Prediction method

Abstract

To provide a prediction method for predicting dynamic performance of a tire in a short time.SOLUTION: A prediction method includes: a first step S1 of acquiring design parameter information; a second step s2 for acquiring an actual measurement value of a response to a measurement condition that changes with time; a third step S3 for preparing an approximation function showing a relationship between a measurement condition and a response; a fourth step S4 for identifying a coefficient of an approximation function by regression analysis on the basis of an actual measurement value; a fifth step S5 for generating a learning model with design parameter information in the first step as an input layer and the coefficient in the fourth step as an output layer; a sixth step S6 that inputs design parameter information of a predicted tire to a learning model and calculating a coefficient of an approximation function for a predicted tire; and a seventh step S7 for predicting dynamic performance by using an approximation function including the coefficient in the sixth step S6.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a method for predicting the dynamic performance of tires.

Background Art

[0002] Conventionally, a method for predicting the dynamic performance of tires by calculation using the finite element method has been known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, even when using the method described in Patent Document 1 above, the calculation takes time, and it has been difficult to predict the dynamic performance of tires in a short time.

[0005] The present invention has been devised in view of the above actual situation, and the main object thereof is to provide a method capable of predicting the dynamic performance of tires in a short time.

Means for Solving the Problems

[0006] The present invention is a method for predicting the dynamic performance of tires, a first step of obtaining a plurality of design parameter information from a plurality of tires having different sizes; a second step of obtaining measured values of the respective responses of the plurality of tires to measurement conditions that change over time as the dynamic performance of the plurality of tires; a third step of preparing an approximate function indicating the relationship between the measurement conditions and the response, including a plurality of coefficients; For each of the plurality of tires, a fourth step of identifying the coefficients of the approximation function by performing regression analysis on the approximation function based on the measured values; A fifth step of generating a learning model having, as an input layer, the design parameter information obtained in the first step and, as an output layer, the coefficients identified in the fourth step; A sixth step of inputting the design parameter information of the tire to be predicted into the learning model and calculating the coefficients of the approximation function for the tire to be predicted; A seventh step of predicting the dynamic performance of the tire to be predicted using the approximation function including the coefficients calculated in the sixth step.

Advantages of the Invention

[0007] Since the prediction method of the present invention has the above configuration, it is possible to predict the dynamic performance of a tire in a short time without performing complicated calculations using the finite element method.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

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Modes for Carrying Out the Invention

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the method for predicting the dynamic performance of the tire of the present embodiment (hereinafter, sometimes simply referred to as the "prediction method"), the performance of the tire is predicted using a computer. The tire for which the dynamic performance is predicted in the present invention, that is, the tire to be predicted, is a tire that does not exist at the time of implementing the present invention.

[0010] FIG. 1 is a perspective view showing a computer 1 for executing the prediction method of the tire of the present embodiment. The computer 1 of the present embodiment includes, for example, a main body 11, a keyboard 12, a mouse 13, and a display device 14. In this main body 11, for example, an arithmetic processing unit (CPU), a memory, and a storage device are provided (not shown). Further, software for executing the prediction method of the present embodiment and the like are stored in advance in the storage device.

[0011] FIG. 2 is a flowchart showing the procedure of the prediction method of the present embodiment. The prediction method 100 includes a first step S1 of acquiring tire design parameter information, a second step S2 of acquiring measured values of the dynamic performance of the tire, a third step S3 of preparing an approximation function including a plurality of coefficients, a fourth step S4 of specifying the coefficients of the approximation function, a fifth step S5 of generating a learning model, a sixth step S6 of calculating the coefficients of the approximation function, and a seventh step S7 of predicting the dynamic performance of the tire to be predicted. Each of the above steps of the prediction method 100 is executed by the computer 1.

[0012] In the first step S1, design parameter information of a plurality of tires having different sizes is acquired. The "design parameter information" is information regarding parameters for designing a tire, and a plurality of design parameter information is acquired from one tire.

[0013] The plurality of tires having different sizes in the first step S1 are a plurality of tires having different outer diameters, cross-sectional widths, cross-sectional heights, etc., and tires having different rim sizes may be mixed. The plurality of tires from which the design parameter information is acquired in the first step S1 are existing tires other than the tire to be predicted.

[0014] Examples of the design parameter information include, for example, parameter information regarding tire size, parameter information regarding usage conditions, parameter information regarding structural design, parameter information regarding rubber compounding, and the like.

[0015] Examples of the parameter information regarding tire size include information regarding the outer diameter, maximum width, cross-sectional height, etc. of the tire.

[0016] Examples of the parameter information regarding usage conditions include information regarding the used rim, internal pressure, etc.

[0017] Examples of the parameter information regarding structural design include information regarding the case structure, belt angle, cord twist structure, thickness of each part of the tire, tread rigidity, etc. The thickness of each part of the tire is, for example, the thickness of the tread part at the tire equator, the thickness of the shoulder part, the thickness of the sidewall part at the maximum width position of the tire, the maximum thickness of the bead part, etc. Tread rigidity is the rigidity in the front-rear, lateral, and longitudinal directions of the tread part with various patterns applied.

[0018] Examples of the parameter information regarding rubber compounding include information regarding the complex elastic modulus of the rubber constituting each part of the tire, the presence or absence of a filler, etc.

[0019] Note that the above parameter information is illustrative, and the design parameter information obtained in the first step S1 is not limited to these.

[0020] The design parameter information is stored in the above storage device or the like by being input into the computer 1 shown in FIG. 1. That is, the computer 1 acquires the design parameter information of a plurality of tires with different sizes.

[0021] The design parameter information may be stored in a storage device or the like of another computer that is connected to Computer 1 by wire or wirelessly. The design parameter information input to Computer 1 is used in the fifth step S5 described later.

[0022] In the second step S2, as the dynamic performance of the tire, the measured values of the responses to the measurement conditions that change over time are acquired. The tires for which the dynamic performance is measured are a plurality of tires having different sizes for which the design parameter information was acquired in the first step S1, but may include a plurality of tires having the same size but different structures, patterns, and rubber compounds. In the second step S2, a plurality of dynamic performances are measured for each of the tires.

[0023] In the second step S2, for example, the dynamic performance of a tire that is incorporated in a standard rim, filled with a standard internal pressure, and loaded with a standard load is acquired.

[0024] The "standard rim" is the rim defined for each tire in a standard system including the standard on which the tire is based. For example, in the case of JATMA, it is the "standard rim", in the case of TRA, it is the "Design Rim", and in the case of ETRTO, it is the "Measuring Rim".

[0025] The "standard internal pressure" is the air pressure defined for each tire in a standard system including the standard on which the tire is based. In the case of JATMA, it is the "maximum air pressure", in the case of TRA, it is the maximum value described in the table "TIRE LOAD LIMITS AT VARIOUS COLD INFLATION PRESSURES", and in the case of ETRTO, it is the "INFLATION PRESSURE". When the tire is for a passenger car, the standard internal pressure is, for example, 180 kPa.

[0026] The "normal load" refers to the load defined for each tire in a standard system including the standards on which the tire is based. In the case of JATMA, it is the "maximum load capacity"; in the case of TRA, it is the maximum value described in the table "TIRE LOAD LIMITS AT VARIOUS COLD INFLATION PRESSURES"; and in the case of ETRTO, it is the "LOAD CAPACITY". When the tire is for a passenger car, the normal load is, for example, a load corresponding to 88% of the above load.

[0027] In the second step S2, the dynamic performance of a tire incorporated in a rim different from the normal rim may be obtained. Also, in the second step S2, the dynamic performance of a tire filled with an internal pressure different from the normal internal pressure may be obtained. Further, in the second step S2, the dynamic performance of a tire loaded with a load different from the normal load may be obtained.

[0028] What is actually measured in the second step S2 is the response of the dynamic performance of the tire to measurement conditions that change over time. Examples of measurement conditions that change over time include, for example, load, slip angle, camber angle, slip ratio, speed, etc. Examples of the above response include cornering force, cornering power, self-aligning torque, camber thrust, braking force (driving force), etc.

[0029] In the case of some tires where the calculation of dynamic performance has already been executed by simulation or the like, the calculated values may be included in the actually measured values obtained in the second step S2. In this case, the plurality of tires from which the design parameter information is obtained in the first step S1 includes tires that do not actually exist.

[0030] The information on the actually measured dynamic performance, like the above design parameter information, is input into the computer 1 shown in FIG. 1 and then stored in the storage device or the like. The information on the dynamic performance input into the computer 1 is used in the fourth step S4 described later.

[0031] In the third step S3, an approximation function showing the relationship between the measurement conditions that change over time in the second step S2 and the actually measured response is prepared. The approximation function prepared in the third step S3 includes a plurality of coefficients.

[0032] Each coefficient is different for each tire. And at the stage of the third step S3, each coefficient is an unknown number that has not been specified. A specific example of the approximation function will be described later.

[0033] In the fourth step S4, by performing regression analysis on the approximation function for each tire, the coefficients included in the approximation function are specified. For the regression analysis of the present embodiment, a method such as the least squares method is applied.

[0034] In the fourth step S4, for each of the plurality of tires, based on the actually measured values measured in the second step S2, the approximation function prepared in the third step S3 is subjected to regression analysis. As a result, the above coefficients of the approximation function are specified.

[0035] In the fifth step S5, a learning model for calculating an approximation function for the tire to be predicted is generated.

[0036] FIG. 3 shows an example of the learning model generated in the fifth step S5. The learning model 20 has the design parameter information (explanatory variables) 21a, 21b, 21c... 21n obtained in the first step S1 as the input layer 21, and the coefficients (output variables) 23a, 23b, 23c specified in the fourth step S4 as the output layer 23, and is defined by an intermediate layer 22 generated by machine learning.

[0037] The intermediate layer 22 includes a combination of a plurality of neurons (nodes) 24 hierarchically layered in multiple stages and a plurality of optimized weighting coefficients 25 (parameters). Each neuron 24 is connected by a weighting coefficient 25. Such an intermediate layer 22 is called a neural network. That is, the learning model 20 of the present embodiment includes a neural network.

[0038] In the sixth step S6, the coefficients of the approximation function for the tire to be predicted are calculated.

[0039] FIG. 4 shows the sixth step S6 of calculating the coefficients of the approximation function using the learning model 20.

[0040] In the sixth step S6, by inputting the design parameter information 21o, 21p, 21q... 21z of the tire to be predicted into the input layer 21 of the learning model 20 generated in the fifth step S5, the coefficients 23x, 23y, 23z of the approximation function for the tire to be predicted are output from the output layer 23, and the approximation function for the tire to be predicted is specified.

[0041] Then, in the seventh step S7, the dynamic performance of the tire to be predicted is predicted. In the seventh step S7, the dynamic performance of the tire to be predicted is predicted using the approximation function including the coefficients 23x, 23y, 23z calculated in the sixth step.

[0042] In the seventh step S7, if good dynamic performance is not predicted, that is, if the predicted dynamic performance does not reach the target value, return to the sixth step S6, change the design parameter information 21o, 21p, 21q... 21z of the input layer 21, and re-specify the coefficients 23x, 23y, 23z of the approximation function. Then, in the seventh step S7, the dynamic performance of the tire to be predicted is predicted again. In this way, it is desirable to repeatedly execute the sixth step S6 and the seventh step S7 until good dynamic performance is obtained.

[0043] Since the prediction method of the present invention has the above configuration, it is possible to accurately predict the dynamic performance of the tire in a short time without complicated calculations using the finite element method.

[0044] As already described, the measurement conditions that change over time in the second step S2 may include the load applied to the tire.

[0045] FIG. 5 shows the transition of the cornering power: CP when the load: W applied to a certain tire is changed, with the horizontal axis being W and the vertical axis being CP / W.

[0046] According to this figure, in the region where the slope of CP / W is near zero, the change in CP / W with respect to the change in the load W becomes small. From this, it is expected that by optimizing the range of the load W, the change in CP / W can be suppressed and the approximation accuracy can be improved.

[0047] That is, as a result of intensive research, the inventor of the present application has found that in the fourth step S4, when performing regression analysis on the approximation function, the range of the load W has an impact. And when verifying a plurality of tires with different sizes, it was confirmed that good approximation accuracy can be obtained by setting the minimum load (the load at which the approximation formula starts) for performing regression analysis to 25% of the maximum load represented by the load index.

[0048] That is, when the load applied to the tire includes the load that changes over time in the measurement conditions in the second step S2, the range of the load when performing regression analysis on the approximation function in the fourth step S4 is preferably determined based on the maximum load represented by the load index, and it was confirmed that it is desirable that the above range is 25% or more of the maximum load.

[0049] The "maximum load represented by the load index" is the maximum load determined for each tire in the standard system including the standard on which the tire is based. For JATMA, it is the "maximum load capacity", for TRA, it is the maximum value described in the table "TIRE LOAD LIMITS AT VARIOUS COLD INFLATION PRESSURES", and for ETRTO, it is the "LOAD CAPACITY".

[0050] As an example of the approximation function prepared in the third step S3, when D, C, B, and E are coefficients, the measurement condition is the slip parameter x, and the force or moment obtained for the dynamic performance of the tire is y, the general form of the magic formula represented by the following formula (1) can be mentioned. y = D·sin { C·arctan [ Bx - E·(Bx - arctan (Bx)) ]} (1)

[0051] In this case, in the fourth step S4, the coefficients D, C, B, and E in the above formula (1) are specified.

[0052] As another example of the approximation function prepared in the third step S3, when α, β, and C are coefficients, the measurement condition is the load W, and the dynamic performance of the tire is the cornering power CP, the following formula (2) can be cited. CP / W = α·cos { β·arctan (W - C)) (2)

[0053] In this case, in the fourth step S4, the coefficients α, β, and C in the above formula (2) are specified. By using formula (2), the number of coefficients is reduced and the specification thereof becomes easier.

[0054] As described above, although the prediction method 100 of the present invention has been described in detail, the present invention is not limited to the above specific embodiments and can be implemented in various modes.

Example

[0055] Using the prediction method of the present invention, the cornering power when the load was continuously changed from 1 kN to 9 kN for a pneumatic tire of size 235 / 55R18 was calculated.

[0056] As a comparative example, the cornering power of the same tire when the load was 3 kN, 5 kN, 7 kN, and 9 kN was calculated using the finite element method.

[0057] In FIG. 6, as an example, the cornering power calculated using the prediction method of the present invention is shown by a solid line. Also, as a comparative example, the cornering power calculated using the finite element method is shown by a triangular mark.

[0058] As is clear from FIG. 6, the prediction method of the example obtained almost the same calculation results as the comparative example. Particularly regarding the sagging of the cornering power in the high load region, the calculation results of the same tendency as the comparative example were obtained.

[0059] Note that, while the time required for the calculation of the comparative example was about 6 hours, the time required for the calculation of the example was less than 1 minute. According to the prediction method of the present invention, it was confirmed that it is possible to accurately predict the dynamic performance of the tire in a short time without complicated calculations.

[0060] [Appendix] The present invention includes the following aspects.

[0061] [Invention 1] A method for predicting the dynamic performance of a tire, a first step of obtaining a plurality of design parameter information from a plurality of tires having different sizes; a second step of obtaining, as the dynamic performance of the plurality of tires, measured values of respective responses of the plurality of tires to measurement conditions that change over time; a third step of preparing an approximate function showing the relationship between the measurement conditions and the response, the approximate function including a plurality of coefficients; a fourth step of specifying the coefficients of the approximate function by performing regression analysis on the approximate function based on the measured values for each of the plurality of tires; a fifth step of generating a learning model having the design parameter information obtained in the first step as an input layer and the coefficients specified in the fourth step as an output layer; a sixth step of inputting the design parameter information of a tire to be predicted into the learning model and calculating the coefficients of the approximate function for the tire to be predicted; and a seventh step of predicting the dynamic performance of the tire to be predicted using the approximate function including the coefficients calculated in the sixth step. Prediction method. [Invention 2] The prediction method according to Invention 1, wherein the measurement conditions include the load applied to the tire. [Invention 3] The prediction method according to Invention 2, wherein the range of the load is determined based on the maximum load represented by the load index. [Invention 4] The prediction method according to any one of Inventions 1 to 3, wherein the measurement conditions include the slip angle of the tire. [Invention 5] The prediction method according to any one of Inventions 1 to 4, wherein the measurement conditions include the slip ratio of the tire. [Invention 6] The prediction method according to any one of Inventions 1 to 5, wherein the measurement conditions include the camber angle of the tire. [Invention 7] The prediction method according to any one of Inventions 1 to 6, wherein the measurement conditions include the speed of the tire. [Invention 8] When the approximation function has coefficients D, C, B, and E, the measurement conditions as the slip parameter x, and the dynamic performance as the obtained force or moment y, it is in the general form of the Magic Formula y = D·sin { C·arctan [ Bx - E·(Bx - arctan (Bx)) ]} The prediction method according to any one of Inventions 1 to 7, which is represented by [Invention 9] When the approximation function has the coefficients α, β, and C, the measurement conditions as the load W, and the dynamic performance as the cornering power CP, CP / W = α·cos { β·arctan (W - C)) The prediction method according to any one of Inventions 1 to 7, which is represented by

Explanation of Signs

[0062] 20: Learning model 21: Input layer 21o: Design parameter information 21p: Design parameter information 23: Output layer 23x: Coefficient 23y: Coefficient 23z: Coefficient 100: Prediction method S1: First step S2: Second step S3: Third step S4: Fourth step S5: Fifth step S6: Sixth step S7: Seventh step

Claims

1. A method for predicting the dynamic performance of a tire, comprising: a first step of obtaining a plurality of design parameter information from a plurality of tires having different sizes; a second step of obtaining, as the dynamic performance of the plurality of tires, measured values of respective responses of the plurality of tires to measurement conditions that change over time; a third step of preparing an approximate function that represents the relationship between the measurement conditions and the response, the approximate function including a plurality of coefficients; a fourth step of specifying the coefficients of the approximate function by performing regression analysis on the approximate function for each of the plurality of tires based on the measured values; a fifth step of generating a learning model having the design parameter information obtained in the first step as an input layer and the coefficients specified in the fourth step as an output layer; a sixth step of inputting the design parameter information of a tire to be predicted into the learning model and calculating the coefficients of the approximate function for the tire to be predicted; a seventh step of predicting the dynamic performance of the tire to be predicted using the approximate function including the coefficients calculated in the sixth step. A prediction method.

2. The prediction method according to claim 1, wherein the measurement conditions include the load applied to the tire.

3. The prediction method according to claim 2, wherein the range of the load is determined based on the maximum load represented by a load index.

4. The prediction method according to claim 1, wherein the measurement conditions include the slip angle of the tire.

5. The prediction method according to claim 1, wherein the measurement conditions include the slip ratio of the tire.

6. The prediction method according to claim 1, wherein the measurement conditions include the camber angle of the tire.

7. The prediction method according to claim 1, wherein the measurement conditions include the speed of the tire.

8. When the approximate function has D, C, B, and E as the coefficients, the measurement conditions as the slip parameter x, and the dynamic performance as the obtained force or moment y, the approximate function is in the general form of the Magic Formula y = D·sin { C·arctan [ Bx - E·( Bx - arctan ( Bx ) ) ]} The prediction method according to claim 1, represented by.

9. When the approximate function has α, β, and C as the coefficients, the measurement conditions as the load W, and the dynamic performance as the cornering power CP, CP / W = α・cos { β・arctan ( W - C ) ) The prediction method according to claim 1, which is represented by

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