Image generation method and image generation apparatus

Abstract

This technology provides data that more accurately represents the cross-section of a tire when tension is applied to the fibrous material. [Solution] The data generation method comprises the following: identifying feature points representing the features of the contour or internal structure in first data representing the cross-sectional contour and internal structure of the tire in a first state; identifying corresponding points corresponding to the feature points in second data representing at least the outer shape of the tire in a second state; deriving a transformation formula for converting the feature points to the corresponding points based on the coordinates of the feature points and the coordinates of the corresponding points; and performing a coordinate transformation on the first data using the transformation formula. The first state is a state in which no tension is applied to the fibrous material contained in the tire, and the second state is a state in which tension is applied to the fibrous material.

Description

Technical Field

[0001] The present invention relates to an image generation method and an image generation apparatus.

Background Art

[0002] Patent Document 1 discloses a method for estimating measurement values of various tires based on a cross-sectional image representing the cross-sectional structure of a tire. In this method, a learned machine learning model estimates measurement points for measuring predetermined dimensions with respect to a cross-sectional image generated from a cut sample of a tire. Conventionally, the measurement points are points specified by a person with respect to the cut sample or the cross-sectional image.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] A cross-sectional image generated from a cut sample of a tire clearly represents the internal structure and contour of the tire in the cut cross-section. However, it is difficult to say that the cross-section of the actual tire is fully reflected in terms of the fact that the tension is not applied to the fiber material of the tire included in the cut sample. However, in order to create a model for numerical analysis of a tire, data representing the cross-section of the tire in a state where tension is applied to the fiber material is required. Such data can be obtained, for example, by photographing a tire with a rim filled with air by a non-destructive method, but there is a problem that the internal structure cannot be captured in detail as compared with the cross-sectional image generated from the cut sample.

[0005] [[ID=…]] An object of the present invention is to provide a technique for generating data that more specifically represents the cross-section of a tire in a state where tension is applied to the fiber material.

Means for Solving the Problems

[0006] The data generation method relating to the first aspect is a data generation method performed by one or more processors, comprising the following: In the first data representing the cross-sectional contour and internal structure of the tire in the first state, feature points representing the features of the contour or internal structure are identified. In the second data representing at least the outer shape of the tire in the second state, the corresponding points corresponding to the feature points are identified. To derive a transformation formula for converting the feature point to the corresponding point based on the coordinates of the feature point and the coordinates of the corresponding point, The first data is transformed using the aforementioned transformation formula. The first state is a state in which no tension is applied to the fibrous material contained in the tire, and the second state is a state in which tension is applied to the fibrous material.

[0007] The data generation method relating to the second viewpoint is the data generation method relating to the first viewpoint, wherein the first data is an image obtained based on a cut sample cut from the tire.

[0008] The data generation method relating to the third viewpoint is a data generation method relating to the first viewpoint or the second viewpoint, wherein the second data is an image obtained by photographing the cross-section of the tire in the second state in a non-destructive manner.

[0009] The data generation method relating to the fourth viewpoint is a data generation method relating to either the first viewpoint or the third viewpoint, wherein the second data represents at least the outer shape of the tire after it has been inflated to a predetermined first internal pressure and then deflated to a second internal pressure lower than the first internal pressure.

[0010] The data generation method relating to the fifth viewpoint is a data generation method relating to either the first viewpoint or the fourth viewpoint, further comprising determining a virtual point derived from the feature point and identifying a virtual corresponding point in the second data that corresponds to the virtual point.

[0011] The data generation program relating to the sixth perspective causes one or more processors to execute a data generation method relating to either the first or fifth perspective.

[0012] The data generation device relating to the seventh viewpoint comprises a specification unit, a derivation unit, and a conversion unit. The specification unit identifies feature points representing the features of the contour or internal structure in first data representing the contour and internal structure of the tire cross-section in a first state, and identifies corresponding points corresponding to the feature points in second data representing at least the outer shape of the tire in a second state. The derivation unit derives a conversion formula for converting the feature points to the corresponding points based on the coordinates of the feature points and the coordinates of the corresponding points. The conversion unit performs a coordinate transformation on the first data using the conversion formula. The first state is a state in which no tension is applied to the fibrous material contained in the tire, and the second state is a state in which tension is applied to the fibrous material. [Effects of the Invention]

[0013] From the above perspective, it is possible to generate data that more accurately represents the cross-section of a tire when tension is applied to the fibrous material. [Brief explanation of the drawing]

[0014] [Figure 1] A block diagram showing the electrical configuration of a data generation device according to one embodiment. [Figure 2] A flowchart showing the flow of a data generation method according to one embodiment. [Figure 3] An example of data representing the cross-section of a tire in the first state. [Figure 4] An example of data representing the cross-section of a tire in the second state. [Figure 5]An example of feature points and virtual points specified in the first data. [Figure 6] An example of corresponding points and virtual corresponding points specified in the second data. [Figure 7] A diagram in which the third data generated in the embodiment and the second data are superimposed.

Mode for Carrying Out the Invention

[0015] Hereinafter, a tire data generation method and a data generation apparatus according to an embodiment of the present invention will be described.

[0016] <1. Outline> FIG. 1 is a block diagram showing an electrical configuration of a data generation apparatus 1 (hereinafter, also simply referred to as "generation apparatus 1") according to an embodiment of the present invention. The generation apparatus 1 generates third data that represents a cross section of the tire 2 in the second state in more detail than the second data, based on the first data representing a cross section of the tire 2 in the first state and the second data representing a cross section of the tire 2 in the second state. The first data and the second data are data that can be obtained by known methods, respectively, as will be described later. The generated third data can be used, for example, to create a structural model of the tire 2. The created model can be used to numerically analyze characteristics of the tire 2 (for example, basic characteristics such as various rigidities, spring constants, load characteristics, etc., dynamic characteristics such as braking / driving characteristics, CF characteristics, etc.), mechanical behavior, durability, and the like.

[0017] <2. Configuration of the Generation Apparatus> The generation apparatus 1 is a general-purpose computer as hardware, and is realized, for example, as a desktop personal computer, a laptop personal computer, a tablet, or a smartphone. The generation apparatus 1 is manufactured by installing a data generation program 130 (hereinafter, also simply referred to as "program 130") into the general-purpose computer from a computer-readable non-volatile storage medium 131 such as a CD-ROM or a USB memory, from a writing device, or via a network. The program 130 causes the generation apparatus 1 to execute operations described later.

[0018] The generating device 1 includes a control unit 10, a display unit 11, an input unit 12, a storage unit 13, a communication unit 14, and an I / O interface 15. These units 10 to 15 are connected to each other via a bus line 16 and can communicate with each other. The display unit 11 can be composed of a liquid crystal display, an organic EL display, a plasma display, a touch panel display, etc., and displays, for example, the generated third data. The input unit 12 can be composed of a mouse, a keyboard, a touch panel, etc., and receives operations from the user for the generating device 1. The display unit 11 and the input unit 12 may both be composed of the same touch panel display. The communication unit 14 functions as a communication interface for performing data communication via a network. The I / O interface 15 is, for example, a USB (Universal Serial Bus) port, a dedicated port, etc., and is an interface for connecting to an external device.

[0019] The storage unit 13 can be composed of a non-volatile memory such as a hard disk and a flash memory. A program 130 is stored in the storage unit 13. In addition, when executing the generating method described later, the first data and the second data fetched from a storage medium similar to the storage medium 131 or from an external device can be appropriately stored. However, the program 130 may be stored in a ROM (Read Only Memory) described later, and the first data and the second data may be temporarily stored in a RAM (Random Access Memory) described later.

[0020] The control unit 10 can be composed of a CPU (Central Processing Unit), a ROM, a RAM, etc. The control unit 10 virtually operates as a preprocessing unit 10A, a specifying unit 10B, a deriving unit 10C, a conversion unit 10D, and an output unit 10E by reading and executing the program 130 in the storage unit 13. The RAM is appropriately used for the operations of the CPU. The operations of each unit will be described later.

[0021] <3. Generating Method> Figure 2 is a flowchart showing the processing flow of the data generation method executed by the generation device 1. Prior to executing this data generation method, first data and second data are acquired. In this embodiment, both the first data and the second data are acquired as image data. The first data includes at least the coordinates (x and y coordinates) of a number of pixels that form an image, and the RGB values ​​of each pixel. The second data includes at least the coordinates (x and y coordinates) of a number of pixels that form an image, and the brightness of each pixel.

[0022] The first data according to this embodiment is obtained by scanning a cut sample 3 cut from an actual tire 2. The scanner's gradation is, for example, 24-bit color, and the resolution is preferably 300 dpi or higher, and more preferably 600 dpi or higher. The cut sample 3 is taken by cutting two separate locations in the circumferential direction of the tire 2. The two cross-sections of the cut sample 3 are parallel, and the thickness of the cut sample 3 can be, for example, 25 mm as a standard, but can be 15 mm to 35 mm. The cut sample 3 may also be a portion cut from an actual tire 2 corresponding to a predetermined angle centered on the tire's rolling axis. In other words, the two cross-sections of the cut sample 3 do not have to be parallel.

[0023] As shown in Figure 3, the cross-section of cut sample 3 can be said to represent in detail the cross-sectional contour and internal structure of tire 2. The cross-sectional contour of tire 2 includes the outer and inner shapes of tire 2, which are formed by the rubber composition constituting tire 2. The outer shape of tire 2 is the contour of the outer surface of tire 2, and includes, for example, the contour defined by the tread portion 20 and the tread groove 200. The inner shape of tire 2 is the contour of the inner surface of tire 2. The internal structure of tire 2 includes, for example, the belt 21, carcass 22, inner liner 23, bead core 24, etc., as well as other components such as the breaker, bead filler, chafer, and interfaces between different rubber compositions (for example, the interface between the tread rubber and the sidewall rubber, the interface between the clinch apex and the sidewall rubber). However, since no tension is applied to the fibrous materials such as the belt 21, carcass 22, and breaker in cut sample 3, it is difficult to say that the actual cross-sectional appearance of tire 2 is fully reflected in this respect. In particular, the appearance of the fibrous material of tire 2 under circumferential tension is difficult to reproduce even by methods such as applying external force to cut sample 3. Therefore, the first data obtained based on cut sample 3 represents the cross-section of tire 2 in a state where no tension is applied to the fibrous material contained in tire 2 (first state). Note that the fibrous material contained in tire 2 refers to components including cord material, such as belt 21, carcass 22, and breaker, regardless of the material.

[0024] The second data represents the cross-section of the tire 2 in a state where tension is applied to the fibrous material (second state). In this embodiment, the second data is obtained by mounting the actual tire 2 on the rim 4 and filling it with air, as shown in Figure 4, and then scanning the tire 2 with the rim 4 attached using a CT (Computed Tomography) device. This second data is a 256-level grayscale image and, like the first data, represents the contour of the cross-section of the tire 2. This second data also represents at least a part of the internal structure of the tire 2, such as the belt 21 and the bead core 24. However, the information about the internal structure of the tire 2 contained in the second data is less than the information about the internal structure contained in the first data. Interfaces between different rubber compositions may also appear in the second data, but they are more clearly shown in the first data.

[0025] In this embodiment, the tire 2 is first assembled onto the rim 4, and then inflated until the internal pressure of the tire 2 reaches a predetermined normal internal pressure (first internal pressure). By inflating the tire 2 in this way, the gap between the tire 2 and the rim 4 is eliminated. Subsequently, the tire 2 is deflated until it reaches a predetermined internal pressure (second internal pressure) lower than the normal internal pressure. The second internal pressure is, for example, 20 kPa, but an internal pressure suitable for modeling can be appropriately selected. In this embodiment, an internal pressure suitable for modeling is an internal pressure at which the shape of the tire 2 is stable and the tension applied to the fiber material is as small as possible. In this embodiment, the state of the tire 2 at the second internal pressure is set as the initial state of the simulation. Note that the first internal pressure is not limited to the normal internal pressure, but is acceptable as long as it is an internal pressure at which the tire 2 and the rim 4 are fitted together without any gaps. Also, the second internal pressure is an internal pressure lower than the first internal pressure, and is acceptable as long as tension (preferably the minimum tension) is applied to the fiber material.

[0026] Referring again to Figure 2, in step S1, the preprocessing unit 10A takes in the first data and the second data and stores them in the storage unit 13 or RAM.

[0027] Next, the preprocessing unit 10A performs preprocessing on the first and second data saved in step S1 as needed (step S2). For example, the preprocessing unit 10A removes unnecessary parts from at least one of the first and second data, such as the presence of components other than the tire 2, cut sample 3, and rim 4. Alternatively, the preprocessing unit 10A rotates at least one of the first and second data so that the orientation of the tire 2 is the same in both the first and second data. The orientation of the tire 2 can be aligned, for example, so that the tread portion 20 faces in the positive y-axis direction and the bead core 24 faces in the negative y-axis direction. Alternatively, the preprocessing unit 10A may trim or resize at least one of the first and second data so that their sizes (number of pixels in height × width) are roughly the same. When resizing, the preprocessing unit 10A may perform pixel interpolation using, for example, bicubic interpolation. The size of the first and second data can be, for example, 3000 to 4000 pixels vertically and 5500 to 6500 pixels horizontally. The preprocessing by the preprocessing unit 10A can use any known image processing method. Furthermore, this preprocessing may be performed by the user giving instructions via the input unit 12 as appropriate, or it may be performed entirely automatically according to a predetermined algorithm.

[0028] Furthermore, it is preferable that the cross-sections of the tire 2 appearing in the first data and the second data be of roughly similar size. Also, it is preferable that the sizes of the first data and the second data be of roughly similar size, but they do not have to be exactly the same. If there is first data and second data that have been preprocessed in step S2, the preprocessing unit 10A further stores them in the storage unit 13 or RAM. Note that the data illustrated in Figures 3 and 4 are data after unnecessary parts have been trimmed from the original data, the orientation of the tire 2 has been aligned, and they have been made to be roughly the same size.

[0029] Next, the identification unit 10B extracts the contour of the tire 2 from the first data and the second data after preprocessing (step S3). The contour of the tire 2 can be extracted as data of a continuous group of points (pixels) using a known method. However, since the extracted contour may contain noise, the identification unit 10B may perform noise reduction processing or the like as needed to improve the accuracy of contour extraction. The identification unit 10B further stores the extracted contour of the tire 2 in the storage unit 13 or RAM.

[0030] Next, the identification unit 10B identifies feature points that represent the features of the contour of the tire 2 extracted from the first data (step S4). For example, as shown in Figure 5, the identification unit 10B identifies points P1 to P6, which correspond to the corners and edges of the contour, as feature points from the point cloud data that constitutes the contour of the tire 2. These points P1 to P6 show greater changes in brightness and color compared to neighboring pixels. The changes in brightness and color in the small region containing points P1 to P6 exhibit a unique pattern corresponding to a specific part, such as the upper end of the tread groove 200 or the corner of the bead. Therefore, by creating a template image in which the above unique pattern is expressed in advance and comparing the contour of the tire 2 extracted from the first data with the template image, points P1 to P6 can be identified as feature points. Alternatively, for example, the identification unit 10B identifies point Q1 (pixel) located on the center line L1 of the contour of the tire 2 as a feature point. The center line L1 is, for example, a straight line parallel to the y-axis that has the x-coordinate midway between the two points furthest apart in the x-axis direction among the point cloud constituting the contour line of tire 2. Thus, the identification unit 10B may use points derived from the data of the point cloud constituting the contour line of tire 2 as feature points. However, the corresponding points of the feature points, as described later, must be included in the data of the point cloud constituting the contour line of tire 2 extracted from the second data. It is preferable that there are multiple points identified as feature points, but for the sake of processing efficiency, it is preferable that there be 20 or fewer. The contour line of tire 2 shown in Figure 5 is an example of a contour line extracted from the first data line in Figure 3.

[0031] Furthermore, the identification unit 10B determines one or more virtual points based on the contour line of the tire 2 and the feature points identified in step S4 (step S5). The virtual points can be determined in any way, as long as they are determined according to a predetermined algorithm based on the coordinates of the feature points. However, the virtual points must be such that the virtual corresponding points described later are included in the data of the point cloud that constitutes the contour line of the tire 2 extracted from the second data. The identification unit 10B determines the virtual points, for example, as follows: First, a straight line L2 connecting points P5 and P6 is defined, and the intersection point with the center line L1 is set to point R1. Next, with point R1 as the center, straight lines are created that divide the space between the center line L1 and the straight line L2 at equal angles. In the example in Figure 5, four straight lines L3 to L6 are created. Of the point cloud that constitutes the contour line of the tire 2, the points through which the straight lines L3 to L6 pass are set to virtual points P7 to P10. In the example shown in Figure 5, the points corresponding to the outer shape of tire 2 are designated as virtual points P7 to P10. However, the points corresponding to the inner shape of tire 2 may also be designated as virtual points, or both the former and the latter may be designated as virtual points.

[0032] Next, the identification unit 10B identifies corresponding points P21-P26 and Q21 (pixels) corresponding to feature points P1-P6 and Q1, and virtual corresponding points P27-P30 (pixels) corresponding to virtual points P7-P10, from the point cloud data constituting the contour of the tire 2 extracted from the second data (steps S6 and S7). In the same manner as in step S4, the identification unit 10B compares the contour of the tire 2 extracted from the second data with a predetermined template image for the contour extracted from the second data and performs template matching. As a result, the identification unit 10B identifies points P21-P26 corresponding to feature points P1-P6 as corresponding points (see Figure 6). Furthermore, the identification unit 10B identifies point Q21 corresponding to feature point Q1 by deriving a straight line L21 corresponding to the center line L1. Furthermore, the identification unit 10B defines a straight line L22 connecting points P25 and P26, and sets the intersection point with straight line L21 as point R2. Straight line L22 corresponds to straight line L2, and point R2 corresponds to point R1. The identification unit 10B further creates straight lines L23 to L26 that divide the space between straight lines L21 and L22 at equal angles, centered at point R2. The identification unit 10B sets the points that the straight lines L23 to L26 pass through among the point group constituting the contour of the tire 2 as virtual corresponding points P27 to P30. Straight lines L23 to L26 correspond to straight lines L3 to L6, and virtual corresponding points P27 to P30 correspond to virtual points P7 to P10. In this way, by performing the same procedure as in steps S4 and S5 on the contour of the tire 2 extracted from the second data, points having common features between the contour of the tire 2 derived from the first data and the contour of the tire 2 derived from the second data are identified. Note that the outline of tire 2 shown in Figure 6 is an example of an outline extracted from the second data in Figure 4.

[0033] Next, the derivation unit 10C derives a transformation formula for transforming the coordinates of feature points P1-P6 and Q1, virtual points P7-P10, corresponding points P21-P26 and Q21, and virtual corresponding points P27-P30, respectively, based on the coordinates of the feature points P1-P6 and Q1, virtual points P7-P10, and virtual points P21-P26 and Q21, and virtual corresponding points P27-P30, respectively, as identified in steps S4-S7 (step S8). This transformation formula only needs to be able to map pixels specified in the first data (including data derived from the first data) to pixels specified in the second data (including data derived from the second data), and should be capable of geometric transformations including curvature. As such a transformation formula, for example, the "polynomial" transformation type of the "fitgeotform2d" function published by MathWorks can be used. The derivation unit 10C derives coefficients that define the above function based on the coordinates of feature points P1 to P6 and Q1, virtual points P7 to P10, corresponding points P21 to P26 and Q21, and virtual corresponding points P27 to P30. This derives the above transformation formula.

[0034] Next, the transformation unit 10D generates third data by performing a coordinate transformation on the first data based on the transformation formula derived in step S8 (step S9). The transformation unit 10D generates third data by specifying the first data and the above transformation formula as arguments to the "imwarp" function published by MathWorks, for example. The first data used as an argument is the first data in the state saved in step S1 or S2 (first data representing the internal structure of tire 2). This generates third data that simulates the case when the contour of the cross-section and the internal structure of tire 2 represented by the first data are in the second state. The third data is image data, just like the first data. The transformation unit 10D saves the third data to the storage unit 13 or RAM.

[0035] Next, the output unit 10E outputs the generated third data to the display unit 11 (step S10). This allows the user of the generation device 1 to view the image of the third data.

[0036] <4. Features> According to the generation device 1 of the above embodiment, based on first and second data that can be obtained by known methods, third data is generated that takes into account the state of the tire 2 (the state in which tension is applied to the fiber material) represented by the second data, in addition to the cross-sectional contour and internal structure of the tire 2 represented by the first data. Since the first data of the above embodiment is data obtained based on a cut sample 3, the internal structure of the tire 2 is reflected in particular in detail. For this reason, the third data reflects the internal structure of the tire 2 in more detail than the second data. In other words, the generation device 1 can generate data suitable for creating a model for numerical analysis of a tire based on data that can be obtained relatively easily.

[0037] <5. Variation> Although one embodiment of the present invention has been described above, the present invention is not limited to the above embodiment, and various modifications are possible without departing from the spirit of the invention. The gist of the modifications shown below can be combined as appropriate.

[0038] (1) The second data may be data obtained by imaging the tire 2 in the second state using another non-destructive method. In other words, the second data may be data obtained by imaging the tire 2 in the second state using an MRI (Magnetic Resonance Imaging) device or the like. Alternatively, the second data may be external shape data obtained by measuring the external shape of the tire 2 in the second state. Such external shape data can be obtained by measuring the external shapes of the tread, shoulder, and sidewall portions that constitute the outer surface of the tire 2 using laser light or ultrasound. In other words, the second data only needs to represent at least the external shape of the tire 2 in the second state, and does not necessarily need to represent the internal shape or internal structure of the tire 2. Furthermore, the second data does not have to be image data as in the above embodiment, but can be any data that can be converted into image data.

[0039] (2) In the above embodiment, the identification unit 10B extracted the outline of the tire 2 from the first data and the second data, respectively. However, the step S3 of extracting the outline of the tire 2 may be omitted, and feature points may be identified instead. For example, the generation device 1 may be configured to output the first data and the second data in image form to the display unit 11, and to allow the user to specify feature points in the first data and corresponding points in the second data via the input unit 12. For such processing, for example, the control point selection tool (cpselect) published by MathWorks can be used. With this control point selection tool, when the user specifies the feature points and corresponding points in the target first data and second data, the identification unit 10B identifies the coordinates of the feature points and corresponding points based on this operation, and infers the spatial transformation between the feature points and corresponding points. By executing steps S8 and S9 based on the above feature points and corresponding points, the third data can be generated.

[0040] (3) In addition to or instead of points representing the contour features of tire 2, points representing the internal structure of tire 2 (e.g., belt 21 and bead core 24) may be identified as feature points. However, the second data must also show points representing the corresponding internal structure features of tire 2. In other words, if a particular part of the contour or internal structure of tire 2 is present in both the first and second data, points representing the features of these particular parts can be identified as feature points.

[0041] (4) The method for identifying feature points is not limited to template matching as in the above embodiment, but can be selected from known feature point extraction methods such as DP (Dynamic Programming) matching. For example, the identification unit 10B may have the user specify pixels on the screen via the input unit 12, etc., and identify feature points based on the user's specification. Furthermore, the method for identifying corresponding points is not limited to the method in the above embodiment and can be changed as appropriate. Note that the identification of virtual points and virtual corresponding points may be omitted.

[0042] (5) In the above embodiment, the generation device 1 is configured as a single device, but the functions of each part 10A to 10E and the storage unit 13 may be distributed among multiple devices.

[0043] (6) The control unit 10 may include a GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), CPLD (Complex Programmable Logic Device), etc., in addition to or instead of a CPU. Furthermore, the operation of the control unit 10 may be performed by one or more processors. [Examples]

[0044] The following describes embodiments of the present invention. However, the following embodiments are merely illustrative examples of the present invention, and the present invention is not limited to these.

[0045] Image data, as shown in Figures 3 and 4, was prepared as the first and second data sets, respectively. Using the control point selection tool (cpselect) described above, 59 feature points in the first data set and 59 corresponding points in the second data set were manually identified. The feature points included points corresponding to the upper and lower ends of each tread groove, points corresponding to the boundary between the tread and shoulder sections, points corresponding to a part of the bead core, and points on the inner liner. Next, based on the coordinates of the identified feature points and corresponding points, coefficients were derived to define the "fitgeotform2d" function of "polynomial" described above. Furthermore, using these coefficients and the "imwarp" function described above, the first data set shown in Figure 3 was transformed to generate the third data set.

[0046] Observing the generated third data, it was observed that the background line of the first data was curved in a roughly sector shape, widening in the x-axis direction as it moved along the y-axis, confirming that the first data had undergone a geometric transformation including curvature. Furthermore, an image (Figure 7) was created by overlaying the generated third data onto the original second data to verify whether the second state of the tire was reflected in the internal structures such as the belt and carcass. As shown in Figure 7, the tire contour, belt, and bead core generally matched between the second and third data, confirming that the second state was reflected to some extent. Regarding the carcass, some parts were observed where the geometric transformation seemed inappropriate, which is thought to be because relatively few feature points were identified, particularly near the tire's sidewall. In other words, it is thought that by increasing the number of feature points identified near the sidewall, it may be possible to generate third data that more appropriately reflects the second state.

[0047] 1 generator 2 tires 3 Cut Samples 10 Control Unit 10A Pre-treatment section 10B Specific part 10C Derivation part 10D conversion unit 10E Output Section

Claims

1. A data generation method performed by one or more processors, In the first data representing the contour and internal structure of the tire cross-section in the first state, feature points representing the features of the contour or internal structure are identified. In the second data representing at least the outer shape of the tire in the second state, the corresponding points corresponding to the feature points are identified, Based on the coordinates of the feature point and the coordinates of the corresponding point, a transformation formula is derived for converting the feature point to the corresponding point. The first data is transformed using the aforementioned transformation formula. Equipped with, The first state is a state in which no tension is applied to the fibrous material contained in the tire, and the second state is a state in which tension is applied to the fibrous material. Data generation method.

2. The first data is an image obtained based on a cut sample taken from the tire. The data generation method according to claim 1.

3. The second data is an image obtained by photographing the cross-section of the tire in the second state using a non-destructive method. The data generation method according to claim 1.

4. The second data represents at least the outer shape of the tire after it has been inflated to a predetermined first internal pressure and then deflated to a second internal pressure lower than the first internal pressure. The data generation method according to claim 1.

5. Determining a virtual point derived from the aforementioned feature point, and identifying a virtual corresponding point in the second data that corresponds to the aforementioned virtual point. Furthermore, The data generation method according to claim 1.

6. The data generation method described in any one of claims 1 to 5 is to be executed by one or more processors. Data generation program.

7. In first data representing the cross-sectional contour and internal structure of the tire in a first state, a specification unit identifies feature points that represent the features of the contour or internal structure, and in second data representing at least the outer shape of the tire in a second state, a specification unit identifies corresponding points that correspond to the feature points. A derivation unit that derives a conversion formula for converting the feature point to the corresponding point based on the coordinates of the feature point and the coordinates of the corresponding point, A transformation unit that performs coordinate transformation on the first data using the aforementioned transformation formula. Equipped with, The first state is a state in which no tension is applied to the fibrous material contained in the tire, and the second state is a state in which tension is applied to the fibrous material. Data generation device.

Images