Method for predicting tire wear state

Abstract

To provide a method capable of predicting a tire wear state within a short time after traveling by a prescribed distance.SOLUTION: This method for predicting the tire wear state comprises: a step of sequentially acquiring, in time-series, data containing first data relating to a tire traveling distance and second data relating to tire wear in the traveling distance by running the tire under a predetermined condition; a step of obtaining an approximation equation H for approximating a relationship between the first data and the second data using the data, and updating the approximation equation H for each additional acquisition of data; and a step of calculating a wear state with a traveling distance P exceeding a distance by which the tire has traveled using an updated approximation equation H2 when a distance D between the updated approximation equation H2 and an approximation equation H1 yet to be updated is within a predetermined range.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to a method for predicting the wear state of a tire. [Background technology]

[0002] Conventionally, various methods for predicting the wear state of a tire have been proposed (for example, see Patent Document 1 below). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-108709 A Summary of the Invention [Problem to be solved by the invention]

[0004] When evaluating the wear resistance of a tire, the wear state of the tire after it has been driven a predetermined distance may be obtained. In this case, for example, a wear test in which the tire is actually driven a predetermined distance or a wear progression simulation using a tire model may be performed, but both of these methods have the problem of taking a lot of time.

[0005] The present invention has been devised in consideration of the above-mentioned circumstances, and its main object is to provide a method capable of predicting the state of tire wear after traveling a specified distance in a short period of time. [Means for solving the problem]

[0006] The present invention is a method for predicting a tire wear state, comprising the steps of: running the tire under predetermined conditions and sequentially acquiring data in a chronological order, the data including first data on a mileage of the tire and second data on the wear of the tire at the mileage; using the acquired data to obtain an approximation equation that approximates the relationship between the first data and the second data, wherein the approximation equation is updated each time additional data is acquired; and, when a difference between the updated approximation equation and the approximation equation before the update falls within a predetermined range, using the updated approximation equation to calculate the wear state at a mileage that exceeds the distance the tire has already traveled. Effect of the Invention

[0007] By employing the above steps, the method for predicting the wear state of a tire according to the present invention makes it possible to predict the wear state of a tire after traveling a predetermined distance in a short period of time. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a perspective view showing an example of a computer for executing a method for predicting a tire wear state. [Diagram 2] 1 is a cross-sectional view showing an example of a tire whose wear state is predicted; [Diagram 3] 3 is a flowchart showing a processing procedure of a tire wear state prediction method according to the present embodiment. [Figure 4] FIG. 1 is a perspective view conceptually showing a drum testing machine. [Diagram 5] 1 is a graph showing the relationship between first data (mileage index) and second data (wear index). [Figure 6] This is a graph when new data is additionally acquired from Figure 5. [Figure 7] 10 is a flowchart showing a processing procedure of a method for predicting a tire wear state according to another embodiment of the present invention. [Figure 8] FIG. 2 is a perspective view showing an example of a tire model and a road surface model. [Figure 9] FIG. 2 is a cross-sectional view showing an example of a tire model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. It should be understood that the drawings include exaggerated expressions and expressions different from the dimensional ratio of the actual structure in order to help understand the contents of the invention. Furthermore, the same or common elements are given the same reference numerals throughout the embodiments, and duplicated explanations are omitted. Furthermore, the specific configurations shown in the embodiments and drawings are for understanding the contents of the present invention, and the present invention is not limited to the specific configurations shown in the drawings.

[0010] In the tire wear prediction method of this embodiment (hereinafter sometimes referred to as the "prediction method"), the wear state of the tread portion of the tire is calculated. In the prediction method of this embodiment, a computer is used.

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

[0012] [tire] 2 is a cross-sectional view showing an example of a tire 2 whose wear state is predicted. The tire 2 of this embodiment is exemplified by a pneumatic tire for a passenger vehicle, but is not particularly limited thereto. The tire 2 may be, for example, a tire for heavy loads such as a truck or bus, or an airless tire, or other tire of another category.

[0013] The tire 2 of this embodiment is provided with a carcass 6 extending from the tread portion 2a through the sidewall portion 2b to the bead core 5 of the bead portion 2c, and a belt layer 7 disposed on the tire radial outer side of the carcass 6 and inside the tread portion 2a. A tread pattern is provided on an outer surface 2S of the tread portion 2a of this embodiment.

[0014] The tread portion 2a is provided with a tread rubber 8 disposed on the outer side of the belt layer 7. The tread rubber 8 (tread portion 2a) is provided with a circumferential groove 9 that extends continuously in the tire circumferential direction. As a result, the tread rubber 8 is provided with a plurality of land portions 10 separated by the circumferential grooves 9.

[0015] The circumferential groove 9 of this embodiment includes a pair of crown circumferential grooves 9a, 9a and a pair of shoulder circumferential grooves 9b, 9b. The pair of crown circumferential grooves 9a, 9a are arranged on both outer sides of the tire equator C in the tire axial direction. The pair of shoulder circumferential grooves 9b, 9b are arranged between the crown circumferential groove 9a and the tread edge 2t. Note that the circumferential grooves 9 are not limited to this embodiment, and for example, some of the circumferential grooves 9 may be omitted, or other circumferential grooves 9 may be further provided.

[0016] In this specification, in the case where the tire 2 is a pneumatic tire, the "tread edge 2t" is specified as the axially outermost contact position when the tire 2 in a normal state is placed on a flat surface with a normal load and a camber angle of 0°. The normal state refers to an unloaded state in which the tire 2 is mounted on a normal rim 11 and inflated to a normal internal pressure. In this specification, unless otherwise specified, the dimensions of each part of the tire are indicated as values ​​measured in a normal state.

[0017] A "genuine rim" is a rim that is determined for each tire by a standard system that includes the standard on which the tire 2 is based. Therefore, a genuine rim is, for example, a "standard rim" for JATMA, a "design rim" for TRA, and a "measuring rim" for ETRTO.

[0018] The "normal internal pressure" is the air pressure that is determined for each tire by each standard in a system of standards including the standard on which the tire 2 is based. Therefore, the normal internal pressure is, for example, the "maximum air pressure" in the case of JATMA, the maximum value listed in the table "TIRE LOAD LIMITS AT VARIOUS COLD INFLATION PRESSURES" in the case of TRA, and the "INFLATION PRESSURE" in the case of ETRTO.

[0019] The "normal load" is the load that is determined for each tire by each standard in the standard system including the standard on which the tire 2 is based. Therefore, the normal load is, for example, "maximum load capacity" in the case of JATMA, the maximum value listed in the table "TIRE LOAD LIMITS AT VARIOUS COLD INFLATION PRESSURES" in the case of TRA, and "LOAD CAPACITY" in the case of ETRTO.

[0020] The land portion 10 of this embodiment includes a crown land portion 10a, a pair of middle land portions 10b, 10b, and a pair of shoulder land portions 10c, 10c. The crown land portion 10a is divided between a pair of crown circumferential grooves 9a, 9a. The pair of middle land portions 10b, 10b are divided by the crown circumferential groove 9a and the shoulder circumferential groove 9b. The pair of shoulder land portions 10c, 10c are divided by the shoulder circumferential groove 9b and the tread edge 2t. Note that the land portion 10 is not limited to such an embodiment, and for example, some of the land portions 10 may be omitted, or other land portions 10 may be further provided. In addition, each land portion 10 may be provided with, for example, a lateral groove (not shown) extending in a direction intersecting with the circumferential groove 9.

[0021] The carcass 6 is composed of at least one carcass ply 6A, one carcass ply in this embodiment. The carcass ply 6A has carcass cords (not shown) arranged at an angle of, for example, 75 to 90 degrees with respect to the tire equator C.

[0022] The belt layer 7 includes two inner and outer belt plies 7A and 7B in which belt cords (not shown) are arranged at an angle of, for example, 10 to 35 degrees with respect to the tire circumferential direction. These belt plies 7A and 7B are overlapped with the belt cords crossing each other.

[0023] [Method for predicting tire wear state (first embodiment)] Incidentally, when evaluating the wear resistance of the tire 2, the wear state of the tire 2 after it has been driven a predetermined distance may be acquired. The predetermined distance is set appropriately according to the wear state to be evaluated, and may be set to, for example, 30,000 to 50,000 km. To acquire the wear state, for example, a wear test is performed in which the tire 2 is actually driven a predetermined distance, or a wear progression simulation is performed using a tire model in which the tire 2 is modeled. However, there is a problem that both of these methods require a lot of time.

[0024] As a result of extensive research, the inventors have found that as the mileage increases, a certain trend appears in the wear of the tire 2. Then, when this trend appears, they have found that by acquiring an approximation equation that indicates the relationship between first data on the mileage of the tire 2 and second data on the tire wear at the mileage, it is possible to calculate the wear state at a predetermined distance without having to drive the tire 2 a predetermined distance that exceeds the distance already traveled.

[0025] In the prediction method of this embodiment, an approximation equation is obtained when a certain trend appears in the wear of the tire 2, and the wear state of the tire 2 after traveling a predetermined distance can be predicted in a short time. Fig. 3 is a flowchart showing the processing procedure of the prediction method of the wear state of the tire 2 of this embodiment.

[0026] [Drive tires under specified conditions] In the prediction method of this embodiment, first, a tire 2 (shown in FIG. 2) is run under predetermined conditions (step S1). The tire 2 can be run as appropriate. In this embodiment, a drum test machine used in conventional wear tests is used for running (rolling) the tire 2.

[0027] 4 is a perspective view conceptually showing the drum testing machine 12. The drum testing machine 12 is configured to include a drum 13 for running the tire 2, and a support body 14 for supporting the tire 2. The drum testing machine 12 allows the tire 2 to run based on predetermined conditions. Note that the running of the tire 2 is not limited to an embodiment in which the drum testing machine 12 is used, and for example, a vehicle (not shown) may be used.

[0028] The conditions under which the tire 2 is run are appropriately set as long as a certain trend in the wear of the tire 2 can be exhibited as the running distance of the tire 2 increases. When the drum testing machine 12 is used as in this embodiment, examples of the conditions include the mounting conditions of the tire 2, the load application conditions of the tire 2, and the running speed in the drum testing machine 12. The mounting conditions include, for example, the conditions for achieving the above-mentioned normal state (normal rim 11, normal internal pressure). The load application conditions include, for example, the above-mentioned normal load. The running speed is set to a constant value (e.g., 60 km / h).

[0029] In addition, when a vehicle (not shown) is used to run the tire 2, examples of the conditions include the mounting conditions of the tire 2, the type of vehicle, the running speed of the vehicle, and the running course. The mounting conditions include, for example, the conditions for achieving the above-mentioned normal state. The type of vehicle may be, for example, a passenger car (engine displacement: 2000cc), but may be, for example, a truck or a bus depending on the category of the tire 2. The running speed is set to, for example, the average speed of the vehicle (for example, 60km / h). The running course is preferably set to, for example, a circuit course that allows the running conditions of the vehicle (frequency of free rolling, braking, driving, and turning) and the running speed to be kept constant. The running course is not limited to a circuit course, and may be, for example, a public road (including a highway, a mountain road, and a general road) whose route is predetermined.

[0030] In this manner, in this embodiment, by running the tire 2 under certain conditions, it becomes possible for the wear of the tire 2 to show a certain tendency as the running distance of the tire 2 increases.

[0031] [Acquire data including the first data and the second data in chronological order] Next, in the prediction method of the present embodiment, data including first data on the mileage of the tire 2 and second data on the wear of the tire 2 over the mileage is acquired in chronological order (step S2).

[0032] The first data is not particularly limited as long as it is related to the mileage of the tire 2. The first data may be the actual mileage (km) of the tire 2, or an index (hereinafter, sometimes referred to as "mileage index") obtained by dividing the actual mileage by a predetermined interval. The interval may be set to, for example, 50 to 500 km (100 km in this example). In this embodiment, the mileage index is used as the first data.

[0033] The second data may be set as appropriate as long as it is related to the wear of the tire 2 in terms of the mileage. The second data in this embodiment preferably includes the wear amount and wear index of the tire 2. The second data in this embodiment is specified as the wear index of the tire 2 measured for each mileage index (100 km in this example), but is not particularly limited. For example, the wear index may be acquired for each different mileage index (for example, 50 km, 200 km, etc.) depending on the driving conditions, or may be an accumulated value of the wear index accumulated for each mileage index. The wear index may be acquired as appropriate using the wear amount measured by a conventional procedure. The wear index in this embodiment is specified as a value obtained by dividing the total mileage up to the mileage index by the total wear amount up to the mileage index.

[0034] The second data (in this example, the wear index) may be measured at any position in the tread portion 2a shown in Fig. 2. In this embodiment, the second data is measured at at least one of the multiple land portions 10 (in this example, the crown land portion 10a, the pair of middle land portions 10b, 10b, and the shoulder land portions 10c, 10c). The second data of each land portion 10 may be a measurement value at any position in the tire circumferential direction, or may be an average value of measurement values ​​measured at multiple points in the tire circumferential direction.

[0035] In this embodiment, in step S2, each time a piece of data including the first data (mileage index) and the second data (wear index) is acquired (in this example, each time the tire 2 travels 100 km), the next step S3 is performed. Then, by repeatedly performing step S2, data including the first data (mileage index) and the second data (wear index) is acquired in chronological order. The acquired data is stored in a computer 1 (shown in FIG. 1).

[0036] [Find an approximate equation between the first and second data] Next, in the prediction method of this embodiment, an approximation equation that approximates the relationship between the first data and the second data is obtained using the acquired data (step S3). In this embodiment, the approximation equation is obtained by the computer 1 (shown in FIG. 1), but the approximation equation may be obtained by, for example, an operator or the like.

[0037] Fig. 5 is a graph showing the relationship between the first data (mileage index) and the second data (wear index). Fig. 5 shows the wear index when the mileage index is 1 to 13. In this graph, the wear index repeatedly increases and decreases when the mileage index is 1 to 8, but after that (mileage index 9 to 13), the wear index decreases at a substantially constant rate. In this way, a certain trend appears in the wear of the tire 2 as the mileage index (mileage) increases.

[0038] The approximation formula H may be obtained by linear approximation or curve approximation of the relationship between the first data (mileage index) and the second data (wear index). In this embodiment, the approximation formula H is preferably obtained by curve approximation in order to appropriately approximate the second data (wear index) which varies in the magnitude of increase or decrease. Such an approximation formula H can be easily obtained, for example, by commercially available numerical analysis software (for example, "MATLAB" manufactured by The MathWorks, Inc.).

[0039] In this embodiment, in step S2, each time a piece of data including the first data (mileage index) and the second data (wear index) is acquired (in this example, each time the tire 2 travels 100 km), step S3 is performed to obtain one approximation formula H. In this embodiment, each time new data is acquired, an approximation formula H that approximates the relationship between the first data and the second data is obtained using the data acquired so far. Then, steps S2 and S3 are repeatedly performed, and the approximation formula H can be updated (additionally acquired) each time additional data including the first data and the second data is acquired. In this way, the approximation formula H can be successively updated to more accurately represent the actual wear tendency of the tire 2.

[0040] Fig. 6 is a graph when new data is additionally acquired from Fig. 5. Fig. 6 shows the updated approximation formula H2 and the pre-update approximation formula H1.

[0041] The updated approximation formula H2 approximates the relationship between the first data and the second data for the data acquired up to the current step S2 (in FIG. 6, the mileage index is 1 to 14). On the other hand, the pre-update approximation formula H1 approximates the relationship between the first data and the second data for the data acquired up to the previous step S2 (in FIG. 6, the mileage index is 1 to 13). These approximation formulas H1 and H2 are stored in the computer 1 (shown in FIG. 1).

[0042] [Determine whether the difference between the approximate formula before and after the update is within a specified range] Next, in the prediction method of this embodiment, it is determined whether or not the difference between the updated approximation formula H2 and the pre-update approximation formula H1 is within a predetermined range (step S4). In this embodiment, the determination is made by the computer 1 (shown in FIG. 1), but it may also be made by an operator or the like.

[0043] In this embodiment, it is determined whether the difference between the updated approximation formula H2 using the data (mileage index is 1 to 14) acquired up to the current step S2 and the pre-update approximation formula H1 using the data (mileage index is 1 to 13) acquired up to the previous step S2 is within a predetermined range, but this is not limited to the embodiment. For example, it may be further determined whether the difference between the updated approximation formula H2 and the pre-update approximation formula (not shown) using the data (mileage index is 1 to 12) acquired up to the step S2 before the previous one is within a predetermined range.

[0044] It can be appropriately determined whether the difference between the updated approximation formula H2 and the pre-update approximation formula H1 (hereinafter, sometimes referred to as the "difference between the approximation formulas") is within a predetermined range. In this embodiment, it is determined whether the difference D between the second data (wear index) predicted by the updated approximation formula H2 and the second data (wear index) predicted by the pre-update approximation formula H1 is within a predetermined range.

[0045] The difference D of the second data (wear index) can be specified at a predetermined mileage index. In this embodiment, the mileage index at which the difference D of the second data is specified is specified at a mileage exceeding the distance already traveled by the tire 2 (a predetermined distance indicated by the mileage index P in FIG. 6). As a result, the difference of the approximation formula (difference D of the second data) is obtained based on the second data (wear index) predicted at a mileage exceeding the distance already traveled by the tire 2.

[0046] The mileage index for determining the second data difference D may be determined, for example, by the distance already traveled by the tire 2 (for example, the mileage index "14" in FIG. 6). In this way, the difference in the approximation formula (second data difference D) is determined based on the second data (wear index) predicted based on the actual travel distance of the tire 2.

[0047] The range used to determine the difference between the approximation expressions (difference D of the second data) can be set appropriately depending on the prediction accuracy of the wear state of the tire 2. In this embodiment, a range of ±5% of the maximum value of the second data (wear index) can be set.

[0048] In step S4, if it is determined that the difference between the approximation formulas (difference D of the second data) is within the above range ("Yes" in step S4), the second data predicted by the updated approximation formula H2 and the second data predicted by the pre-update approximation formula H1 are approximately the same. In this case, it is determined that these approximation formulas H1 and H2 were acquired when a certain trend appeared in the wear of the tire 2, and the next step S5 can be performed.

[0049] In step S4, if it is determined that the difference between the approximation expressions (difference D between the second data) is outside the above range ("No" in step S4), the second data predicted by the updated approximation expression H2 is deviated from the second data predicted by the pre-update approximation expression H1. In this case, it is determined that at least the pre-update approximation expression H1 was not acquired when a certain trend appeared in the wear of the tire 2, and steps S2 to S4 are performed again. As a result, the approximation expression H is updated using new data including the first data and the second data, so that the approximation expression H (including the updated approximation expression H2) is successively updated so as to more accurately represent the actual wear trend of the tire 2. Therefore, in the prediction method of this embodiment, the difference between the approximation expressions (difference D between the second data) can be set within the above range, and it is possible to acquire the approximation expressions H1 and H2 when a certain trend appears in the wear of the tire 2.

[0050] [Calculate wear condition] Next, in the prediction method of this embodiment, the wear state at a mileage exceeding the distance already traveled by the tire 2 is calculated (step S5). In this embodiment, the wear state is calculated by the computer 1 (shown in FIG. 1), but the wear state may be calculated by, for example, an operator or the like. Also, in step S5 of this embodiment, the travel of the tire 2 is terminated.

[0051] In this embodiment, the approximation formula H obtained in step S3 is used to calculate the wear state at a mileage exceeding the distance that the tire 2 has already traveled. As described above, if the determination in step S4 is affirmative (Yes), the approximation formula H1 before the update and the approximation formula H2 after the update are obtained when a certain trend appears in the wear of the tire 2. Therefore, the wear state can be calculated with high accuracy regardless of which of the approximation formulas H1 and H2 is used, but in this embodiment, the approximation formula (updated approximation formula H2) updated to accurately represent the actual wear trend of the tire 2 is used. As a result, the prediction method of this embodiment can calculate the wear state of the tire 2 with higher accuracy.

[0052] As shown in Fig. 6, when a wear index for each mileage index is specified as the second data, in step S5, a total value of the second data (wear index) predicted by the updated approximation formula H2 is calculated for each mileage index 1 to P. This makes it possible to obtain a total wear index when the new tire 2 has traveled up to the mileage indicated by the mileage index P. Note that when a cumulative value of the wear index for each mileage index is specified as the second data, the mileage index P is substituted into the updated approximation formula H2. This makes it possible to obtain a total wear index when the new tire 2 has traveled up to the mileage indicated by the mileage index P.

[0053] There is no particular limitation on the mileage index P as long as it is a mileage that exceeds the distance already traveled by the tire 2. The mileage index P in this embodiment is set to the above-mentioned predetermined distance (30,000 to 50,000 km).

[0054] In this way, in the prediction method of this embodiment, the wear state at a mileage (in this example, the mileage index P) exceeding the distance already traveled by the tire 2 can be calculated with high accuracy by using the approximation formula F2 updated at the stage when a tendency for wear appears. Therefore, in this embodiment, even if the tire 2 is not driven up to that mileage, the wear state at that mileage can be calculated, so that the wear state of the tire 2 can be predicted in a short time. The predicted wear state (in this example, the total wear index) is stored in the computer 1 (shown in FIG. 1).

[0055] [Evaluate tire wear condition] Next, in the prediction method of the present embodiment, the predicted wear state of the tire 2 is evaluated as to whether it is good or not (step S6). The wear state may be evaluated by the computer 1 (shown in FIG. 1) or may be evaluated by an operator or the like.

[0056] The wear state can be evaluated as appropriate. In the present embodiment, when the predicted total wear index is equal to or less than a predetermined threshold, the wear state can be determined to be good. The threshold can be set as appropriate according to the wear resistance performance required for the tire 2.

[0057] If it is determined in step S6 that the predicted wear state is good ("Yes" in step S6), the tire 2 meets the standard for wear resistance performance. In this case, the tire 2 is manufactured and produced (step S7). On the other hand, if it is determined in step S6 that the predicted wear state is not good ("No" in step S6), the tire 2 does not meet the above-mentioned standard. For this reason, a new tire 2 is prototyped by improving the structure of the tire 2 shown in FIG. 2 and the compounding of the tread rubber 8 (step S8), and steps S1 to S6 are performed again. As a result, the prediction method of this embodiment makes it possible to reliably manufacture and produce a tire 2 with excellent wear resistance performance.

[0058] [Method for predicting tire wear state (second embodiment)] In the above-described embodiment, the second data includes the wear index of the tire 2, but is not limited to such an embodiment. The second data may include the wear energy of the tire 2.

[0059] The second data in this embodiment is specified as the wear energy of the tire 2 measured at each mileage index (in this example, at intervals of 100 km) shown in Figures 5 and 6, but is not particularly limited thereto, and may be, for example, a cumulative value of the wear energy accumulated at each mileage index. For measuring the wear energy, for example, a wear energy tester described in a patent document (JP Patent Publication No. 2023-042390) is used.

[0060] In this embodiment, similarly to the previous embodiments, the approximate formula H is obtained using the acquired data (step S3), and it is determined whether or not the difference between the updated approximate formula H2 and the pre-update approximate formula H1 is within a predetermined range (step S4). If it is determined in step S4 that the difference between the approximate formulas is within the predetermined range ("Yes" in step S4), the wear state at a mileage exceeding the distance already traveled by the tire 2 is calculated (step S5).

[0061] When the wear energy at each mileage index is specified as the second data as in this embodiment, the updated approximation formula H2 is integrated in step S5 for the mileage index range of 1 to P. This makes it possible to obtain the total wear energy when the new tire 2 has traveled up to the mileage indicated by the mileage index P. Note that when a cumulative value of the wear energy for each mileage index is specified as the second data, the mileage index P is substituted into the updated approximation formula H2. This makes it possible to obtain the total wear energy when the new tire 2 has traveled up to the mileage indicated by the mileage index P.

[0062] Then, by multiplying the total wear energy by the wear index of the tread rubber 8 (shown in FIG. 2), the total amount of wear when the new tire 2 runs up to the mileage indicated by the mileage index P can be obtained. Here, the wear index of the tread rubber 8 indicates the relationship between the amount of wear of the tread rubber 8 and the wear energy of the tread rubber 8. Such a wear index can be obtained by a wear test using an indoor wear tester (such as a Lambourn wear tester), for example, as described in a patent document (JP Patent Publication 2019-218021 A).

[0063] In the prediction method of this embodiment, as in the previous embodiments, the predicted tire wear state is evaluated as to whether it is good or not (step S6). If it is determined to be good, the tire 2 is manufactured and produced (step S7). On the other hand, if it is determined to be not good, a new tire 2 is prototyped with an improved structure of the tire 2 and an improved compounding of the tread rubber 8 (step S8), and steps S1 to S6 are performed again. As a result, in the prediction method of this embodiment, as in the previous embodiments, it is possible to reliably manufacture and produce a tire 2 with excellent wear resistance.

[0064] [Method for predicting tire wear state (third embodiment)] In the above embodiments, the tire 2 shown in FIG. 2 is run under a predetermined condition, and data including the first data and the second data is acquired in a time-series order, but the present invention is not limited to such an embodiment. For example, a tire model in which the tire 2 is modeled may be run under a predetermined condition, and data including the first data and the second data may be acquired in a time-series order while the wear of the tire model progresses. FIG. 7 is a flowchart showing a processing procedure of a method for predicting the wear state of a tire 2 according to another embodiment of the present invention. A computer 1 (shown in FIG. 1) is used in the prediction method of this embodiment.

[0065] [Enter tire model] In the prediction method of this embodiment, first, a tire model in which a tire 2 (shown in FIG. 2) is modeled is input to the computer 1 (step S11). Fig. 8 is a perspective view showing an example of the tire model 22 and the road surface model 31. Fig. 9 is a cross-sectional view showing an example of the tire model 22. Note that the tire model 22 in Fig. 8 is shown in a simplified manner, with the tread pattern, element F(i), and the like omitted.

[0066] 9, in step S11 of this embodiment, the tire 2 is discretized using a finite number of elements F(i) (i=1, 2, ...) that can be handled by a numerical analysis method, based on the information on the tire 2 shown in FIG. 2. As a result, in step S11, a tire model 22 is set. As the numerical analysis method, for example, the finite element method, the finite volume method, the difference method, or the boundary element method is appropriately adopted. In this embodiment, the finite element method is adopted.

[0067] For example, a tetrahedral solid element, a pentahedral solid element, or a hexahedral solid element is used as the element F(i). Each element F(i) has a plurality of nodes 32. Furthermore, each element F(i) is provided with linear edges 33 connecting the nodes 32, 32. For each element F(i), numerical data such as an element number, a node 32 number, and a coordinate value of the node 32 are defined. Furthermore, for each element F(i), numerical data such as material properties (e.g., density, Young's modulus, damping coefficient, loss tangent (tan δ), and / or complex modulus of elasticity E*) of the tire components (e.g., tread rubber 8) shown in FIG. 2 are defined.

[0068] In the tire model 22, a carcass ply model 26A that models the carcass ply 6A (shown in FIG. 2) and belt ply models 27A and 27B that model the belt plies 7A and 7B (shown in FIG. 2), respectively, are set.

[0069] Furthermore, a tread rubber model 28 that models the tread rubber 8 (shown in FIG. 2) is set in the tire model 22. The tread pattern shown in FIG. 2 is reproduced on an outer surface 22S of the tread portion 22a (tread rubber model 28).

[0070] The tread portion 22a (tread rubber model 28) of this embodiment is provided with a circumferential groove 29 and a land portion 30, similar to the tread portion 2a (tread rubber 8) shown in FIG. 2. The circumferential groove 29 includes a pair of crown circumferential grooves 29a, 29a and a pair of shoulder circumferential grooves 29b, 29b. The land portion 30 includes a crown land portion 30a, a pair of middle land portions 30b, 30b, and a pair of shoulder land portions 30c, 30c. The tire model 22 is input to a computer 1 (shown in FIG. 1).

[0071] [Enter road surface model] Next, in the prediction method of this embodiment, a road surface model 31 (shown in FIG. 8) that models a road surface (not shown) is input to the computer 1 shown in FIG. 1 (step S12). In step S12, based on information about the road surface, the road surface is discretized using a finite number of elements G(i) (i=1, 2, ...) that can be handled by a numerical analysis method (in this embodiment, the finite element method). Thus, in step S12, the road surface model 31 is set.

[0072] Element G(i) is a rigid plane element that is set to be undeformable. Element G(i) has a plurality of nodes 34. Furthermore, element G(i) is defined with numerical data such as an element number and the coordinate values ​​of the nodes 34.

[0073] In this embodiment, the road surface model 31 has been exemplified as having a smooth surface, but as necessary, the road surface model may be provided with minute irregularities, irregular steps, depressions, undulations, or irregularities approximating the actual road surface such as ruts, as in an asphalt road surface. The road surface model 31 is input to a computer 1 (shown in FIG. 1).

[0074] [Run the tire model under specified conditions] Next, in the prediction method of this embodiment, the computer 1 (shown in FIG. 1) runs the tire model 22 under predetermined conditions to advance the wear of the tire model 22 (step S13). The conditions for running the tire model 22 are appropriately set as long as a certain trend can be expressed in the wear of the tire model 22 by the advancement of wear with an increase in the running distance of the tire model 22. Examples of the conditions include the mounting conditions of the tire model 22, the load application conditions of the tire model 22, and the running speed of the tire model 22. The mounting conditions include, for example, the conditions for achieving the above-mentioned normal state (normal rim 11, normal internal pressure). The load application conditions include the above-mentioned normal load. The running speed is set to, for example, a constant (for example, 60 km / h).

[0075] In step S13 of this embodiment, a state in which the tire model 22 runs (rolls) on the road surface model 31 is calculated, similarly to the procedure described in the patent document (JP 2019-91302 A). The running of the tire model 22 is calculated based on the above conditions. Then, in step S13, the movement of each node 32 of the tread portion 22a is calculated based on the wear energy calculated at each node 32 of the tread portion 22a (tread rubber model 28) shown in FIG. 9. As a result, in step S13, a state in which wear of the tire model 22 (tread portion 22a) progresses can be calculated.

[0076] In this manner, in this embodiment, the running and wear progression of the tire model 22 are calculated under certain conditions, so that it is possible to obtain a certain trend in the wear of the tire model 22 as the running distance of the tire model 22 increases.

[0077] [Acquire data including the first data and the second data in chronological order] Next, in the prediction method of this embodiment, data including first data on the travel distance of the tire model 22 and second data on the wear of the tire model 22 over the travel distance are acquired in chronological order (step S14).

[0078] The first data is not particularly limited as long as it is related to the travel distance of the tire model 22. The first data may be the travel distance (km) that the tire model 22 has traveled on the road surface model 31, or may be an index (travel distance index) obtained by dividing the travel distance by a predetermined interval. The interval may be set to, for example, 50 to 500 km (100 km in this example) as in the previous embodiments. The travel distance index is used as the first data in this embodiment.

[0079] The second data may be set as appropriate as long as it is related to wear of the tire model 22 over the mileage. The second data in this embodiment includes the wear energy of the tire model 22. The second data in this embodiment is specified as the wear energy of the tire model 22 calculated for each mileage index (100 km in this example), but is not particularly limited to this. For example, the wear energy may be acquired for each different mileage index (e.g., 50 km, 200 km, etc.), or may be an accumulated value of the wear energy accumulated for each mileage index. The wear energy may be calculated when the wear of the tire model 22 is advanced in step S13.

[0080] In this embodiment, in step S14, each time a piece of data including the first data (mileage index) and the second data (wear energy) is calculated (in this example, each time the tire model 22 travels 100 km), the next step S15 is performed. Then, by repeatedly performing step S14, data including the first data (mileage index) and the second data (wear energy) are calculated in chronological order. The calculated data is stored in the computer 1 (shown in FIG. 1).

[0081] [Find an approximate equation between the first and second data] Next, in the prediction method of this embodiment, the computer 1 (shown in FIG. 1) uses the calculated data to obtain an approximation equation that approximates the relationship between the first data and the second data (step S15). In step S15 of this embodiment, an approximation equation H (shown in, for example, FIGS. 5 and 6) is obtained in the same procedure as in the previous embodiments.

[0082] In this embodiment, similarly to the previous embodiments, step S15 is performed to obtain one approximation formula H every time a piece of data including the first data and the second data is calculated in step S14 (in this example, every time the tire model 22 travels 100 km). In this embodiment, every time new data is calculated, an approximation formula H that approximates the relationship between the first data and the second data is obtained using data acquired up to that point. Then, steps S14 and S15 are repeatedly performed, so that the approximation formula H can be updated every time data including the first data and the second data is additionally calculated. In this way, the approximation formula H can be successively updated so as to more accurately represent the wear tendency of the tire model 22 (actual tire 2). The approximation formula H is stored in the computer 1 (shown in FIG. 1).

[0083] [Determine whether the difference between the approximate formula before and after the update is within a specified range] Next, in the prediction method of this embodiment, the computer 1 (shown in FIG. 1) judges whether or not the difference between the updated approximation formula and the approximation formula before the update is within a predetermined range (step S16). In this embodiment, based on the same procedure as in the previous embodiments, it is judged whether or not the difference between the approximation formula (i.e., the difference between the updated approximation formula and the approximation formula before the update) is within the above range.

[0084] If it is determined in step S16 that the difference between the approximation expressions (difference D of the second data shown in FIG. 6) is within the above range ("Yes" in step S16), the next step S17 may be performed. On the other hand, if it is determined in step S16 that the difference between the approximation expressions (difference D of the second data) is outside the above range ("No" in step S16), steps S13 to S16 may be performed again. As a result, the approximation expression H is updated using new data including the first data and the second data, so that the approximation expression H is successively updated so as to more accurately represent the wear tendency of the tire model 22 (actual tire 2). Therefore, it is possible to obtain the approximation expressions H1 and H2 when a certain tendency appears in the wear of the tire model 22.

[0085] [Calculate wear condition] Next, in the prediction method of this embodiment, the computer 1 (shown in FIG. 1) calculates the wear state after a mileage that exceeds the distance already traveled by the tire model 22 (step S17). In step S17, the wear state after a mileage that exceeds the distance already traveled by the tire model 22 is calculated using the approximation formula H (shown in FIG. 6, for example) obtained in step S15. Also, in step S17 of this embodiment, the traveling of the tire model 22 has ended.

[0086] In this embodiment, as in the previous embodiments, of the pre-update approximation formula H1 and the post-update approximation formula H2, the post-update approximation formula H2 is used, which is updated so as to accurately represent the wear tendency of the actual tire model 22. As a result, the prediction method of this embodiment can calculate the wear state more accurately.

[0087] When the wear energy is calculated for each mileage index as the second data as in this embodiment, in step S17, the updated approximation formula H2 is integrated over the range of mileage index from 1 to P. This makes it possible to obtain the total wear energy when the new tire model 22 has traveled the mileage indicated by the mileage index P. Note that when a cumulative value of the wear energy for each mileage index is specified as the second data, the mileage index P is substituted into the updated approximation formula H2. This makes it possible to obtain the total wear energy when the new tire model 22 has traveled the mileage indicated by the mileage index P.

[0088] Then, by multiplying the total wear energy by the wear index of the actual tread rubber 8 (shown in FIG. 2), the total amount of wear when the new tire model 22 is driven up to the mileage indicated by the mileage index P can be calculated.

[0089] In this manner, in the prediction method of this embodiment, as in the previous embodiments, the approximation formula H updated at the stage when a tendency for wear appears can be used to accurately calculate the wear state at a mileage (in this example, the mileage index P) that exceeds the distance already traveled by the tire model 22. Therefore, in this embodiment, even if the tire model 22 is not driven up to that mileage, the wear state at that mileage can be calculated, so that the wear state of the tire 2 (tire model 22) can be predicted in a short time. The predicted wear state (in this example, the total wear amount) is stored in the computer 1 (shown in FIG. 1).

[0090] [Evaluate tire wear condition] Next, in the prediction method of this embodiment, the predicted wear state of the tire model 22 (tire 2) is evaluated as to whether it is good or not (step S18). The wear state may be evaluated by the computer 1 (shown in FIG. 1) or by an operator or the like.

[0091] The wear state can be evaluated as appropriate. In this embodiment, the wear state can be determined to be good when the predicted total wear amount is equal to or less than a predetermined threshold. The threshold can be set as appropriate according to the wear resistance performance required for the tire 2 shown in FIG. 2.

[0092] If it is determined in step S18 that the predicted wear state is good ("Yes" in step S18), the tire model 22 meets the standard for wear resistance performance. In this case, the tire 2 (shown in FIG. 2) is manufactured and produced based on the design factors of the tire model 22 (step S19). On the other hand, if it is determined in step S18 that the predicted wear state is not good ("No" in step S18), the above standard is not met. For this reason, the structure and design factors of the tire 2 are changed (step S20), and steps S11 to S18 are performed again. As a result, the prediction method of this embodiment makes it possible to reliably manufacture and produce a tire 2 with excellent wear resistance performance.

[0093] [Method for predicting tire wear state (fourth embodiment)] In the embodiment (third embodiment) described above, the second data includes the wear energy of the tire model 22, but is not limited to this. The second data may include the wear amount and wear index of the tire model 22.

[0094] The second data in this embodiment is specified as the amount of wear of the tire model 22 measured at each mileage index (in this example, at intervals of 100 km), but is not particularly limited thereto, and may be, for example, an accumulated value of the amount of wear accumulated at each mileage index. The amount of wear can be calculated based on the amount of wear progression of the tire model 22 (the amount of movement of the node 32).

[0095] In this embodiment, similarly to the previous embodiments, an approximation formula H is obtained using the acquired data (step S15), and it is determined whether or not the difference between the updated approximation formula H2 and the pre-update approximation formula H1 is within a predetermined range (step S16). If it is determined in step S16 that the difference between the approximation formulas is within the predetermined range ("Yes" in step S16), the wear state at a mileage exceeding the distance already traveled by the tire model 22 is calculated (step S17).

[0096] In this embodiment, when the wear amount at each mileage index is calculated as the second data, in step S17, the sum of the second data (wear amount) predicted by the updated approximation formula H2 is calculated for each mileage index from 1 to P. This allows the total wear amount when the new tire model 22 has traveled the mileage indicated by the mileage index P to be obtained. Note that, when the cumulative wear amount is calculated for each mileage index as the second data, the mileage index P is substituted into the updated approximation formula H2. This allows the total wear amount when the new tire model 22 has traveled the mileage indicated by the mileage index P to be obtained.

[0097] In the prediction method of this embodiment, as in the previous embodiments, the predicted wear state of the tire model is evaluated as to whether it is good or not (step S18). Then, if it is judged to be good, the tire 2 (shown in FIG. 2) is manufactured and produced based on the design factors of the tire model 22 (step S19). On the other hand, if it is judged to be not good, the design factors of the tire model 22 are changed (step S20), and steps S11 to S18 are performed again. As a result, in the prediction method of this embodiment, as in the previous embodiments, it is possible to reliably manufacture and produce a tire 2 with excellent wear resistance.

[0098] Although a particularly preferred embodiment of the present invention has been described in detail above, the present invention is not limited to the illustrated embodiment and can be modified and carried out in various forms.

[0099] [Note] The present invention includes the following aspects.

[0100] [Invention 1] 1. A method for predicting a tire wear state, comprising: running the tire under a predetermined condition and sequentially acquiring data in a time series, the data including first data on a travel distance of the tire and second data on wear of the tire over the travel distance; A step of obtaining an approximation equation that approximates a relationship between the first data and the second data by using the acquired data, the approximation equation being updated each time the data is additionally acquired; a step of calculating a wear state of the tire at a mileage exceeding a distance already traveled by the tire, using the updated approximation equation when a difference between the updated approximation equation and the approximation equation before the update falls within a predetermined range; Includes How to predict tire wear condition. [Invention 2] The method for predicting the wear state of a tire according to the first aspect of the present invention, wherein the second data includes the wear energy of the tire. [Invention 3] 3. The method for predicting a wear state of a tire according to claim 1 or 2, wherein the second data includes an amount of wear of the tire. [Invention 4] 1. A method for predicting a tire wear state, comprising: inputting a tire model obtained by modeling the tire into a computer; a step of the computer running the tire model under a predetermined condition to progress wear of the tire model, and sequentially calculating data including first data on a travel distance of the tire model and second data on wear of the tire model for the travel distance in a time series manner; A step of obtaining an approximation equation that approximates a relationship between the first data and the second data by using the calculated data, the approximation equation being updated each time the data is additionally calculated; a step of calculating a wear state of the tire model at a mileage exceeding a distance already traveled by the tire model, by using the updated approximation equation when a difference between the updated approximation equation and the approximation equation before the update falls within a predetermined range; Includes How to predict tire wear condition. [Invention 5] A method for predicting a tire wear state according to the present invention 4, wherein the second data includes the wear energy of the tire model. [Invention 6] 6. The method for predicting a tire wear state according to claim 4 or 5, wherein the second data includes an amount of wear of the tire model. [Explanation of symbols]

[0101] H1 Approximation after update H2 Approximation before update

Claims

1. 1. A method for predicting a tire wear state, comprising: a step of running the tire under a predetermined condition and sequentially acquiring data in a time series manner, the data including first data on a running distance of the tire and second data on wear of the tire over the running distance; A step of obtaining an approximation equation that approximates a relationship between the first data and the second data by using the acquired data, the approximation equation being updated each time the data is additionally acquired; a step of calculating a wear state of the tire at a mileage exceeding a distance already traveled by the tire, using the updated approximation equation when a difference between the updated approximation equation and the approximation equation before the update falls within a predetermined range; Includes How to predict tire wear condition.

2. The method for predicting a wear state of a tire according to claim 1 , wherein the second data includes a wear energy of the tire.

3. The method for predicting a wear state of a tire according to claim 1 , wherein the second data includes an amount of wear of the tire.

4. 1. A method for predicting a tire wear state, comprising: inputting a tire model obtained by modeling the tire into a computer; a step of the computer running the tire model under a predetermined condition to progress wear of the tire model, and sequentially calculating data including first data on a travel distance of the tire model and second data on wear of the tire model for the travel distance in a time series manner; a step of obtaining an approximation equation that approximates a relationship between the first data and the second data by using the calculated data, the approximation equation being updated each time the data is additionally calculated; a step of calculating a wear state of the tire model at a mileage exceeding a distance already traveled by the tire model, by using the updated approximation equation when a difference between the updated approximation equation and the approximation equation before the update falls within a predetermined range; Includes How to predict tire wear condition.

5. The method of claim 4 , wherein the second data includes a wear energy of the tire model.

6. The method for predicting a tire wear state according to claim 4 , wherein the second data includes a wear amount of the tire model.

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