Tire performance prediction device, performance prediction method, and learned model generation method

Abstract

To provide a tire performance prediction device capable of estimating tire performance on the basis of an image showing a tire skeleton shape in a tire meridian cross section SOLUTION: A derivation unit that inputs a datum 21 including an image 21A of a target tire to a learned model 24B that is machine-learned so as to output a performance datum 22 of the tire when a datum 21 including an image 21A representing a tire frame shape in a tire meridian cross-section is inputted, and outputs the performance datum 22 of the target tire.SELECTED DRAWING: Figure 7

Description

[Technical Field]

[0001] The present invention relates to a tire performance prediction device, a performance prediction method, and a method for generating a trained model. [Background technology]

[0002] Patent Document 1 listed below describes a tire image recognition method. This method involves inputting an image of the contact patch of a target tire to be recognized into a machine learning model that has learned the relationship between an image representing the tire contact patch and the contour of the tire contact patch, deriving an output, and estimating the contour of the contact patch of the target tire based on the derived output. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2024-048936 Summary of the Invention [Problem to be solved by the invention]

[0004] It is generally known that tire performance varies depending on the tire frame shape in the tire meridian cross section. Therefore, if tire performance values ​​and the like could be predicted from the tire frame shape, there would be an advantage in that the costs of prototyping and testing could be reduced. However, no specific method has been proposed for predicting tire performance based on the tire frame shape.

[0005] The present invention has been devised in view of the above-described circumstances, and has as its main object to provide a tire performance prediction device capable of estimating tire performance based on an image representing the tire skeleton shape in a tire meridian cross section. [Means for solving the problem]

[0006] The present invention is a tire performance prediction device that includes a derivation unit that inputs data including an image of a target tire into a trained model that has been machine-learned to output tire performance data when data including an image representing the tire skeletal shape at a tire meridian cross section is input, and outputs the performance data of the target tire. [Effects of the Invention]

[0007] The tire performance prediction device of the present invention has the above-described configuration, and its main object is to provide a tire performance prediction device that can estimate tire performance based on an image representing the tire skeleton shape in a tire meridian cross section. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram illustrating an example of a tire performance prediction device and a trained model generation device. [Figure 2] 1 is a tire meridian cross-sectional view showing an example of a tire. [Figure 3] FIG. 2 is a development view of the tread portion of the tire. [Figure 4] 1 is a flowchart illustrating an example of a processing procedure for a method for generating a trained model. [Figure 5] 10 is a flowchart showing an example of a processing procedure of a preparation step. [Figure 6] FIG. 10 is a diagram illustrating an example of a training dataset. [Figure 7] FIG. 1 is a conceptual diagram illustrating an example of a machine learning model (trained model). [Figure 8] 1 is a flowchart showing an example of a processing procedure of a tire performance prediction method. [Figure 9] FIG. 10 is a diagram illustrating an example of a training dataset according to another embodiment of the present invention. [Figure 10] FIG. 1 is a conceptual diagram illustrating an example of a machine learning model (trained model) into which condition data is input. DETAILED DESCRIPTION OF THE INVENTION

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

[0010] [Tire performance prediction device] FIG. 1 is a block diagram showing an example of a tire performance prediction device 1A and a trained model generation device 1B. The tire performance prediction device (hereinafter, sometimes referred to as a "prediction device") 1A is a device for predicting the performance of a target tire (a tire to be evaluated). The prediction device 1A of this embodiment is also configured as a device (hereinafter, sometimes referred to as a "generation device") 1B for generating a trained model. Note that the prediction device 1A and the generation device 1B may be configured separately.

[0011] The prediction device 1A (generation device 1B) of this embodiment is configured as a general-purpose computer 1. Examples of the computer 1 include a desktop personal computer, a laptop personal computer, a tablet, a smartphone, and a cloud server.

[0012] The prediction device 1A (generation device 1B) of this embodiment includes an input device 3, an output device 4, and a calculation processing device 5.

[0013] [Input section, output section, arithmetic processing unit] The input device 3 is configured as an input device. Examples of this input device include a keyboard or a mouse. The output device 4 is configured as an output device. Examples of this output device include a display device or a printer. The arithmetic processing device 5 is used to predict the performance of a target tire. The arithmetic processing device 5 of this embodiment is configured to include one or more processors (arithmetic units) 5A that perform various calculations, a storage unit 5B in which data, programs, etc. are stored, and a working memory 5C.

[0014] [Processor] The processor 5A in this embodiment is configured as a central processing unit (CPU), but is not particularly limited thereto and may be configured as, for example, a microprocessor or other processing unit. In this embodiment, a mode is exemplified in which the tire performance prediction method (hereinafter sometimes referred to as the "prediction method") and the trained model generation method (hereinafter sometimes referred to as the "generation method") are executed by one processor 5A, but they may also be executed by multiple processors (parallel processing).

[0015] [Storage] The storage unit 5B is a non-volatile information storage device formed of, for example, a magnetic disk, an optical disk, an SSD, etc. The storage unit 5B is provided with a data unit 7 and a program unit 8.

[0016] [Data section] The data unit 7 of this embodiment is for storing data and the like necessary for executing the prediction method and the generation method. The data unit 7 of this embodiment includes a target data storage unit 7A, a target performance data storage unit 7B, a learning dataset storage unit 7C, and a model storage unit 7D. Note that the data unit 7 is not limited to this configuration, and may include storage units for storing other data as needed, or some of these may be omitted. The data stored in each storage unit 5B will be explained in each step of the prediction method and generation method described below.

[0017] [Program section] The program unit 8 of this embodiment is a program (application) required to execute the prediction method and the generation method. When such program unit 8 is executed by the processor (calculation unit) 5A, the computer 1 (prediction device 1A and generation device 1B) can function as a specific means. The program unit 8 of this embodiment includes an input unit 8A, a derivation unit 8B, a preparation unit 8C, a learning unit 8D, and an evaluation unit 8E. Note that the program unit 8 is not limited to this configuration, and other programs may be included as needed, or some of these may be omitted. The functions of each program unit 8 will be explained in the respective steps of the prediction method and generation method described below.

[0018] [tire] 2 is a tire meridian cross-sectional view showing an example of a tire 11. The tire 11 of this embodiment is exemplified as a pneumatic tire mounted on an SUV or a passenger car, but is not limited to this. The tire 11 may also be a tire of another category, such as a heavy-duty tire for trucks, buses, etc.

[0019] The tire 11 of this embodiment includes a tread portion 12, a pair of sidewall portions 13, and a pair of bead portions 14. The bead portions 14 have, for example, an annularly extending bead core 15. The tire 11 of this embodiment includes a carcass 16 and a belt layer 17.

[0020] [Carcass] The carcass 16 extends between the pair of bead portions 14. The carcass 16 includes at least one carcass ply 16A (one in this example).

[0021] The carcass ply 16A includes, for example, a main body portion 16a and a turned-up portion 16b. The main body portion 16a extends, for example, between the two bead portions 14. The turned-up portion 16b is continuous with the main body portion 16a and is turned up around the bead core 15 from the axially inner side to the outer side. A bead apex 18 extending from the bead core 15 to the radially outer side of the tire is disposed between the main body portion 16a and the turned-up portion 16b. The carcass ply 16A has a plurality of carcass cords (not shown) arranged in parallel. These carcass cords may be made of organic fiber cords such as aramid or rayon. The framework of the tire 11 may be formed by such a carcass 16 (carcass ply 16A).

[0022] [Belt layer] The belt layer 17 is disposed in the tread portion 12. The belt layer 17 includes at least one belt ply 17A, 17B, two in this embodiment. These belt plies 17A, 17B have a plurality of belt cords (not shown) arranged in parallel. Steel cords are used as the belt cords, but highly elastic organic fiber cords such as aramid and rayon can also be used as needed.

[0023] [Tire frame shape] As described above, the skeleton of the tire 11 is formed by the carcass 16 (carcass ply 16A). Therefore, in the tire meridian cross section, the contour line 19a of the carcass 16 represents the tire skeleton shape 20. Furthermore, the contour line 19b of the tire outer surface 11o, the contour line 19c of the tire cavity surface 11i, and the contour line 19d of the belt layer 17 (belt plies 17A, 17B), which are formed based on the carcass 16, also represent the tire skeleton shape 20.

[0024] [Tread section] Fig. 3 is a development view of the tread portion 12 of the tire 11. As shown in Fig. 2 and Fig. 3, the outer surface 12o of the tread portion 12 of this embodiment includes a contact patch 31 that contacts the road surface (not shown) and grooves 32 that are recessed radially inward from the contact patch 31. Furthermore, the outer surface 12o of the tread portion 12 may include sipes 33 that are narrower than the grooves 32 (for example, 1.5 mm or less).

[0025] As shown in FIG. 3 , the groove 32 of this embodiment includes a longitudinal groove 32A and a lateral groove 32B. The longitudinal groove 32A of this embodiment extends continuously in a zigzag pattern in the tire circumferential direction, but may extend continuously in a straight line. In this embodiment, four longitudinal grooves 32A are provided to divide the land portion 34 into five portions, but this is not limited to this. Furthermore, the lateral groove 32B extends in the land portion 34 in the tire axial direction. This divides the land portion 34 into a plurality of blocks 35.

[0026] In the tread portion 12 of this embodiment, a tread pattern is formed in which a plurality of pitches (not shown), which are pattern constituent units, are arranged in the tire circumferential direction by the longitudinal grooves 32A and the lateral grooves 32B. Each pitch may be of only one type having the same circumferential length, or may be of multiple types having different circumferential lengths.

[0027] Incidentally, it is known that the performance of a tire 11 varies depending on the tire frame shape 20 in the tire meridian cross section shown in Fig. 2. Therefore, if tire performance values ​​and the like could be predicted from the tire frame shape 20, there would be an advantage in that the costs of prototyping and testing could be reduced. However, no specific method has been proposed for predicting tire performance based on the tire frame shape 20.

[0028] In the prediction method of this embodiment (prediction device 1A shown in FIG. 1), data including an image representing a tire skeleton shape 20 (shown in FIG. 2) of a target tire is input to a trained model (machine learning model) that has been trained in advance by machine learning. This makes it possible to estimate performance data of the target tire (hereinafter sometimes referred to as "performance data").

[0029] In this embodiment, prior to the implementation of the prediction method, a trained model is generated based on the generation method. Note that, when the prediction method is implemented, if a prediction model has already been generated, the generation method may be omitted.

[0030] [How to generate a trained model] In the generation method of this embodiment, a trained model is generated. The trained model of this embodiment is a model that has been machine-learned so as to output performance data of a target tire (a tire to be evaluated) when data including an image representing the tire skeleton shape 20 of the target tire is input. In order to create such a trained model, it is effective to train the machine learning model using a training dataset that combines data and performance data acquired from multiple types of tires 11.

[0031] 4 is a flowchart showing an example of a processing procedure of a method for generating a trained model. Each step of the generation method of this embodiment is executed by one or more processors 5A included in the generation device 1B (computer 1) shown in FIG.

[0032] [Prepare the training dataset (preparation process)] In the generation method of this embodiment, first, a training dataset is prepared (preparation step S1). In the preparation step S1 of this embodiment, first, a preparation unit 8C included in the program unit 8 shown in FIG. 1 is loaded into the working memory 5C. The preparation unit 8C is a program for preparing the training dataset. When this preparation unit 8C is executed by the processor 5A, the computer 1 (generation device 1B) can function as a means for preparing the training dataset.

[0033] As described above, the training data set is used to train the machine learning model. This training data set is a combination of data including an image representing the tire skeleton shape 20 shown in FIG. 2 and performance data of the tire 11.

[0034] The training dataset is preferably acquired based on multiple types of tires 11. The categories of the multiple types of tires 11 are not particularly limited. For example, the multiple types of tires 11 may be classified as the same category from the viewpoint of improving the prediction accuracy of the trained model (machine learning model), or may be classified as different categories from the viewpoint of improving the versatility of the trained model (machine learning model). The multiple types of tires 11 preferably differ from each other in at least one of, for example, category, tire size, tread pattern, and internal structure (including carcass and belt layer). FIG. 5 is a flowchart showing an example of the processing procedure of the preparation step S1. FIG. 6 is a diagram showing an example of the training dataset 23. FIG. 6 shows a training dataset 23 acquired from one tire 11 as a representative.

[0035] [Acquire data including images showing the tire frame shape] In the preparation step S1 of this embodiment, first, data 21 including an image (hereinafter sometimes referred to as a "tire skeleton image") 21A representing the tire skeleton shape 20 shown in Fig. 2 is acquired (step S11). In step S11 of this embodiment, data 21 including the tire skeleton image 21A is acquired for each of a plurality of types of tires 11.

[0036] The data 21 may be only the tire skeleton image 21A, or may further include other data in addition to the tire skeleton image 21A. The data 21 of this embodiment includes only the tire skeleton image 21A.

[0037] The tire skeleton image 21A can be acquired as appropriate. For example, the tire skeleton image 21A may be acquired by actually photographing a meridian cross section of the tire 11 shown in FIG. 2, or may be acquired from design data (CAD data) of the tire 11, or may be a hand-drawn image by an operator. The tire skeleton image 21A may also be acquired from a tire model (not shown) modeled based on the design data of the tire 11. This tire model can be modeled as appropriate based on, for example, a known method (for example, the method described in Japanese Patent Application Laid-Open No. 2023-084539). The tire skeleton image 21A of the present embodiment can be acquired from the tire model. This allows the tire skeleton image 21A to be acquired at low cost and in a short time without actually manufacturing multiple types of tires 11.

[0038] The tire skeleton image 21A may be configured as, for example, a color image, a grayscale image, or a black-and-white image. The tire skeleton image 21A of this embodiment is configured as a grayscale image. Such a grayscale image has the advantage of being able to identify the tire skeleton shape with high resolution compared to a black-and-white image. On the other hand, a grayscale image has the advantage of having a smaller data size compared to a color image.

[0039] The tire skeleton image 21A can be acquired as appropriate. The tire skeleton image 21A of this embodiment can be acquired of an unempuffed tire 11 (shown in FIG. 2). This allows the tire skeleton image 21A to be acquired in a shorter time than when the tire is inflated. The acquisition of the tire skeleton image 21A is not limited to a mode in which the tire 11 is unempuffed. For example, the tire skeleton image 21A may be acquired of a tire 11 mounted on a rim and inflated to a predetermined pressure, or may be acquired of a tire 11 to which a predetermined load is applied. This stabilizes the shape of the tire 11, and further allows tire skeleton images 21A of multiple types of tires 11 to be acquired under the same conditions.

[0040] The rim (not shown) is exemplified as a genuine rim. A "genuine rim" is a rim defined for each tire by a standard system that includes the standard on which the tire 11 is based. Therefore, a genuine rim is, for example, a "standard rim" for JATMA, a "design rim" for TRA, or a "measuring rim" for ETRTO.

[0041] The internal pressure is exemplified as a normal internal pressure. The "normal internal pressure" is the air pressure determined for each tire by each standard in a standard system including the standard on which the tire 11 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.

[0042] The load is exemplified as a normal load. The "normal load" is the load determined for each tire by each standard in a standard system including the standard on which the tire 11 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, or "LOAD CAPACITY" in the case of ETRTO.

[0043] The tire skeleton image 21A may include a contour line 19b of the tire outer surface 11o. This tire outer surface 11o comes into contact with the road surface (not shown) during running. Therefore, the contour line 19b of the tire outer surface 11o has a strong correlation with tire performance (performance data 22), such as the rolling resistance coefficient of the tire 11, the tire contact shape, and the air resistance value of the tire 11.

[0044] The tire skeleton image 21A may include a contour line 19c of the tire cavity surface 11i. The shape of the tire cavity surface 11i can identify, for example, the tire thickness between the tire outer surface 11o and the presence or absence of reinforcing rubber in a run-flat tire. Therefore, the contour line 19c of the tire cavity surface 11i has a strong correlation with tire performance (performance data 22) including, for example, the rolling resistance coefficient of the tire 11 and the rigidity of the tire 11.

[0045] The tire skeleton image 21A may include a contour line 19a of the carcass 16. As described above, the carcass 16 forms the skeleton of the tire 11. Therefore, the contour line 19a of the carcass 16 has a strong correlation with tire performance (performance data 22) including, for example, the rolling resistance coefficient of the tire 11, the contact shape of the tire 11, the rigidity of the tire 11, and the air resistance value of the tire 11.

[0046] The tire skeleton image 21A may include a contour line 19d of the belt layer 17. This belt layer 17 tightens the carcass 16 to increase the rigidity of the tread portion 12. Therefore, the contour line 19d of the belt layer 17 has a strong correlation with tire performance (performance data 22) including, for example, the rolling resistance coefficient of the tire 11, the contact shape of the tire 11, the rigidity of the tire 11, and the air resistance value of the tire 11.

[0047] The tire skeleton image 21A of this embodiment includes a contour line 19b of the tire outer surface 11o, a contour line 19c of the tire cavity surface 11i, a contour line 19a of the carcass 16, and a contour line 19d of the belt layer 17. The tire skeleton image 21A may include at least one contour line selected from these contour lines 19a to 19d (for example, only the contour line 19a of the carcass 16). The tire skeleton image 21A may also include other contour lines (not shown), such as the bead core 15 shown in FIG. 2. As described above, the tire skeleton image 21A including at least one of these contour lines 19a to 19d has a strong correlation with tire performance (performance data 22).

[0048] The tire skeleton image 21A in this embodiment represents the tire skeleton shape 20 on both sides in the tire axial direction with respect to the tire equator C, but is not particularly limited thereto. The tire skeleton image 21A may represent, for example, the tire skeleton shape 20 on either side in the tire axial direction. Data 21 including the tire skeleton image 21A is input to the learning dataset storage unit 7C shown in FIG. 1.

[0049] [Get tire performance data] Next, in the preparation step S1 of this embodiment, performance data 22 (shown in FIG. 6) of each tire 11 is acquired (step S12). In step S12 of this embodiment, the performance data 22 is acquired for each of a plurality of types of tires 11.

[0050] The performance data 22 is not particularly limited as long as it relates to the performance of the tire 11. The performance data 22 in this embodiment includes at least one of the rolling resistance coefficient, the tire contact shape, the tire stiffness, and the tire air resistance value of the tire 11. These performance data 22 affect the shapes (contours) of the tire outer surface 11o, the tire cavity surface 11i, the carcass 16, and the belt layer 17, and therefore have a strong correlation with the tire skeleton image 21A.

[0051] The performance data 22 can be appropriately acquired by known procedures. The rolling resistance coefficient of the tire can be acquired, for example, by using a known rolling resistance tester. The contact shape of the tire can be acquired, for example, by the footprint of the tire 11. The stiffness of the tire 11 can be acquired, for example, by measuring the longitudinal spring constant of the tire 11. The air resistance value of the tire can be acquired, for example, by a wind tunnel test of the tire 11. Furthermore, the performance data 22 may be acquired by a simulation using a computer 1 (shown in FIG. 1). For such a simulation, it is preferable to use the tire model described above.

[0052] The performance data 22 may be at least one of the rolling resistance coefficient of the tire 11, the contact shape of the tire 11, the stiffness of the tire 11, and the air resistance value of the tire 11, or all of these, or other performance data may be further acquired. In this embodiment, the rolling resistance coefficient 22A of the tire 11 is acquired as the performance data 22. FIG. 6 shows a case where the rolling resistance coefficient 22A is "6.0". The performance data 22 is input to the learning dataset storage unit 7C shown in FIG. 1.

[0053] [Create a training dataset] Next, in the preparation step S1 of this embodiment, a learning dataset 23 (shown in FIG. 6) is created (step S13). In step S13 of this embodiment, data 21 (in this example, a tire skeleton image 21A) and performance data 22 (in this example, rolling resistance coefficient 22A) are combined for each of a plurality of types of tires 11. In this way, a plurality of learning datasets 23 are created.

[0054] In this embodiment, in step S2 described below, training data sets 23 of multiple types of tires 11 are used to train the machine learning model. This makes it possible to generate a trained model that can output performance data 22 (in this example, rolling resistance coefficient 22A) of the target tire when data 21 (in this example, tire skeleton image 21A) of the target tire is input. The multiple training data sets 23 are input to the training data set storage unit 7C shown in FIG. 1.

[0055] [Training the machine learning model] Next, in the generation method of this embodiment, learning of a machine learning model is performed using the learning dataset 23 shown in Fig. 6 (step S2). In step S2, the machine learning model is trained so that when data 21 including a tire skeleton image 21A of a target tire (in this example, the tire skeleton image 21A) is input, performance data 22 of the target tire (in this example, the rolling resistance coefficient 22A of the tire 11) is output.

[0056] In step S2 of this embodiment, first, the training dataset 23 (shown in FIG. 6) stored in the training dataset storage unit 7C shown in FIG. 1 and a training unit 8D included in the program unit 8 are loaded into the working memory 5C. The training unit 8D is a program for training a machine learning model using the training dataset 23. When the training unit 8D is executed by the processor 5A, the computer 1 (generation device 1B) can function as a means for training the machine learning model.

[0057] The machine learning model is not particularly limited as long as it is capable of performing machine learning so that, when data 21 including a tire skeleton image 21A of a target tire is input, performance data 22 of the target tire is output. In this embodiment, it is desirable to use a deep learning model equipped with a neural network as the machine learning model. This enables the machine learning model to extract the feature quantities of the input data 21 and estimate the performance data 22. Such a machine learning model can be appropriately configured based on known techniques such as multiple regression, ridge regression, random forest, Gaussian process regression, CNN, and GNN.

[0058] As described above, image data (in this example, a grayscale image) is input to the machine learning model as data 21 (in this example, a tire skeleton image 21A). In this case, the machine learning model (deep learning model) is preferably configured with a convolutional neural network (CNN) capable of extracting features of the image data.

[0059] The convolutional neural network (CNN) may be a known one, or a customized version of the known one. The machine learning model 24A (trained model 24B) of this embodiment may be a customized version of VGG16, a known CNN. Figure 7 is a conceptual diagram illustrating an example of the machine learning model 24A (trained model 24B).

[0060] The machine learning model 24A (trained model 24B) includes an input layer 25, an output layer 26, a convolution layer 27, a pooling layer 28, and a fully connected layer 29.

[0061] The input layer 25 can receive input of data 21 including a tire skeleton image 21A. Unlike VGG16, the output layer 26 (third fully connected layer 29C) has one node. As a result, the machine learning model 24A of this embodiment can output the numerical value of one piece of performance data 22 (in this example, tire rolling resistance coefficient 22A) by receiving input of data 21 including the tire skeleton image 21A into the input layer 25.

[0062] In the convolutional layers 27, filtering is performed on the data 21 including the tire skeleton image 21A, thereby generating a feature map. A convolutional layer 27 is defined in each of the multiple convolutional blocks 30. These convolutional blocks 30 include a first convolutional block 30A and a second convolutional block 30B. Two convolutional layers 27 are defined in each of the first convolutional block 30A and the second convolutional block 30B.

[0063] The pooling layer 28 reduces the size of the feature map generated in the convolutional layer 27. The pooling layer 28 includes a first pooling layer 28A and a second pooling layer 28B. The first pooling layer 28A is defined between the first convolutional block 30A and the second convolutional block 30B. The second pooling layer 28B is defined between the second convolutional block 30B and the fully connected layer 29.

[0064] The fully connected layer 29 connects the feature maps and outputs performance data 22 (in this example, the rolling resistance coefficient of the tire) to the output layer 26. The fully connected layer 29 of this embodiment includes a first fully connected layer 29A, a second fully connected layer 29B, and a third fully connected layer 29C. Of the first fully connected layer 29A to the third fully connected layer 29C, the third fully connected layer 29C is configured as the output layer 26.

[0065] In step S2 of the present embodiment, first, data 21 (in this example, a tire skeleton image 21A) included in one training dataset 23 shown in FIG. 6 among a plurality of training datasets 23 (not shown) is input to the machine learning model 24A. As a result, performance data 22 (in this example, a tire rolling resistance coefficient 22A) estimated by the machine learning model 24A can be output. Next, in step S2 of the present embodiment, an error between the output performance data (estimated data) 22 and the performance data (ground truth data) 22 included in the training dataset 23 shown in FIG. 6 is calculated. Then, various parameters of the machine learning model 24A (e.g., weighting coefficients, biases, etc.) are updated to minimize this error. In this way, a series of steps from inputting the training dataset 23 to updating the parameters is performed for each of the plurality of training datasets 23, thereby optimizing the machine learning model 24A.

[0066] In step S2 of this embodiment, a machine learning model 24A is optimized using a plurality of learning datasets 23 (not shown). As a result, when data 21 including at least a tire skeleton image 21A of a target tire is input, a trained model 24B capable of outputting performance data 22 of the target tire (in this example, rolling resistance coefficient 22A) is generated. Such trained model 24B makes it possible to estimate the performance data 22 of the target tire without requiring the experience or intuition of a skilled person. The trained model 24B is input to a model storage unit 7D.

[0067] As described above, the performance of the tire 11 shown in FIG. 2 varies depending on the tire skeleton shape 20, and therefore the performance data 22 shown in FIG. 6 has a strong correlation with the tire skeleton image 21A. By inputting data 21 including such a tire skeleton image 21A into the machine learning model 24A shown in FIG. 7, feature quantities for estimating the performance data 22 can be effectively extracted. Therefore, the generation method (generation device 1B) of this embodiment makes it possible to generate a trained model 24B (shown in FIG. 7) that can accurately estimate the performance data 22.

[0068] In this embodiment, the trained model 24B is generated based on a conventional model (VGG16), which allows the trained model 24B to be created in a short time.

[0069] [Tire performance prediction method (first embodiment)] Next, an example of a processing procedure for a tire performance prediction method will be described. The prediction method of this embodiment uses a trained model 24B (shown in FIG. 7) generated based on the generation method shown in FIGS. 4 and 5.

[0070] 8 is a flowchart showing an example of a processing procedure of a tire performance prediction method. Each step of the prediction method of this embodiment is executed by one or more processors 5A included in the prediction device 1A (computer 1) shown in FIG.

[0071] [Enter data including an image showing the tire frame shape of the target tire] In the prediction method of this embodiment, first, data 21 including a tire skeleton image 21A of a target tire is input to the trained model 24B shown in Fig. 7 (step S3). This trained model 24B has been trained by machine learning so that when the data 21 (in this example, the tire skeleton image 21A) is input, the trained model 24B outputs performance data 22 of the tire 11 (in this example, the tire rolling resistance coefficient 22A).

[0072] In step S3 of this embodiment, first, the trained model 24B (shown in FIG. 7) input to the model storage unit 7D shown in FIG. 1 and the input unit 8A included in the program unit 8 are loaded into the working memory 5C. The input unit 8A is a program for inputting data 21 including a tire skeleton image 21A of a target tire to the trained model 24B. Execution of this input unit 8A by the processor 5A allows the computer 1 (prediction device 1A) to function as a means for inputting the data 21 to the trained model 24B.

[0073] In step S3 of this embodiment, first, a tire skeleton image 21A of the target tire is acquired. In this embodiment, the tire skeleton image 21A of the target tire shown in FIG. 7 is acquired based on the processing procedure in step S11 of the preparation step S1 shown in FIG. 4. The acquired tire skeleton image 21A of the target tire is input to the target data storage unit 7A (shown in FIG. 1). Furthermore, in step S3 of this embodiment, the tire skeleton image 21A of the target tire is input as image data to the trained model 24B. This makes it possible to output performance data 22 of the target tire (in this example, the tire rolling resistance coefficient) from the trained model 24B.

[0074] [Output performance data of target tire] Next, in the prediction method of this embodiment, the performance data 22 of the target tire is output from the trained model 24B (step S4).

[0075] In step S4 of this embodiment, first, the derivation unit 8B included in the program unit 8 shown in Fig. 1 is loaded into the working memory 5C. The derivation unit 8B is a program for outputting performance data of the target tire from the trained model 24B. Execution of this derivation unit 8B by the processor 5A causes the computer 1 to function as a means for outputting performance data 22 (shown in Fig. 7) of the target tire.

[0076] In this embodiment, in step S3, data 21 of the target tire (in this example, a tire skeleton image 21A) is input to the trained model 24B, as shown in Fig. 7. As a result, in step S4, performance data 22 of the target tire (in this example, a tire rolling resistance coefficient 22A) can be output from the trained model 24B. Therefore, the prediction method of this embodiment makes it possible to estimate the performance data 22 of the target tire without requiring the experience or intuition of a skilled person.

[0077] The performance data 22 (in this example, tire rolling resistance coefficient 22A) output from the trained model 24B is stored in the target performance data storage unit 7B shown in Fig. 1. In addition, in step S4, the performance data 22 output from the trained model 24B may be output (displayed) to an output device 4 including, for example, a display device. This allows an operator or the like to understand the performance data 22 and further enables evaluation of the performance of the target tire.

[0078] [Evaluate the performance data of the target tires] Next, in the prediction method of this embodiment, the quality of the performance data 22 (shown in FIG. 7) of the target tire is evaluated (step S5). The quality of the performance data 22 may be evaluated by the prediction device 1A (computer 1) shown in FIG. 1, or may be evaluated by an operator or the like.

[0079] In step S5 of this embodiment, first, the performance data 22 (in this example, tire rolling resistance coefficient 22A) input to the target performance data storage unit 7B shown in FIG. 1 and an evaluation unit 8E included in the program unit 8 are read into the working memory 5C. This evaluation unit 8E is a program for determining whether the performance data 22 of the target tire is good or not based on the output performance data 22. Execution of this evaluation unit 8E by the processor 5A causes the computer 1 to function as a means for evaluating the performance data 22.

[0080] The quality of the performance data 22 can be evaluated as appropriate. In the case where the performance data 22 of this embodiment is the rolling resistance coefficient 22A of the tire, for example, if the rolling resistance coefficient of the tire is less than a predetermined threshold, it can be determined that the rolling resistance performance is excellent and good. The threshold can be set as appropriate, for example, depending on the performance (rolling resistance performance, etc.) required of the target tire.

[0081] If the performance data 22 of the target tire is determined to be good ("Yes" in step S5), the target tire is manufactured based on the tire skeleton shape 20 of the target tire input to the trained model 24B (step S6). On the other hand, if the performance data 22 of the target tire is determined to be bad ("No" in step S5), at least a part of the tire skeleton shape 20 of the target tire (shown in FIG. 2) is changed (step S7), and steps S3 to S5 are performed again. When the tire rolling resistance coefficient 22A is output as in this embodiment, the tire skeleton shape 20 can be changed in step S7 to reduce rolling resistance. Such changes can be performed by an operator or the like, or by the computer 1 based on a known optimization method or the like. This allows a tire 11 having desired performance (e.g., rolling resistance performance) to be reliably designed and manufactured.

[0082] [Method for generating trained model (second embodiment)] In the above embodiments, as shown in Fig. 7, only the tire skeleton image 21A has been exemplified as the data 21 input to the trained model 24B (machine learning model 24A), but this is not limiting. The data 21 may further include a pattern image of the tread portion 12 of the tire 11 shown in Fig. 3 (hereinafter, sometimes referred to as a "pattern image").

[0083] In the generation method of this embodiment, a tire skeleton image 21A (shown in FIG. 6) and a pattern image are acquired in step S11 (shown in FIG. 5) of acquiring data 21. The tire skeleton image 21A can be acquired in the same procedure as in the previous embodiments.

[0084] The pattern image may be identified based on, for example, a tire 11 (shown in FIG. 3) that is not in contact with a road surface (not shown). In this case, unlike the contact patch image of Patent Document 1, the pattern image does not require the tire 11 to be inflated with internal pressure and in contact with the road surface. Therefore, the pattern image can be acquired in a shorter time than the contact patch image.

[0085] The pattern image is an image for identifying a pattern (tread pattern) engraved on the outer surface 12o of the tread portion 12 shown in Fig. 3. Such a pattern is constituted by, for example, grooves 32 (longitudinal grooves 32A and lateral grooves 32B) and sipes 33 provided on the outer surface 12o.

[0086] In the case where a tread pattern is formed by arranging a plurality of pitches (not shown) in the tire circumferential direction, as in the tread portion 12, a pattern image at a specific (one or more) pitches may be acquired. Alternatively, pattern images at all pitches may be acquired. In this embodiment, pattern images at a plurality of pitches (e.g., three pitches) are acquired. In this case, the pitches from which pattern images are acquired are appropriately selected according to performance data estimated by the machine learning model, and preferably include, for example, the pitch with the highest ground contact pressure among all pitches. This allows pattern images of a plurality of types of tires 11 to be acquired under the same conditions. The pitch with the highest ground contact pressure can be easily identified, for example, by measuring or calculating the ground contact pressure of all pitches.

[0087] In the pattern image of this embodiment, the contour of the tread pattern (grooves 32, sipes 33, etc.) is identified for each position (pixel) on the outer surface 12o of the plurality of pitches. The contour of the tread pattern is identified based on, for example, color information or shading information. The color information is used to identify the color. The shading information is used to identify the grayscale gradation.

[0088] The pattern image in this embodiment is configured as a grayscale image. As a result, the contours of the tread pattern (i.e., the contours of the grooves 32, sipes 33, etc.) can be distinguished by shades of light and dark. Furthermore, compared to color images, grayscale images have the advantage of suppressing an increase in the amount of data. Note that the pattern image is not limited to a grayscale image, and may be configured as, for example, a color image or a black and white image.

[0089] Fig. 9 is a diagram showing an example of a training data set 23 according to another embodiment of the present invention. Fig. 9 shows a training data set 23 acquired from one tire 11 as a representative. In this training data set 23, pattern images 21B are shown for a plurality of pitches (including, in this example, the pitch with the highest ground contact pressure). Furthermore, the pattern image 21B is configured as a grayscale image.

[0090] In the pattern image 21B of this embodiment, contours including the contact patch 31, grooves 32 (including longitudinal grooves 32A and lateral grooves 32B), and sipes 33 can be identified. These contours make it possible to identify the uneven shape (longitudinal grooves 32A, lateral grooves 32B, and sipes 33) of the tread portion 12. Such uneven shape affects the rigidity and contact shape of the tread portion 12, and therefore has a strong correlation with the performance data 22 (in this example, the tire rolling resistance coefficient 22A) output from the trained model.

[0091] The pattern image 21B can be acquired as appropriate. For example, the pattern image 21B can be acquired by actually photographing the tread portion 12 of the tire 11, or can be acquired from tire design data (CAD data), or can be an image hand-drawn by an operator. The pattern image 21B can also be acquired from a tire model (not shown) modeled based on the design data of the tire 11. This tire model can be modeled as appropriate based on, for example, the known methods described above.

[0092] The pattern image 21B in this embodiment represents the outline of the tread pattern of the tread portion 12 of the tire 11 on both sides in the tire axial direction with respect to the tire equator C shown in Fig. 3, but is not particularly limited thereto. For example, the pattern image 21B may represent the outline of the tread pattern of the tread portion 12 on either side in the tire axial direction.

[0093] In step S11 of this embodiment, a tire skeleton image 21A and a pattern image 21B shown in Fig. 9 are acquired for each of a plurality of types of tires 11. Data 21 including the tire skeleton image 21A and the pattern image 21B is input to the learning dataset storage unit 7C shown in Fig. 1.

[0094] Next, in the generation method of this embodiment, in step S13 shown in FIG. 5, a training data set 23 is created as shown in FIG. 9. In this step S13, the training data set 23 is created for each of a plurality of types of tires 11, combining data 21 (in this example, a tire skeleton image 21A and a pattern image 21B) and performance data 22 (in this example, a tire rolling resistance coefficient 22A). Then, in step S2 shown in FIG. 4, the machine learning model 24A shown in FIG. 7 is optimized (trained) using the plurality of training data sets 23. As a result, when the data 21 (tire skeleton image 21A and a pattern image 21B) of the target tire is input, a trained model 24B (shown in FIG. 7) capable of outputting the performance data 22 (rolling resistance coefficient 22A) of the target tire is generated. Such a trained model 24B makes it possible to estimate the performance data 22 of the target tire without the need for the experience or intuition of a skilled person.

[0095] In this embodiment, as shown in FIG. 9, in addition to a tire skeleton image 21A, a pattern image 21B is input to a machine learning model 24A (shown in FIG. 7). The tire skeleton image 21A and the pattern image 21B are related to each other in terms of affecting the rigidity and contact shape of the tread portion 12, and further, are strongly correlated with the performance data 22 (tire rolling resistance coefficient 22A) output from the trained model 24B. Therefore, the tire skeleton image 21A and the pattern image 21B can generate a trained model 24B that can accurately estimate the performance data 22. Furthermore, in this embodiment, since the pattern image 21B is configured as image data, for example, the well-known VGG 16 can be easily customized, and the trained model 24B can be generated in a short time.

[0096] [Tire performance prediction method (second embodiment)] In the prediction method of this embodiment, as in the previous embodiments, a trained model 24B (shown in FIG. 7) generated based on the generation method shown in FIG. 4 is used.

[0097] In the prediction method of this embodiment, as in the previous embodiments, in step S3 shown in Fig. 8, data 21 including a tire skeleton image 21A and a pattern image 21B of the target tire shown in Fig. 9 is input to the trained model 24B shown in Fig. 7. This makes it possible to output performance data 22 of the target tire (in this example, tire rolling resistance coefficient 22A) from the trained model 24B.

[0098] Next, in the prediction method of this embodiment, as in the previous embodiments, in step S4 shown in FIG. 8, performance data 22 of the target tire (in this example, tire rolling resistance coefficient 22A) is output from the trained model 24B shown in FIG. 7. As a result, the prediction method of this embodiment makes it possible to estimate the performance data 22 of the target tire without requiring the experience or intuition of a skilled person. Then, if it is determined that the performance data 22 is not satisfactory ("No" in step S5), in step S7, at least a portion of the tire skeleton shape 20 of the target tire shown in FIG. 2 and the tread pattern of the tread portion 12 shown in FIG. 3 are changed based on the performance data 22. This allows a tire 11 having desired performance (for example, rolling resistance performance) to be reliably designed and manufactured.

[0099] [Method for generating trained model (third embodiment)] In the embodiments described above, the tire skeleton image 21A and the pattern image 21B shown in FIG. 9 are exemplified as the data 21 input to the trained model 24B (machine learning model 24A), but the present invention is not limited to such an embodiment. In addition to the tire skeleton image 21A, the data 21 may further include, for example, condition data. Note that the data 21 of this embodiment may further include the pattern image 21B of the embodiments described above.

[0100] In the generation method of this embodiment, in step S11 (shown in FIG. 5) of acquiring data 21, a tire skeleton image 21A of the tread portion 12 shown in FIG. 6 and condition data are acquired. The tire skeleton image 21A can be acquired in the same procedure as in the previous embodiments.

[0101] The condition data relates to tire design conditions and / or evaluation conditions. In this embodiment, the condition data includes at least one of the following: tread gauge, category, load, internal pressure, aspect ratio, tire width, outer diameter, complex modulus of elasticity E* of the tread portion, rim width, running speed, and camber angle. These design conditions and / or evaluation conditions have a strong correlation with the performance data 22 (tire rolling resistance coefficient 22A) output from the trained model 24B shown in FIG. 7.

[0102] 10 is a conceptual diagram showing an example of a machine learning model 24A (trained model 24B) to which condition data 21C is input. The condition data 21C in FIG. 10 is composed of numerical data specifying the tread gauge, category, load, internal pressure, aspect ratio, tire width, outer diameter, and complex modulus of elasticity E* of the tread portion for one tire 11. In step S11 of this embodiment, the condition data 21C is acquired for multiple types of tires 11. The condition data 21C is input to the training dataset storage unit 7C shown in FIG. 1.

[0103] Next, in the generation method of this embodiment, a training dataset 23 is created in step S13 shown in FIG. 5. In this step S13, a training dataset is created for each of a plurality of types of tires 11, combining data 21 (tire skeleton image 21A and condition data 21C) and performance data 22 (tire rolling resistance coefficient 22A), as shown in FIG. 10. Then, in step S2 shown in FIG. 3, a machine learning model 24A is optimized (trained) using the plurality of training datasets 23. As a result, when data 21 of a target tire (in this example, the tire skeleton image 21A and the condition data 21C) is input, a trained model 24B capable of outputting the rolling resistance coefficient 22A of the target tire is generated. Such a trained model 24B makes it possible to estimate the rolling resistance coefficient 22A of the target tire without the need for the experience or intuition of a skilled person.

[0104] As described above, the condition data 21C is numerical data that specifies the tread gauge, etc., and therefore differs from image data such as the tire skeleton image 21A and the pattern image 21B shown in FIG. 9. For this reason, the condition data 21C cannot be input as image data to the input layer 25 of the machine learning model 24A shown in FIG. 10. In this embodiment, the condition data 21C is input to the second pooling layer 28B of the machine learning model 24A. This second pooling layer 28B includes features extracted by convolving the tire skeleton image 21A. Performance data 22 (in this example, the rolling resistance coefficient of the tire) can be output from the output layer 26 based on features obtained by adding the condition data 21C to the features of the tire skeleton image 21A.

[0105] In this embodiment, in addition to the tire skeleton image 21A, condition data 21C is input to the machine learning model 24A. The tire skeleton image 21A and the condition data 21C are related to each other in that they affect the rigidity and contact shape of the tread portion 12, and further have a strong correlation with the performance data 22 (tire rolling resistance coefficient 22A) output from the trained model 24B. Therefore, the tire skeleton image 21A and the condition data 21C can be used to generate a trained model 24B that can accurately estimate the performance data 22. Furthermore, in this embodiment, by inputting the condition data 21C to the second pooling layer 28B of the machine learning model 24A, it is possible to easily customize, for example, the well-known VGG16, and the trained model 24B can be generated in a short time.

[0106] [Tire performance prediction method (third embodiment)] In the prediction method of this embodiment, as in the previous embodiments, a trained model 24B (shown in FIG. 10) generated based on the generation method shown in FIG. 4 is used.

[0107] In the prediction method of this embodiment, as in the previous embodiments, in step S3 shown in Fig. 8, data 21 including a tire skeleton image 21A and condition data 21C of a target tire is input to a trained model 24B shown in Fig. 10. This makes it possible to output performance data 22 of the target tire (in this example, tire rolling resistance coefficient 22A) from the trained model 24B.

[0108] Next, in the prediction method of this embodiment, as in the previous embodiments, in step S4 shown in FIG. 8, performance data 22 of the target tire (in this example, tire rolling resistance coefficient 22A) is output from the trained model 24B shown in FIG. 10. As a result, the prediction method of this embodiment makes it possible to estimate the performance data 22 of the target tire without requiring the experience or intuition of a skilled person. Then, if it is determined that the performance data 22 is not satisfactory ("No" in step S5), at least a part of the tire skeleton shape 20 (shown in FIG. 2), design conditions, evaluation conditions, etc. of the target tire are changed based on the performance data 22 in step S7. As a result, a tire 11 having desired performance (for example, rolling resistance performance) can be reliably designed and manufactured.

[0109] 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 implemented in various ways.

[0110] [Note] The present invention includes the following aspects.

[0111] [Invention 1] a derivation unit that inputs data including an image of a target tire into a trained model that has been machine-learned to output tire performance data when data including an image representing a tire skeleton shape at a tire meridian cross section is input, and outputs the performance data of the target tire. Tire performance prediction device. [Invention 2] The tire performance prediction device according to the first aspect of the present invention, wherein the image includes a contour line of the outer surface of the tire. [Invention 3] 3. The tire performance prediction device according to claim 1 or 2, wherein the image includes a contour line of the tire cavity surface. [Invention 4] 4. The tire performance prediction device according to any one of claims 1 to 3, wherein the image includes a contour line of a carcass. [Invention 5] 5. The tire performance prediction device according to any one of claims 1 to 4, wherein the image includes a contour line of a belt layer disposed in a tread portion. [Invention 6] 6. The tire performance prediction device according to any one of claims 1 to 5, wherein the data further comprises condition data relating to design conditions and / or evaluation conditions of the tire. [Invention 7] 7. The tire performance prediction device according to any one of claims 1 to 6, wherein the data further comprises a pattern image of a tread portion of the tire. [Invention 8] 8. The tire performance prediction device according to any one of claims 1 to 7, wherein the performance data includes at least one of the rolling resistance coefficient of the tire, the contact shape of the tire, the rigidity of the tire, and the air resistance value of the tire. [Invention 9] 1. A method for predicting tire performance executed by one or more processors, comprising: A step of inputting data including an image of a target tire into a trained model that has been machine-learned so as to output tire performance data when data including an image representing a tire frame shape at a tire meridian cross section is input; and outputting performance data of the target tire from the trained model. A method for predicting tire performance. [Invention 10] A step of preparing a learning dataset that combines data including an image representing a tire skeleton shape at a tire meridian cross section and tire performance data; and training a machine learning model using the training dataset so that when data including an image of a target tire is input, performance data of the target tire is output. How to generate a trained model. [Explanation of symbols]

[0112] 21 Data 21A Image showing tire frame shape 22 Performance Data 24B trained model

Claims

1. a derivation unit that inputs data including an image of a target tire into a trained model that has been machine-learned to output tire performance data when data including an image representing a tire skeleton shape at a tire meridian cross section is input, and outputs the performance data of the target tire. Tire performance prediction device.

2. The tire performance prediction device according to claim 1 , wherein the image includes a contour line of the outer surface of the tire.

3. The tire performance prediction device according to claim 1 , wherein the image includes a contour line of a tire cavity surface.

4. The tire performance prediction device according to claim 1 , wherein the image includes a contour line of a carcass.

5. The tire performance prediction device according to claim 1 , wherein the image includes a contour line of a belt layer disposed in a tread portion.

6. The tire performance prediction device according to claim 1 , wherein the data further includes condition data relating to design conditions and / or evaluation conditions of the tire.

7. The tire performance prediction device according to claim 1 , wherein the data further comprises a pattern image of a tread portion of the tire.

8. The tire performance prediction device according to claim 1 , wherein the performance data includes at least one of a rolling resistance coefficient of the tire, a contact shape of the tire, a stiffness of the tire, and an air resistance value of the tire.

9. 1. A method for predicting tire performance executed by one or more processors, comprising: A step of inputting data including an image of a target tire into a trained model that has been machine-learned so as to output tire performance data when data including an image representing a tire frame shape at a tire meridian cross section is input; and outputting performance data of the target tire from the trained model. A method for predicting tire performance.

10. A step of preparing a learning dataset that combines data including an image representing a tire skeleton shape at a tire meridian cross section and tire performance data; and training a machine learning model using the training dataset so that when data including an image of a target tire is input, performance data of the target tire is output. How to generate a trained model.

Images