How to predict tire performance

Abstract

To provide a method for accurately predicting performance of a tire.SOLUTION: There is provided a method for predicting performance of a tire. The method includes a first step S1 of inputting time-series temperature data of a rubber member when an unvulcanized tire containing an unvulcanized rubber member is vulcanized and molded to a computer. The method executes a second step S2 of predicting physical properties of the rubber member after vulcanization on the basis of the temperature data of the rubber member, and a third step S3 of predicting performance of a tire after vulcanization on the basis of the physical properties of the rubber member.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a method for predicting tire performance. [Background technology]

[0002] A method for designing a pneumatic tire is described in Patent Document 1. In this method, the thermal energy imparted to each rubber component of the tire under vulcanization conditions is calculated, and the physical property values ​​of the rubber components are calculated based on the calculated thermal energy. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 5128853 Summary of the Invention [Problem to be solved by the invention]

[0004] In recent years, there has been a demand for greater efficiency in tire development, and there has also been a strong demand for improved accuracy in predicting tire performance.

[0005] The present disclosure has been devised in view of the above circumstances, and has as its main object to provide a method capable of predicting tire performance with high accuracy. [Means for solving the problem]

[0006] The present disclosure provides a method for predicting tire performance, which includes a first step of inputting time-series temperature data of an unvulcanized rubber component when the unvulcanized tire including the unvulcanized rubber component is vulcanized and molded into a computer, and the computer executes a second step of predicting physical properties of the rubber component after vulcanization based on the temperature data of the rubber component, and a third step of predicting performance of the tire after vulcanization based on the physical properties of the rubber component. [Effects of the Invention]

[0007] The tire performance prediction method of the present disclosure employs the above steps, making it possible to predict tire performance with high accuracy. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a perspective view showing a computer for executing the tire prediction method of the present embodiment. [Figure 2] FIG. 1 is a cross-sectional view showing an example of a tire to be evaluated according to the present embodiment. [Figure 3] FIG. 2 is a partial cross-sectional view of a mold, a bladder, and an uncured tire during the curing process. [Figure 4] 1 is a flowchart showing the processing steps of a tire performance prediction method. [Figure 5] 10 is a flowchart showing the processing procedure of the first step. [Figure 6] 1A and 1B are diagrams illustrating an example of a mold model, an unvulcanized tire model, and a bladder model. [Figure 7] 1 is a graph showing time-series temperature data (relationship between temperature and vulcanization time) of a rubber member. [Figure 8] 10 is a flowchart showing a processing procedure of a fourth step in this embodiment. [Figure 9] 1 is a graph showing time series temperature data of rubber constituting the tread rubber. [Figure 10] 10 is a flowchart showing the processing procedure of a second step in this embodiment. [Figure 11] 10 is a flowchart showing a processing procedure of a third step in this embodiment. [Figure 12] FIG. 2 is a perspective view showing a tire model and a road surface model after vulcanization. [Figure 13] FIG. 2 is a cross-sectional view of a tire model. [Figure 14] 10 is a flowchart showing the processing procedure of a third step according to another embodiment of the present disclosure. [Figure 15] 1 is a graph showing the relationship between predicted values ​​and actually measured values ​​of the complex elastic modulus of a rubber member. [Figure 16] 10 is a graph showing the relationship between predicted and measured loss tangent values ​​of a rubber member. [Figure 17] 1 is a graph showing the relationship between predicted values ​​and actually measured values ​​of rolling resistance in Examples 1 and 2. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. It should be understood that the drawings include exaggerated representations and representations that differ from the dimensional ratios of actual structures in order to facilitate understanding of the contents of the disclosure. Furthermore, identical or common elements are designated by the same reference numerals throughout the embodiments, and redundant explanations will be omitted. Furthermore, the specific configurations shown in the embodiments and drawings are intended to facilitate understanding of the contents of the present disclosure, and the present disclosure is not limited to the specific configurations shown in the drawings.

[0010] In the tire performance prediction method of this embodiment (hereinafter sometimes simply referred to as the "prediction method"), tire performance is predicted using a computer.

[0011] [computer] 1 is a perspective view showing a computer for executing the tire prediction method of this embodiment. The computer 1 of this embodiment includes, for example, a main body 1a, a keyboard 1b, a mouse 1c, and a display device 1d. The main body 1a is provided with, for example, a central processing unit (CPU), a ROM, a working memory, a storage device such as a magnetic disk, and disk drive devices 1a1 and 1a2. The storage device also stores in advance software and the like for executing the prediction method of this embodiment.

[0012] [tire] FIG. 2 is a cross-sectional view showing an example of a tire 2 to be evaluated in this embodiment. The tire 2 in this embodiment is configured as, for example, a pneumatic tire for a passenger car. However, the tire 2 is not limited to this form and may be configured as, for example, a pneumatic tire for heavy loads or a tire for a motorcycle. The tire 2 in this embodiment is configured to include a rubber member 3 and a fibrous member 4.

[0013] The fibrous member 4 of this embodiment includes, for example, a carcass 4a, an inner belt 4b, and an outer belt 4c. The carcass 4a extends from the tread portion 2a through the sidewall portion 2b to the bead cores 5 of the bead portions 2c. The inner belt 4b and the outer belt 4c are disposed outside the carcass 4a in the tire radial direction and inside the tread rubber 3a.

[0014] The rubber members 3 of this embodiment include, for example, a tread rubber 3a, a sidewall rubber 3b, a clinch rubber 3c, a bead apex rubber 3d, and an inner liner rubber 3e. The tread rubber 3a is disposed on the outer side of the outer belt 4c in the tread portion 2a. The sidewall rubber 3b is disposed on the outer side of the carcass 4a in the sidewall portion 2b. The clinch rubber 3c is fixed to the inner side of the sidewall rubber 3b in the tire radial direction. The bead apex rubber 3d extends from the bead core 5 outward in the tire radial direction. The inner liner rubber 3e is disposed on the inner surface of the carcass 4a.

[0015] [Vulcanization molding] The tire (vulcanized tire) 2 of this embodiment is manufactured by conventionally vulcanizing and molding an unvulcanized tire (shown in FIG. 3) including an unvulcanized rubber member 3. Here, "unvulcanized" includes all states that have not yet reached complete vulcanization, and the so-called semi-vulcanized state is included in this "unvulcanized" state. FIG. 3 is a partial cross-sectional view of a mold 11, a bladder 12, and an unvulcanized tire 2L during the vulcanization process.

[0016] In the vulcanization process of this embodiment, for example, a mold 11 for shaping the outer surface of the tire 2 and a bladder 12 that expands within the cavity of the unvulcanized tire 2L set in the mold 11 are used.

[0017] The mold 11 of this embodiment is configured to include, for example, a pair of sidewall molding dies 13, 13 having sidewall molding surfaces 13s, and a tread molding die 14 having a tread molding surface 14s. The tread molding die 14 is divided in the tire circumferential direction. The sidewall molding die 13 and the tread molding die 14 are fitted together to form a molding surface 11s that can mold the outer surface 2o of the tire 2. A heating means (not shown), such as an electric heater, is arranged in the mold 11.

[0018] The bladder 12 of this embodiment is made of, for example, an expandable rubber-like elastic body. A high-pressure fluid (not shown) is supplied to the internal space 12s of the bladder 12 from, for example, a supply means (not shown). The high-pressure fluid may be, for example, a mixture of water vapor and at least one inert gas, such as nitrogen, or a plurality of inert gases. The temperature of the high-pressure fluid is set to, for example, approximately 140 to 220°C.

[0019] In the vulcanization step, the unvulcanized tire 2L is heated and pressurized between the mold 11 and the bladder 12 to produce a vulcanized and molded tire 2 (shown in FIG. 2).

[0020] During the vulcanization process, the temperature of each rubber member 3 changes from moment to moment. These temperatures tend to vary depending on the position, size, and other factors of each rubber member 3. These temperatures affect the vulcanization speed of each rubber member 3 and the physical properties of the rubber after vulcanization, and ultimately have a significant effect on the performance of the tire 2 after vulcanization. Therefore, in order to accurately predict the physical properties of the rubber after vulcanization and the performance of the tire 2, it is important to consider the temperature of the rubber member 3, which changes from moment to moment during vulcanization molding.

[0021] [Tire performance prediction method (first embodiment)] Next, the prediction method of this embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart showing the processing steps of the tire performance prediction method of this embodiment.

[0022] [Step 1 (Enter time-series temperature data)] In the prediction method of this embodiment, first, time-series temperature data of the rubber member 3 when vulcanizing and molding an unvulcanized tire 2L including the unvulcanized rubber member 3 shown in Fig. 3 is input to a computer 1 (shown in Fig. 1) (first step S1). In the first step S1 of this embodiment, time-series temperature data of the rubber member 3 is acquired from the start to the end of the vulcanization process (including, for example, the heating process and the cooling process), but only part of this temperature data (for example, only the temperature data for the heating process) may be acquired.

[0023] The time-series temperature data of the rubber member 3 may be obtained, for example, using a temperature sensor (not shown) installed in the mold 11, or may be obtained by carrying out a simulation using a computer 1 (shown in FIG. 1). In the first step S1 of this embodiment, the time-series temperature data of the rubber member 3 is obtained by carrying out a simulation. FIG. 5 is a flowchart showing the processing procedure of the first step S1. FIG. 6 is a diagram showing an example of the mold model 21, the unvulcanized tire model 32, and the bladder model 22.

[0024] [Input mold model] In the first step S1 of this embodiment, first, a mold model 21 is input to a computer 1 (shown in FIG. 1) (step S11). In this embodiment, for example, based on design data (e.g., CAD data) of the mold 11 (shown in FIG. 3), the mold 11 is discretized (modeled) into a plurality (a finite number) of elements F(i) (i=1, 2, ...) that can be handled by a numerical analysis method. This sets up the mold model 21 having an internal space 21i for arranging an unvulcanized tire model 32.

[0025] As the numerical analysis method, for example, the finite element method, the finite volume method, the difference method, or the boundary element method can be appropriately adopted. In this embodiment, the finite element method is adopted. For each element F(i), for example, a tetrahedral solid element or the like is adopted. In the case of a two-dimensional model, a quadrilateral element or the like can be adopted.

[0026] Each element F(i) has a plurality of nodes 34. Numerical data such as an element number, a node number, a node coordinate value, and material properties (rigidity, Young's modulus, thermal conductivity, density, specific heat, thermal expansion coefficient, etc.) of the mold 11 (shown in FIG. 3) are defined for each element F(i). Such a mold model 21 can be easily set (modeled) by using, for example, commercially available meshing software.

[0027] The mold model 21 includes a pair of first mold models 23, 23 that model a pair of sidewall molds 13, 13 (shown in FIG. 3), and a second mold model 24 that model a tread mold 14 (shown in FIG. 3). The first mold model 23 and the pair of second mold models 24, 24 are combined together to form a molding surface 21s for molding the outer surface 32o of the unvulcanized tire model 32. The mold model 21 is input into a computer 1 (shown in FIG. 1).

[0028] [Enter the uncured tire model] Next, in the first step S1 of this embodiment, an unvulcanized tire model 32 is input to a computer 1 (shown in FIG. 1) (step S12). In this embodiment, for example, based on design data (e.g., CAD data) of a mold 11 (shown in FIG. 3), an unvulcanized tire 2L (shown in FIG. 3) is discretized (modeled) into a plurality (a finite number) of elements G(i) (i=1, 2, ...) that can be handled by a numerical analysis method. In this way, the unvulcanized tire model 32 is set.

[0029] In the unvulcanized tire model 32 of this embodiment, for example, a rubber component model 35 that models the rubber component 3 (shown in Figure 3) of the unvulcanized tire 2L, and a fiber component model 36 that models the fiber component 4 (shown in Figure 3) of the unvulcanized tire 2L are defined.

[0030] The rubber member model 35 includes a tread rubber model 35a that models the tread rubber 3a (shown in FIG. 3) and a sidewall rubber model 35b that models the sidewall rubber 3b (shown in FIG. 3). The rubber member model 35 further includes a clinch rubber model 35c that models the clinch rubber 3c (shown in FIG. 3) and a bead apex rubber model 35d that models the bead apex rubber 3d (shown in FIG. 3). The rubber member model 35 further includes an inner liner rubber model 35e that models the inner liner rubber 3e (shown in FIG. 3).

[0031] The elements G(i) are the same as the elements F(i) of the mold model 21. Each element G(i) is configured to include a plurality of nodes 37. Numerical data such as an element number, a node number, node coordinate values, and material properties (rigidity, Young's modulus, thermal conductivity, density, specific heat, thermal expansion coefficient, etc.) of the unvulcanized rubber member 3 and the fiber member 4 are defined for each element G(i). The unvulcanized tire model 32 is stored in the computer 1 (shown in FIG. 1).

[0032] [Enter bladder model] Next, in the first step S1 of this embodiment, a bladder model 22 is input to a computer 1 (shown in FIG. 1) (step S13). In this embodiment, for example, based on design data (e.g., CAD data) of the mold 11 and bladder 12 shown in FIG. 3, the bladder 12 is modeled (discretized) using a plurality (a finite number) of elements H(i) (i = 1, 2, ...) that can be handled by a numerical analysis method. In this way, the bladder model 22 is set.

[0033] The elements H(i) are similar to the elements F(i) of the mold model 21 and the elements G(i) of the unvulcanized tire model 32. Each element H(i) is configured to include a plurality of nodes 38. Numerical data such as an element number, node numbers, node coordinate values, and material properties (rigidity, Young's modulus, thermal conductivity, density, specific heat, thermal expansion coefficient, etc.) of the bladder 12 (shown in FIG. 3) are defined for each element H(i). The bladder model 22 is stored in the computer 1.

[0034] [Layout of unvulcanized tire model and bladder model] Next, in the first step S1 of this embodiment, the unvulcanized tire model 32 and the bladder model 22 are placed in the internal space 21i of the mold model 21 (step S14). In step S14 of this embodiment, the unvulcanized tire model 32 and the bladder model 22 can be placed in the internal space 21i of the mold model 21 based on, for example, a procedure similar to the placement step of a patent document (Japanese Patent No. 6871528).

[0035] Boundary Condition Definition Next, in the first step S1 of this embodiment, boundary conditions for calculating heat transfer of the unvulcanized tire model 32, mold model 21, and bladder model 22 are defined in the computer 1 (shown in FIG. 1) (step S15). The boundary conditions of this embodiment include, for example, the initial temperatures of the mold model 21, unvulcanized tire model 32, and bladder model 22, as well as the temperature conditions of the mold model 21 and bladder model 22 during the vulcanization process. The temperature conditions of this embodiment include the time series temperatures of the mold 11 and bladder 12 shown in FIG. 3 during the heating and cooling processes. These boundary conditions can be defined, for example, based on a procedure similar to the boundary condition definition step in a patent document (Japanese Patent No. 6871528). These boundary conditions are input into the computer 1 (shown in FIG. 1).

[0036] Calculate heat transfer Next, in the first step S1 of this embodiment, the computer 1 (shown in FIG. 1) calculates the heat transfer of the unvulcanized tire model 32, the mold model 21, and the bladder model 22 (step S16). In step S16 of this embodiment, the mold model 21 with an increased temperature is calculated based on the initial temperature and temperature conditions of the mold model 21. As a result, in step S16, the heat transfer at the contact surface 40 between the unvulcanized tire model 32 and the mold model 21 is calculated.

[0037] Furthermore, in step S16 of the present embodiment, the bladder model 22 with an increased temperature is calculated based on the initial temperature and temperature conditions of the bladder model 22. As a result, in step S16, heat transfer at the contact surface 42 between the unvulcanized tire model 32 and the bladder model 22 is calculated.

[0038] In this way, in step S16 of the present embodiment, the unvulcanized tire model 32 with an increased temperature can be calculated based on the temperatures of the mold model 21 and the bladder model 22. As a result, in step S16 of the present embodiment, the temperature of the rubber member 3 (rubber member model 35) that changes from moment to moment during vulcanization molding (in this example, the heating process and the cooling process) can be calculated, similar to the actual vulcanization process shown in Fig. 3 .

[0039] In step S16 of this embodiment, a thermal analysis (heat transfer calculation) is performed for each unit step of the simulation. The thermal analysis can be performed using commercially available finite element analysis application software such as Abaqus manufactured by Dassault Systèmes, LS-DYNA manufactured by LSTC, or NASTRAN manufactured by MSC.

[0040] In this embodiment, the temperature of the rubber member model 35 is calculated for each unit step of the simulation at the node 37 of the element G(i) that constitutes the rubber member model 35. As a result, in step S16 of this embodiment, time-series temperature data of the rubber member model 35 (rubber member 3) is acquired.

[0041] In step S16 of this embodiment, time-series temperature data is acquired for each of a plurality of regions 43 obtained by dividing the rubber member 3 (rubber member model 35). The plurality of regions 43 are appropriately divided, for example, according to the structure of the unvulcanized tire 2L (vulcanized tire 2) and the rate of change in the physical properties of the rubber member 3 with respect to the temperature during vulcanization and molding. In this embodiment, for example, a tread rubber model 35a, a sidewall rubber model 35b, a clinch rubber model 35c, a bead apex rubber model 35d, and an inner liner rubber model 35e are set in the regions 43. Note that the regions 43 are not limited to this embodiment. For example, more detailed components of the tread rubber model 35a to the inner liner rubber model 35e (for example, cap rubber and base rubber in the case of the tread rubber model 35a) may be set as the regions 43. Furthermore, the regions 43 may be, for example, each node 37 of the element G(i).

[0042] In step S16 of this embodiment, time-series temperature data is acquired for each region 43 (in this example, the tread rubber model 35a to the inner liner rubber model 35e). This time-series temperature data is acquired at predetermined nodes (representative points) among the multiple nodes 37 that make up each region 43, but is not particularly limited to these. FIG. 7 is a graph showing time-series temperature data (the relationship between temperature and vulcanization time) of the rubber member 3. In FIG. 7, for example, the temperature data of the tread rubber model 35a is shown as a representative. The time-series temperature data is stored in the computer 1 (shown in FIG. 1).

[0043] [Step 4 (obtaining an approximate response function)] Next, in the prediction method of this embodiment, a computer 1 (shown in FIG. 1) calculates an approximate response function that indicates the relationship between the temperature during vulcanization of multiple types of rubber, the equivalent vulcanization amount of the rubber, and the physical properties of the rubber after vulcanization (fourth step S4). The multiple types of rubber can be selected as appropriate. For the multiple types of rubber of this embodiment, rubbers with the same compounding as the rubber members 3 (tread rubber 3a to inner liner rubber 3e, etc.) shown in FIGS. 2 and 3 are used. FIG. 8 is a flowchart showing the processing procedure of the fourth step S4 of this embodiment.

[0044] [Getting the temperature during rubber vulcanization] In the fourth step S4 of this embodiment, first, temperatures during vulcanization of multiple types of rubber are acquired (step S41). As the temperatures during vulcanization of this embodiment, for example, time-series temperature data when each rubber is vulcanized is acquired using a known vulcanization tester (not shown).

[0045] The temperature data for each rubber is obtained based on a plurality of different vulcanization conditions (temperature conditions). The vulcanization conditions in this embodiment include temperature rise conditions in the heating process and temperature fall conditions in the cooling process. Fig. 9 is a graph showing time-series temperature data for the rubber constituting the tread rubber 3a. This graph shows the relationship between the temperature of the rubber during vulcanization and time, and representatively shows temperature data under two vulcanization conditions (first vulcanization condition and second vulcanization condition).

[0046] The number of vulcanization conditions is set appropriately depending on, for example, the prediction accuracy required for the approximate response function. The number of vulcanization conditions in this embodiment is set to, for example, 10 to 100 (about 40 in this example). Temperature data during vulcanization molding of multiple types of rubber is stored in a computer 1 (shown in FIG. 1).

[0047] [Get equivalent vulcanization amount of rubber] Next, in the fourth step S4 of this embodiment, the equivalent vulcanization amounts of multiple types of rubber are acquired (step S42). The equivalent vulcanization amount ECU is used to identify the speed of the vulcanization reaction, which changes depending on the temperature of the rubber during vulcanization molding (shown in FIG. 9). The speed of such a vulcanization reaction is closely related to the physical properties of the rubber.

[0048] To obtain multiple types of equivalent vulcanization amounts (ECU), for example, the following formula (1) is used. The following formula (1) is based on the Arrhenius equation. Note that the equivalent vulcanization amount of rubber is not limited to the form obtained by the following formula (1).

[0049]

number

[0050] In the above formula (1), the activation energy E, gas energy R, and reference temperature T0 can be set appropriately depending on, for example, the rubber compounding and vulcanization conditions. The activation energy E is set to, for example, 83.72 kJ / mol. The gas energy R is set to, for example, 8.318 J / mol·deg. The reference temperature is set to, for example, 414.86 K.

[0051] In step S42 of this embodiment, for each of the multiple types of rubber, for each vulcanization condition (e.g., the first vulcanization condition and the second vulcanization condition in FIG. 9), each temperature T identified from the temperature data (shown in FIG. 9) and the elapsed time t at that temperature T are respectively substituted into the above formula (1). As a result, in step S42 of this embodiment, for each of the multiple types of rubber, an equivalent vulcanization amount is respectively obtained for each of the vulcanization conditions (e.g., the first vulcanization condition, the second vulcanization condition, ...). The equivalent vulcanization amounts of the multiple types of rubber are stored in computer 1 (shown in FIG. 1).

[0052] [Obtain physical properties after vulcanization] Next, in the fourth step S4 of this embodiment, the physical properties of the multiple types of rubber after vulcanization are obtained (step S43). The physical properties of the vulcanized rubber are, for example, the same types as the physical properties of the rubber member 3 predicted in the second step S2 described below. The physical properties in this embodiment include, for example, at least one of the loss tangent tanδ and the complex modulus of elasticity E* (in this embodiment, both the loss tangent tanδ and the complex modulus of elasticity E*). However, the physical properties are not limited to these forms.

[0053] In step S43 of this embodiment, first, the physical properties of the vulcanized rubber from which the temperature data (shown in FIG. 9) has been acquired are measured using a viscoelasticity spectrometer in accordance with the provisions of JIS-K6394. The measurement conditions are, for example, as follows: Initial distortion: 10% Amplitude: ±1% Frequency: 10Hz Deformation mode: tension Measurement temperature: 70℃ Viscoelasticity Spectrometer: Iwamoto Manufacturing Co., Ltd.

[0054] In this embodiment, the physical properties of multiple types of rubber vulcanized under each vulcanization condition (for example, first vulcanization condition, second vulcanization condition, etc.) are measured. The measured physical properties of the multiple types of rubber after vulcanization are stored in a computer 1 (shown in FIG. 1).

[0055] [Calculate the approximate response function] Next, in the fourth step S4 of this embodiment, an approximate response function showing the relationship between the rubber temperature, the equivalent vulcanization amount of the rubber, and the physical properties of the rubber after vulcanization is obtained for multiple types of rubber (step S44).

[0056] The approximate response function is used to predict the physical properties of rubber vulcanized under unknown vulcanization conditions (e.g., temperature rise conditions in the heating process and temperature fall conditions in the cooling process) by complementing them with the physical properties of rubber vulcanized under known vulcanization conditions (e.g., first vulcanization conditions, second vulcanization conditions, etc.). Therefore, by constructing such an approximate response function in advance, it becomes possible to predict the performance of rubber vulcanized under any vulcanization conditions (rubber member 3 shown in Figure 2).

[0057] The temperature used in the approximate response function may be the time-series temperature data (shown in FIG. 9) of multiple types of rubber. Note that if such temperature data is used, the number of variables in the approximate response function will increase, which may increase the time required to calculate the approximate response function and the time required to predict physical properties from the approximate response function. Furthermore, since the temperature data is reflected in the equivalent vulcanization amount, its contribution to the accuracy of physical property prediction is not high. Meanwhile, among the temperature data, the maximum temperature reached (for example, the maximum temperature reached T1 under the first vulcanization conditions and the maximum temperature reached T2 under the second vulcanization conditions in FIG. 9) has a significant effect on the physical properties of the rubber. Therefore, the maximum temperature reached for each temperature data (for example, the maximum temperatures reached T1 and T2 shown in FIG. 9) is used as the temperature in this embodiment.

[0058] Furthermore, as shown in FIG. 9, the physical properties after vulcanization are significantly affected not only by the rubber temperature (e.g., maximum temperatures T1 and T2) and the equivalent vulcanization amount, but also by the time (duration) U during which the rubber is continuously heated above a predetermined reference temperature T3 (e.g., 60 to 130°C (80°C in this example)). For this reason, in the fourth step S4 of this embodiment, it is desirable to include the duration U of the temperature above the reference temperature T3 in the construction of the approximate response function. In FIG. 9, the duration U1 under the first vulcanization conditions and the duration U2 under the second vulcanization conditions are shown as representatives. The duration U is obtained from each temperature data (shown in FIG. 9).

[0059] In this embodiment, an approximate response function is calculated for each of the multiple types of rubber. As a result, in this embodiment, by using the approximate response function of the same rubber as the type of rubber whose physical properties are to be predicted, it becomes possible to predict the physical properties of the rubber with high accuracy, from the approximate response functions set for each of the multiple types of rubber.

[0060] To construct the approximate response function for each rubber in this embodiment, the temperature during vulcanization molding (in this example, the highest temperatures reached, T1 and T2 in Figure 9, etc.), the equivalent vulcanization amount, the duration U at or above the reference temperature T3 (shown in Figure 9), and the physical properties after vulcanization are used for multiple vulcanization conditions.

[0061] The approximate response function can be constructed by various methods according to convention. For example, the response surface methodology (RSM), the radial basis function (RBF), or the Kriging method are preferably used for the approximate response function. The approximate response function may be constructed by deep learning, which multiplies the intermediate layers of a neural network, in order to improve the ability to deal with nonlinearity. The approximate response function for each rubber is stored in a computer 1 (shown in FIG. 1).

[0062] [Second step (predicting the physical properties of rubber components after vulcanization)] Next, in the prediction method of this embodiment, the computer 1 (shown in FIG. 1) predicts the physical properties of the rubber member 3 after vulcanization based on temperature data of the rubber member 3 (for example, shown in FIG. 7) (second step S2). Fig. 10 is a flowchart showing the processing procedure of the second step S2 of this embodiment.

[0063] [Get equivalent vulcanization amount of rubber material] In the second step S2 of this embodiment, first, an equivalent vulcanization amount of the rubber member 3 (shown in FIG. 3) is acquired based on temperature data (shown in FIG. 7, for example) of the rubber member 3 (step S21). In step S21 of this embodiment, for each region 43 (tread rubber model 35a to inner liner rubber model 35e) shown in FIG. 6, each temperature T identified from the temperature data (shown in FIG. 7, for example) and the elapsed time t at that temperature T are substituted into the above formula (1). As a result, in step S22, an equivalent vulcanization amount ECU of each region 43 is acquired.

[0064] [Predicting the physical properties of rubber components] Next, in a second step S2 of this embodiment, the physical properties of the rubber member 3 are predicted (step S22) based on the temperature data of the rubber member 3 (for example, as shown in FIG. 7) and the equivalent vulcanization amount of the rubber member 3. In step S22 of this embodiment, the temperature of the temperature data of the rubber member 3 and the equivalent vulcanization amount of the rubber member 3 are substituted into the approximate response function obtained in the fourth step S4 to calculate the physical properties of the rubber member 3.

[0065] In step S22 of this embodiment, first, from the approximate response functions set for each of the multiple types of rubber, an approximate response function of a rubber having the same compounding as each region 43 (in this example, the tread rubber model 35a to the inner liner rubber model 35e) is selected. Next, in step S22, from the temperature data of each region 43 (for example, as shown in FIG. 7), the maximum temperature (for example, the maximum temperature T5 in FIG. 7) and the duration of the temperature being equal to or higher than the reference temperature T3 (shown in FIG. 9) are acquired. Then, the maximum temperature, the equivalent vulcanization amount, and the duration of the temperature being equal to or higher than the reference temperature are substituted into the selected approximate response function, thereby calculating the physical properties of each region 43 after vulcanization.

[0066] In this way, in this embodiment, the temperature of the rubber member 3 (each region 43), which changes from moment to moment during the vulcanization of the tire 2L (shown in FIG. 3), can be taken into account in calculating the physical properties of the rubber member after vulcanization based on time-series temperature data (e.g., shown in FIG. 7) of the rubber member 3 (each region 43). As a result, the prediction method of this embodiment can predict the physical properties of the actual rubber member after vulcanization with higher accuracy than when, for example, the temperature of the rubber member, which changes from moment to moment, is not taken into account. The physical properties of each region 43 after vulcanization are stored in the computer 1 (shown in FIG. 1).

[0067] [Step 3 (predicting tire performance)] Next, in the prediction method of this embodiment, the computer 1 (shown in FIG. 1) predicts the performance of the tire 2 (shown in FIG. 2) after vulcanization based on the physical properties of the rubber member 3 (third step S3). Fig. 11 is a flowchart showing the processing procedure of the third step S3 of this embodiment.

[0068] [Enter tire model] In the third step S3 of this embodiment, first, a vulcanized tire model, which is a model of a vulcanized tire 2 (shown in FIG. 2), is input to the computer 1 (step S31). Fig. 12 is a perspective view showing a vulcanized tire model 45 and a road surface model 48. Fig. 13 is a cross-sectional view of the tire model 45.

[0069] In step S31 of this embodiment, a vulcanized tire model 45 is set using the same processing procedure as in step S12, in which the unvulcanized tire model 32 (shown in FIG. 6) is input. As shown in FIG. 13, the vulcanized tire model 45 includes rubber elements J(i) (i = 1, 2, ...) that can define the physical properties of the vulcanized rubber member 3 (shown in FIG. 2). The rubber elements J(i) are the same as the elements G(i) of the unvulcanized tire model 32 shown in FIG. 6, except for the material properties (physical properties defined in step S32, which will be described later).

[0070] In the vulcanized tire model 45 of this embodiment, for example, a rubber member model 35 and a fiber member model 36 are defined, similar to the unvulcanized tire model 32 (shown in FIG. 6).

[0071] The rubber member model 35 of this embodiment is a model of the rubber member 3 (shown in FIG. 2) of the vulcanized tire 2. The rubber member model 35 includes a tread rubber model 35a, a sidewall rubber model 35b, a clinch rubber model 35c, a bead apex rubber model 35d, and an inner liner rubber model 35e.

[0072] The fiber member model 36 of this embodiment is a model of the fiber member 4 (shown in FIG. 2) of the tire 2 after vulcanization. The fiber member model 36 includes a carcass model 36a, an inner belt model 36b, and an outer belt model 36c. The tire model 45 after vulcanization is stored in the computer 1 (shown in FIG. 1).

[0073] [Define the properties of rubber materials] Next, in the third step S3 of this embodiment, the physical properties of the rubber member 3 after vulcanization are defined for the rubber elements J(i) of the vulcanized tire model 45 shown in Fig. 13 (step S32). In step S32 of this embodiment, the physical properties of the rubber member 3 after vulcanization (shown in Figs. 2 and 3) predicted in the second step S2 are defined for each rubber element J(i).

[0074] In this embodiment, the post-vulcanization physical properties are predicted for each of a plurality of regions 43 (in this example, tread rubber model 35a to inner liner rubber model 35e) into which the rubber member 3 is divided. Therefore, in step S32 of this embodiment, the post-vulcanization physical properties are defined for each of the regions 43 of the post-vulcanization tire model 45.

[0075] As described above, the physical properties of each region 43 after vulcanization are predicted taking into consideration the temperature of the rubber members that changes from moment to moment during vulcanization molding of the unvulcanized tire 2L (shown in FIG. 3). Therefore, in the vulcanized tire model 45 of this embodiment, physical properties that approximate those of the actual vulcanized tire 2 (shown in FIGS. 2 and 3) can be defined. The vulcanized tire model 45 is stored in the computer 1 (shown in FIG. 1).

[0076] [Predicting tire performance] Next, in the third step S3 of this embodiment, the tire performance is predicted (step S33). The predicted performance of the tire 2 is not particularly limited, but it is desirable to select, for example, a performance that will affect the physical properties of the rubber member 3 (shown in FIGS. 2 and 3) after vulcanization. The performance of the tire 2 of this embodiment includes, for example, at least one of the longitudinal spring constant, lateral spring constant, rolling resistance, and ground contact shape of the tire 2.

[0077] In step S33 of this embodiment, first, as shown in FIG. 12, a road surface model 48 used to predict the above-mentioned tire performance is set. In this embodiment, based on information about the road surface (not shown), the road surface is discretized using a finite number of elements K(i) (i=1, 2, ...) that can be handled by a numerical analysis method (in this embodiment, the finite element method). In this way, the road surface model 48 is set. The elements K(i) are defined as rigid plane elements that are defined to be undeformable. The elements K(i) have a plurality of nodes 50. Furthermore, for the elements K(i), numerical data such as element numbers and coordinate values ​​of the nodes are defined.

[0078] Next, in step S33 of this embodiment, a tire model 45 after internal pressure filling is calculated. In this embodiment, as in the conventional simulation method, for example, bead portions 45c, 45c of the vulcanized tire model 45 shown in Fig. 13 are constrained, and deformation based on a uniformly distributed load corresponding to the internal pressure condition is calculated, thereby calculating the tire model 45 after internal pressure filling. For example, commercially available finite element analysis application software such as LS-DYNA manufactured by JSOL Corporation is used to calculate the deformation of the tire model 45.

[0079] Next, in step S33 of this embodiment, as shown in FIG. 12, the tire model 45 after internal pressure filling is brought into contact with a road surface model 48, and physical quantities related to the performance of the tire 2 (shown in FIG. 2) are calculated.

[0080] In this embodiment, the tire model 45 after internal pressure inflation is brought into contact with the road surface model 48 based on a predetermined load condition (vertical load L). As a result, in step S33, the vertical spring constant and the contact shape of the tire 2 are calculated. Furthermore, a lateral load (not shown) is set on the tire model 45 after internal pressure inflation, thereby calculating the lateral spring constant.

[0081] In this embodiment, for example, the rolling resistance is calculated by rolling the tire model 45 after loading on the road surface model 48 based on predetermined rolling conditions (traveling speed (angular velocity V1 and translational velocity V2), slip angle, etc.).

[0082] In the vulcanized tire model 45 of this embodiment, physical properties that are similar to those of the actual vulcanized tire 2 (shown in FIG. 2) are defined, and therefore it is possible to accurately predict the performance of the tire 2. The predicted performance of the tire 2 is stored in the computer 1.

[0083] [Evaluating tire performance] Next, in the prediction method of this embodiment, as shown in FIG. 4, the performance of the tire 2 (shown in FIG. 2) is evaluated (step S5). In this embodiment, it is determined whether the predicted performance of the tire 2 is good or not. The performance evaluation criteria are set appropriately depending on the performance required of the tire 2. Furthermore, the performance evaluation of the tire 2 may be performed by the computer 1 (shown in FIG. 1) or by an operator.

[0084] If it is determined in step S5 that the performance of the tire 2 (shown in FIG. 2) is good ("Yes" in step S5), the tire 2 is manufactured (step S6), for example, based on the design factors (CAD data) of the tire 2. On the other hand, if it is determined in step S5 that the performance of the tire 2 is not good ("No" in step S5), the design factors of the tire 2 are changed (step S7), and the first step S1 to step S5 are performed again.

[0085] In the prediction method of this embodiment, tire performance can be predicted with high accuracy using a tire model 45 in which physical properties similar to those of an actual vulcanized tire 2 (shown in FIG. 2) are defined, and therefore tire performance can be evaluated accurately. Therefore, in this embodiment, it is possible to reliably design and manufacture a tire 2 having desired performance.

[0086] [Tire performance prediction method (second embodiment)] In the second step S2 of the above-described embodiments, the physical properties of all the regions 43 (the tread rubber 3a to the inner liner rubber 3e shown in FIG. 3) into which the rubber member 3 is divided are predicted, but the present invention is not limited to such an embodiment. In the second step S2, for example, the physical properties of the region 43 in which the amount or rate of change in the physical properties of the rubber member 3 with respect to temperature is greater than a predetermined threshold may be predicted.

[0087] The amount or rate of change of a physical property with respect to temperature can be determined as appropriate. In this embodiment, for a predetermined first temperature during vulcanization molding and a second temperature higher than the first temperature, the amount of change of the physical property with respect to temperature is determined by subtracting the physical property (loss tangent tanδ) at the first temperature from the physical property (loss tangent tanδ) at the second temperature. The threshold value is set as appropriate (for example, 0.005 to 0.01) based on, for example, the prediction accuracy of the determined physical property.

[0088] In a region 43 where the amount of change (rate of change) is large (for example, the sidewall rubber 3b), the physical properties are more sensitive to the temperature during vulcanization than in a region 43 where the amount of change (rate of change) is small (for example, the tread rubber 3a). From this perspective, in the second step S2 of this embodiment, the physical properties are predicted based on the procedure of the second step S2 described above for the region 43 of each region 43 shown in FIG. 3 where the amount of change (rate of change) in the physical properties of the rubber member 3 with respect to temperature is larger than a threshold value. This makes it possible to accurately predict the performance of the tire 2 (shown in FIG. 2) after vulcanization in the third step S3.

[0089] On the other hand, in a region 43 where the amount of change (rate of change) is small (e.g., tread rubber 3a), the sensitivity of the physical properties to the temperature during vulcanization is lower than in a region 43 where the amount of change (rate of change) is large (e.g., sidewall rubber 3b). In such a region 43 where the amount of change (rate of change) is small, even if the physical properties after vulcanization cannot be predicted based on the procedure in the second step S2, the impact on the prediction accuracy of the performance of the tire 2 (shown in FIG. 2) predicted in the third step S3 is small. From this perspective, in this embodiment, the physical properties obtained based on a procedure other than the second step S2 are set for the region 43 of each region 43 shown in FIG. 3 where the amount of change (rate of change) of the physical properties of the rubber member 3 with respect to temperature is equal to or less than a threshold value. In this embodiment, for example, the physical properties after vulcanization under one vulcanization condition for an unvulcanized rubber having the same formulation as the region 43 where the rate of change is small are used. As a result, the prediction method of this embodiment can maintain the prediction accuracy of tire performance while reducing the number of steps required to predict the physical properties in the second step S2.

[0090] [Tire performance prediction method (third embodiment)] In the third step S3 of the embodiments described above, the performance of the tire 2 is predicted by defining the physical properties of the rubber member 3 predicted in the second step S2 for the rubber element J(i) of the tire model 45 shown in FIG. 13 , but the present disclosure is not limited to this. For example, if predetermined reference physical properties of the rubber member 3 and the reference performance of the tire 2 at the reference physical properties are known, the performance of the tire 2 may be predicted based on the difference between the predicted physical properties and the reference physical properties, and the reference performance. FIG. 14 is a flowchart showing the processing procedure of the third step of another embodiment of the present disclosure.

[0091] In the third step S3 of this embodiment, predetermined reference physical properties of the rubber member 3 and reference performance of the tire at the reference physical properties are specified (step S34). The reference physical properties and the reference performance are specified as appropriate.

[0092] The reference performance (e.g., rolling resistance, etc.) in this embodiment is obtained, for example, by an experiment using a vulcanized tire 2 having the same configuration (e.g., carcass, etc.) as the tire 2 to be evaluated. The reference performance may also be obtained by a simulation using the above-mentioned vulcanized tire model 45 (shown in FIG. 13).

[0093] In this embodiment, the reference physical properties are determined by manufacturing rubber components (not shown) of existing tires with the same compounding, and obtaining the physical properties (loss tangent tanδ, etc.) using the method described above. The reference physical properties and reference performance are stored in the computer 1.

[0094] Next, in a third step S3 of this embodiment, the performance of the tire 2 is predicted based on the difference between the physical properties of the rubber member 3 and the reference physical properties and the reference performance (step S35). In this embodiment, the following formula (2) is used to predict the performance of the tire 2.

[0095]

number

[0096] In the above formula (2), the reference performance (for example, a value indicating rolling resistance) obtained in step S34 is substituted for the reference performance. In the above formula (2), the derivative f'(x) of the basic formula for basic performance (the fundamental formula differentiated) is multiplied by the difference Δx between the physical property predicted in the second step S2 and the reference physical property obtained in step S34, thereby obtaining the change in basic performance due to the change in physical property from the reference physical property.

[0097] The change in the basic performance (i.e., f'(x) × Δx) is added to the reference performance f(x), thereby determining the performance of the tire 2 (shown in FIG. 2) based on the predicted physical properties. Note that, when physical properties are predicted for each region 43 (shown in FIG. 3), the difference Δx between the predicted physical property and the reference physical property may be, for example, an average value of the differences Δx determined for each region 43.

[0098] The basic formula for the reference performance can be set appropriately depending on the required reference performance. For example, when the reference performance is rolling resistance, it is defined by the following formula (3).

[0099]

number

[0100] In the above formula (3), the rolling resistance RR is found, for example, by dividing the hysteresis loss HL acting on the tire model 45 (shown in FIG. 12) that has made one rotation by the circumference P of the tire model 45. The hysteresis loss HL is found by adding up the total values ​​of the hysteresis losses in six directions (forward, backward, rightward, leftward, upward, and downward) acting on each rubber element J(i) shown in FIG. 13 for all rubber elements J(i). The hysteresis loss in one direction acting on each rubber element J(i) (for example, forward direction: variable j=1) can be calculated by, for example, dividing the storage modulus E in that one direction. i1 , the difference εdiff between the strain when internal pressure is applied in one direction and the strain when grounded i1 , and loss tangent tanδ in one direction i1 Each can be found by multiplying by

[0101] 11 to 13, the performance of the tire 2 after vulcanization can be easily calculated (predicted) based on the difference Δx between the physical property predicted in the second step S2 and the reference physical property and the reference performance. Therefore, in this embodiment, it is possible to predict the performance of the tire 2 in a short time while maintaining the prediction accuracy of the performance of the tire 2 after vulcanization.

[0102] [Tire performance prediction method (fourth embodiment)] In the embodiments described above, the approximate response function obtained in the fourth step S4 is used to predict the physical properties of the rubber member 3, but the present invention is not limited to this. For example, the physical properties of the rubber member 3 may be predicted using a learning model generated by deep learning using artificial intelligence (AI). Such a learning model is useful for accurately predicting the physical properties of rubber vulcanized under unknown vulcanization conditions (for example, temperature rise conditions in the heating process and temperature fall conditions in the cooling process).

[0103] Although particularly preferred embodiments of the present disclosure have been described in detail above, the present disclosure is not limited to the illustrated embodiments and can be modified and implemented in various forms. [Example]

[0104] Based on the procedure shown in FIG. 4, tire performance was predicted (Example 1 and Example 2).

[0105] In Examples 1 and 2, first, a first step was carried out in which time-series temperature data of the rubber members when an unvulcanized tire including the unvulcanized rubber members was vulcanized and molded was input into a computer. In the first step, a mold model, an unvulcanized tire model, and a bladder model were input based on the processing procedure shown in Fig. 5, and heat transfer of these models was calculated to obtain time-series temperature data of the rubber members.

[0106] Next, in Examples 1 and 2, a fourth step was carried out to obtain an approximate response function showing the relationship between the rubber temperature, the equivalent vulcanization amount of the rubber, and the physical properties of the rubber. In the fourth step, the temperature, equivalent vulcanization amount, and physical properties of multiple types of rubber during vulcanization molding were obtained based on the processing procedure shown in Fig. 8, and the approximate response function of each rubber was obtained using these.

[0107] Next, in Examples 1 and 2, a second step was carried out to predict the physical properties of the rubber member after vulcanization based on the temperature data of the rubber member acquired in the first step. In the second step, the equivalent vulcanization amount of the rubber member was acquired based on the temperature data of the rubber member according to the processing procedure shown in Fig. 10. Then, the temperature of the temperature data of the rubber member and the equivalent vulcanization amount of the rubber member were substituted into an approximate response function to predict the physical properties of the rubber member.

[0108] Fig. 15 is a graph showing the relationship between the predicted and measured values ​​of the complex modulus E* of a rubber member. Fig. 16 is a graph showing the relationship between the predicted and measured values ​​of the loss tangent tanδ of a rubber member. Figs. 15 and 16 show physical properties predicted based on temperature data when rubber members with the same compound are vulcanized and molded under multiple vulcanization conditions.

[0109] As shown in FIGS. 15 and 16, in Examples 1 and 2, it was possible to predict physical properties that were close to the actually measured values.

[0110] Next, in Example 1, the physical properties of the rubber components after vulcanization were defined for the rubber elements of the tire model after vulcanization based on the processing procedure shown in Figure 11, and tire performance was predicted. On the other hand, in Example 2, the reference physical properties of the rubber components and the reference performance of the tire at the reference physical properties were specified based on the processing procedure shown in Figure 14, and tire performance was predicted using the above equations (2) and (3). In Examples 1 and 2, tire performance was predicted using the physical properties of the rubber components predicted based on temperature data under three vulcanization conditions. The common specifications are as follows: Tire size: 215 / 65R17 Rubber area: Tread rubber (cap rubber, base rubber) Sidewall rubber Clinch Rubber Bead apex rubber Inner liner rubber

[0111] 17 is a graph showing the relationship between predicted and measured values ​​of rolling resistance for Examples 1 and 2. As a result of the test, rolling resistance values ​​close to the measured values ​​could be obtained for Examples 1 and 2. Therefore, for Examples 1 and 2, tire performance could be predicted with high accuracy.

[0112] Furthermore, while experiments to obtain actual measured values ​​require several months to obtain the above results, in Examples 1 and 2, actual measured values ​​could be obtained within a few hours to a few days. Therefore, Examples 1 and 2 were able to predict tire performance with high accuracy. Furthermore, in Example 2, the time required to obtain the above results was shorter than in Example 1, in which a simulation was performed.

[0113] [Note] The present disclosure includes the following aspects.

[0114] [Disclosure 1] 1. A method for predicting tire performance, comprising: a first step of inputting time-series temperature data of the rubber member when the unvulcanized tire including the unvulcanized rubber member is vulcanized and molded into a computer; The computer a second step of predicting physical properties of the rubber member after vulcanization based on the temperature data of the rubber member; and a third step of predicting performance of the tire after vulcanization based on the physical properties of the rubber member. How to predict tire performance. [Disclosure 2] The first step includes acquiring time-series temperature data for each of a plurality of regions in which the rubber member is divided, The tire performance prediction method according to Disclosure 1, wherein the second step predicts the physical properties of each of the regions after vulcanization based on the temperature data of each of the regions. [Disclosure 3] The second step is a method for predicting tire performance described in Disclosure 2, which predicts the physical properties of areas among the areas where the change or rate of change in the physical properties of the rubber member with respect to the temperature is greater than a predetermined threshold value. [Disclosure 4] the second step is a step of acquiring an equivalent vulcanization amount of the rubber member based on the temperature data of the rubber member; A method for predicting tire performance described in any one of Disclosures 1 to 3, comprising a step of predicting the physical properties of the rubber component based on the temperature data of the rubber component and the equivalent vulcanization amount of the rubber component. [Disclosure 5] The method further includes, prior to the second step, a fourth step of acquiring the temperatures during vulcanization molding of a plurality of types of rubber, the equivalent vulcanization amounts of the rubbers, and the physical properties of the rubbers after vulcanization, and determining an approximate response function showing the relationship between the temperatures of the rubbers, the equivalent vulcanization amounts of the rubbers, and the physical properties of the rubbers, The tire performance prediction method described in Disclosure 4, wherein the second step includes a step of substituting the temperature of the temperature data of the rubber component and the equivalent vulcanization amount of the rubber component into the approximate response function to calculate the physical properties of the rubber component. [Disclosure 6] The method for predicting tire performance according to any one of Disclosures 1 to 5, wherein the third step includes a step of defining the physical properties of the rubber component in a rubber element of a tire model that models the tire, and predicting the tire performance. [Disclosure 7] the third step includes a step of specifying predetermined reference physical properties of the rubber member and reference performance of the tire at the reference physical properties; A method for predicting tire performance described in any one of Disclosures 1 to 5, comprising a step of predicting the performance of the tire based on the difference between the physical properties of the rubber component and the reference physical properties and the reference performance. [Disclosure 8] The tire performance prediction method according to any one of Disclosures 1 to 7, wherein the physical property of the rubber member includes at least one of loss tangent tanδ and complex modulus E*. [Disclosure 9] The tire performance prediction method according to any one of Disclosures 1 to 8, wherein the tire performance includes at least one of the tire's longitudinal spring constant, lateral spring constant, rolling resistance, and ground contact shape. [Explanation of symbols]

[0115] S1 1st process S2 2nd process S3 3rd process

Claims

1. 1. A method for predicting tire performance, comprising: a first step of inputting time-series temperature data of the rubber member when the unvulcanized tire including the unvulcanized rubber member is vulcanized and molded into a computer, The computer a second step of predicting physical properties of the rubber member after vulcanization based on the temperature data of the rubber member; and a third step of predicting performance of the tire after vulcanization based on the physical properties of the rubber member. the first step includes acquiring time-series temperature data for each of a plurality of regions in which the rubber member is divided, the second step predicts physical properties of each of the regions after vulcanization based on the temperature data of each of the regions; the second step predicts the physical properties of a region among the regions in which a change amount or a change rate of the physical properties of the rubber member with respect to temperature is greater than a predetermined threshold value. How to predict tire performance.

2. A method for predicting tire performance, comprising: a first step of inputting time-series temperature data of the rubber member when the unvulcanized tire including the unvulcanized rubber member is vulcanized and molded into a computer, The computer a second step of predicting physical properties of the rubber member after vulcanization based on the temperature data of the rubber member; and a third step of predicting performance of the tire after vulcanization based on the physical properties of the rubber member. The method further includes, prior to the second step, a fourth step of acquiring the temperatures during vulcanization molding of a plurality of types of rubber, the equivalent vulcanization amounts of the rubbers, and the physical properties of the rubbers after vulcanization, and determining an approximate response function that indicates the relationship between the temperatures of the rubbers, the equivalent vulcanization amounts of the rubbers, and the physical properties of the rubbers, the second step is a step of acquiring an equivalent vulcanization amount of the rubber member based on the temperature data of the rubber member; and calculating the physical properties of the rubber member by substituting the temperature of the temperature data of the rubber member and the equivalent vulcanization amount of the rubber member into the approximate response function. How to predict tire performance.

3. A method for predicting tire performance, comprising: a first step of inputting time-series temperature data of the rubber member when the unvulcanized tire including the unvulcanized rubber member is vulcanized and molded into a computer, The computer a second step of predicting physical properties of the rubber member after vulcanization based on the temperature data of the rubber member; and a third step of predicting performance of the tire after vulcanization based on the physical properties of the rubber member. the third step includes a step of specifying predetermined reference physical properties of the rubber member and reference performance of the tire at the reference physical properties; and predicting performance of the tire based on a difference between the physical property of the rubber member and the reference physical property and the reference performance. How to predict tire performance.

4. A method for predicting tire performance described in any one of claims 1 to 3, wherein the third step includes a step of defining the physical properties of the rubber component in the rubber elements of a tire model that models the tire, and predicting the performance of the tire.

5. A method for predicting tire performance described in any one of claims 1 to 4, wherein the physical properties of the rubber member include at least one of loss tangent tanδ and complex modulus of elasticity E*.

6. A method for predicting tire performance described in any one of claims 1 to 5, wherein the tire performance includes at least one of the tire's vertical spring constant, lateral spring constant, rolling resistance, and contact shape.

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