JP2012093108A - Method of predicting friction coefficient - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method of predicting the friction coefficient of tires by using highly accurate indices.SOLUTION: A method of prediction comprises (1) a step of measuring the slip velocity of a rubber block running on a road surface; (2) a step of scanning the road surface with a surface roughness meter and measuring the height in a position on the road surface; (3) a step of calculating a height difference correlation function by using these measured data, a dispersion function and a gamma function and obtaining the maximum horizontal correlation length; (4) a step of estimating, by dividing the slip velocity by the maximum horizontal correlation length, the frequency of vibration generated by the contact of the rubber block with the road surface; (5) a step of preparing a reference sample of a rubber composition equivalent to the contact part of the rubber block with the road surface; (6) a step of measuring the loss tangent of the reference sample and drawing a master curve; and (7) a step of estimating a loss tangent matching the frequency by using the master curve.

Description

  The present invention relates to a friction coefficient prediction method.

  The tire tread is subjected to vibration due to road surface irregularities. In order to evaluate the braking characteristics of a tire, it is essential to understand the viscoelastic characteristics of the crosslinked rubber constituting the tread.

  It has been proposed to use tan δ at 0 ° C. for calculation of the coefficient of friction for a wet road surface (for example, Non-Patent Document 1). This is based on the idea that the vibration of about 1 MHz that the tread receives from the road surface corresponds to the vibration of about 10 Hz at 0 ° C. Japanese Patent Application Laid-Open No. 2007-163142 discloses a method of evaluating the frictional force against a wet road surface using tan δ at 0 ° C.

  From the viewpoint of improving evaluation accuracy, various studies have been made on measuring devices and measuring methods for viscoelastic properties. An example of this study is disclosed in Japanese Patent Laid-Open No. 2006-177734.

  As described above, the tread receives vibration of about 1 MHz from the road surface. This vibration is high frequency. Ultrasonic waves may be used to measure tan δ in the high frequency range. Examples of measuring tan δ using ultrasonic waves are disclosed in Japanese Unexamined Patent Application Publication Nos. 2007-47130 and 2010-85340.

JP 2007-163142 A JP 2006-177734 A JP 2007-47130 A JP 2010-85340 A

Keizo Ninagawa, Journal of Japan Rubber Association, Vol. 80, No. 10, pp. 394-401 (2007)

  On the road surface made of asphalt, various irregularities exist. When the road surface is uneven, the tire receives high-frequency vibration from the road surface. Even if the road surface is rough, if the tire travels at high speed on the road surface, the tire receives high-frequency vibrations from the road surface. As described above, the vibration conditions (hereinafter referred to as the input frequency) that the tire receives from the road surface are combined with the driving conditions such as the road surface condition and the driving speed. Since there is no means for quantitatively organizing this complex involvement, there is a problem that the friction coefficient of the tire cannot be accurately calculated for each traveling condition.

  FIG. 10 is a cross-sectional view schematically showing the state of the road surface 2. The road surface 2a shown in FIG. 10 (a) includes a reference surface 4a and a large number of convex portions 6 protruding from the reference surface 4a. These convex portions 6 have the same height Ha. These convex portions 6 are arranged at a constant pitch. The road surface 2b shown in FIG. 10B is composed of a reference surface 4b and a large number of recesses 8 that are recessed from the reference surface 4b. These recesses 8 have the same depth Db. These recesses 8 are arranged at a constant pitch. The pitch of the concave portions 8 is equal to the pitch of the convex portions 6 described above.

  In FIG. 10, when the height Ha of the convex portion 6 and the depth Db of the concave portion 8 have the same length, the ten-point average roughness Rz of the road surface 2a is indicated by a value equivalent to that of the road surface 2b. The maximum height Ry of the road surface 2a is indicated by a value equivalent to that of the road surface 2b. However, the friction coefficient of the tire traveling on the road surface 2a is different from the friction coefficient of the tire traveling on the road surface 2b. Thus, even if parameters such as the ten-point average roughness Rz and the maximum height Ry have the same value, the friction coefficient of the road surface 2 may be different. In addition, this parameter reflects the height Ha of the convex portion 6 or the depth Db of the concave portion 8, and does not reflect the frequency of vibration generated when the tire contacts the road surface 2. With such parameters, there is a limit to predicting the friction coefficient of the tire with high accuracy in association with the running conditions.

  In the method described in JP 2007-163142 A, the average value of the tip radii of the protrusions existing on the road surface is used. This average value is obtained from a profile of minute irregularities on the road surface. In this method, the road surface state is represented by an index focusing only on the uneven shape. Moreover, tan δ and elastic modulus at 0 ° C. are used as another index. In this method, the correlation between the vibration received by the tire from the road surface and the viscoelastic property is insufficient. The effect of the road surface is not sufficiently reflected in the friction coefficient obtained by this method. Even in this method, there is a limit to predicting the friction coefficient of the tire with high accuracy in association with the running condition.

  An object of the present invention is to provide a method for predicting a friction coefficient of a tire using a highly accurate index.

（この数式（２）及び（３）において、λは波長を表す。） The prediction method of the friction coefficient according to the present invention is as follows:
(1) a step of measuring the sliding speed Vs of the rubber block traveling on the road surface;
(2) scanning the road surface with a surface roughness meter, measuring the height z (x) at the position x of the road surface, and quantifying the state of the road surface;
(3) The data composed of the position x and the height z (x), the dispersion function σ (x) expressed by the following formula (1), and the gamma function Γz (λ) expressed by the following formula (2) To calculate the altitude difference correlation function Cz (λ) represented by the following formula (3) to obtain the longest horizontal correlation length ξh;
(4) dividing the sliding speed Vs by the longest horizontal correlation length ξh to estimate a frequency N of vibration generated when the rubber block is in contact with the road surface;
(5) a step of preparing a reference sample comprising a rubber composition equivalent to the contact portion of the rubber block with the road surface;
(6) measuring the loss tangent of the reference sample, and creating a master curve of the loss tangent;
(7) using the master curve, estimating a loss tangent corresponding to the frequency N of the contact portion.

(In the equations (2) and (3), λ represents a wavelength.)

  Preferably, in this method of predicting the friction coefficient, the length of the portion where the road surface is scanned is 50 times or more and 1000 times or less of the longest horizontal correlation length ξh in the numerical step.

  Preferably, in this friction coefficient prediction method, in the master curve production step, the reference temperature of the master curve is the temperature of the road surface or the temperature of the surface of the rubber block.

  In the method for predicting a friction coefficient according to the present invention, traveling conditions such as road surface conditions and traveling speed are quantitatively arranged, and a frequency N of vibration generated when the rubber block comes into contact with the road surface is estimated. This frequency N is accurate. The loss tangent estimated based on the frequency N is a highly accurate index. According to this prediction method, the friction coefficient of the tire can be predicted with high accuracy in association with the running condition.

FIG. 1 is a flowchart illustrating a friction coefficient prediction method according to an embodiment of the present invention. FIG. 2 is a plan view showing a measurement state of the road surface state. FIG. 3 is a graph in which the altitude difference correlation function Cz (λ) is plotted against the wavelength λ. FIG. 4 is a graph showing the calculation result of the altitude difference correlation function Cz (λ) for a road surface different from FIG. FIG. 5 is a graph showing the calculation result of the altitude difference correlation function Cz (λ) for another road surface different from FIG. FIG. 6 is a graph showing a master curve of loss tangent (tan δ). FIG. 7 is a graph showing the correlation between tan δ and the friction coefficient μ. FIG. 8 is a graph showing the correlation between tan δ and the friction coefficient μ under a traveling condition different from that in FIG. FIG. 9 is a graph showing the correlation between tan δ and the coefficient of friction μ in another traveling condition different from FIG. FIG. 10 is a cross-sectional view schematically showing the state of the road surface.

  Hereinafter, the present invention will be described in detail based on preferred embodiments with appropriate reference to the drawings.

FIG. 1 shows a flowchart of a friction coefficient prediction method according to an embodiment of the present invention. This prediction method is
(1) Step of preparing a rubber block (STEP 1),
(2) A step of moving the rubber block to obtain the sliding speed Vs (STEP 2),
(3) Step of converting the state of the road surface on which the rubber block travels (STEP 3),
(4) Step of analyzing the digitized data (STEP 4)
And (5) a step of estimating a loss tangent (hereinafter referred to as tan δ) based on the analysis result (STEP 5)
Is included.

  In the preparation step (STEP 1), a reference rubber composition containing a base rubber and a filler is prepared using a kneader such as a kneader or a roll. The rubber composition is crosslinked to prepare a rubber block. Although not shown, this rubber block has a donut shape. This rubber block is used in a friction tester described later. The rubber block may be a tire. In this case, the tread of the tire is composed of the rubber composition.

  In this prediction method, five types of reference rubber compositions having different compositions are prepared. The composition of these rubber compositions is shown in Table 1 below. In Table 1, “Nippol NS116” is a styrene butadiene rubber manufactured by Nippon Zeon. “KR7” is natural rubber. “Ubepol BR150L” is a butadiene rubber manufactured by Ube Industries. “ZEOSIL 115Gr” is silica manufactured by Rhodia. “N220” is carbon black. “Sant Flex 13” is an antioxidant manufactured by Flexis. “Noxeller NS” is a vulcanization accelerator manufactured by Ouchi Shinsei Chemical Co., Ltd. “Suncellor TBZTD” is a vulcanization accelerator manufactured by Sanshin Chemical Co., Ltd.

In the measurement step (STEP 2), a friction test is performed using a rubber block. By this friction test, the sliding speed Vs and the friction coefficient μ are obtained. The sliding speed Vs is expressed by the following mathematical formula (a) using the traveling speed V ₀ and the slip ratio S of the rubber block.
Vs = V ₀ × S (a)

The slip ratio S is expressed by the following mathematical formula (b) using the traveling speed V ₀ , the rolling radius r and the rotational angular speed ω of the rubber block.
S = (V ₀ −rω) / V ₀ (b)

  In this prediction method, a trade name “LAT-100” manufactured by VMI is used as a friction tester. This friction tester is configured to run a rubber block at a predetermined sliding speed Vs and measure the frictional force F against the road surface.

  A grindstone is used on the road surface of the friction tester. In this prediction method, a grindstone whose count is 60 (hereinafter, 60th grindstone) or a grindstone whose count is 120 (hereinafter 120th grindstone) is used as the road surface. Although not shown, a rubber block is placed on the road surface. A load of 40N is applied to the rubber block. This load can be appropriately changed in consideration of the purpose of the test.

  In this measurement step (STEP 2), the friction force F is measured by running the rubber block on the road surface at a predetermined sliding speed Vs. In this prediction method, the average value of the frictional force F when the rubber block travels 300 m is measured. By dividing this average value by the aforementioned load (40 N), the friction coefficient μ can be obtained. In this measurement, the road surface temperature is adjusted to 20 ° C. The travel distance of the rubber block for obtaining the road surface temperature and the average value of the frictional force F can be appropriately changed in consideration of the purpose of the test.

  In this prediction method, when the slip speed Vs is set to 20 km / h or 40 km / h using a # 60 grindstone on the road surface, and when the slip speed Vs is set to 20 km / h using a # 120 grindstone on the road surface , The friction coefficient μ is measured. The measurement results are shown in Table 2 below.

  In the digitizing step (STEP 3), the road surface state is digitized using a surface roughness meter. Although not shown, the road surface includes a large number of convex portions and a large number of concave portions. In this prediction method, a part of the road surface is scanned with a surface roughness meter, and the height at each position is measured.

  As the surface roughness meter, a contact type or a non-contact type can be used. Examples of the contact type include a surface roughness meter using an atomic force microscope and a probe. Examples of the non-contact type include a laser measurement type surface roughness meter and a confocal type laser microscope. In this prediction method, a confocal laser microscope (trade name “VK9500” manufactured by Keyence Corporation) is used as a surface roughness meter.

  FIG. 2 shows a state of measurement of the state of the road surface 10. FIG. 2 shows a state in which the road surface 10 is viewed from directly above. In FIG. 2, what is indicated by a symbol C is a camera provided in the surface roughness meter. An area R can be scanned by the camera C. The scanning region R is set so that its outline has a rectangular shape.

  As shown in the figure, the outline of the scanning region R has a long shape in the direction indicated by the arrow x. In this prediction method, the contour is set such that the length direction thereof coincides with the traveling direction of the rubber block traveling on the road surface 10. Therefore, the direction indicated by the arrow x coincides with the traveling direction of the rubber block on the road surface 10. In FIG. 2, the length of the scanning region R is indicated by a double arrow L, and the width of the scanning region R is indicated by a double arrow W. The length L and width W are set in a surface roughness meter.

  In the numerical process (STEP 3), the surface roughness meter measures the height z (x, y) at each position (x, y) in the scanning region R while moving the camera C. By this measurement, the state of the road surface 10 is replaced with a data group including a large number of data composed of the position (x, y) and the height z (x, y).

  In the analysis step (STEP 4), using the data group obtained in the above-mentioned numericalization step (STEP 3), the dispersion function expressed by the following mathematical formula (1) and the gamma function expressed by the following mathematical formula (2). The altitude difference correlation function Cz (λ) expressed by the following mathematical formula (3) is calculated. As described above, the rubber block advances on the road surface 10 in the direction indicated by the arrow x. For this reason, when calculating the function Cz (λ), an amount to be shifted for analyzing the x component is taken into consideration. In this prediction method, this shift amount is shown as the wavelength λ. In the following description, the description of the y component is omitted from the viewpoint of convenience. In formulas (1) and (2), <> represents an ensemble average. <Z> represents the average value of the measured z (x).

  The altitude difference correlation function Cz (λ) is expressed by the following equation (4) using the Hurst exponent H, the vertical correlation length ξv, and the longest horizontal correlation length ξh based on the fractal theory. In this prediction method, the calculation result of the function Cz (λ) is analyzed based on the mathematical formula (4).

  FIG. 3 is a graph in which the altitude difference correlation function Cz (λ) is plotted against the wavelength λ. The horizontal and vertical axes are logarithmic. FIG. 3 relates to the road surface 10 made of a # 60 grindstone. The multiple plots shown in FIG. 3 are based on a data group obtained from a scanning region R having a width W of 1.28 mm and a length L of 10.1 mm. This data group is composed of data of 468 points in the width direction and 3917 points in the length direction.

  As shown in the drawing, in the region where the wavelength λ is small, the function Cz (λ) increases monotonously with respect to the wavelength λ, as indicated by the straight line S1. As the wavelength λ further increases, the function Cz (λ) converges to a constant value indicated by the straight line T1. The straight line S1 is obtained by approximating a portion where the function Cz (λ) monotonously increases to a linear function. Straight lines S2 and S3 to be described later are also obtained in the same manner as the straight line S1. In this specification, the average value from the data after the monotonous increase of the function Cz (λ) to the tenth data in the series of data is shown as the convergence value.

  In FIG. 3, what is indicated by a symbol U1 is an intersection of the straight line S1 and the straight line T1. In this prediction method, the x component of this intersection U1 gives the longest horizontal correlation length ξh, and the y component of this intersection U1 gives the square of the vertical correlation length ξv. The longest horizontal correlation length ξh corresponds to the maximum length of the aggregate constituting the road surface 10. In the road surface 10 made of the # 60 grindstone shown in FIG. 3, the maximum horizontal correlation length ξh is estimated to be 458 μm, and the vertical correlation length ξv is estimated to be 148 μm. As described above, the analysis step (STEP 4) uses the data group obtained in the numerical step (STEP 3), the variance function σ (x) and the gamma function Γz (λ), and uses the altitude difference correlation function Cz ( (λ) is calculated to obtain the longest horizontal correlation length ξh.

  FIG. 4 shows a calculation result of the altitude difference correlation function Cz (λ) for a road surface 10 different from that in FIG. FIG. 4 relates to the road surface 10 made of a 120th grindstone. Also in FIG. 4, in the region where the wavelength λ is small, the function Cz (λ) increases monotonously with respect to the wavelength λ, as indicated by the straight line S2. As the wavelength λ further increases, the function Cz (λ) converges to a constant value indicated by the straight line T2. In the figure, reference numeral U2 indicates an intersection of the straight line S2 and the straight line T2. In the road surface 10 composed of the # 120 grindstone shown in FIG. 4, the maximum horizontal correlation length ξh is estimated to be 280 μm, and the vertical correlation length ξv is estimated to be 121 μm.

  FIG. 5 shows the calculation result of the altitude difference correlation function Cz (λ) for another road surface 10 different from FIG. FIG. 5 relates to a road surface 10 made of asphalt. Also in FIG. 5, in the region where the wavelength λ is small, the function Cz (λ) increases monotonously with respect to the wavelength λ, as indicated by the straight line S3. As the wavelength λ further increases, the function Cz (λ) converges to a constant value indicated by the straight line T3. In the figure, the point U3 indicates the intersection of the straight line S3 and the straight line T3. In the asphalt road surface 10 shown in FIG. 5, the maximum horizontal correlation length ξh is estimated to be 3.86 mm, and the vertical correlation length ξv is estimated to be 1.88 mm.

  In this analysis step (STEP 4), using the longest horizontal correlation length ξh, the frequency of vibration (hereinafter, input frequency N) generated when the rubber block comes into contact with the road surface 10 is further estimated. This input frequency N is obtained by dividing the sliding velocity Vs by the longest horizontal correlation length ξh. This analysis step (STEP 4) includes a step of estimating the input frequency N by dividing the sliding velocity Vs by the longest horizontal correlation length ξh.

  In this prediction method, when the slip speed Vs is set to 20 km / h or 40 km / h using a No. 60 grindstone on the road surface 10, and when the slip speed Vs is set to 20 km / h using a No. 120 grindstone on the road surface 10. The input frequency N is calculated. The calculation results are shown in Table 3 below.

  In this prediction method, the viscoelastic characteristics of the portion in contact with the road surface 10 of the rubber block are estimated using the input frequency N obtained in the analysis step (STEP 4) (STEP 5). In this viscoelastic property estimation step (STEP 5), a rubber sheet as a reference sample molded from the reference rubber composition shown in Table 1 is prepared. This estimation step (STEP 5) includes a step of preparing a rubber sheet made of a rubber composition equivalent to the contact portion of the rubber block with the road surface 10.

In the estimation step (STEP 5), the viscoelastic properties (storage elastic modulus G ′, loss elastic modulus G ″ and loss tangent (tan δ)) of the rubber sheet are measured. This viscoelastic property is determined according to “JIS K”. It is measured in accordance with the provision of “6394”. The measurement conditions are as follows.
Viscoelastic spectrometer: Trade name “VA4500” manufactured by Metraviv
Initial strain: 10%
Dynamic strain: ± 0.5%
Frequency: 1-100Hz
Deformation mode: Tensile Measurement temperature: -30 ° C, -20 ° C, -10 ° C, 0 ° C, 20 ° C, 40 ° C, 60 ° C, 80 ° C

  A master curve of viscoelastic properties is created according to the time-temperature conversion rule using data on viscoelastic properties obtained by measurement. The estimation step (STEP 5) of this prediction method further includes a step of measuring the viscoelastic characteristics of the rubber sheet and creating a master curve of the viscoelastic characteristics.

  In this prediction method, the surface temperature of the rubber block in the above-described sliding speed measuring step (STEP 2) is adopted as the reference temperature when producing the master curve. In addition, when the temperature of the road surface 10 is adjusted and the surface temperature of the rubber block is controlled, the temperature of the road surface 10 may be employed as the reference temperature. FIG. 6 shows a master curve of loss tangent (tan δ) of the blend D in Table 1. The temperature (20 ° C.) of the road surface 10 is adopted as the reference temperature for this master curve.

  In the estimation step (STEP 5), tan δ corresponding to the input frequency N shown in Table 3 is estimated using the master curve. This tan δ represents the tan δ of the portion in contact with the road surface 10 of the rubber block that is traveling on the road surface 10. This estimation step (STEP 5) further includes a step of estimating tan δ corresponding to the input frequency N of the contact portion with the road surface 10 using the master curve. In Table 4 below, tan δ corresponding to the frequency shown in Table 3 for each formulation shown in Table 1 is shown together with tan δ at 0 ° C. and 10 Hz used in the conventional method.

  FIG. 7 is a graph showing the correlation between tan δ and the friction coefficient μ. FIG. 7 shows the correlation between tan δ and the friction coefficient μ when using a # 60 grindstone on the road surface 10 and running the rubber block at a sliding speed Vs of 20 km / h. In the graph shown in FIG. 7A, tan δ estimated by this prediction method is used. In the graph shown in FIG. 7B, tan δ at 0 ° C. and 10 Hz estimated by a conventional method is used.

  The correlation coefficient between tan δ and the friction coefficient μ in FIG. 7A is 0.990, and the correlation coefficient between tan δ and the friction coefficient μ in FIG. 7B is 0.862. The variation of the friction coefficient μ with respect to tan δ estimated by this prediction method is smaller than that of the friction coefficient μ with respect to tan δ estimated by the conventional method. As is clear from the comparison between FIG. 7A and FIG. 7B, tan δ estimated by this prediction method can be well correlated with the friction coefficient μ.

  FIG. 8 is a graph showing the correlation between tan δ and the friction coefficient μ under a traveling condition different from that in FIG. FIG. 8 shows the correlation between tan δ and the friction coefficient μ when using a No. 60 grindstone on the road surface 10 and running the rubber block at a sliding speed Vs of 40 km / h. In the graph shown in FIG. 8A, tan δ estimated by this prediction method is used. In FIG. 8B, tan δ at 0 ° C. and 10 Hz estimated by the conventional method is used.

  The correlation coefficient between tan δ and the friction coefficient μ in FIG. 8A is 0.950, and the correlation coefficient between tan δ and the friction coefficient μ in FIG. 8B is 0.862. The variation of the friction coefficient μ with respect to tan δ estimated by this prediction method is smaller than that of the friction coefficient μ with respect to tan δ estimated by the conventional method. As is clear from the comparison between FIG. 8A and FIG. 8B, tan δ estimated by this prediction method can be well correlated with the friction coefficient μ.

  FIG. 9 is a graph showing the correlation between tan δ and the coefficient of friction μ in another traveling condition different from FIG. FIG. 9 shows the correlation between tan δ and the friction coefficient μ when using a No. 120 grindstone on the road surface 10 and running the rubber block at a sliding speed Vs of 20 km / h. In the graph shown in FIG. 9A, tan δ estimated by this prediction method is used. In the graph shown in FIG. 9B, tan δ at 0 ° C. and 10 Hz estimated by the conventional method is used.

  The correlation coefficient between tan δ and the friction coefficient μ in FIG. 9A is 0.952, and the correlation coefficient between tan δ and the friction coefficient μ in FIG. 9B is 0.654. The variation of the friction coefficient μ with respect to tan δ estimated by this prediction method is smaller than that of the friction coefficient μ with respect to tan δ estimated by the conventional method. As is clear from the comparison between FIG. 9A and FIG. 9B, tan δ estimated by this prediction method can be well correlated with the friction coefficient μ.

  In this prediction method, the driving conditions such as the state of the road surface 10 and the slip speed Vs are quantitatively arranged, and the input frequency N to the portion of the rubber block in contact with the road surface 10 is calculated. This input frequency N is accurate. For this reason, the running condition is sufficiently reflected in tan δ estimated by this prediction method. This tan δ is a highly accurate index. According to this prediction method, it is possible to predict the friction coefficient of the tire with high accuracy in association with the running condition. In other words, this prediction method can predict with high accuracy a friction coefficient reflecting a change in running conditions such as a change in the road surface 10 and a change in speed. This prediction method can contribute to the efficiency of tire development.

  In this prediction method, a good correlation is realized between tan δ and the friction coefficient μ. In other words, according to this prediction method, tan δ having a high correlation with the friction coefficient μ can be estimated. For this reason, if the state of the road surface 10 on which the tire is scheduled to travel is digitized using a surface roughness meter and the tire slip speed is set, the friction coefficient of the tire during traveling can be easily and highly accurately determined. Can be predicted.

  In this prediction method, the length L of the scanning region R of the road surface 10 is preferably 50 times or more and 1000 times or less of the longest horizontal correlation length ξh in the numerical step (STEP 3). By setting this length L to be 50 times or more of the correlation length ξh, it is possible to confirm the convergence of the altitude difference correlation function Cz (λ) to a constant value in the analysis step (STEP 4). An advantageous calculation can be made. The data group obtained in this way can contribute to the prediction of the friction coefficient with high accuracy. From this viewpoint, the length L is preferably 70 times or more the correlation length ξh. By setting the length L to 1000 times or less of the correlation length ξh, the number of data included in the data group is appropriately maintained. For this reason, an excessive load on the computer used for this calculation is effectively prevented.

  The prediction method according to the present invention is preferably implemented using a computer and software from the viewpoint of efficiency. Of course, the present invention can also be implemented by hand calculation. The essence of the present invention is not in computer software.

  The method described above can also be applied to various tire performance predictions.

2, 2a, 2b, 10 ... road surface 4a, 4b ... reference surface 6 ... convex portion 8 ... concave portion

Claims (3)

Measuring the sliding speed Vs of the rubber block traveling on the road surface;
Scanning the road surface with a surface roughness meter, measuring a height z (x) at a position x of the road surface, and quantifying the state of the road surface;
Using the data consisting of the position x and the height z (x), the dispersion function σ (x) expressed by the following formula (1), and the gamma function Γz (λ) expressed by the following formula (2) Calculating the altitude difference correlation function Cz (λ) represented by the following mathematical formula (3) to obtain the longest horizontal correlation length ξh;
Dividing the sliding speed Vs by the longest horizontal correlation length ξh to estimate the frequency N of vibration generated when the rubber block contacts the road surface;
A step of preparing a reference sample made of a rubber composition equivalent to the contact portion of the rubber block with the road surface;
Measuring the loss tangent of this reference sample, creating a master curve of this loss tangent,
Estimating the loss tangent corresponding to the frequency N of the contact portion using the master curve.

(In the equations (2) and (3), λ represents a wavelength.)   2. The method for predicting a friction coefficient according to claim 1, wherein, in the quantification step, a length of a portion for scanning the road surface is 50 times or more and 1000 times or less of the longest horizontal correlation length ξh. In the master curve production process,
The friction coefficient prediction method according to claim 1 or 2, wherein the reference temperature of the master curve is the temperature of the road surface or the temperature of the surface of the rubber block.

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