JP2018077064A - Friction characteristic prediction method of tire - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide friction characteristic prediction method of a tire.SOLUTION: Coefficient of static friction of the tire and coefficient of kinetic friction of the tire are predicted from coefficient of static friction of a rubber and coefficient of kinetic friction of a rubber. The first measurement step 1 measures the coefficient of static friction of the tire and the coefficient of kinetic friction of the tire by using a plurality of reference tires and measures the coefficient of static friction of the rubber and the coefficient of kinetic friction of the rubber by using its rubber sample. The second prediction style of creation step 2 performs regression analysis about its measured data and creates prediction style 1a putting the coefficient of the static friction of the tire as explanatory variable, the coefficient of static friction of the rubber as objective variable, prediction style 1b putting the coefficient of kinetic friction of the tire as explanatory variable, and the coefficient of the kinetic friction of the rubber as objective variable. The second measurement step 3 measures the coefficient of static friction of the rubber and the coefficient of kinetic friction of the rubber by using the test sample. The prediction step 4 predicts the coefficient of static friction and the coefficient of kinetic friction of the virtual tire on the basis of the prediction styles 1a, 1b.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a tire friction characteristic prediction method that can predict at least a static friction coefficient and a dynamic friction coefficient in a virtual tire when the rubber is used as a tread rubber from a static friction coefficient and a dynamic friction coefficient of rubber.

  In order to improve the clip performance of tires, various researches and developments have been made on tread rubber having excellent friction characteristics. For the developed rubber, it is necessary to evaluate the performance when used for tires.

  Therefore, in the past, a tire using the developed rubber as a tread rubber was prototyped, and the friction coefficient, μ-S characteristics, etc. of this prototype tire were put on an actual road surface (reference road surface) using, for example, a traction vehicle. Is measured.

  However, such a conventional method has a problem in that the efficiency of research and development is inferior because much cost and time are required for manufacturing and testing of the prototype tire.

  The following Patent Document 1 describes a friction test apparatus and a measurement method for measuring a friction coefficient of a rubber sample. However, estimating the friction coefficient of a tire from the friction coefficient of a rubber sample has not been done in the past, and is insufficient for improving the efficiency of research and development.

  Non-Patent Document 1 below describes a tire μ-S characteristic equation in equation (7.7.25) on page 189. However, since the friction coefficient μd of the rubber in the above formula does not take into consideration factors such as the road surface condition and the contour shape of the tire, a deviation from the actual μ-S characteristic occurs, and the evaluation accuracy of the tire friction characteristic is improved. It ’s difficult.

JP2015-107769A Hideo Sakai, “From Introduction to Application of Tire Engineering”, Grand Prix Publishing, Inc., 1987

  The present invention can predict a static friction coefficient and a dynamic friction coefficient of a virtual tire from a static friction coefficient and a dynamic friction coefficient of rubber without making a tire prototype, and can predict the friction characteristics of a tire that can greatly contribute to the efficiency of tire research and development. It is an issue to provide.

The present invention is a tire friction characteristic prediction method for predicting at least a static friction coefficient and a dynamic friction coefficient in a virtual tire when the rubber is employed in a tread rubber from a static friction coefficient and a dynamic friction coefficient of rubber,
Except the physical properties of the tread rubber by using a reference tire T _A a plurality of forming the same structure, measured tire static friction coefficient .mu.s _A and the tire dynamic friction coefficient [mu] d _A at least the reference road surface, and the tread rubber of the reference tire T _{A A} first measurement step of measuring a rubber static friction coefficient μrs _A and a rubber dynamic friction coefficient μrd _A on at least a reference road surface using the rubber sample GA;
The tire static friction coefficient μs _A measurement data and the rubber static friction coefficient μrs _A measurement data are subjected to regression analysis, the tire static friction coefficient μs is an explanatory variable, and the prediction formula (1a) having the rubber static friction coefficient μrs as an objective variable, and the tire Regression analysis of dynamic friction coefficient μd _A measurement data and rubber dynamic friction coefficient μrd _A measurement data to create a prediction formula (1b) that uses tire dynamic friction coefficient μd as an explanatory variable and rubber dynamic friction coefficient μrd as an objective variable Process,
The reference to the tread rubber of the tire T _A by using the test sample G _B of different rubber, the second measurement step of measuring a rubber static friction coefficient Myurs _B and rubber dynamic friction coefficient Myurd _B at least in the reference road surface,
In addition, based on the prediction equations (1a) and (1b), the friction for predicting the tire static friction coefficient μs _B and the tire dynamic friction coefficient μd _{B of} the virtual tire from the rubber static friction coefficient μrs _B and the rubber dynamic friction coefficient μrd _B. It is characterized by comprising a prediction step including coefficient prediction.

In the tire friction characteristic prediction method according to the present invention, the prediction formulas (1a) and (1b) are preferably linear function regression equations.
μs = α ₁ × μrs + α ₂ −−− (1a)
μd = β ₁ × μ rd + β ₂ −−− (1b)
(Α ₁ , α ₂ , β ₁ , β ₂ are regression coefficients.)

The frictional characteristic estimation method of a tire according to the present invention, the first measuring step, the reference tire at the ground measurements width W and the contact length L and the ground pressure P, the reference road surface of the reference tire T _A in the ground contact load Fz measurement of mu-S characteristic of T _a, and includes a measurement of the Young's modulus _{E a} of the rubber sample _{G a,}
And said second measuring step preferably includes the measurement of Young's modulus E _B of the test sample G _B.

The frictional characteristic estimation method of a tire according to the present invention, the prediction expression creation process is based on the following equation (2), along with determining the lateral elastic constant Cx _A rubber block of the reference tire T _A, the lateral elastic constant Cx and _a, and data regression analysis of Young modulus E _a of the rubber sample G _a, the lateral elastic constant Cx explanatory variables, the step of creating prediction equation of the power function aimed variable Young's modulus E (3) It is preferable to contain.
Kx _A = (1/2) x Cx _A x W x L ^2- (2)
Cx = C1 × E ⁿ --- (3)
(Kx _A is blade Kings stiffness of the reference tire _{T A,} C1, n are regression coefficients.)

The frictional characteristic estimation method of a tire according to the present invention, the prediction process is based on the prediction equation (3), the Young's modulus E _B in the test sample G _B together determine the lateral elastic constant Cx _B of the rubber blocks of the virtual tire From the transverse elastic constant Cx _B , the tire static friction coefficient μs _{B of} the virtual tire obtained by the prediction of the friction coefficient, and the tire dynamic friction coefficient μd _B , the virtual tire is calculated based on the following equations (4) and (5). It is preferable to include the μ-S characteristic prediction for predicting the μ-S characteristic.
μs _B × P = Cx _B × S × Lh (4)
_{μ = Cx B × S × W} × Lh 2 / (2 × Fz)
^{_{+ 20 × μd B / (L}} 5) × [(L / 2) 4 × (L-Lh) - (1/5) {(L / 2) 5 - (Lh-L / 2) 5}]
---- (5)
(S is the slip ratio, and Lh is the length from the contact start point to the slip start point at which the contact surface starts to slide from the road surface on the tire contact surface during braking.)

  The present invention can predict the static friction coefficient and the dynamic friction coefficient of the virtual tire from the static friction coefficient and the dynamic friction coefficient of the rubber for the reason described in the column of “Mode for Carrying Out the Invention” below. Can greatly contribute to the improvement of efficiency.

It is a flowchart which shows the friction characteristic prediction method of the tire of this invention. (A), (B) is a graph which shows an example of prediction formula (1a) by the prediction formula creation process, and (1b). It is a graph of the mu-S curve illustrating the blade Kings stiffness Kx _A. It is a graph which shows an example of prediction formula (3) by a prediction formula creation process. (A) is a prediction result of the μ-S characteristic of the virtual tire, and (B) is a graph showing the μ-S characteristic of the actual tire. It is a graph which illustrates the correlation with the μ-S characteristic of a virtual tire, and the μ-S characteristic of an actual tire. It is a graph which illustrates the correlation with the μ-S characteristic of a virtual tire, and the μ-S characteristic of an actual tire in a comparative example. It is drawing explaining Lh in Formula (4), (5).

Hereinafter, embodiments of the present invention will be described in detail.
As shown in FIG. 1, the tire friction characteristic prediction method of the present embodiment includes a first measurement step 1, a prediction formula creation step 2, a second measurement step 3, and a prediction step 4. Thus, predicting the static friction coefficient of the rubber μrs and dynamic coefficients of friction Myurd, at least the static friction coefficient .mu.s _B in the virtual tire T _B in the case of adopting the rubber tread _rubber, and a dynamic friction coefficient [mu] d _B.

In this embodiment, the frictional characteristic estimation method, the test sample G _B of the rubber, as well as predicting the coefficient of static friction .mu.s _B and the dynamic friction coefficient [mu] d _B of the virtual tire T _B, based on the prediction result, further virtual tire T _B The case where the μ-S characteristic is predicted is shown.

The first measuring step 1 and prediction expression creation step 2 is a preparatory stage for the prediction step 4, the first measuring step 1, the tread rubber of the plurality of reference tire T _A, and the reference tire T _A It is carried out with the rubber sample G _a. As the number of reference tire _{T A} (m), 5 or more, more preferably 10 or more.

Each reference tire T _A, except the physical properties of the tread rubber respectively constituting the same structure. Further, the rubber samples G _A is the rubber of the tread rubber having the same composition of the reference tire T _A can be formed by vulcanizing molding, may be formed by cutting directly from the reference tire T _A.

The first measurement process 1 includes tire measurement and rubber sample measurement. In the tire measured at a reference road surface, measuring at least tire static friction coefficient .mu.s _A and the tire dynamic friction coefficient [mu] d _A reference tire T _A.

In this example, because the mu-S characteristic estimation, the ground contact load Fz, as well as measuring the ground contact width W of the reference tire T _A and the ground length L and a contact pressure P, the reference road surface, the reference tire T _A mu -Measure S characteristics.

Also in the rubber sample measurement, the reference road surface, measuring at least rubber static friction coefficient Myurs _A and rubber dynamic friction coefficient Myurd _A rubber sample G _A. In this example, because the mu-S characteristic estimation, further measuring the Young's modulus E _A rubber sample G _A.

  Here, the “reference road surface” is a surface serving as a reference for evaluating the friction performance of the tire, and can be individually defined by those skilled in the art. The reference road surface includes, for example, an actual road surface such as an asphalt road surface, a road surface obtained by cutting out a part thereof, or an approximated simulated road surface. If the surface property is substantially the same, an actual road surface may be used for tire measurement and a simulated road surface may be used for rubber sample measurement.

Reference tire T _A tire static friction coefficient at .mu.s _A, the measurement of the tire dynamic friction coefficient [mu] d _A, and mu-S characteristics, for example a traction vehicle (Ltd. T & Tea, Inc., etc.) commercially available measuring device suitably employed such as Yes. Reference tire T _A ground contact load in Fz, ground contact width W, and the contact length L, the measurement of the contact pressure P, for example a commercially available measuring device such as a tire contact surface analyzer (XSensor Technology Corporation Ltd. X3) is preferably employed sell. The measurement of the rubber static friction coefficient Myurs _A and rubber dynamic friction coefficient Myurd _A in the rubber samples G _A, for example a commercially available measuring device such as a dynamic friction tester (Nipposangyo Co., Ltd.) can be preferably employed. Also the measurement of Young's modulus E _A in the rubber samples G _A, for example rubber viscoelasticity measuring machine (Iwamoto Seisakusho viscoelasticity spectrometer) commercially available measuring device such as may be suitably used. Note that although the size of the rubber samples G _A, is not particularly restricted, is set to the measuring apparatus in accordance with the defined size of the.

  Next, the prediction formula creation step 2 includes creation of a prediction formula (1a) and creation of a prediction formula (1b).

In the preparation of the prediction formula (1a), the tire static friction coefficient μs _A measurement data and the rubber static friction coefficient μrs _A measurement data are subjected to regression analysis, the tire static friction coefficient μs is an explanatory variable, and the rubber static friction coefficient μrs is an objective variable. The regression equation to be created is created as the prediction equation (1a).

Specifically, in the first measurement step 1, m tire static friction coefficient μs _A measurement data (μs _A1 , μs _A2 ,... Μs _Am ) and m rubber static friction coefficient μrs _A measurement data. (Μrs _A1 , μrs _A2 ,... Μrs _Am ). Then, this is subjected to regression analysis, and as shown in FIG. 2A, a regression equation having the tire static friction coefficient μs as an explanatory variable and the rubber static friction coefficient μrs as an objective variable is obtained by a least square method. In this example, regression analysis is performed using a linear function as a model formula based on the correlation between the explanatory variable and the objective variable.
μs = α ₁ × μrs + α ₂ −−− (1a)
(Α ₁ and α ₂ are regression coefficients.)

Similarly, in the creation of the prediction formula (1b), the regression analysis is performed on the measurement data of the tire dynamic friction coefficient μd _{A and} the measurement data of the rubber dynamic friction coefficient μrd _A , the tire dynamic friction coefficient μd is the explanatory variable, and the rubber dynamic friction coefficient μrd is the purpose. A regression equation as a variable is created as a prediction equation (1b).

Specifically, in the first measurement step 1, m tire dynamic friction coefficient μd _A measurement data (μd _A1 , μd _A2 ,... Μd _Am ) and m rubber dynamic friction coefficient μrd _A measurement data are measured. (Μrd _A1 , μrd _A2 ,... Μrd _Am ). Then, this is subjected to regression analysis, and as shown in FIG. 2B, a regression equation having the tire dynamic friction coefficient μd as an explanatory variable and the rubber dynamic friction coefficient μrd as an objective variable is obtained by a least square method. In this example, regression analysis is performed using a linear function as a model formula based on the correlation between the explanatory variable and the objective variable.
μd = β ₁ × μ rd + β ₂ −−− (1b)
(Β ₁ and β ₂ are regression coefficients.)

Next, in the second measuring step 3, the tread rubber of the reference tire T _A by using the test sample G _B of different rubber to measure the rubber static friction coefficient Myurs _B and rubber dynamic friction coefficient Myurd _B at least in the reference road surface . This measurement, as in the case of the rubber sample G _A, for example, a traction wheel (Ltd. T & Tea, Inc., etc.) commercially available measuring device such as may be suitably used. Also in this embodiment, for the mu-S characteristic prediction, Young's modulus E _B in the test sample G _B is further measured. This measurement, as in the case of the rubber sample G _A, for example, rubber viscoelasticity measuring machine (Iwamoto Seisakusho viscoelasticity spectrometer) commercially available measuring device such as may be suitably used.

In the first and second measurement steps 1 and 3, the rubber dynamic friction coefficients μrd _{A and} μrd _B are measured at the same sliding speed. The sliding speed is preferably in the range of 3.0 to 10.0 km / h. Note that the prediction formula (1b) varies depending on the sliding speed.

Next, the prediction step 4, the prediction equation (1a), based on (1b), and a rubber static friction coefficient Myurs _B and rubber dynamic friction coefficient Myurd _B, and tire coefficient of static friction of the virtual tire T _B .mu.s _B, the tire dynamic friction coefficient Friction coefficient prediction to predict μd _B is included.

Specifically, the value of the rubber static friction coefficient Myurs _B test samples G _B measured in the second measurement step 3 is substituted into the prediction equation (1a). This makes it possible to calculate the predicted value of the tire static friction coefficient .mu.s _B of the virtual tire T _B. Similarly, the value of the rubber dynamic friction coefficient Myurd _B test samples G _B measured in the second measurement step 3 is substituted into the prediction equation (1b). This makes it possible to calculate the predicted values of the tire dynamic friction coefficient [mu] d _B of the virtual tire T _B.

Then, the predicted value .mu.s _B, based on [mu] d _B, illustrating a case where further predicting mu-S characteristics of the virtual tire T _B. In this example, the prediction formula creation step 2 includes a step of creating a prediction formula (3) for μ-S characteristic prediction.
Kx _A = (1/2) x Cx _A x W x L ^2- (2)
Cx = C1 × E ⁿ --- (3)

Specifically, the prediction formula (3) is created as follows. Based on the above formula (2), determine the lateral elastic constant Cx _A rubber block of the reference tire T _A. Then, the the lateral elastic constant Cx _A, and data of the Young's modulus E _A rubber sample G _A regression analysis, lateral elastic constant Cx explanatory variables, the prediction type of the power function of the Young's modulus E and the objective variable ( Create 3).

Here, the code Kx _A in formula (2), a brake Kings stiffness of the reference tire T _A, Equation (2) corresponds to Formula (7.5.10) described in Non-Patent Document 1 described above. W is a grounding width W, and L is a grounding length L. The brake Kings stiffness Kx _A, as illustrated in FIG. 3, obtained as the gradient of the slip ratio S = 0 in the mu-S curve of the reference tire _A measured by the first measuring step 1. Therefore, in this example, m pieces of braking stiffness Kx _A data (Kx _A1 , Kx _A2 ,... Kx _Am ) are obtained, and m pieces of transverse elastic constant Cx _A data ( Cx _A1 , Cx _A2 ,... Cx _Am ) are obtained. Note each reference tire T _A is the ground-contacting tread width W, the contact length L may be substituted by measurements of substantially equal, therefore a reference tire T _A.

Then, as illustrated in FIG. 4, m pieces of transverse elastic constant Cx _A data (Cx _A1 , Cx _A2 ,... Cx _Am ) and m pieces of Young modulus E _A data (E _A1 , E _A2). ,... E _Am ), and a regression equation having the transverse elastic constant Cx as an explanatory variable and the Young's modulus E as an objective variable can be obtained by the least square method. In this example, regression analysis is performed using a power function as a model formula based on the correlation between the explanatory variable and the objective variable.

Next, the prediction process 4 of this example includes μ-S characteristic prediction. This mu-S characteristics predicted based on the prediction equation (3), the Young's modulus E _B in the test sample G _B, obtains the lateral elastic constant Cx _B of the rubber blocks of the virtual tire T _B.

Then, the lateral elastic constant Cx _B, and the tire static friction coefficient .mu.s _B of the virtual tire T _B previously determined by the friction coefficient prediction, and a tire dynamic friction coefficient [mu] d _B, the following equation (4), based on (5) , to predict the mu-S characteristics of the virtual tire _{T B.}
μs _B × P = Cx _B × S × Lh (4)
_{μ = Cx B × S × W} × Lh 2 / (2 × Fz)
^{_{+ 20 × μd B / (L}} 5) × [(L / 2) 4 × (L-Lh) - (1/5) {(L / 2) 5 - (Lh-L / 2) 5}]
---- (5)

Specifically, the relationship between the slip ratio S and Lh is shown from the equation (4). That is, Lh is obtained as a function f (S) of the slip ratio S. The tire static friction coefficient μs _B in the equation (4) is obtained by the friction coefficient prediction in the prediction step 4. The ground pressure P is determined by the first measurement process 1. The transverse elastic constant Cx _B is obtained from the above equation (3).

  As shown in FIG. 8, on the tire contact surface at the time of braking at the slip angle 0, the region y1 from the contact start point j1 to a certain point jh adheres to the ground only by deformation of the rubber without slipping. Further, it is considered that slip occurs in the region y2 on the rear side from the point jh. A length from the ground contact start point j1 to the point jh (slip start point jh at which slip starts) is defined as Lh. Equation (3) corresponds to Equation (7.7.8) described in Non-Patent Document 1 described above.

And in the formula (5) by replacing Lh function f (S) of the slip ratio S, it is possible to sell mu-S characteristics of the virtual tire T _B. The transverse elastic constant Cx _B in the equation (5) is obtained from the equation (3). The grounding width W, the grounding load Fz, and the grounding length are obtained by the first measurement process 1. The tire dynamic friction coefficient μd _B is obtained by the friction coefficient prediction in the prediction step 4.

Equation (5) corresponds to the equation (7.7.25) described in Non-Patent Document 1 described above, where n = 4 and divided by the contact load Fz, and the contact pressure distribution is expressed by a quaternary equation (n = 4). This corresponds to the friction coefficient of the sliding region when approximated by. Note difference from the equation (7.7.25) of the Non-Patent Document 1, as a dynamic friction coefficient, rather than the dynamic friction coefficient of the rubber, due to its use of dynamic friction coefficient of the virtual tire T _B predicted by the prediction equation (1b), and in the formula (4), as the static friction coefficient, rather than the static friction coefficient of the rubber, in adopting the static friction coefficient of the virtual tire T _B predicted by the prediction equation (1a). Also the predicted value of the static friction coefficient and dynamic friction coefficient of the virtual tire T _B, the surface of the elements of the road surface is taken into account. Therefore, the prediction accuracy of static friction coefficient and dynamic friction coefficient and the prediction accuracy of μ-S characteristics are improved.

  As mentioned above, although especially preferable embodiment of this invention was described in full detail, this invention can be put into embodiment of illustration. Without being limited, the present invention can be carried out with various modifications.

In order to confirm the effect of the present invention, tires T ₀ and T _{6 to} T ₁₀ (tire size 195 / 65R15) in which rubbers G _{0 and} G _{6 to} G ₁₀ having the rubber compositions shown in Table 1 are adopted as tread rubbers were manufactured as trial products. . The tires T ₀ and T _{6 to} T ₁₀ are the same except for the rubber composition of the tread rubber.

Then, in accordance with the friction characteristic estimation method of the present invention, a tire using the rubber G ₀ as a reference tire T _A, to create a prediction equation. The use of the prediction equation, the prediction of a rubber _G 6 _{~G 10,} a mu-S characteristics of the virtual tire _T B6 _{through T B10} (a hypothetical tire and employing the rubber _G 6 _{~G 10} in the tread rubber.) did. The prediction result of the μ-S characteristics of the virtual tires T _{B6 to} TB ₁₀ is shown in FIG. And it compared with the μ-S characteristic of actual tires T _{J6 to} T _J10 in which rubbers G _{6 to} G ₁₀ were actually adopted as tread rubber. The μ-S characteristics of actual tires T _{J6 to} T _J10 are shown in FIG.

FIG. 6 shows the maximum value (μs peak expected value) μmax of each μ-S characteristic in virtual tires T _{B6 to} T _B10 and the maximum value (μs peak actually measured value) of each μ-S characteristic in actual tires T _{J6 to} T _J10 . ) Shows the relationship with μmax. The correlation coefficient was 0.9 or more, and it was confirmed that the μ-S characteristic of the virtual tire can be accurately predicted from the rubber sample by the friction characteristic prediction method of the present invention.

Details of the friction characteristic prediction will be described below.
<First and second measurement steps>
(1) The tire static friction coefficient μs, tire dynamic friction coefficient μd, and μ-S characteristics of each tire were measured using a traction vehicle (manufactured by T & T Corporation).
The measurement conditions are as follows.
Tire size: 195 / 65R15
Rim size: 15x6J
Reference road surface (measurement road surface): wet asphalt road speed: 65km / h
Load: 5kN
Temperature: 25 degrees (2) The contact load Fz, contact width W, contact length L, and contact pressure P of each tire were measured using a tire contact surface analyzer (X3 manufactured by XSENSOR Technology Corporation).
(3) The rubber static friction coefficient μrs and the rubber dynamic friction coefficient μrd of each rubber sample were measured using a dynamic friction tester S type (manufactured by Nihon Sangyo Co., Ltd.).
The measurement conditions are as follows.
Reference road surface (measurement road surface): Cut out wet asphalt road surface (Extracted road surface size: 30cm x 30cm x 5cm
Sliding speed: 0-16km / h
Load: 2kN
Temperature: 25 degrees (4) Young's modulus E of rubber sample is a rubber viscoelasticity measuring machine (Viscoelasticity spectrometer manufactured by Iwamoto Seisakusho)
The measurement conditions are as follows.
Elongation rate: 5%
Frequency: 10Hz
Measurement temperature: 25 ° C

For comparison, the lateral elastic constant Cx of the rubber block is assumed to be a linear function of the Young's modulus E of the rubber instead of the formula (3), and the virtual tire μ is calculated using the formulas (4) and (5). -S characteristics were predicted. FIG. 7 shows the maximum value (μs peak expected value) μmax of each μ-S characteristic in the virtual tires T _{B6 to} T _{B10 and} the maximum value (μs peak) of each μ-S characteristic in the actual tires T _{J6 to} T _J10 . The relationship with (measured value) μmax was shown. The correlation coefficient is 0.8, which is inferior in prediction accuracy as compared with the case of Equation (3).

The chemicals in the table are as follows.
Styrene butadiene rubber (SBR): E15 manufactured by Asahi Kasei Chemicals Corporation
Silica: Ultrasil VN3 manufactured by Degussa
Carbon: Carbon black N326 manufactured by Showa Cabot Co., Ltd.
Oil: Process X-260 (aromatic oil) manufactured by Japan Energy Co., Ltd.
Coupling agent: Tetrasulfidesilane (Si69) manufactured by Degussa
Stearic acid: Stearic acid “Kashiwa” manufactured by Nippon Oil & Fats Co., Ltd.
Zinc oxide: Zinc oxide manufactured by Mitsui Mining & Smelting Co., Ltd. Anti-aging agent: Antigen 6C (N- (1,3-dimethylbutyl) -N′-phenyl-p-phenylenediamine) manufactured by Sumitomo Chemical Co., Ltd.
Wax: Sunnock N manufactured by Ouchi Shinsei Chemical Industry Co., Ltd.
Powdered sulfur: Powdered sulfur from Karuizawa Sulfur Co., Ltd. Vulcanization accelerator CZ: Noxeller CZ from Ouchi Shinsei Chemical Industry Co., Ltd.
Vulcanization accelerator DPG: Noxeller D (1,3-diphenylguanidine) manufactured by Ouchi Shinsei Chemical Industry Co., Ltd.

1 First measurement process 2 Prediction formula creation process 3 Second measurement process 4 Prediction process

Claims (5)

From a static friction coefficient and a dynamic friction coefficient of rubber, a tire friction characteristic prediction method for predicting at least a static friction coefficient and a dynamic friction coefficient in a virtual tire when the rubber is employed in a tread rubber,
Except the physical properties of the tread rubber by using a reference tire T _A a plurality of forming the same structure, measured tire static friction coefficient .mu.s _A and the tire dynamic friction coefficient [mu] d _A at least the reference road surface, and the tread rubber of the reference tire T _{A A} first measurement step of measuring a rubber static friction coefficient μrs _A and a rubber dynamic friction coefficient μrd _A on at least a reference road surface using the rubber sample GA;
The tire static friction coefficient μs _A measurement data and the rubber static friction coefficient μrs _A measurement data are subjected to regression analysis, the tire static friction coefficient μs is an explanatory variable, and the prediction formula (1a) having the rubber static friction coefficient μrs as an objective variable, and the tire Regression analysis of dynamic friction coefficient μd _A measurement data and rubber dynamic friction coefficient μrd _A measurement data to create a prediction formula (1b) that uses tire dynamic friction coefficient μd as an explanatory variable and rubber dynamic friction coefficient μrd as an objective variable Process,
The reference to the tread rubber of the tire T _A by using the test sample G _B of different rubber, the second measurement step of measuring a rubber static friction coefficient Myurs _B and rubber dynamic friction coefficient Myurd _B at least in the reference road surface,
In addition, based on the prediction equations (1a) and (1b), the friction for predicting the tire static friction coefficient μs _B and the tire dynamic friction coefficient μd _{B of} the virtual tire from the rubber static friction coefficient μrs _B and the rubber dynamic friction coefficient μrd _B. A method for predicting a frictional characteristic of a tire, comprising a prediction step including coefficient prediction. The tire prediction method according to claim 1, wherein the prediction equations (1a) and (1b) are regression equations of linear functions.
μs = α ₁ × μrs + α ₂ −−− (1a)
μd = β ₁ × μ rd + β ₂ −−− (1b)
(Α ₁ , α ₂ , β ₁ , β ₂ are regression coefficients.) Wherein the first measurement step, the measurement of the mu-S characteristic of the reference tire T _A and ground contact width W measured between the contact length L and the ground pressure P, the reference road surface of the reference tire T _A in ground contact load Fz, and the includes measurements of Young's modulus _{E a} rubber sample _{G a,}
And the second measuring step, the frictional characteristic estimation method of a tire according to claim 1 or 2, characterized in that it comprises a measurement of the Young's modulus E _B of the test sample G _B. The prediction formula making process is based on the following equation (2), along with determining the lateral elastic constant Cx _A rubber block of the reference tire T _A, the transverse elastic constant Cx _A, the Young's modulus E of the rubber sample G _A 4. The method according to claim 3, further comprising the step of performing regression analysis on the data with _A, and generating a prediction formula (3) of a power function with the transverse elastic constant Cx as an explanatory variable and the Young's modulus E as an objective variable. Prediction method of tire friction characteristics.
Kx _A = (1/2) x Cx _A x W x L ^2- (2)
Cx = C1 × E ⁿ --- (3)
(Kx _A is blade Kings stiffness of the reference tire _{T A,} C1, n are regression coefficients.) The prediction step includes
Based on the prediction equation (3), along with determining the lateral elastic constant Cx _B of the rubber blocks of the virtual tire from the Young's modulus E _B in the test sample G _B,
From the lateral elastic constant Cx _B , the tire static friction coefficient μs _{B of} the virtual tire determined by the prediction of the friction coefficient, and the tire dynamic friction coefficient μd _B , based on the following equations (4) and (5), 5. The method for predicting a frictional characteristic of a tire according to claim 4, further comprising a [mu] S characteristic prediction for predicting the [mu] S characteristic.
μs _B × P = Cx _B × S × Lh (4)
_{μ = Cx B × S × W} × Lh 2 / (2 × Fz)
^{_{+ 20 × μd B / (L}} 5) × [(L / 2) 4 × (L-Lh) - (1/5) {(L / 2) 5 - (Lh-L / 2) 5}]
---- (5)
(S is the slip ratio, and Lh is the length from the contact start point to the slip start point at which the contact surface starts to slide from the road surface on the tire contact surface during braking.)

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