Method for predicting occurrence of tire vulcanization failure and tire manufacturing method

Abstract

To provide a vulcanization failure occurrence prediction method capable of easily and highly accurately predicting the possibility of a vulcanization failure occurrence by using design specification data of a green tire and sectional shape data of a molded surface of a used mold, and a tire manufacturing method using this prediction method.SOLUTION: A vulcanization failure occurrence prediction method stores, in an arithmetic unit 2, a prediction model PM generated by machine-learning design specification data D1 including a sectional shape of a green tire G, sectional shape data D2 of a molded surface 7, and vulcanization failure occurrence data D3 in a tire T obtained by vulcanizing a green tire G molded on the basis of multiple different design specification data D1 by a mold 6 as teacher data, and predicts the occurrence presence of a vulcanization failure in a target tire Ta obtained by vulcanizing the green tire Ga by the mold 6 by assigning the design specification data D1 of the green tire Ga to be predicted and the sectional shape data D2 of a molded surface 7 to the prediction model PM to execute an arithmetic operation.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for predicting the occurrence of vulcanization failures in tires and a manufacturing method for tires, and more specifically, to a simple prediction method that can predict the possibility of vulcanization failures occurring with high accuracy using design specification data of green tires and cross-sectional shape data of the molding surface of a mold to be used, and a manufacturing method for tires utilizing this prediction method. [Background technology]

[0002] Tires are manufactured by vulcanizing unvulcanized green tires in a vulcanization mold. Green tires are molded to preset specifications, but depending on the tire specifications, they may not match well with the mold used, causing vulcanization defects in the vulcanized tire. For example, vulcanized tires may have vulcanization defects such as localized excess or deficiency of rubber, excess or deficiency of vulcanization, and deformation.

[0003] In order to prevent such failures, it has been proposed to use the size of the gap between the predicted cross-sectional shape of a green tire and the molding surface of the mold used, calculated using cross-sectional shape data, as an index of vulcanization failures (see Patent Document 1). In the invention proposed in Patent Document 1, a large number of coordinate points are extracted at equal intervals in the tire radial direction on the outer surface of the tire of the predicted cross-sectional shape of the green tire, and the size of the gap between the outer surface of the tire and the molding surface at each coordinate point is calculated. A correlation coefficient between the size of this gap and the failure rate is calculated, and it is determined that failures are likely to occur in areas (positions) where the absolute value of this correlation coefficient is large.

[0004] In this invention, the parts (positions) where failures are likely to occur are identified, but the clear appropriate range of the size of the gap that can prevent the occurrence of failures is not identified. Therefore, it is difficult to accurately predict whether or not a vulcanization failure will occur when the green tire to be judged is vulcanized using a specified mold, and there is room for improvement in predicting the possibility of vulcanization failures with a higher degree of accuracy while still being simple. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2010-18010 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 possibility of vulcanization failure using design specification data of a 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 method for predicting the occurrence of curing defects in tires of the present invention comprises, when molding a green tire based on design specification data including a cross-sectional shape of the green tire, molding a large number of green tires with different design specification data, vulcanizing each of the molded green tires using a predetermined mold to obtain occurrence data of curing defects that occur in each tire manufactured by vulcanizing the green tires, and using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the occurrence data of the curing defects as training data, performing machine learning using the training data to generate a prediction model that predicts whether or not a curing defect will occur in 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 predicting the occurrence or not of a curing defect in a target tire manufactured by vulcanizing the green tire to be predicted using the mold by calculation processing using the prediction model based on the input design specification data and the cross-sectional shape data of the molding surface. The design specification data includes viscosity data of unvulcanized rubber that contacts the molding surface of the green tire. It is characterized by: Another method for predicting the occurrence of vulcanization defects in tires according to the present invention includes the steps of: when molding green tires based on design specification data including a cross-sectional shape of the green tires, molding a large number of green tires with different design specification data, vulcanizing each of the molded green tires using a predetermined mold to obtain data on the occurrence of vulcanization defects in each tire manufactured; and using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the occurrence data of the vulcanization defects as training data, machine learning is performed using the training data to generate a prediction model for predicting whether or not a vulcanization defect will occur in a tire manufactured by vulcanization, and storing this prediction model. The present invention is characterized in that design specification data of a green tire to be predicted is input into the arithmetic device, and based on this input design specification data and the cross-sectional shape data of the molding surface, the arithmetic device performs calculation processing using the prediction model to predict whether or not a vulcanization fault will occur in a target tire to be manufactured by vulcanizing the green tire to be predicted using the mold, vulcanization condition data including at least the vulcanization temperature when vulcanizing the green tire is added as the teaching data, the vulcanization condition data of the green tire to be predicted is input into the arithmetic device, and the input vulcanization condition data is also used for predicting whether or not a vulcanization fault will occur in the target tire. A further method of predicting the occurrence of vulcanization defects in tires according to the present invention includes, when molding a green tire based on design specification data including a cross-sectional shape of the green tire, molding a large number of green tires with different design specification data, vulcanizing each of the molded green tires using a predetermined mold to obtain occurrence data of vulcanization defects that occur in each tire manufactured by vulcanizing the green tires, and using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the vulcanization defect occurrence data as training data, to generate a prediction model that predicts whether or not a vulcanization defect will occur in 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 predicting the occurrence or not of a vulcanization defect in a target tire manufactured by vulcanizing the green tire to be predicted using the mold by calculation processing using the prediction model based on this input design specification data and the cross-sectional shape data of the molding surface, and including in the design specification data a volume of unvulcanized rubber outside the carcass layer of the green tire.

[0008] The tire manufacturing method of the present invention is characterized in that, based on the results predicted by the tire vulcanization fault occurrence prediction method described above, the design specification data of the green tire in which the vulcanization fault will not occur in the target tire is determined, a green tire is molded based on this determined design specification data, and the molded green tire is vulcanized using a mold having the same specifications as the mold.

[0009] According to the tire vulcanization fault occurrence 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 occurrence data of the vulcanization fault 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 into the prediction model and performing calculation processing by the calculation device, it becomes possible to predict with higher accuracy and more simply whether or not a vulcanization fault will occur in a target tire manufactured by vulcanizing the green tire to be predicted using the mold. 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 whether or not a vulcanization fault will occur in the target tire becomes simple.

[0010] According to the tire manufacturing method of the present invention, the design specification data can be grasped based on the results predicted by the tire vulcanization failure prediction method. Then, a green tire is molded based on the grasped design specification data that does not cause vulcanization failure, and the molded green tire is vulcanized using a mold with the same specifications as the mold. Therefore, it is possible to stably and efficiently manufacture tires that do not cause vulcanization failure. [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 an appropriate range of the outer surface (profile) of the cross-sectional shape of a green tire predicted using a prediction model, together with the cross-sectional shape of the molding surface. [Figure 6] FIG. 6 is an explanatory diagram illustrating the appropriate range in FIG. 5 together with the profile of a green tire to be predicted. [Figure 7] FIG. 2 is an explanatory diagram illustrating a profile of a green tire formed based on the prediction results using a prediction model, together with the cross-sectional shape and appropriate range of the molding surface. [Figure 8] 8 is an explanatory diagram illustrating, in cross section, a vulcanizing device that vulcanizes the green tire of FIG. 7.

[0023] 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 method for predicting the occurrence of a vulcanization fault in a tire and a method for manufacturing a tire according to the present invention will be specifically described with reference to the embodiments shown in the drawings.

[0013] The tire curing failure occurrence prediction method of the present invention is carried out using a prediction system 1 exemplified in Fig. 1. This prediction system 1 has a calculation device 2, an input unit 3, a display unit 4, and further has a design system 5. 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 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 molding (manufacturing) 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] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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. If there is a portion that is known to be prone to vulcanization failure, cross-sectional shape data D2 of the molding surface 7 including the portion where that vulcanization failure is likely to occur (the portion where it is most likely to occur) can also be used.

[0024] 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, it is possible to predict the occurrence or non-occurrence of vulcanization failures 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.

[0025] 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.

[0026] 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 possibility of occurrence of a vulcanization failure (presence or absence of occurrence) in a tire T manufactured by vulcanizing a green tire G. An example of a procedure for generating this prediction model PM will be described.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] The green tire G is vulcanized to manufacture a tire T as shown in FIG. 10. Then, the presence or absence of a vulcanization fault and the location of the vulcanization fault (location data) are confirmed for each manufactured tire T. Note that 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.

[0031] The presence or absence of a vulcanization failure and the location of the occurrence thereof confirmed in the manufactured tire T are adopted as vulcanization failure occurrence data D3. The vulcanization failure occurrence data D3 includes data in cases where a vulcanization failure has occurred and data in cases where a vulcanization failure has not occurred.

[0032] Regardless of the type or degree of the vulcanization failure, the data can be used as the vulcanization failure occurrence data D3 by distinguishing between cases where a vulcanization failure has occurred and cases where a vulcanization failure has not occurred. Alternatively, the type of vulcanization failure (excess or shortage of rubber, excess or shortage of vulcanization, deformation, etc.) and the degree of the vulcanization failure (small, medium, large) can be classified and confirmed. Then, a combination of these classifications can be used as the vulcanization failure occurrence data D3.

[0033] The vulcanization fault to be predicted can also be specified to one or more desired types of faults. In this case, the occurrence data D3 of the vulcanization fault is adopted as data indicating the occurrence of a vulcanization fault only when the specified type of vulcanization fault occurs, and is adopted as data indicating the absence of a vulcanization fault in other cases. Specifically, when the type of vulcanization fault to be predicted is specified to one type, rubber excess or shortage, if rubber excess or shortage occurs in the tire T, it is adopted as data indicating the occurrence of a vulcanization fault, and if there is no rubber excess or shortage even if there is over or under vulcanization, it is adopted as data indicating the absence of a vulcanization fault.

[0034] In this manner, data D3 on the occurrence of vulcanization failures is obtained. In addition, design specification data D1 used in molding the green tires G of each manufactured tire T and cross-sectional shape data D2 of the molding surface 7 of the mold 6 in which these green tires G were vulcanized are prepared. Each design specification data D1 includes the cross-sectional shape of each green tire G.

[0035] The above-mentioned design specification data D1, cross-sectional shape data D2 of the molding surface 7, and vulcanization fault occurrence data D3 are used as basic data (i.e., teacher data) for predicting the occurrence or non-occurrence of vulcanization faults, and a prediction model PM is generated by machine learning. Specifically, the above-mentioned 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 presence or non-occurrence of vulcanization faults is linked to the relationship between the design specification of the green tire G and the cross-sectional shape of the molding surface 7, and the influence of the design specification of the green tire G and the cross-sectional shape of the molding surface 7 on the occurrence of vulcanization faults is analyzed and evaluated.

[0036] The incidence of vulcanization failures in 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 incidence of vulcanization failures.

[0037] 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, rubber defects are likely to occur, which may result in deformation of the tire. The portion with the rubber defects may become insufficiently vulcanized relative to the unvulcanized rubber R due to insufficient pressure or heat. 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, excess rubber and tire deformation are likely to occur. The portion with the excess rubber may become over-vulcanized relative to the unvulcanized rubber R due to excessive pressure.

[0038] 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.

[0039] 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 incidence of vulcanization failures. 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 incidence of vulcanization failures. The above-mentioned factors each affect each other in a complex way, and thus the incidence of vulcanization failures changes.

[0040] 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 between the combinations of the data D1 and D2 when a curing failure occurs and when a curing failure does not occur, and the characteristics of each case. As a machine learning method, various well-known methods such as deep learning using a neural network can be exemplified. In deep learning, a network is used in which a multi-layer structure is formed by an input layer, a plurality of intermediate layers, and an output layer by a well-known method, and 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 curing failure occurrence data D3 are input from the input layer, and the predicted value of the curing failure occurrence data D3 is output to the output layer. The calculated predicted value is compared with the actual measured value of the curing failure occurrence 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.

[0041] Next, an example of a procedure for predicting whether or not a vulcanization failure will occur in a target tire Ta manufactured by vulcanizing a green tire Ga that is a prediction target in a predetermined mold 6 will be described.

[0042] 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, it is determined whether or not a vulcanization failure will occur in the target tire Ta, and the prediction result is displayed on the display unit 4.

[0043] This prediction is based on the premise that the bead cores of the pair of bead portions m2 of the green tire Ga are fixed to the correct positions on the molding surface 7 and vulcanized. Therefore, the vulcanization failure occurrence data D3 also uses data obtained when the bead cores of the pair of bead portions m2 are fixed to the correct positions on the molding surface 7 and vulcanized.

[0044] The prediction result using the prediction model PM is displayed as the design specification data D1, in which the appropriate range AR of the outer surface (profile) of the cross-sectional shape of the green tire Ga is indicated by a range enclosed by dashed lines, as shown in Fig. 5 and Fig. 6. In Fig. 5, this appropriate range AR is shown together with the cross-sectional shape of the molding surface 7, and in Fig. 6, this appropriate range AR is shown together with the outer surface of the cross-sectional shape of the green tire Ga. The appropriate range AR is exaggerated in order to make it easier to see on the drawings.

[0045] Any range outside this appropriate range AR becomes an inappropriate range for the outer surface of the cross-sectional shape of the green tire Ga. Therefore, in the prediction result, if the entire outer surface of the cross-sectional shape of the green tire Ga falls within the appropriate range AR, it is predicted that no vulcanization failure will occur in the target tire Ta. On the other hand, if there is a part on the outer surface of the cross-sectional shape of the green tire Ga that falls within the inappropriate range, it is predicted that a vulcanization failure will occur in the target tire Ta in that part. Therefore, it is possible to predict whether or not a vulcanization failure will occur in the target tire Ta (green tire Ga) and the distribution of the occurrence positions. Note that the prediction result is not limited to the display in Figures 5 and 6, and it is sufficient if it is possible to grasp whether or not a vulcanization failure will occur in the target tire Ta and the appropriate range AR.

[0046] 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 at which position of the green tire Ga a vulcanization failure will occur. Therefore, it is easy to determine which position of the green tire Ga is too large or too small relative to the mold 6 (molding surface 7). Furthermore, since it is possible to grasp the distance to the outer surface of the cross-sectional shape of the green tire Ga relative to the appropriate range AR and the distance to the molding surface 7 in the cross section, it becomes clear to what extent the cross-sectional shape of the green tire Ga should be changed in the design specification data D1 in order to prevent the occurrence of a vulcanization failure. Therefore, it is extremely useful for obtaining a cross-sectional shape of the green tire Ga that can prevent the occurrence of a vulcanization failure.

[0047] In this prediction method, a prediction model PM is used that is generated by machine learning the above-mentioned design specification data D1, the cross-sectional shape data D2 of the molding surface 7, and the occurrence data D3 of vulcanization failures as teacher data. Therefore, 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, it is possible to predict vulcanization failures that occur due to the complex interactions of various factors with each other with even greater accuracy.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] By using data classifying the type of vulcanization failure (excess or insufficient rubber, over or under vulcanization, deformation, etc.) and the degree of vulcanization failure (small, medium, large), it becomes possible to make clear predictions for each type of vulcanization failure or combination of types, as well as a clear prediction of the degree of vulcanization failure.

[0053] 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 incidence of vulcanization failures. Therefore, including the vulcanization temperature in the training data is advantageous for improving the accuracy of predicting whether or not a vulcanization failure will occur in 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 incidence of vulcanization failures.

[0054] 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 whether or not a vulcanization failure occurs in 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 may not be commensurate with the increased burden. Therefore, it is advisable to add the vulcanization condition data D4 to the teacher data as necessary.

[0055] 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 data D3 on the occurrence of vulcanization failures under the given vulcanization conditions that are the assumption is used.

[0056] The tire manufacturing method of the present invention utilizes the curing failure occurrence prediction method of the present invention. An example of the procedure of the tire manufacturing method will be described.

[0057] In this tire manufacturing method, design specification data D1 of a green tire Ga in which no vulcanization failure occurs in the manufactured target tire Ta is obtained based on the results predicted using the prediction model PM. Then, the green tire Ga is molded by a known method based on the obtained design specification data D1. For example, as shown in FIG. 7, the entire outer surface of the cross-sectional shape of the green tire Ga is made to have a cross-sectional shape that falls within an appropriate range AR in which no vulcanization failure is predicted to occur. In this manner, the green tire Ga is molded by reflecting the prediction results using the prediction model PM.

[0058] Next, as illustrated in Fig. 8 and Fig. 9, the molded green tire Ga 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 Ga is vulcanized in the closed mold 6 by a known method, thereby producing a tire Ta illustrated in Fig. 10.

[0059] In this tire manufacturing method, a green tire Ga is molded based on design specification data D1 that is predicted with high accuracy to cause no vulcanization failure. Then, this green tire Ga is vulcanized using a mold 6 having the same specifications as a predetermined mold 6, so that a tire Ta that does not cause vulcanization failure can be stably and efficiently manufactured.

[0060] In a test case for improving a vulcanization failure caused by an excess or deficiency of rubber on the outer side of a tire Ta for a general passenger vehicle, a tire Ta was manufactured by molding and vulcanizing a green tire Ga so that the outer surface of the cross-sectional shape of the green tire Ga was within the appropriate range AR based on the results of the method for predicting the occurrence of vulcanization failure of the present invention. As a result, it was confirmed that vulcanization failure in the tire Ta could be prevented. [Explanation of symbols]

[0061] 1. Prediction System 2 Arithmetic unit 3. Input section 4 Display section 5. Design System 6 Mold 6a Side mold 6b Sector Mall 7 Molding surface 8. Vulcanizing equipment 9 Vulcanizing bladder PM Prediction Model G, Ga Green Tire m1 Inner liner layer m2 carcass layer m3 bead part m4 belt layer R Unvulcanized rubber Rc Vulcanized Rubber T, Ta tires (vulcanized tires)

Claims

1. a tire vulcanization failure occurrence prediction method, comprising: when molding a green tire based on design specification data including a cross-sectional shape of the green tire, molding a large number of green tires with different design specification data, vulcanizing each of the molded green tires using a specified mold to obtain occurrence data of vulcanization failures that occur in each tire manufactured by vulcanizing the green tires; using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the vulcanization failure occurrence data as training data, machine learning is performed using the training data to generate a prediction model that predicts whether or not vulcanization failures will occur in tires manufactured by vulcanization; inputting design specification data of the green tire to be predicted into a calculation device in which this prediction model is stored, and predicting whether or not vulcanization failures will occur in a target tire manufactured by 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; and including viscosity data of unvulcanized rubber in contact with the molding surface of the green tire in the design specification data.

2. When green tires are molded based on design specification data including the cross-sectional shape of the green tire, a large number of green tires having different design specification data are molded, and data on occurrence of vulcanization defects that have occurred in each tire manufactured by vulcanizing each of the molded green tires using a predetermined mold is obtained, and each of the design specification data, cross-sectional shape data of the molding surface of the mold, and data on occurrence of vulcanization defects are used as training data to perform machine learning using the training data to generate a prediction model for predicting whether or not a vulcanization defect will occur in a tire manufactured by vulcanization, and the prediction model is stored in a calculation device that stores the prediction model. A method for predicting the occurrence of vulcanization failures in a tire, comprising: inputting design specification data for a tire; and, based on the input design specification data and the cross-sectional shape data of the molding surface, performing calculation processing by the calculation device using the prediction model to predict whether or not a vulcanization failure will occur in a target tire manufactured by vulcanizing the green tire that is the prediction target using the mold; adding vulcanization condition data including at least the vulcanization temperature when vulcanizing the green tire as the teaching data; inputting the vulcanization condition data for the green tire that is the prediction target into the calculation device; and using the input vulcanization condition data to predict whether or not a vulcanization failure will occur in the target tire.

3. a tire vulcanization failure occurrence prediction method, comprising: when molding a green tire based on design specification data including a cross-sectional shape of the green tire, molding a large number of green tires with different design specification data, vulcanizing each of the molded green tires using a specified mold to obtain occurrence data of vulcanization failures that have occurred in each tire manufactured by vulcanizing the green tires, and using each of the design specification data, cross-sectional shape data of the molding surface of the mold, and the vulcanization failure occurrence data as training data, to generate a prediction model that predicts whether or not vulcanization failures will occur in tires manufactured by vulcanization by carrying out machine learning using the training data; inputting design specification data of the green tire to be predicted into a calculation device in which this prediction model is stored, and predicting whether or not vulcanization failures will occur in a target tire manufactured by vulcanizing the green tire to be predicted using the mold by calculation processing using the prediction model based on the input design specification data and the cross-sectional shape data of the molding surface; and including a volume of unvulcanized rubber outside the carcass layer of the green tire in the design specification data.

4. A method for predicting the occurrence of vulcanization failures in a tire as described in claim 2 or 3, wherein the design specification data includes a volume of unvulcanized rubber outside the carcass layer of the green tire.

5. A tire manufacturing method comprising the steps of: determining the design specification data of a green tire in which the vulcanization fault will not occur in the target tire based on a result predicted by a tire vulcanization fault prediction method described in 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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