Physical property prediction system, physical property prediction device, and physical property prediction method

Abstract

To provide a physical property prediction system, a physical property prediction device, and a physical property prediction method by which a physical property of a composition composed of multiple raw materials can be predicted, and further, a feature amount of a specific raw material contained in the composition can be displayed in association with a predicted value.SOLUTION: A prediction system includes a prediction device 10, an information terminal 20, and a database 30. A property prediction unit 111 of the prediction device 10 predicts a physical property value of prediction target rubber on the basis of raw material information about multiple raw materials included in the prediction target rubber. A display processing unit of the information terminal 20 displays, on a display part, a prediction value predicted by the property prediction unit 111 and a feature amount of a specific raw material among the multiple raw materials in association with each other.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a property prediction system, a property prediction device, and a property prediction method for predicting the properties of a composition composed of a plurality of materials. [Background technology]

[0002] A prediction method for determining a specific performance of a rubber-like elastomer, which is a material for tires and the like mounted on automobiles, is known (see Patent Document 1). The prediction method determines a specific performance (first performance) of the rubber-like elastomer using a relational equation that shows the relationship between molecular information (first data) about individual materials including multiple polymers and multiple additives, material compounding ratios (second data) of multiple types of rubber-like elastomers after vulcanization, vulcanization conditions (third data), and information on the first performance to be predicted (fourth data). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-147460 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional prediction methods allow for the prediction of specific performance values ​​of a rubber-like elastomer in advance based on various information about the rubber-like elastomer to be predicted. Therefore, developers of rubber-like elastomers can obtain a rubber-like elastomer composition having desired performance values ​​without actually manufacturing the rubber-like elastomer by repeatedly inputting composition information, including the combination and distribution amounts of multiple materials that make up the rubber-like elastomer, to obtain the predicted value. This can speed up the development of rubber-like elastomers and reduce development costs. However, conventional prediction methods do not allow developers to grasp the relationship between the characteristic values ​​of any of the materials in the rubber-like elastomer to be predicted and the predicted value. If developers could grasp this relationship, they could use the relationship as a starting point to find a rubber-like elastomer composition having the desired performance value more quickly, which would contribute to the development of rubber-like elastomers.

[0005] An object of the present invention is to provide a property prediction system, a property prediction device, and a property prediction method that are capable of not only predicting the properties of a composition made up of multiple materials, but also displaying the predicted values ​​in association with the feature quantities of specific materials contained in the composition. [Means for solving the problem]

[0006] (1) According to one aspect of the present invention, a physical property prediction system predicts the physical properties of a composition composed of multiple materials. The physical property prediction system includes: a material information storage unit that stores material information about the multiple materials, the material information including feature quantities of a specific material among the multiple materials; a prediction unit that predicts the value of the physical property based on the material information stored in the material information storage unit; and a display processing unit that displays the predicted value predicted by the prediction unit in association with the feature quantities stored in the material information storage unit.

[0007] With this configuration, the display processing unit can display the predicted values ​​of the physical properties of the composition on a predetermined display unit. For example, if the user is a developer of the composition, the user can easily grasp at a glance the relationship between the characteristic quantities of the specific materials constituting the composition and the predicted values ​​of the physical properties of the composition. As a result, the user can use the relationship as a starting point to more quickly identify the composition having the desired physical properties, which can greatly contribute to the development of the composition. Examples of the composition include a polymer composition containing a polymer composed of multiple materials and a rubber material made of the polymer composition.

[0008] (2) The physical property prediction system of the present invention further includes a feature quantity change unit that changes the feature quantity displayed on a predetermined display unit by the display processing unit. When the feature quantity is changed by the feature quantity change unit, the prediction unit re-predicts the value of the physical property based on the material information including the changed feature quantity.

[0009] According to this configuration, the physical property values ​​of the composition are re-predicted based on the material information including the changed feature values, and the re-predicted predicted values ​​are displayed on a predetermined display unit, thereby allowing a user to easily understand the relationship between the changed feature values ​​and the re-predicted predicted values.

[0010] (3) The physical property prediction system of the present invention further includes a first selection unit that selects the physical property whose predicted value is to be displayed on the display processing unit, in which the display processing unit displays the predicted value of the physical property selected by the first selection unit in association with the feature amount.

[0011] According to this configuration, the user can designate any desired physical property as the physical property to be predicted by the prediction unit.

[0012] (4) The physical property prediction system of the present invention further includes a second selection unit that selects the specific material for which the feature is to be displayed on the display processing unit. In this case, the display processing unit displays the predicted value predicted by the prediction unit and the feature of the specific material selected by the second selection unit in association with each other.

[0013] According to this configuration, the user can designate any material as the specific material to be displayed in association with the predicted value.

[0014] (5) The display processing unit causes a predetermined display unit to display a characteristics graph showing the predicted values ​​corresponding to the possible quantitative ranges of the feature amounts of the specific material selected by the second selection unit.

[0015] According to this configuration, the user can easily grasp at a glance the change trend of the predicted value within the quantitative range.

[0016] (6) The physical property prediction system of the present invention further includes a range change unit that changes the possible quantitative range of the feature of the specific material selected by the second selection unit, in which case the display processing unit displays the characteristic graph corresponding to the quantitative range changed by the range change unit on a predetermined display unit.

[0017] According to this configuration, the user can easily grasp the change tendency of the predicted value in response to a change in the quantitative range from the characteristic graph redisplayed by the display processing unit.

[0018] (7) When the values ​​of the physical properties of each of the multiple types of compositions are predicted by the prediction unit, the display processing unit displays the predicted values ​​corresponding to each composition in association with the feature values ​​of the specific material corresponding to each predicted value.

[0019] According to this configuration, the user can easily grasp at a glance the relationship between the predicted value and the feature amount for each of the different types of compositions.

[0020] (8) The physical property prediction system of the present invention further includes an input receiving unit that receives input of material information of the composition that is the target of prediction by the prediction unit. In this case, the prediction unit predicts the value of the physical property of the composition based on a prediction model based on training data including feature quantities of multiple pieces of material information for each of multiple other compositions and values ​​of the physical property for each of the multiple other compositions, and on the material information received by the input receiving unit.

[0021] (9) The characteristic amount preferably includes at least one of the blend amount of the specific material and the physical property value of a predetermined physical property of the specific material.

[0022] (10) The physical properties preferably include at least one of hardness, dynamic viscoelasticity at low and high temperatures, tensile strength, elongation at break, 100% modulus (M100), 200% modulus (M200), 300% modulus (M300), toluene swelling index, glass transition temperature, abrasion resistance, vulcanization characteristic value, scorch time, and Mooney viscosity. The dynamic viscoelasticity may include complex modulus E*, storage modulus E', loss modulus E", loss tangent tanδ, complex shear modulus G*, storage shear modulus G', loss shear modulus G", etc.

[0023] (11) The composition preferably contains at least a polymer and an additive. In this case, the material information is information about the molecules of the polymer and the additive, and preferably includes at least one of weight-average molecular weight, number-average molecular weight, molecular weight distribution, and degree of branching of molecular chains.

[0024] (12) It is preferable that the material information includes at least one of the cis-isomer ratio, trans-isomer ratio, oil extension amount, glass transition temperature, solubility parameter, styrene amount, vinyl amount, butadiene rubber amount, and viscoelasticity property of the polymer.

[0025] (13) Preferably, the additive includes a filler, and the material information includes at least one of the particle size, CTAB adsorption specific surface area, BET adsorption specific surface area, and surface polarity of the filler.

[0026] (14) According to another aspect of the present invention, there is provided a physical property prediction device for predicting the physical properties of a composition composed of a plurality of materials, the physical property prediction device including: a material information storage unit that stores material information about the plurality of materials, the material information including feature quantities of a specific material among the plurality of materials; a prediction unit that predicts the value of the physical property based on the material information stored in the material information storage unit; and a display processing unit that displays the predicted value predicted by the prediction unit in association with the feature quantities stored in the material information storage unit.

[0027] (15) A physical property prediction method according to another aspect of the present invention is a method for predicting a value of a physical property of a composition composed of a plurality of materials, the method including: a prediction step of predicting a value of the physical property based on material information about the plurality of materials; and a display processing step of displaying the predicted value predicted in the prediction step in association with a feature value of a specific material among the plurality of materials. [Effects of the Invention]

[0028] According to the present invention, it is possible not only to predict the physical properties of a composition made up of multiple materials, but also to display the predicted values ​​in association with the feature quantities of specific materials contained in the composition. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 is a diagram showing the configuration of a rubber performance prediction system according to an embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the configuration of a prediction device included in the rubber performance prediction system. [Figure 3] FIG. 3 is a block diagram showing the configuration of an information terminal provided in the rubber performance prediction system. [Figure 4]FIG. 4 is a diagram showing an example of an input screen (mixing ratio input screen) displayed on an information terminal used by a user. [Figure 5] FIG. 5 is a diagram showing an input operation of a material category on the blending ratio input screen of the information terminal. [Figure 6] FIG. 6 is a diagram showing the input operation of materials on the blending ratio input screen of the information terminal. [Figure 7] FIG. 7 is a diagram showing the operation of inputting the mixture ratio on the mixture ratio input screen of the information terminal. [Figure 8] FIG. 8 is a diagram showing an input operation for shifting from the blending ratio input screen to the material property input screen of the information terminal. [Figure 9] FIG. 9 is a diagram showing an input operation of material properties on the material property input screen of the information terminal. [Figure 10] FIG. 10 is a diagram showing the input operation of material properties on the material property input screen of the information terminal. [Figure 11] FIG. 11 is a diagram showing an output setting screen displayed on an information terminal used by a user. [Figure 12] FIG. 12 is a diagram showing an input operation for predicted performance on the output setting screen of the information terminal. [Figure 13] FIG. 13 is a diagram showing a predicted execution operation on the output setting screen of the information terminal. [Figure 14] FIG. 14 is a diagram showing an example of a prediction result display screen displayed on an information terminal used by a user. [Figure 15] FIG. 15 is a diagram showing a characteristic value change operation on the prediction result display screen of the information terminal. [Figure 16] FIG. 16 is a diagram showing an example of a prediction result display screen after re-prediction on the prediction result display screen of the information terminal. [Figure 17] FIG. 17 is a flowchart illustrating an example of a procedure for a performance prediction process executed by a control unit of the prediction device. [Figure 18] FIG. 18 is a diagram illustrating an example of a prediction result display screen on which a prediction graph is displayed. [Figure 19]FIG. 19 is a diagram illustrating an example of a prediction result display screen in which a coordinate point on a prediction graph is selected. [Figure 20] FIG. 20 is a diagram showing a display frame in which a three-dimensional coordinate system is displayed. DETAILED DESCRIPTION OF THE INVENTION

[0030] Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings. Note that the following embodiment is an example of the present invention, and does not limit the technical scope of the present invention.

[0031] FIG. 1 is a diagram showing the configuration of a rubber performance prediction system 100 (hereinafter simply referred to as the prediction system 100) according to an embodiment of the present invention. The prediction system 100 of this embodiment is composed of a prediction device 10, an information terminal 20, and a database 30. The prediction system 100 is an example of a physical property prediction system according to the present invention. The prediction device 10 is also an example of a physical property prediction device according to the present invention.

[0032] The prediction system 100 is a system for predicting specific performance of a rubber-like elastomer (an example of a composition of the present invention) made of a polymer composition composed of multiple materials including at least a polymer and an additive. The rubber-like elastomer is obtained by vulcanizing the polymer composition, and specifically, is a rubber material used in the manufacture of tire products such as pneumatic tires mounted on vehicles such as automobiles. In other words, the prediction system 100 of this embodiment predicts specific performance characteristics that indicate the properties of the rubber-like elastomer that constitutes a tire product. The prediction results (predicted values) by the prediction system 100 are used in the development of the tire product.

[0033] In this embodiment, a rubber material used in manufacturing the tire product is exemplified as an example of the rubber-like elastomer. However, the rubber-like elastomer may be the tire product itself. In this case, the prediction system 100 predicts a specific performance among a plurality of tire performance characteristics of the tire product. The rubber-like elastomer may be an industrial rubber product itself, such as an anti-vibration rubber, or a rubber material used in manufacturing the industrial rubber product. In this embodiment, a rubber product obtained by vulcanizing the polymer composition is exemplified. However, as described below, the prediction system may also predict a specific performance of a polymer composition in an unvulcanized state (i.e., unvulcanized rubber). In this case, the unvulcanized polymer composition (unvulcanized rubber) is an example of the composition of the present invention.

[0034] The polymer composition is an unvulcanized rubber obtained by kneading a plurality of materials including one or more polymers and one or more additives, and the rubber-like elastomer is obtained by vulcanizing the polymer composition.

[0035] The polymer is, for example, unvulcanized raw rubber blended into the polymer composition, such as natural rubber (NR), isoprene rubber (IR), butadiene rubber (BR), and styrene-butadiene rubber (SBR).

[0036] Examples of the additives include fillers such as carbon black and silica, antioxidants, vulcanization accelerators, oils, zinc oxide, stearic acid, sulfur, and processing aids.

[0037] [Configuration of prediction system 100] 1, the prediction system 100 includes a prediction device 10, an information terminal 20, and a database 30, which are communicably connected to each other via a network N1. The network N1 is, for example, a wired communication network connected by a LAN or the like, or a wireless communication network such as a dedicated line or a public line.

[0038] In this embodiment, a configuration in which the database 30 is connected to the network N1 is exemplified, but for example, the database 30 may be provided in the prediction device 10 or the information terminal 20. In addition, in this embodiment, a configuration in which the information terminal 20 is connected to the network N1 is exemplified, but the components and various functions provided in the information terminal 20 may be installed in the prediction device 10.

[0039] The prediction device 10 is one element constituting the prediction system 100. The prediction device 10 predicts the specific performance of the rubber-like elastomer to be predicted (hereinafter, the rubber-like elastomer to be predicted may be referred to as the "predicted rubber") using input information transmitted from the information terminal 20 and a pre-constructed prediction model 123 (see FIG. 2), and outputs the prediction result to the information terminal 20. The prediction device 10 is an information processing device capable of executing various arithmetic processes, and is, for example, a server computer, a cloud server, or a personal computer connected to a network N1. Note that the prediction device 10 is not limited to a single computer, and may also be a computer system in which multiple computers operate in cooperation, or a cloud computing system. Furthermore, the various processes executed by the prediction device 10 may be executed in a distributed manner by one or multiple processors. A program or computer software for operating the prediction system 100 is installed in the prediction device 10.

[0040] The prediction device 10 constructs a prediction model 123 (see FIG. 2) based on training data stored in the database 30, and predicts the specific performance of the rubber to be predicted using the prediction model 123. In this embodiment, the prediction device 10 predicts the specific performance of the rubber-like elastic body used in the tire product, and the prediction result is evaluated as a predicted value of the specific performance of the rubber to be predicted, or as a predicted value of the specific performance of the tire product. The prediction device 10 and the prediction model 123 will be described later.

[0041] The information terminal 20 is an information processing device or terminal device used by a user. The information terminal 20 is a so-called desktop personal computer, a notebook computer, or a portable terminal such as a smartphone or a tablet terminal that can be carried around. The user inputs various information about the rubber to be predicted through the information terminal 20. The information terminal 20 also displays the prediction results transmitted from the prediction device 10 on a display screen. Therefore, a program or computer software is installed in the information terminal 20 for cooperating with the prediction system 100 to transmit the various information to the prediction device 10 and display the prediction results on a display screen.

[0042] The database 30 is a data group in which various data handled by the prediction system 100 is stored in a storage medium based on a predetermined data management method. The database 30 is managed in various forms, such as a storage device, an information processing device, a cloud server, or a data server communicably connected to the network N1. The database 30 includes training data for generating a prediction model 123 (see FIG. 2) used in the prediction process by the prediction device 10.

[0043] The training data is information used to generate the prediction model 123. Specifically, the training data includes material information of a large number of sample rubbers Tk (k = 1, 2, . . . , n) consisting of a large number of rubber-like elastomers with known properties, and physical property values ​​(so-called training information) of various properties possessed by each sample rubber Tk. The sample rubbers Tk are rubber products (tire products, industrial rubber products, etc.) previously manufactured as products, prototype rubber products manufactured during research for the development of the rubber products, or test pieces made of the rubber-like elastomers manufactured during the research. A data set including the material information and physical property values ​​of each of the large number of sample rubbers Tk is stored in the database 30 as the training data. The sample rubbers Tk are an example of the multiple other compositions of the present invention.

[0044] The material information includes, for example, information regarding the characteristics of each material, such as the polymer Pk (k=1, 2,..., n) and additives that make up the sample rubber Tk (hereinafter referred to as material property information), and the compounding ratio of each material in the sample rubber Tk.

[0045] Here, the material property information of the polymer is preferably at least one of the weight average molecular weight (Mw), number average molecular weight (Mn), molecular weight distribution (Mw / Mn), degree of molecular chain branching (polymer linearity), and peak top molecular weight (Mp). The material property information of the polymer may also include at least one of the amount of isoprene rubber, the amount of styrene, the amount of vinyl, the amount of butadiene rubber, the ratio of the amount of trans-butadiene rubber, and the ratio of the amount of cis-butadiene rubber. In this embodiment, the weight average molecular weight (Mw), number average molecular weight (Mn), molecular weight distribution (Mw / Mn), the amount of isoprene rubber, the amount of styrene, the amount of vinyl, the ratio of trans-butadiene rubber, and the ratio of cis-butadiene rubber, which are exemplified as the material property information of the polymer, are included in the training data. Table 1 shows an example of the material property information of various polymers Pk (k = 1, 2, . . . , n) included in the training data. The material property information of the polymer Pk may include at least one of the amount of oil extension, glass transition temperature, solubility parameter, viscoelasticity, type of modifying group, and viscosity.

[0046] [Table 1]

[0047] Furthermore, when the additive is a filler such as carbon black or silica, the material property information of the additive is preferably at least one of the type of the filler (carbon black, silica, etc.), particle size (primary particle size), CTAB adsorption specific surface area, BET adsorption specific surface area, or surface polarity.

[0048] An example of the compounding ratio of each material in the sample rubber Tk (T1, T2, . . . , Tn) is shown in Table 2. In Table 2, the compounding ratio of the materials (polymers, additives, etc.) in each sample rubber Tk is expressed in parts by mass, and for each additive Ak (k = 1, 2, . . . , n), the compounding ratio is shown when the total value of each polymer is 100 parts by mass.

[0049] [Table 2]

[0050] The various performance (physical properties) of the sample rubber Tk preferably include at least one of hardness, dynamic viscoelasticity at low and high temperatures, tensile strength, elongation at break, 100% modulus (M100), 200% modulus (M200), 300% modulus (M300), toluene swelling index, glass transition temperature, abrasion resistance, vulcanization characteristic values, scorch time, and Mooney viscosity. The vulcanization characteristic values ​​are measured using a predetermined vulcanization tester (curelastometer), such as induction time tC(10), 50% vulcanization time tC(50), and 90% vulcanization time tC(90). The scorch time and Mooney viscosity are measured using a predetermined Mooney viscometer. The dynamic viscoelasticity includes the complex modulus E*, storage modulus E', loss modulus E", loss tangent tanδ, complex shear modulus G*, storage shear modulus G', and loss shear modulus G". In this embodiment, the training data includes the physical property values ​​of all the above-mentioned performances exemplified as the various performances of the sample rubber Tk. Table 3 shows examples of the physical property values ​​(measured values) of the various performances of each of the sample rubbers Tk (T1, T2, . . . , Tn).

[0051] [Table 3]

[0052] The material property information (see Table 1) contained in the training data, the information on the physical properties of the sample rubber Tk (see Table 3), and the method for measuring this information are well known and are also described in detail in the above-mentioned prior art document (JP 2018-147460 A), so a detailed explanation will be omitted here.

[0053] In this embodiment, as described above, the training data includes the material information and the physical property values, but the training data is not limited to the examples given in this embodiment. For example, the material information may include the kneading conditions for kneading the polymer composition. This is because the kneading conditions may affect the physical property values ​​of the rubber to be predicted. The kneading conditions may include at least one of the following: the volume of the kneader chamber, the amount of polymer composition filled during kneading, the kneading time, and the discharge temperature of the polymer composition after kneading. The material information may also include vulcanization conditions for vulcanizing the polymer composition. This is because the vulcanization conditions may affect the physical property values ​​of the rubber to be predicted. The vulcanization conditions may be, for example, vulcanization temperature conditions set for the polymer composition before vulcanization of each sample rubber Tk. Note that the kneading conditions and vulcanization conditions are well known and are described in detail in the aforementioned prior art (JP 2018-147460 A), so a detailed description thereof will be omitted here.

[0054] [Prediction Device 10] The configuration of the prediction device 10 will be described below with reference to Fig. 2. Fig. 2 is a block diagram showing the configuration of the prediction device 10.

[0055] The prediction device 10 is for realizing the prediction system 100 of this embodiment, and as shown in Figure 2, it includes a control unit 11, a memory unit 12, a communication unit 13, a display unit 14, and an operation unit 15.

[0056] The communication unit 13 is a communication interface that connects the prediction device 10 to the network N1 and performs data communication with each device connected to the network N1 in accordance with a predetermined communication protocol. Specifically, the communication unit 13 performs data communication with the information terminal 20 and the database 30 through the network N1.

[0057] The storage unit 12 is a non-volatile storage medium such as an HDD, SSD, or flash memory that stores various information and data. The storage unit 12 stores a control program 121 and a prediction model 123. Note that the prediction model 123 may be realized as an electronic circuit including a memory in which the prediction model 123 is stored.

[0058] The control program 121 may be non-temporarily recorded on a computer-readable recording medium such as a CD or a DVD, read from the recording medium by a reading device (not shown) such as a CD drive or a DVD drive electrically connected to the prediction device 10, and copied to the storage unit 12. Alternatively, the control program 121 may be read from an external storage connected to the network N1, input via the communication unit 13, and copied to the storage unit 12.

[0059] The control program 121 is a program for causing the control unit 11 to execute a prediction process (see FIG. 17) described later using the prediction model 123.

[0060] Prediction model 123 is a trained model used in the prediction process (see FIG. 17) described below, and calculates a predicted value of the physical property value of a specific performance of the rubber-like elastic material (predicted rubber) that is the prediction target. In this embodiment, when the compounding ratio of the materials of the rubber-like elastic material that is the prediction target and the physical property values ​​of the characteristics of each material are input to an input unit, prediction model 123 predicts the physical property value of the specific performance of the rubber-like elastic material and outputs the predicted value from an output unit. Note that prediction model 123 may include a well-known function (prediction function) that returns a predicted value (output data) for an input value (input data).

[0061] The prediction model 123 is generated by machine learning by the control unit 11 based on the training data stored in the database 30 and a predetermined algorithm. Note that the control unit 11 updates the prediction model 123 each time the content of the training data is updated. Note that the prediction model 123 may be one that has been learned and generated by a control unit other than the control unit 11, transferred from an external unit, and stored in the storage unit 12.

[0062] In this embodiment, the prediction model 123 can be constructed by various methods. For example, algorithms required for constructing the prediction model 123 include multiple regression, generalized linear regression, principal component regression, ridge regression, lasso regression, kernel regression, random forest regression, Gaussian process regression, multilayer neural network, clustering, support vector machine, and RBF network defined by radial basis function. The prediction model 123 may also be constructed by deep learning, which multiplies the intermediate layer of a neural network. The prediction model 123 may use one of the above-mentioned algorithms, or may use multiple algorithms. In this embodiment, the prediction model 123 uses the generalized linear regression, which has an excellent balance between accuracy and cost.

[0063] In this embodiment, the input values ​​(design variables) for the prediction model 123 are material information of each material (polymer, additive) contained in the sample rubber Tk, and the output values ​​(objective variables) are physical property values ​​of various performances possessed by the sample rubber Tk.

[0064] Such a prediction model 123 can be constructed by using commercially available computer software (for example, MATLAB (registered trademark) manufactured by The MathWorks, Inc., modeFRONTIER manufactured by ESTECO, Inc., etc.).

[0065] The display unit 14 is a display device such as a liquid crystal display or an organic EL display that displays various information. The operation unit 15 is an input device such as a mouse, keyboard, or touch panel that accepts operations by an operator.

[0066] The control unit 11 controls the operation of each unit of the prediction device 10. The control unit 11 has control devices such as a CPU, a ROM, and a RAM. The CPU is a processor that executes various arithmetic operations. The ROM is a non-volatile storage medium that pre-stores control programs such as a BIOS and an OS for causing the CPU to execute various arithmetic operations. The RAM is a volatile or non-volatile storage medium that stores various information and is used as a temporary storage memory (work area) for the various arithmetic operations executed by the CPU. The control unit 11 controls the prediction device 10 by having the CPU execute various control programs pre-stored in the ROM or the storage unit 12.

[0067] As shown in FIG. 2, the control unit 11 includes various processing units such as a performance prediction unit 111 (an example of a prediction unit of the present invention). The control unit 11 functions as the performance prediction unit 111 by the CPU executing various arithmetic processes in accordance with the control program. The control unit 11 or the CPU is an example of a computer that executes the control program. Note that part or all of the performance prediction unit 111 may be configured with an electronic circuit. The control program may also be a program for causing multiple processors to function as the performance prediction unit 111. The control program may also be a program for causing a graphics processing unit (GPU) to function as the performance prediction unit 111.

[0068] The performance prediction unit 111 predicts specific performance (specific performance) of the rubber to be predicted based on material information on multiple materials (polymers, additives, etc.) that make up the rubber to be predicted. The predicted specific performance includes various properties of the rubber to be predicted, such as hardness, dynamic viscoelasticity at low and high temperatures, tensile strength, elongation at break, 100% modulus (M100), 200% modulus (M200), 300% modulus (M300), toluene swelling index, glass transition temperature, abrasion resistance, vulcanization characteristics, scorch time, and Mooney viscosity. The vulcanization characteristics are measured using a predetermined vulcanization tester (curelastometer), such as induction time tC(10), 50% vulcanization time tC(50), and 90% vulcanization time tC(90). The scorch time and Mooney viscosity are measured using a predetermined Mooney viscometer. The dynamic viscoelasticity includes the complex modulus E*, storage modulus E', loss modulus E'', loss tangent tanδ, complex shear modulus G*, storage shear modulus G', and loss shear modulus G''.

[0069] The material information is input by a user at information terminal 20, and the input information is transferred to prediction device 10 via network N1. The material information of the rubber to be predicted includes the compounding ratio of the materials (polymers, additives, etc.) of the rubber to be predicted, and material property information of the polymers, additives, etc. that make up the rubber to be predicted. The material information transferred to prediction device 10 is stored in RAM in control unit 11 of prediction device 10 or in memory unit 12. In this case, the RAM or memory unit 12 is an example of a material information memory unit of the present invention.

[0070] When the performance prediction unit 111 acquires the material information of the rubber to be predicted from the information terminal 20, it predicts the specific performance of the rubber to be predicted using the prediction model 123 and the acquired material information. Specifically, the performance prediction unit 111 inputs the material information to an input unit of the prediction model 123, causes the prediction model 123 to predict the specific performance of the rubber to be predicted, and acquires the physical quantity indicating the specific performance output from an output unit of the prediction model 123 as a prediction result (predicted value).

[0071] The predicted value predicted by the performance predicting unit 111 is transferred to the information terminal 20 by the communication unit 13 so as to be displayed on the display unit 23 of the information terminal 20 .

[0072] In this embodiment, the specific performance predicted by the performance prediction unit 111 is selected by a first selection processing unit 212 (described later) of the information terminal 20. Selection information regarding the specific performance selected by the first selection processing unit 212 is transferred from the information terminal 20 to the prediction device 10. Upon receiving the selection information, the control unit 11 sets the performance indicated by the selection information as the target for prediction by the performance prediction unit 111. In this case, the performance prediction unit 111 predicts the specific performance selected by the first selection processing unit 212.

[0073] The performance prediction unit 111 is not limited to predicting only the performance selected by the first selection processing unit 212, but may also predict, for example, some or all of the various performances possessed by the rubber to be predicted (hardness, dynamic viscoelasticity at low and high temperatures, tensile strength, elongation at break, 100% modulus (M100), 200% modulus (M200), 300% modulus (M300), toluene swelling index, glass transition temperature, abrasion resistance, vulcanization characteristic value, scorch time, and Mooney viscosity).

[0074] When the prediction model 123 makes a prediction using the prediction function, for example, when the material information is x and the predicted value of the specific performance is y, the prediction function is expressed as y = f(x). When the material information x and the specific performance y of each of n sample rubbers Tk exist as training data, the prediction function can be estimated from n paired data D expressed by the following formula. D = {(x1,y1),(x2,y2),…,(xN,yN)}

[0075] [Information terminal 20] The configuration of the information terminal 20 will be described below with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of the information terminal 20.

[0076] 3, the information terminal 20 includes a control unit 21, a storage unit 22, a display unit 23, an operation unit 24, and a communication unit 25. The information terminal 20 is an information processing device or terminal device used by a user. The user inputs various pieces of information about the rubber to be predicted, which are necessary for the prediction process by the prediction device 10, from the information terminal 20.

[0077] The communication unit 25 is a communication interface for connecting the information terminal 20 to the network N1 and for executing data communication with the prediction device 10 via the network N1 in accordance with a predetermined communication protocol.

[0078] The storage unit 22 is a non-volatile storage medium such as a flash memory that stores various types of information. The storage unit 22 stores a control program 221 for causing the control unit 21 to execute various types of arithmetic processing, data used in the various types of arithmetic processing, and the like.

[0079] The display unit 23 is a display device such as a liquid crystal display or an organic EL display that displays various information. The operation unit 24 is an input device such as a mouse, keyboard, or touch panel that accepts input operations by the user.

[0080] In this embodiment, the predicted value predicted by the performance prediction unit 111 of the prediction device 10 and the characteristic value of a pre-selected specific material (hereinafter referred to as the material characteristic value) are displayed on the display unit 23. Specifically, a coordinate point indicating the relationship between the material characteristic value and the predicted value is displayed in a display frame 65 on a prediction result display screen 234 (see FIG. 14) displayed on the display unit 23. The material characteristic value is an example of a feature quantity of the present invention.

[0081] The control unit 21 controls the operation of each unit of the information terminal 20. The control unit 21 has control devices such as a CPU, a ROM, and a RAM. The CPU is a processor that executes various types of arithmetic processing. The ROM is a non-volatile storage medium that stores in advance control programs such as a BIOS and an OS for causing the CPU to execute various types of arithmetic processing. The RAM is a volatile or non-volatile storage medium that stores various types of information and is used as a temporary storage memory (work area) for the various types of arithmetic processing executed by the CPU. The control unit 21 controls the information terminal 20 by having the CPU execute various control programs that are stored in advance in the ROM or the storage unit 22.

[0082] As shown in FIG. 3 , the control unit 21 includes various processing units, such as an input receiving unit 211 (an example of an input receiving unit of the present invention), a first selection processing unit 212 (an example of a first selection unit of the present invention), a second selection processing unit 213 (an example of a second selection unit of the present invention), a characteristic value changing unit 214 (an example of a feature value changing unit of the present invention), a range changing unit 215 (an example of a range changing unit of the present invention), and a display processing unit 216 (an example of a display processing unit of the present invention). The control unit 21 functions as the various processing units when the CPU executes various arithmetic processes in accordance with the control program. The control unit 21 or the CPU is an example of a computer that executes the control program. Note that some or all of the processing units included in the control unit 21 may be configured with electronic circuits. Furthermore, the control program may be a program that causes multiple processors to function as the various processing units.

[0083] The input receiving unit 211 performs processing to receive input of the material information of the rubber to be predicted, which is the target of prediction by the performance prediction unit 111 of the prediction device 10. Specifically, the input receiving unit 211 displays a plurality of input screens 231, 232 on the display unit 23, and acquires information input into each of the input screens 231, 232 by the operation unit 24. The acquired information is then transmitted to the prediction device 10 by the communication unit 25.

[0084] 4 to 8 are diagrams showing a compounding ratio input screen 231 (an example of an input screen) displayed on the display unit 23 of the information terminal 20. The compounding ratio input screen 231 is an input screen for inputting the compounding ratios of all materials that make up the predicted rubber.

[0085] 4, the blending ratio input screen 231 includes an input frame 41 for inputting a material category, an input frame 42 for inputting a material, an input frame 43 for inputting a blending ratio, and a display frame 44 for displaying the blending ratio. The blending ratio input screen 231 also has a back key 31 for returning the displayed content to the previous screen, a registration key 32 for confirming the input content, and a next key 33 for moving the displayed content to the next screen. The user can input any information into the input frames 41 to 43 by operating the operation unit 24.

[0086] The input frame 41 is for inputting the category of the material that constitutes the predicted rubber. The input frame 41 is provided with a pull-down key 41A, and when the pull-down key 41A is pressed by the operation unit 24, a pull-down menu 41B (see FIG. 5) containing category information of multiple items that have been registered in advance is displayed.

[0087] 5 is a diagram showing a state in which pull-down menu 41B is opened by pressing pull-down key 41A. As shown in FIG. 5, pull-down menu 41B includes the following items as the category information: "Polymer," "Filler," "Vulcanization Accelerator," "Coupling Agent," and "Other." When a category of a material constituting the rubber to be predicted is selected from these, the selected category is displayed in input frame 41. For example, when "Polymer" is selected from pull-down menu 41B after pressing pull-down key 41A, "Polymer" is displayed in input frame 41.

[0088] The input frame 42 is used to input a specific material belonging to a predetermined category. Once the input of the category in the input frame 41 is completed, the material can be input in the input frame 42. For example, if "polymer" is input in the input frame 41, multiple polymers that can be used as the material of the rubber-like elastic body can be input in the input frame 42. The input frame 42 is provided with a pull-down key 42A, and when the pull-down key 42A is pressed by the operation unit 24, a pull-down menu 42B (see FIG. 6) containing multiple pre-registered polymers Pk (P1, P2, . . . , Pn) is displayed.

[0089] Fig. 6 is a diagram showing a state in which a pull-down menu 42B is opened by pressing a pull-down key 42A. As shown in Fig. 6, when a specific polymer constituting the rubber to be predicted is selected from the plurality of polymers displayed in the pull-down menu 42B, the selected polymer is displayed in the input frame 42. For example, when "Polymer P1" is selected from the pull-down menu 42B after pressing the pull-down key 42A, "Polymer P1" is displayed in the input frame 42 (see Fig. 7).

[0090] The input frame 43 is used to input the compounding ratio of the material input in the input frame 42. As shown in FIG. 7, when the input frame 43 is selected by the operation unit 24, a numeric keypad input panel 45 is displayed below it. This numeric keypad input panel 45 allows the user to input the numerical value of the compounding ratio of the material that makes up the rubber to be predicted. FIG. 7 shows an example in which, when "polymer P1" is input in the input frame 42, "20 phr" is input in the input frame 43 as the compounding ratio of that polymer P1. When the registration key 32 is pressed after the compounding ratio is input in the input frame 43, the compounding ratio of the specific material that makes up the rubber to be predicted is registered, and the information is displayed in the display frame 44.

[0091] By performing the above-described input operation for each of the materials that make up the rubber to be predicted, the registration of the compounding ratios of all materials is completed. Fig. 8 shows the state in which the compounding ratios of each material of the rubber to be predicted Tx, which is an example of the prediction target, have been entered and displayed in the display frame 44. Here, Table 4 shows the compounding ratios of each material of the rubber to be predicted Tx.

[0092] [Table 4]

[0093] In Fig. 8, when the next key 33 is pressed after the blending ratio for each material belonging to each category has been input, all registered blending ratios are stored in the memory unit 22, and information on the registered blending ratios is transmitted to the prediction device 10 by the communication unit 25. In addition, the screen displayed on the display unit 23 changes to a material property input screen 232 (see Figs. 9 and 10).

[0094] 9 and 10 are diagrams showing a material property input screen 232 (an example of an input screen) displayed on the display unit 23 of the information terminal 20. The material property input screen 232 is an input screen for inputting the material property information of the polymer or additive input on the blending ratio input screen 231.

[0095] 9, the material property input screen 232 includes an input frame 51 for inputting a material, an input frame 52 for inputting material properties, an input frame 53 for inputting a property value of the material property (material property value, an example of a feature of the present invention), and a display frame 54 for displaying the input material property value. The material property input screen 232 also has a back key 31, a registration key 32, and a next key 33. The user can input any information into the input frames 51 to 53 by operating the operation unit 24.

[0096] The input frame 51 is for inputting the polymers or additives that make up the rubber to be predicted. When the pull-down key 51A of the input frame 51 is pressed by the operation unit 24, a pull-down menu (not shown) containing pre-registered materials (in the case of the rubber to be predicted Tx, polymers P1, P4, P6, and each additive) is displayed, and by selecting one of these, the input target of the material characteristic value is input into the input frame 51. Figure 9 shows the state in which "polymer P1" has been input into the input frame 51.

[0097] Input frame 52 is used to input specific material properties whose property values ​​are to be input. When input of the material in input frame 51 is completed, input of the material properties of that material becomes possible in input frame 52. For example, when "polymer P1" is input in input frame 51, input of multiple material properties of polymer P1 becomes possible in input frame 52. Input frame 52 is provided with pull-down key 52A, and when pull-down key 52A is pressed by operation unit 24, pull-down menu 52B (see FIG. 9) containing multiple pre-registered material properties is displayed.

[0098] As shown in Fig. 9, when one of the material properties is selected from the multiple material properties displayed in pull-down menu 52B, the selected material property is displayed in input frame 52, and then it becomes possible to input a material property value in input frame 53. When register key 32 is pressed after the material property value is input in input frame 53, the property value of the material property of the specific material that makes up the rubber to be predicted is registered, and the information is displayed in display frame 54. Fig. 9 shows the state in which the material property values ​​of polymers P1 and P4 of rubber Tx to be predicted shown in Table 5 have been input and displayed in display frame 54.

[0099] [Table 5]

[0100] When inputting the characteristic values ​​of the material properties of an additive such as a filler, as shown in Fig. 10, a registered additive is selected in input frame 51, the material properties are selected from pull-down menu 52B in input frame 52, and then the material property values ​​are input in input frame 53. Fig. 10 shows the state in which "Additive A2" has been input in input frame 51.

[0101] When the next key 33 is pressed after all the material property values ​​have been entered on the material property input screen 232, all the registered material property values ​​are stored in the memory unit 22, and the registered blending ratio information is transmitted to the prediction device 10 by the communication unit 25. In addition, the screen displayed on the display unit 23 changes to the output setting screen 233 (see FIG. 11).

[0102] The first selection processing unit 212 performs a process of selecting, from the specific performance predicted by the performance prediction unit 111, the predicted performance whose predicted value is to be displayed in the display frame 65. Specifically, the first selection processing unit 212 displays an output setting screen 233 on the display unit 23, and acquires the predicted performance input to the output setting screen 233 by the operation unit 24. The acquired predicted performance is then set as the specific performance to be predicted by the performance prediction unit 111, and the setting information is transmitted to the prediction device 10 by the communication unit 25.

[0103] 11 to 13 are diagrams showing output setting screens displayed on the display unit 23 of the information terminal 20. The output setting screen 233 is a setting screen for setting the specific performance to be predicted by the performance prediction unit 111 and the material properties to be displayed in the display frame 65.

[0104] 11, the output setting screen 233 has an input frame 61 for inputting the predicted performance and a display frame 62 for displaying the predicted value. The output setting screen 233 also has a back key 31 for returning the displayed content to the previous screen, and an execution key 34 for inputting a prediction execution instruction. The user can input any information into the input frame 61 by operating the operation unit 24.

[0105] The input frame 61 is used to input the predicted performance as a target to be predicted by the performance prediction unit 111. The input frame 61 is provided with a pull-down key 61A, and when the pull-down key 61A is pressed by the operation unit 24, a pull-down menu 61B (see FIG. 12) including a plurality of performance items indicating a plurality of pre-registered performances is displayed.

[0106] FIG. 12 shows a state in which a pull-down menu 61B is opened by pressing a pull-down key 61A. As shown in FIG. 12, the pull-down menu 61B includes a plurality of performance items selectable as the predicted performance, including hardness, dynamic viscoelasticity at low and high temperatures (complex modulus E*, storage modulus E′, loss modulus E″, loss tangent tanδ, complex shear modulus G*, storage shear modulus G′, and loss shear modulus G″), tensile strength, elongation at break, 100% modulus (M100), 200% modulus (M200), 300% modulus (M300), toluene swelling index, glass transition temperature, abrasion resistance, vulcanization characteristic value, scorch time, and Mooney viscosity. When any one of these performance items is selected, the selected performance item is displayed in the input frame 61, and the selected performance item is set as the specific performance. 13 shows a state in which "low-temperature loss tangent tan δ" is selected as the predicted performance and displayed in the input frame 61.

[0107] The second selection processing unit 213 performs a process of selecting the specific material for which the material characteristic value (feature amount) is to be displayed in the display frame 65. Specifically, the second selection processing unit 213 acquires the material characteristics of the specific material input to the output setting screen 233 by the operation unit 24. Then, the acquired material characteristics are set as a display target by the display processing unit 216, and the setting information is transmitted to the prediction device 10 by the communication unit 25.

[0108] 13, the output setting screen 233 has an input frame 63 for inputting the specific material and an input frame 64 for inputting the characteristic value of the specific material. The user can input any information into the input frames 63 and 64 by operating the operation unit 24.

[0109] Input frame 63 is used to input the specific material to be displayed in display frame 65, and input frame 64 is used to input the material properties of the specific material to be displayed in display frame 65. When pull-down key 63A in input frame 63 is pressed, a pull-down menu containing multiple specific materials is displayed, and any specific material can be selected from the menu. When pull-down key 64A in input frame 64 is pressed, a pull-down menu containing multiple material properties is displayed, and any material property can be selected from the menu. Note that FIG. 13 shows a state in which "Polymer P4" has been selected as the specific material and "weight average molecular weight (Mw)" has been selected as the material property.

[0110] When the execution key 34 is pressed after the predicted performance is selected and the material properties are selected, the predicted performance is transmitted to the prediction device 10 by the communication unit 25 together with a command to execute prediction.

[0111] When the display processing unit 216 receives the predicted value transmitted from the prediction device 10, it performs processing to associate the predicted value with the characteristic value of the material properties of a specific material identified from the multiple materials that make up the predicted rubber, and display them on the prediction result display screen 234 (see Figure 14).

[0112] In this embodiment, when the display processing unit 216 receives the predicted value, it displays the predicted value in the display frame 62. Furthermore, as shown in FIG. 14, the display processing unit 216 associates the predicted value with the characteristic value of the specific material selected by the second selection processing unit 213 and displays them in the display frame 65. The display frame 65 shows a two-dimensional coordinate system with the predicted value on the vertical axis and the characteristic value on the horizontal axis, and the coordinate points in the coordinate system that are in correspondence between the predicted value and the characteristic value are displayed so that they can be grasped at a glance. Note that FIG. 14 shows that the predicted value of the "loss tangent tanδ" to be predicted is "0.28", and the characteristic value of the material characteristic "weight average molecular weight (Mw)" of the specific material "polymer P4" is "5.0×10 5 ", the two-dimensional coordinate system of the display frame 65 has a coordinate point (5.0 × 105 ,0.28) is displayed.

[0113] Furthermore, the display processing unit 216 displays the material characteristic value of the specific material in the display frame 65. For example, as shown in Fig. 14, a display window 66 capable of displaying the material characteristic value is displayed near the horizontal axis of the two-dimensional coordinate system of the display frame 65, and the numerical value of the material characteristic value, i.e., the coordinate of the horizontal axis, is displayed in the display window 66.

[0114] The characteristic value change unit 214 performs processing to change the material characteristic values ​​displayed in the display window 66 of the display frame 65 by the display processing unit 216. The display window 66 is provided with keys 66A and 66B that can increase or decrease the numerical value displayed in the display window 66. As shown in Fig. 15, when the increase key 66A is pressed by the operation unit 24, the characteristic value change unit 214 increases the numerical value according to the time or number of times the key is pressed, and when the decrease key 66B is pressed, the numerical value decreases according to the time or number of times the key is pressed.

[0115] When the numerical value of the material characteristic value displayed in the display window 66 is changed by the characteristic value changing unit 214 and the execute key 34 is then pressed, the changed material characteristic value together with an instruction to execute prediction is transmitted by the communication unit 25 to the prediction device 10. In this case, in the prediction device 10, the performance prediction unit 111 again predicts the physical property value of the specific performance of the rubber to be predicted based on the material information including the changed material characteristic value, and the prediction device 10 transmits the predicted value to the information terminal 20.

[0116] For example, if the weight average molecular weight Mw of the rubber to be predicted, displayed in the display frame 65, is changed from the initial value of 5.0 × 10 5 " to "3.5 x 10 5 " and then when the execution key 34 is pressed (see FIG. 15), the physical property value of the specific performance is predicted again by the performance prediction unit 111 using the reduced numerical value. Then, as shown in FIG. 16, the predicted value "0.32" in this case is displayed in the display frame 62, and the coordinate point (3.5×10 5 ,0.32) is displayed.

[0117] [Prediction processing / display processing] An example of the procedure of the prediction process and the display process executed in the prediction system 100 will be described below with reference to the flowchart in Fig. 17. The prediction process is executed by the control unit 11 in the prediction device 10, and the display process is executed by the control unit 21 in the information terminal 20. Note that one or more steps included in the prediction process or the display process may be omitted as appropriate, and the steps may be executed in a different order as long as the same operational effect is achieved.

[0118] First, in step S11, when the compounding ratio of the rubber to be predicted is input through the compounding ratio input screen 231, the control unit 21 of the information terminal 20 stores the input compounding ratio in the RAM or memory unit 22 as information to be input into the prediction model 123, and further transmits the compounding ratio to the prediction device 10.

[0119] In the next step S12, when the physical property values ​​of the material properties of each material that makes up the rubber to be predicted are input through the material property input screen 232, the control unit 21 stores the input physical property values ​​in RAM or memory unit 22 as information to be input into the prediction model 123, and further transmits the physical property values ​​to the prediction device 10.

[0120] Thereafter, the predicted performance is input into the input frame 61 via the output setting screen 233, and the material properties are input into the input frame 64 to set the output (S13), and then it is determined whether or not a prediction execution instruction has been input by pressing the execution key 34 (S14).

[0121] In step S14, when the prediction execution instruction is input, the control unit 21 transmits the prediction performance together with the prediction execution instruction to the prediction device 10, and causes the performance prediction unit 111 of the prediction device 10 to execute the prediction process.

[0122] In step S15, the control unit 11 of the prediction device 10 inputs the compounding ratio and the material characteristic value into the input unit of the prediction model 123, and performs a prediction process in which the prediction model 123 predicts the physical property value of the specific performance of the rubber to be predicted. Then, the control unit 11 transmits the predicted value output from the prediction model 123 by the prediction process to the information terminal 20. Note that step S15 is an example of a prediction step of the present invention.

[0123] In step S16, when the control unit 21 of the information terminal 20 receives the predicted value from the prediction device 10, the control unit 21 displays the predicted value in the display frame 62 of the prediction result display screen 234. Furthermore, the control unit 21 displays a two-dimensional coordinate system in the display frame 65, with the predicted value on the vertical axis and the characteristic value of the material characteristic set in step S13 on the horizontal axis, and also displays coordinate points that represent the correspondence between the predicted value and the characteristic value (S17). The control unit 21 also displays the numerical value of the characteristic value in the display window 66 of the display frame 65 so that it can be changed (S18). Note that step S17 is an example of a display processing step of the present invention.

[0124] Subsequently, in step S19, the control unit 21 determines whether or not the characteristic value in the display window 66 has changed. For example, if the decrease key 66B is pressed by operating the operation unit 24, it determines that the characteristic value has decreased, and if the increase key 66A is pressed, it determines that the characteristic value has increased.

[0125] If the characteristic value is not changed and an instruction to end prediction is input (Yes in S20), the series of processes ends.

[0126] On the other hand, if it is determined that the characteristic value has been changed and the execution key 34 has been pressed again (S19 Yes), the control unit 21 transmits the changed material characteristic value together with a prediction execution instruction to the prediction device 10, and causes the performance prediction unit 111 to execute the prediction process again (S21). Thereafter, the processes from step S16 onwards are repeated.

[0127] As described above, in this embodiment, the display processing unit 216 displays the predicted value of the specific performance of the rubber to be predicted, which is the target of prediction, on the display unit 23. As a result, for example, if the user is a developer of the rubber-like elastomer, the user can easily grasp at a glance the relationship between the feature quantities of the specific material that constitutes the rubber-like elastomer and the predicted value of the specific performance of the rubber-like elastomer. As a result, the user can use the relationship as a starting point to more quickly find a composition of a rubber-like elastomer that has a desired performance value, which can greatly contribute to the development of the rubber-like elastomer.

[0128] In the above-described embodiment, an example has been described in which a two-dimensional coordinate system is displayed in the display frame 65 to display coordinate points between the predicted value and the characteristic value. For example, as shown in FIG. 18 , a prediction graph L1 (an example of a characteristic graph of the present invention) with the characteristic value on the horizontal axis as a variable may be displayed in the two-dimensional coordinate system shown in the display frame 65. In this case, the performance prediction unit 111 may predict a prediction graph L1 corresponding to a possible range of the characteristic value (an example of a quantitative range of the present invention), transmit the prediction graph L1 to the information terminal 20, and display the prediction graph L1 in the display frame 65. This allows the user to easily grasp at a glance the change trend of the predicted value within the possible range of the characteristic value from the prediction graph L1.

[0129] In this case, it is preferable that a change cursor 67 for changing the lower limit of the range and a change cursor 68 for changing the upper limit of the range are provided so that the range of possible characteristic values ​​of the specific material selected by the second selection processing unit 213 can be changed. The change cursors 67 and 68 are moved by the user operating the operation unit 24. When the change cursors 67 and 68 are moved, the range change unit 215 of the control unit 21 changes the range to a range corresponding to the positions of the change cursors 67 and 68 after the change. The lower limit of the range changed by the change cursor 67 is displayed in a display window 67A provided on the prediction result display screen 234, and the upper limit of the range is displayed in a display window 67B provided on the prediction result display screen 234. The display processing unit 216 also changes the prediction graph L1 to correspond to the changed range and displays it in the display frame 65. This allows the user to easily grasp the trend of change in the predicted value in response to changes in the range of possible characteristic values ​​from the changed prediction graph L1 displayed by the display processing unit 216.

[0130] In the above-described embodiment, the coordinate points of the predicted value and the characteristic value corresponding to the target object are displayed on the horizontal axis of the two-dimensional coordinate system displayed in the display frame 65. However, the present invention is not limited to this configuration. For example, when the performance prediction unit 111 performs a prediction process for each of multiple types of target objects, the display processing unit 216 may display the predicted value corresponding to each target object and the material characteristic value (feature value) of each specific material corresponding to each predicted value in association with each other on the display unit. For example, when the performance prediction unit 111 predicts the predicted value for each of two or more target objects, multiple coordinate points corresponding to the multiple target objects may be displayed on the two-dimensional coordinate system. In this case, the user can easily grasp at a glance the relationship between the predicted value and the feature value for each of the different types of target objects.

[0131] Furthermore, as shown in FIG. 19, when an arbitrary point on the prediction graph L1 is selected by operating the operation unit 24, the display processing unit 216 may display the predicted value on the vertical axis at that coordinate point in the display frame 62, and may display the characteristic value on the horizontal axis at that coordinate point in the display window 66.

[0132] Furthermore, the coordinates displayed in the display frame 65 are not limited to a two-dimensional coordinate system. For example, as shown in FIG. 20, the Z axis may be the predicted value, the X axis may be the weight average molecular weight, which is one of the material properties of the rubber to be predicted, and the Y axis may be the number average molecular weight, which is one of the material properties of the rubber to be predicted, and the relationship between each numerical value may be displayed in the display frame 65 using a three-dimensional coordinate system.

[0133] In the above embodiment, the prediction system 100 is exemplified as having a configuration in which the performance prediction unit 111 is provided in the prediction device 10 and the display processing unit 216 is provided in the information terminal 20, but the present invention is not limited to this configuration. For example, the configuration provided in the information terminal 20 may be provided in the prediction device 10, and the configuration provided in the prediction device 10 may be provided in the information terminal 20.

[0134] In the above embodiment, the ingredient characteristic values ​​are exemplified as the feature quantities to be displayed in the display frame 65 of the prediction result display screen 234 (see FIG. 14), but the feature quantities of the present invention are not limited to this. For example, instead of the ingredient characteristic values, the specific ingredient blend amounts selected by the second selection processing unit 213 may be displayed in the display frame 65 together with the prediction values.

[0135] In the above embodiment, the polymer composition is unvulcanized rubber, and the rubber-like elastomer is a polymer composition that has been vulcanized. However, the present invention is not limited to this configuration. For example, the present invention can predict specific performance of the polymer composition even when the polymer composition is in an unvulcanized state (i.e., unvulcanized rubber).

[0136] In the above-described embodiment, a rubber-like elastomer made of a polymer composition was described as an example of the composition of the present invention. However, the composition is not limited to a rubber-like elastomer, nor is it limited to an unvulcanized polymer composition (unvulcanized rubber). The composition of the present invention may be any organic composition primarily composed of multiple organic substances (e.g., a composition having a total organic substance content of more than 10% by mass), and the above-described polymer compositions are more preferred. Furthermore, rubber compositions primarily composed of multiple rubber materials and resin compositions composed of multiple synthetic resins are particularly preferred. In other words, the present invention can also be applied to predicting the physical properties of specific material performance in these compositions. [Explanation of symbols]

[0137] 100: Rubber performance prediction system, 10: Prediction device 11: Control section 20: Information terminal 21: Control unit 23:Display section 30: Database 111: Performance Prediction Department 121: Control program 123: Predictive Model 211: Input reception unit 212: First selection processing unit 213: Second selection processing unit 214: Characteristic value change section 215: Range change section 216: Display processing unit 221: Control program

Claims

1. A physical property prediction system for predicting specific performance of a rubber-like elastomer obtained by vulcanizing a polymer composition composed of multiple materials including at least a polymer and an additive, comprising: a material information storage unit configured to store material information relating to the plurality of materials, the material information including a feature value indicating a predetermined physical property value of a specific material among the plurality of materials; a prediction unit that predicts a value of the specific performance based on the material information stored in the material information storage unit; a display processing unit that displays the predicted value predicted by the prediction unit in association with the feature amount stored in the material information storage unit.

2. a feature amount changing unit that changes the feature amount displayed on a predetermined display unit by the display processing unit, The physical property prediction system according to claim 1 , wherein, when the feature quantity is changed by the feature quantity change unit, the prediction unit re-predicts the value of the specific performance based on the material information including the feature quantity after the change.

3. a first selection unit that selects the specific performance for which the display processing unit displays the predicted value; The physical property prediction system according to claim 1 , wherein the display processing unit displays the predicted value of the specific performance selected by the first selection unit in association with the feature amount.

4. a second selection unit that selects the specific material for which the feature amount is to be displayed on the display processing unit; 4. The physical property prediction system according to claim 1, wherein the display processing unit displays the predicted value predicted by the prediction unit and the feature amount of the specific material selected by the second selection unit in association with each other.

5. 5. The physical property prediction system according to claim 4, wherein the display processing unit causes a characteristic graph showing the predicted values ​​corresponding to the possible quantitative ranges of the feature quantities of the specific material selected by the second selection unit to be displayed on a predetermined display unit.

6. a range change unit that changes the possible quantitative range of the feature amount of the specific material selected by the second selection unit, The physical property prediction system according to claim 5 , wherein the display processing unit causes the characteristic graph corresponding to the quantitative range changed by the range changing unit to be displayed on a predetermined display unit.

7. 7. A physical property prediction system according to claim 1, wherein when values ​​of the specific performance of each of a plurality of types of rubber-like elastomers are predicted by the prediction unit, the display processing unit displays the predicted values ​​corresponding to each rubber-like elastomer in association with the feature quantities of the specific material corresponding to each predicted value.

8. an input receiving unit that receives input of material information of the rubber-like elastic body that is a prediction target by the prediction unit, 8. A physical property prediction system according to claim 1, wherein the prediction unit predicts the value of the specific performance of the rubber-like elastic body based on a prediction model based on training data including feature quantities of a plurality of pieces of material information for each of a plurality of other rubber-like elastic bodies and values ​​of the specific performance for each of the plurality of other rubber-like elastic bodies, and on the material information accepted by the input accepting unit.

9. 9. The physical property prediction system according to claim 1, wherein the specific performance includes at least one of hardness, dynamic viscoelasticity at low and high temperatures, tensile strength, elongation at break, 100% modulus (M100), 200% modulus (M200), 300% modulus (M300), toluene swelling index, glass transition temperature, abrasion resistance, vulcanization characteristic value, scorch time, and Mooney viscosity.

10. 10. The physical property prediction system according to claim 1, wherein the material information is information about molecules of the polymer and the additive, and includes at least one of weight average molecular weight, number average molecular weight, molecular weight distribution, and degree of branching of molecular chains.

11. 11. The physical property prediction system according to claim 10, wherein the material information includes at least one of the cis-isomer ratio, trans-isomer ratio, amount of oil extension, glass transition temperature, solubility parameter, amount of styrene, amount of vinyl, amount of butadiene rubber, and viscoelastic properties of the polymer.

12. The additive comprises a filler; The physical property prediction system according to claim 10 or 11, wherein the material information includes at least one of a particle size, a CTAB adsorption specific surface area, a BET adsorption specific surface area, and a surface polarity of the filler.

13. A physical property prediction device for predicting specific performance of a rubber-like elastomer obtained by vulcanizing a polymer composition composed of multiple materials including at least a polymer and an additive, comprising: a material information storage unit configured to store material information relating to the plurality of materials, the material information including a feature value indicating a predetermined physical property value of a specific material among the plurality of materials; a prediction unit that predicts a value of the specific performance based on the material information stored in the material information storage unit; a display processing unit that displays the predicted value predicted by the prediction unit and the feature amount stored in the material information storage unit in association with each other.

14. A method for predicting a specific performance value of a rubber-like elastomer obtained by vulcanizing a polymer composition composed of multiple materials including at least a polymer and an additive, comprising: a prediction step of predicting a value of the specific performance based on material information about the plurality of materials; a display processing step of displaying the predicted value predicted in the prediction step in association with a feature value indicating a physical property value of a specified physical property of a specific material among the plurality of materials, the display processing step being executed by one or more processors.

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