Method for predicting tire cross-sectional shape and method for manufacturing tire

Abstract

To provide a prediction method that can more accurately predict a cross sectional shape of a tire that is manufactured by vulcanizing a green tire, using design specification data of the green tire and cross sectional shape data of a molding surface of a mold to be used, and a manufacturing method for a tire utilizing the prediction method.SOLUTION: A prediction model PM, which is created by subjecting design specification data D1 including cross section shapes of green tires G, cross sectional shape data D2 on a molding surface 7 of a mold 6 and cross section image data D3 on a tire T manufactured by vulcanizing a number of green tires G whose design specification data D1 are different using the mold 6, as teaching data, to machine learning, is stored in a calculation device 2; and the design specification data D1 on green tires Ga to be predicted and the cross sectional shape data D2 on the molding surface 7 are substituted in the prediction model PM and are subjected to calculation processing, so as to predict a cross sectional shape of a target tire Ta that is manufactured by vulcanizing the green tires Ga using the mold 6.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a tire cross-sectional shape prediction method and a tire manufacturing method, and more particularly to a simple prediction method that can predict with high accuracy the cross-sectional shape of a tire manufactured by vulcanizing a green tire using green tire design specification data and cross-sectional shape data of the molding surface of a mold to be used, and a tire manufacturing method utilizing this prediction method. [Background technology]

[0002] Tires are manufactured by vulcanizing an unvulcanized green tire in a vulcanization mold. The green tire is molded into a preset shape, and is transformed into its final shape during vulcanization to complete the tire. If the shape of the green tire is not appropriate during the green tire molding process due to an excess or deficiency of rubber volume, a tire with the desired shape may not be obtained. Therefore, if it is possible to accurately predict the final shape that a vulcanized green tire will be transformed into, it is beneficial for ensuring the production of a tire with the desired shape.

[0003] Conventionally, a method for predicting the cross-sectional shape of a tire in an arbitrary manufacturing process has been proposed (see Patent Document 1). In the method proposed in Patent Document 1, the cross-sectional shape of the tire is calculated based on the predicted cross-sectional shape of the carcass. The cross-sectional shape of the carcass is predicted based on manufacturing condition information in the process to be predicted and structural specification information of the carcass, using a predetermined function that determines the cross-sectional shape.

[0004] However, in this proposed method, one of the objectives is to make predictions in a short time, and therefore, as a function for determining the cross-sectional shape of the carcass, a theoretical formula derived from extremely simple theories such as membrane theory and beam theory is adopted. Therefore, there is a limit to how accurately the cross-sectional shape of a tire manufactured by vulcanizing a green tire can be predicted, and there is room for improvement in order to predict the cross-sectional shape of a tire with higher accuracy while still being simple. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2006-168294 A Summary of the Invention [Problem to be solved by the invention]

[0006] An object of the present invention is to provide a simple prediction method capable of predicting with higher accuracy the cross-sectional shape of a tire produced by vulcanizing a green tire, using design specification data of the green tire and cross-sectional shape data of the molding surface of a mold to be used, and a tire manufacturing method utilizing this prediction method. [Means for solving the problem]

[0007] In order to achieve the above object, the tire cross-sectional shape prediction method of the present invention comprises the steps of: when molding a green tire based on design specification data including a cross-sectional shape of the green tire, molding a large number of the green tires with different design specification data, vulcanizing each of the molded green tires using a predetermined mold to obtain cross-sectional image data of each tire manufactured; using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the cross-sectional image data as training data, machine learning is performed using the training data to generate a prediction model for predicting the cross-sectional shape of a tire manufactured by vulcanization; inputting the design specification data of the green tire to be predicted into a calculation device in which this prediction model is stored, and vulcanizing the green tire to be predicted using the mold through calculation processing by the calculation device using the prediction model based on the input design specification data and the cross-sectional shape data of the molding surface This resulted in the final shape. A tire profile prediction method comprising: predicting a profile of a target tire (However, this does not include prediction of the shape of the green tire being vulcanized.) .

[0008] The tire manufacturing method of the present invention is characterized in that, based on the results predicted by the above-mentioned tire cross-sectional shape prediction method, design specification data of a green tire for manufacturing a tire having a target cross-sectional shape is determined, a green tire is molded based on the determined design specification data, and the molded green tire is vulcanized using a mold having the same specifications as the above-mentioned mold.

[0009] According to the tire cross-sectional shape prediction method of the present invention, a prediction model is used that is generated by machine learning the design specification data, the cross-sectional shape data of the molding surface, and the tire cross-sectional image data as the teacher data. Therefore, by inputting the design specification data of the green tire to be predicted and the cross-sectional shape data of the molding surface to the prediction model and performing arithmetic processing by the arithmetic device, it is possible to predict with high accuracy the cross-sectional shape of the target tire manufactured by vulcanizing the green tire to be predicted using the mold. Furthermore, the design specification data and the cross-sectional shape data of the molding surface can be easily obtained without actually measuring the green tire and the mold, because design data can be used, and prediction of the cross-sectional shape of the target tire is simplified.

[0010] According to the tire manufacturing method of the present invention, it is possible to grasp design specification data of a green tire for manufacturing a tire having a target cross-sectional shape based on the highly accurate results predicted by the tire cross-sectional shape prediction method described above. Then, a green tire is molded based on the grasped design specification data and vulcanized using a mold having the same specifications as the above mold, which is advantageous for obtaining a tire having a target cross-sectional shape. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is an explanatory diagram illustrating a prediction system used in the present invention. [Diagram 2] FIG. 2 is an explanatory diagram illustrating a right half of a green tire in cross section. [Diagram 3] FIG. 13 is an explanatory diagram illustrating the right half of the mold in cross section. [Figure 4] FIG. 2 is an explanatory diagram illustrating a cross-sectional view of the right half of a green tire in a state in which the tire is placed in a mold. [Diagram 5] FIG. 2 is an explanatory diagram illustrating an example of the outer surface (profile) of a tire cross-sectional shape predicted using a predictive model, together with the cross-sectional shape of a molding surface. [Figure 6] FIG. 6 is an explanatory diagram illustrating an outer surface of the cross-sectional shape of the tire in FIG. 5 together with the outer surface of the cross-sectional shape of a green tire. [Figure 7] FIG. 2 is an explanatory diagram illustrating an example of the outer surface of a green tire cross-sectional shape corrected based on prediction results using a prediction model, together with the outer surface of the cross-sectional shape before correction. [Figure 8] 8 is an explanatory diagram illustrating a cross-sectional view of a vulcanizer vulcanizing the repaired green tire of FIG. 7. FIG. [Figure 9] 9 is an explanatory diagram illustrating a right half of the green tire in FIG. 8 in cross section. FIG. [Figure 10] FIG. 2 is an explanatory diagram illustrating a right half of a vulcanized tire in cross section. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, a tire cross-sectional shape prediction method and a tire manufacturing method according to the present invention will be specifically described with reference to the embodiments shown in the drawings.

[0013] The tire cross-sectional shape prediction method of the present invention is carried out using a prediction system 1 illustrated in Fig. 1. This prediction system 1 has a calculation device 2, an input unit 3, and a display unit 4, and further has a design system 5 and a cross-sectional image capturing device 5a. The calculation device 2, the input unit 3, and the display unit 4 are connected to each other so as to be able to communicate with each other by wire or wirelessly. Data output by the design system 5 and the cross-sectional image capturing device 5a is input to the calculation device 2 directly or through the input unit 3.

[0014] Various data are input and stored in the arithmetic unit 2, and the arithmetic unit 2 performs arithmetic processing using these data. Various computers can be used as the arithmetic unit 2. Therefore, the arithmetic unit 2 has a memory in which various data are stored, and a CPU that performs arithmetic processing.

[0015] The input unit 3 is an input means for inputting various data to the arithmetic unit 2. As the input unit 3, a keyboard, a mouse, or various terminal devices can be used.

[0016] The display unit 4 is a display means for displaying various data input to the arithmetic device 2 and the results of arithmetic processing using these data (numerical values, tables, drawings, etc.). As the display unit 4, various monitors can be used.

[0017] The design system 5 has a computer in which a program (such as a CAD program) for designing the green tire G is stored. Using this design system 5, design drawing data for molding (manufacturing) the green tire G is created. This design drawing data constitutes design specification data D1 including the cross-sectional shape of the green tire G. In this way, since the design specification data D1 is design drawing data used for manufacturing (molding) the green tire G, in the present invention, it is not necessary to measure the actual green tire G with a measuring device or the like and obtain the design specification data D1 such as its cross-sectional shape data.

[0018] Design specification data D1 including the cross-sectional shape of the green tire G stored in the design system 5 is input to the calculation device 2. In this invention, at least the cross-sectional shape data of the green tire G is input to the calculation device 2 as the design specification data D1. The calculation device 2 of the prediction system 1 may be used as the computer of the design system 5. In other words, the prediction system 1 may be configured to have the functions of the design system 5.

[0019] The cross-sectional image capturing device 5a is a means for acquiring image data of a cross section of a manufactured tire T. Examples of the cross-sectional image capturing device 5a include a known image capturing device such as a digital camera, and a known X-ray imaging device (CT scanner). When an X-ray imaging device (CT scanner) is used, cross-sectional shape data can be measured and acquired without cutting the green tire G into a cross section. When a digital camera or the like is used, the tire T is cut into a cross section and the cut section is photographed.

[0020] As the design specification data D1, cross-sectional shape data of a green tire G as exemplified in FIG. 2 is used. In a typical green tire G, an inner liner layer m1 is disposed on the innermost circumference, a carcass layer m2 is disposed on its outer surface, and unvulcanized rubber R is disposed on the outer surface of the carcass layer m2. A belt layer m4 and the like are embedded in the unvulcanized rubber R of the tread portion. Since the cross-sectional shape of the green tire G does not generally change in the tire circumferential direction, cross-sectional shape data at any circumferential position of the green tire G can be used. Note that the drawings show the right half of the green tire, tire, and mold, but the left half has the same specifications as the right half.

[0021] Other examples of the design specification data D1 include various characteristic data of each component of the green tire G. Specifically, examples include volume data (cross-sectional area data) of the unvulcanized rubber R that forms the green tire G, viscosity data of this unvulcanized rubber R, cross-sectional shape data (and arrangement data) and rigidity data of the carcass layer m2, and cross-sectional shape data (and arrangement data) and rigidity data of the bead portion m3. The design specification data D1 to be adopted may be only one type of cross-sectional shape data of the green tire G, but one or more other types of design specification data D1 may also be added. The two-dot chain line CL in the figure indicates the center in the tire width direction.

[0022] The volume data (cross-sectional area data) of the unvulcanized rubber R, the cross-sectional shape data (and arrangement data) of the carcass layer m2, and the cross-sectional shape data (and arrangement data) of the bead portion m3 are calculated and acquired from the design specification data D1 stored in the design system 5. The viscosity data of the unvulcanized rubber R is acquired using a known viscosity measuring device such as a Mooney viscometer. This viscosity data may be measured at room temperature, but considering that the unvulcanized rubber R is heated in the vulcanization process and its fluidity is improved, it is also possible to adopt a measured value at a temperature at which the fluidity is highest. The stiffness data of the carcass layer m2 and the bead portion m3 is acquired using a known bending tester or the like.

[0023] Specifically, the cross-sectional shape data D2 of the molding surface 7 of the mold 6 is cross-sectional shape data of the molding surface 7 as shown in Fig. 3. The mold 6 shown in Fig. 3 is a sectional type, and is therefore composed of a side mold 6a and a sector mold 6b. The side mold 6a mainly vulcanizes and molds the outside of the side portion and the outside of the bead portion m3 of the green tire G, and the sector mold 6b mainly vulcanizes and molds the tread portion.

[0024] Since the cross-sectional shape of the molding surface 7 generally changes in the tire circumferential direction, cross-sectional shape data of the molding surface 7 at a preset position in the tire circumferential direction is used. For example, cross-sectional shape data D2 of a representative molding surface 7 including the most characteristic grooves, or cross-sectional shape data D2 of a molding surface 7 from which grooves are omitted, can be used.

[0025] Alternatively, it is possible to exclude the portions (sites) of the molding surface 7 whose cross-sectional shape changes in the tire circumferential direction from the prediction target, and to predict only the portions (at least one of the outer side of the side portion and the outer side of the bead portion) whose cross-sectional shape does not substantially change in the tire circumferential direction. In other words, the cross-sectional shape may be predicted only for the outer portion of the side portion, only for the outer portion of the bead portion m3, or only for the outer portion of the side portion and the outer portion of the bead portion m3. In this case, it is sufficient to use the design specification data D1 of the portion corresponding to the prediction target and the cross-sectional shape data D2 of the molding surface 7.

[0026] The cross-sectional shape data D2 of the molding surface 7 does not need to be obtained by new measurement because design drawing data (such as CAD data) used in manufacturing the mold 6 exists. For example, data corresponding to the cross-sectional shape of the molding surface 7 can be extracted from the design drawing data of the mold 6 stored in the design system 5 and used as the cross-sectional shape data of the molding surface 7.

[0027] In the present invention, a prediction model PM generated by machine learning is used. This prediction model PM is a computer program for predicting the cross-sectional shape of a tire T manufactured by vulcanizing a green tire G. An example of a procedure for generating this prediction model PM will be described.

[0028] First, a green tire G is molded based on design specification data D1 including a cross-sectional shape of the green tire G. At this time, a large number of green tires G having different design specification data D1 are molded.

[0029] In the currently established known tire molding method, the cross-sectional shape of the actual green tire G molded based on the design specification data D1 and the cross-sectional shape of this design specification data D1 can be generally guaranteed to be identical for general (general-purpose) tires. Therefore, the cross-sectional shapes of both can be considered to be substantially identical, and in the present invention, the cross-sectional shapes of both are treated as being substantially identical. For the basic tire, the green tire G molded based on the design specification data D1 is measured to grasp its cross-sectional shape and confirm its identity with the cross-sectional shape of the design specification data D1. This makes it possible to ensure the identity of both tires for tires of different sizes or similar types of the confirmed basic tire. In addition, when a tire with specifications significantly different from the basic tire whose identity has been confirmed is to be the prediction target, the actual green tire G molded based on the design specification data D1 is measured to grasp its cross-sectional shape and confirm its identity with the cross-sectional shape of the design specification data D1 before it is used as the prediction target according to the present invention.

[0030] Next, each of the molded green tires G is vulcanized using a predetermined mold 6. As illustrated in Fig. 4, the green tire G to be vulcanized is placed in the mold 6, and then the mold 6 is closed and pressure and heat are applied between the molding surface 7 of the mold 6 and an inflated vulcanization bladder 9. In Fig. 4, the green tire G in its initial shape (before being placed inside the mold 6) is depicted, and the cross-sectional shape of the molding surface 7 and the vulcanization bladder 9 are depicted by dashed lines.

[0031] The green tire G is vulcanized to manufacture a tire T as shown in Fig. 10. Next, cross-sectional image data D3 of the manufactured tire T is acquired using a cross-sectional image photographing device 5a. The predetermined mold 6 is not limited to one specific mold 6, and may be any mold 6 having the same specifications as the predetermined mold 6.

[0032] The cross-sectional image data D3 of the tire T to be acquired may be in a state where the tire T is fitted in the mold 6 as exemplified in Fig. 10. The cross-sectional image data D3 of the tire T may be acquired in a state where the tire T is actually fitted in the mold 6, or the cross-sectional image data D3 may be acquired by removing the tire T from the mold 6 and setting it in a state (shape) similar to the state where it was fitted in the mold 6.

[0033] Along with the acquired cross-sectional image data D3 of the large number of tires T, design specification data D1 including the cross-sectional shape of a green tire G before each manufactured tire T is vulcanized, and cross-sectional shape data D2 of the molding surface 7 of the mold 6 in which the green tire G is vulcanized are prepared. Next, the design specification data D1, the cross-sectional shape data D2 of the molding surface 7, and the tire cross-sectional image data D3 are used as basic data (i.e., teacher data) for predicting the cross-sectional shape of the tire T, and machine learning is performed to generate a prediction model PM.

[0034] Specifically, the above data D1, D2, and D3 are input to the calculation device 2, and machine learning is performed using these data D1, D2, and D3. That is, the relationship between the cross-sectional image of the tire T and the tire specifications of the green tire G and the cross-sectional shape of the molding surface 7 is linked, and the influence of the tire specifications of the green tire G and the cross-sectional shape of the molding surface 7 on the cross-sectional shape of the tire T to be manufactured is analyzed and evaluated.

[0035] The cross-sectional shape of the tire T is greatly affected by the size of the interval (gap) between the outer surface of the green tire G and the molding surface 7 of the mold 6, and the volume of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7. Since the unvulcanized rubber R flows during vulcanization, the viscosity (fluidity) of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7 also affects the cross-sectional shape of the tire T.

[0036] If the distance between the outer surface of the green tire G and the molding surface 7 of the mold 6 is too large, or if the volume of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7 is too small, the tire T may deform due to a lack of rubber. If the distance between the outer surface of the green tire G and the molding surface 7 of the mold 6 is too small, or if the volume of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7 is too large, the tire T may deform due to an excess of rubber.

[0037] If the viscosity of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7 is high, the flowability is low, and therefore deformation of the green tire G is unlikely to occur. If the viscosity is low, the flowability is high, and therefore deformation of the green tire G is likely to occur.

[0038] In addition, the cross-sectional shape (and arrangement) and rigidity of the carcass layer m2 affect the fluidity of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7, and therefore affect the cross-sectional shape of the tire T. The cross-sectional shape (and arrangement) and rigidity of the bead portion m3 also affect the fluidity of the unvulcanized rubber R between the carcass layer m2 and the molding surface 7, and therefore affect the cross-sectional shape of the tire T. The above-mentioned factors each affect each other in a complex manner, and the cross-sectional shape of the tire T changes.

[0039] Therefore, by using the data D1, D2, and D3 as teacher data, it becomes possible to generate a prediction model PM by having an artificial intelligence (AI) learn the differences and the degree of difference in the data D3 (cross-sectional image data) resulting from the combination of the data D1 and D2, and the characteristics in each case. Examples of machine learning methods include various well-known methods such as deep learning using a neural network. In deep learning, a multi-layer structure is formed by a known method, with an input layer, a plurality of intermediate layers, and an output layer, and a network is used in which weights are set between nodes between each layer and linked. Then, the design specification data D1 and the cross-sectional shape data D2 that affect the cross-sectional image data D3 are input from the input layer, and the predicted value of the cross-sectional image data D3 is output to the output layer. The calculated predicted value is compared with the actual measured value of the cross-sectional image data D3, and the weights are changed so as to reduce the error between the two. As a result, a prediction model PM with improved prediction accuracy is generated (constructed). The generated prediction model PM is stored in the calculation device 2.

[0040] Next, an example of a procedure for predicting the cross-sectional shape of a target tire Ta produced by vulcanizing a green tire Ga to be predicted in a predetermined mold 6 will be described.

[0041] The calculation device 2 receives input of design specification data D1 of the green tire Ga to be predicted using the input unit 3. The calculation device 2 substitutes the input design specification data D1 of the green tire Ga and cross-sectional shape data D2 of the molding surface 7 of the mold 6 into the prediction model PM, and performs a calculation process to execute the prediction model PM. By this calculation process, when the green tire Ga to be predicted is vulcanized using the mold 6 to manufacture a target tire Ta, the cross-sectional shape of the target tire Ta is predicted, and the prediction result is displayed on the display unit 4.

[0042] This prediction is premised on the premise that the bead cores of the pair of bead portions m2 of the green tire Ga are fixed at the correct positions on the molding surface 7 and vulcanized. Therefore, the cross-sectional image data D3 of the tire T also uses data of the tire T in which the bead cores of the pair of bead portions m2 are fixed at the correct positions on the molding surface 7 and vulcanized.

[0043] In the prediction result using the prediction model PM, for example, as shown in Fig. 5 and Fig. 6, the outer surface X (profile X) of the cross-sectional shape of the target tire Ta is displayed in a solid line. In Fig. 5, the outer surface X of this cross-sectional shape is shown together with the cross-sectional shape of the molding surface 7 shown in a dashed line. In Fig. 6, the outer surface X of this cross-sectional shape is shown together with the outer surface of the cross-sectional shape of the green tire Ga shown in a dashed line. Note that the prediction result (outer surface X of the cross-sectional shape) is not limited to the displays in Fig. 5 and Fig. 6, and it is sufficient if the outer surface X of the cross-sectional shape of the target tire Ta can be grasped.

[0044] The prediction results are displayed in a planar coordinate system of the tire cross section, so that it is possible to grasp at a glance how each position of the green tire Ga changes in the target tire Ta. Therefore, it is easy to determine which position of the green tire Ga is too large or too small compared to the cross-sectional shape of the target tire. Therefore, it is easy to grasp how much the cross-sectional shape of the green tire Ga should be changed in order to obtain the tire Tg with the target cross-sectional shape.

[0045] This prediction method uses a prediction model PM that is generated by machine learning using the above-mentioned design specification data D1, the cross-sectional shape data D2 of the molding surface 7, and the cross-sectional image data D3 of the tire T as teacher data. Therefore, the cross-sectional shape of the tire Ta that is formed by the complex interactions of various factors can be predicted with even greater accuracy by inputting the design specification data D1 of the green tire Ga to be predicted and the cross-sectional shape data D2 of the molding surface 7 into the prediction model PM and performing calculation processing.

[0046] Furthermore, the cross-sectional shape data of the green tires G, Ga employed as the design specification data D1 does not need to be obtained by measuring the actual green tires G, Ga using a measuring device, but is design data used in molding the green tires G, Ga, and is therefore easy to obtain. Furthermore, since the molding surface 7 is manufactured by precision machining, even if the design data (such as CAD data) used in manufacturing the mold 6 is employed as the cross-sectional shape data D2 of the molding surface 7, it will substantially match the measurement data obtained by measuring the actual molding surface 7 using a measuring device. Therefore, prediction can be made simply and with high accuracy.

[0047] The design specification data D1 may include the volume of the unvulcanized rubber R outside the carcass layer m2 of the green tires G and Ga. This is advantageous for improving prediction accuracy.

[0048] In addition, it is preferable that the design specification data D1 includes viscosity data of the unvulcanized rubber R that contacts the molding surface 7 of the green tires G, Ga. That is, including the viscosity data of the unvulcanized rubber R outside the carcass layer m2 is advantageous for improving prediction accuracy.

[0049] In addition, including the various data described above as the design specification data D1 is advantageous in improving prediction accuracy. However, the more types of design specification data D1 are included, the greater the burden of calculation processing becomes, and the improvement in prediction accuracy may not be commensurate with the increased burden. Therefore, in order to make predictions efficiently and accurately, it is advisable to include at least one type of cross-sectional shape data of the green tire G, which has the greatest influence, and add one or more other types of design specification data D1 as necessary.

[0050] As training data, vulcanization condition data D4 including at least the vulcanization temperature when vulcanizing the green tire G can also be added. The vulcanization temperature affects the fluidity of the unvulcanized rubber R, and therefore affects the cross-sectional shape of the tire T. Therefore, including the vulcanization temperature in the training data is advantageous for improving the prediction accuracy of the cross-sectional shape of the tire Ta. Another example of the vulcanization condition data D4 is the vulcanization pressure applied to the green tire G by the vulcanization bladder 9. The vulcanization pressure also affects the fluidity of the unvulcanized rubber R, and therefore affects the cross-sectional shape of the tire T.

[0051] When the vulcanization condition data D4 is included in the teacher data, the vulcanization condition data D4 of the green tire Ga to be predicted is input to the calculation device 2, and the input vulcanization condition data D4 is also used to predict the cross-sectional shape of the target tire Ta. If the vulcanization condition data D4 is included in the teacher data, the burden of the calculation processing increases, and the improvement in prediction accuracy commensurate with the increase in the burden may not be obtained. Therefore, it is advisable to add the vulcanization condition data D4 to the teacher data as necessary.

[0052] If it is assumed that the vulcanization conditions when using this mold 6 do not change much and vulcanization is performed under substantially the same vulcanization conditions, there is no need to consider the vulcanization condition data D4. In such a case, the cross-sectional image data D3 under the given vulcanization conditions that are assumed are used.

[0053] The tire manufacturing method of the present invention utilizes the cross-sectional shape prediction method of the present invention. An example of the procedure of the tire manufacturing method will be described.

[0054] In this tire manufacturing method, design specification data D1 of the green tire GaN for manufacturing a tire Tg having a target cross-sectional shape is grasped based on the results predicted using the prediction model PM. That is, the positions and the degree of correction required for the green tire Ga are grasped with reference to Figs. 5 and 6. Then, the green tire GaN corrected based on the grasped design specification data D1 is molded by a known method. For example, as shown in Fig. 7, the outer surface of the cross-sectional shape of the green tire Ga before correction is changed based on the prediction results.

[0055] Next, as illustrated in Fig. 8 and Fig. 9, the green tire GaN corrected by reflecting the prediction result using the prediction model PM is placed in a mold 6 having the same specifications as a predetermined mold 6 installed in a known vulcanizing device 8. Then, the green tire GaN is vulcanized in the closed mold 6 by a known method, thereby manufacturing a tire Ta having a target cross-sectional shape illustrated in Fig. 10.

[0056] In this tire manufacturing method, the above-mentioned prediction method is used to grasp the design specification data D1 of the green tire GaN for manufacturing a tire Tg having a target cross-sectional shape. Since the prediction accuracy of the prediction method used is high, it is advantageous to obtain a tire Tg having a target cross-sectional shape by molding the green tire Ga based on the grasped design specification data D1 and vulcanizing it using a mold 6 having the same specifications as the predetermined mold 6. [Explanation of symbols]

[0057] 1. Prediction System 2 Arithmetic unit 3. Input section 4 Display section 5. Design System 5a Cross-sectional image capture device 6 Mold 6a Side mold 6b Sector Mall 7 Molding surface 8. Vulcanizing Equipment 9 Vulcanizing bladder PM Prediction Model G, Ga, GaN Green Tires m1 Inner liner layer m2 carcass layer m3 bead part m4 belt layer R Unvulcanized rubber Rc Vulcanized Rubber T, Ta, Tg Tires (vulcanized tires)

Claims

1. A tire cross-sectional shape prediction method comprising the steps of: when molding a green tire based on design specification data including a cross-sectional shape of the green tire, molding a large number of the green tires with different design specification data, vulcanizing each of the molded green tires using a predetermined mold to produce tires, acquiring cross-sectional image data of each tire, and using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the cross-sectional image data as training data, thereby generating a prediction model that predicts the cross-sectional shape of a tire to be vulcanized and produced, inputting design specification data of the green tire to be predicted into a calculation device in which this prediction model is stored, and predicting the cross-sectional shape of a target tire that has been produced by vulcanizing the green tire to be predicted using the mold and has reached its final shape by calculation processing by the calculation device based on the input design specification data and the cross-sectional shape data of the molding surface (however, predicting the shape of the green tire to be predicted during the process of vulcanizing the green tire to be predicted is excluded).

2. The tire cross-sectional shape prediction method according to claim 1 , wherein the design specification data includes a volume of unvulcanized rubber outside a carcass layer of the green tire.

3. 3. The tire cross-sectional shape prediction method according to claim 1, wherein the design specification data includes viscosity data of unvulcanized rubber that contacts the molding surface of the green tire.

4. The tire cross-sectional shape prediction method according to any one of claims 1 to 3, further comprising the steps of: adding vulcanization condition data including at least a vulcanization temperature when vulcanizing the green tire as the teaching data; inputting the vulcanization condition data of the green tire to be predicted into the arithmetic device; and using the input vulcanization condition data in predicting the cross-sectional shape of the target tire.

5. A tire manufacturing method comprising the steps of: determining design specification data of a green tire for manufacturing a tire having a target cross-sectional shape based on a result predicted by the tire cross-sectional shape prediction method according to any one of claims 1 to 4; molding a green tire based on the determined design specification data; and vulcanizing the molded green tire using a mold having the same specifications as the mold.

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