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JP-2026075421-A - Feature prediction system, feature prediction device, feature prediction method, and program

JP2026075421AJP 2026075421 AJP2026075421 AJP 2026075421AJP-2026075421-A

Abstract

[Problem] To provide a feature prediction system, feature prediction device, feature prediction method, and program that can predict other unknown feature quantities when any two of the feature quantities of a rubber composition—viscoelasticity, surface roughness, and ice friction characteristics—are known. [Solution] The prediction system 100 comprises a prediction device 10, an information terminal 20, and a database 30. The control unit 11 of the prediction device 10 predicts a feature quantity representing another feature based on two known index values representing two of the features of the rubber to be predicted, namely viscoelasticity, surface roughness, and ice friction characteristics. The control unit 21 of the information terminal 20 displays the predicted value along with the index values used to predict the predicted value on the display unit 23. [Selection Diagram] Figure 2

Inventors

  • 市本 大和

Assignees

  • 住友ゴム工業株式会社

Dates

Publication Date
20260508
Application Date
20241022

Claims (10)

  1. An input receiving unit that receives input of two index values representing two of the following characteristics of the rubber composition: the viscoelasticity of the rubber composition, the surface roughness of the contact portion of the rubber composition that contacts the ice surface, and the friction characteristics of the rubber composition on ice. A prediction unit predicts a feature quantity that indicates other features based on the two index values received by the input receiving unit, An output processing unit that outputs the predicted value predicted by the prediction unit to a predetermined output destination, A feature prediction system equipped with the following features.
  2. The feature prediction system according to claim 1, wherein the output processing unit outputs the predicted value and the two index values received by the input receiving unit.
  3. The feature quantity prediction system according to claim 2, wherein the output processing unit outputs the predicted value and the two index values received by the input receiving unit to the predetermined output destination and displays them on a predetermined display unit provided at the predetermined output destination.
  4. The feature prediction system according to claim 1 or 2, wherein the prediction unit predicts the coefficient of friction on ice, which indicates the frictional properties of the rubber composition, based on a first index value indicating viscoelasticity and a second index value indicating surface roughness.
  5. The system further includes an index value changing unit that changes at least one of the two index values used in the prediction by the prediction unit, The feature quantity prediction system according to claim 3, wherein the prediction unit re-predicts the feature quantity that indicates the other features based on the modified index value changed by the index value changing unit.
  6. The output processing unit displays a modified value input screen on the predetermined display unit, which includes a display frame for the predicted value, display frames for each of the two indicator values, and an input frame for inputting the modified value of at least one of the two indicator values. The feature quantity prediction system according to claim 5, wherein the index value changing unit changes at least one of the two index values used in the prediction by the prediction unit to a numerical value entered into the input frame via a predetermined operation unit.
  7. The feature prediction system according to claim 1 or 2, wherein the prediction unit predicts feature quantities representing the other characteristics of the rubber composition based on a prediction model learned from training data including index values representing the viscoelasticity, surface roughness, and ice friction characteristics of each of a plurality of other rubber compositions, and the two index values received by the input receiving unit.
  8. A prediction unit predicts a feature quantity representing the other feature based on two index values representing two of the following features: the viscoelasticity of the rubber composition, the surface roughness of the contact portion where the rubber composition contacts the ice surface, and the ice friction characteristics of the rubber composition. An output processing unit that outputs the predicted value predicted by the prediction unit, A feature prediction device equipped with the following features.
  9. A prediction step in which a feature quantity representing the other feature is predicted based on two index values representing two of the following features: the viscoelasticity of the rubber composition, the surface roughness of the contact portion where the rubber composition contacts the ice surface, and the ice friction characteristics of the rubber composition. An output step which outputs the predicted value predicted by the prediction step, A feature prediction method performed by one or more processors.
  10. A prediction step in which a feature quantity representing the other feature is predicted based on two index values representing two of the following features: the viscoelasticity of the rubber composition, the surface roughness of the contact portion of the rubber composition that contacts the ice surface, and the ice friction characteristics of the rubber composition. An output step which outputs the predicted value predicted by the prediction step, A program that causes one or more processors to run.

Description

This disclosure relates to a feature prediction system, feature prediction device, feature prediction method, and program for predicting the feature quantities of rubber compositions. Conventionally, a method for predicting the ice friction characteristics (coefficient of ice friction) of a rubber block on an ice surface is known (see Patent Document 1). This prediction method assumes that the ice melts due to the frictional heat generated when the rubber block slides on the ice. It calculates the coefficient of ice friction, which represents the aforementioned ice friction characteristics, using a calculation formula that expresses the adhesive friction coefficient in the adhesive region directly in contact with the ice, the lubrication friction coefficient in the lubrication region that contacts the ice via a water film formed by the melted water, the average value of the friction coefficients in the adhesive region and the lubrication region, and the ratio of the adhesive region to the tire's contact area (adhesion rate). Japanese Patent Publication No. 2023-44563 Figure 1 shows the configuration of a feature prediction system according to an embodiment of this disclosure.Figure 2 is a block diagram showing the configuration of the prediction device included in the feature prediction system.Figure 3 is a block diagram showing the configuration of the information terminals included in the feature prediction system.Figure 4 shows an example of a prediction item input screen displayed on the information terminal used by the user.Figure 5 shows an example of an explanatory variable input screen displayed on an information terminal used by a user.Figure 6 shows another example of an explanatory variable input screen displayed on an information terminal used by a user.Figure 7 shows another example of an explanatory variable input screen displayed on an information terminal used by a user.Figure 8 shows an example of a prediction result display screen that appears on the information terminal used by the user.Figure 9 shows another example of a prediction result display screen that appears on the information terminal used by the user.Figure 10 is a flowchart showing an example of the procedure for the prediction process performed by the control unit of the prediction device. The embodiments of this disclosure will be described below with reference to the attached drawings. Note that the following embodiments are merely examples of the embodiments described herein and do not limit the technical scope of this disclosure. [Configuration of Feature Prediction System 100] The feature prediction system 100 according to the embodiment of this disclosure (hereinafter simply referred to as the prediction system 100) is a system that predicts a feature that represents one of the following characteristics of a rubber composition: the viscoelasticity of the rubber composition, the surface roughness of the contact portion of the rubber composition that contacts the ice surface, and the frictional properties of the rubber composition on ice. As shown in Figure 1, the prediction system 100 comprises a prediction device 10 (an example of the feature prediction device of this disclosure), an information terminal 20, and a database 30. These are connected to each other via a network N1 so as to be able to communicate with each other. The network N1 is, for example, a wired communication network connected by a LAN, or a wireless communication network such as a dedicated line or a public line. Note that the prediction system 100 is an example of the feature prediction system of this disclosure. Also, the prediction device 10 is an example of the feature prediction device of this disclosure. In this embodiment, a configuration in which the database 30 is connected to the network N1 is illustrated; however, for example, the database 30 may be provided in the prediction device 10 or the information terminal 20. Furthermore, while this embodiment illustrates a configuration in which the information terminal 20 is connected to the network N1, the various components and functions of the information terminal 20 may be mounted on the prediction device 10. The aforementioned rubber composition is a rubber-like elastic body obtained by vulcanizing a polymer composition composed of multiple materials such as polymers and additives. Specifically, it is a rubber material used in tire products such as pneumatic tires mounted on vehicles such as automobiles. The prediction results (predicted values) from the prediction system 100 are used in the development of the tire product. In this embodiment, the rubber material used in the tire product is given as an example of the rubber composition; however, the rubber composition may be a rubber material used not only in the tire product but also in industrial rubber products such as vibration-damping rubber. The polymer is, for example, an unvulcanized raw rubber compounded into the polymer composition