount of the kneaded material, the pressure difference, the pressure, the filling rate, the residence time, and the shear stress.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

10. The second physical quantity includes at least one of the temperature of the kneaded material and the cooling time.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

11. a first calculation step of calculating a first physical quantity of a kneaded material inside a kneader based on setting of a first condition for the kneader that kneads the material mixture; a second calculation step of calculating a second physical quantity of the kneaded material inside the cooler based on setting of a second condition for the cooler that cools the kneaded material discharged from the kneader; an acquiring step of acquiring physical properties of the kneaded material discharged from the cooler using the first physical quantity and the second physical quantity; An information processing method comprising:

12. In the obtaining step, the physical properties of the kneaded material discharged from the cooling machine are estimated using a trained model that has been trained to input the first physical quantity and the second physical quantity and output the physical properties of the kneaded material discharged from the cooling machine.

12. The information processing method according to claim 11.

13. A program for causing a computer to execute the steps of the information processing method according to claim 11 or 12.

14. 13. A computer-readable storage medium storing a program for causing a computer to execute each step of the information processing method according to claim 11 or 12.

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