Tire manufacturing method and manufacturing system

Abstract

To provide a manufacturing method and a manufacturing system that can estimate the performance of a tire to be manufactured with higher accuracy and can more reliably manufacture the tire having target performance based on the estimation results.SOLUTION: Data TP of a predetermined tire performance of each manufactured tire T is used as an objective variable, and predetermined quality information data QL and predetermined processing condition data Pc for tire components E1 of individual sizes for one tire used in manufacturing each tire T are used as explanatory variables. A relationship between the explanatory variables and the objective variable is calculated by machine learning using a calculation device 9, and a contribution Ct of the predetermined quality information data QL and processing condition data Pc to the predetermined tire performance TP is determined in advance based on the relationship between the explanatory variables and the objective variable. The tire T is manufactured by controlling the conditions for the predetermined quality and predetermined processing for tire components E1 of individual sizes used in the manufactured tire T according to the contribution Ct so that the predetermined tire performance approaches a set target value.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a tire manufacturing method and manufacturing system, and more particularly to a tire manufacturing method and manufacturing system that can estimate the performance of a tire to be manufactured with higher accuracy and more reliably manufacture tires having target performance based on the estimation results. [Background technology]

[0002] Tires are manufactured using various tire components, such as unvulcanized rubber extruded by an extruder or the like, and a reinforcing layer formed of unvulcanized rubber and reinforcing cords. When manufacturing a tire, these various tire components are integrated in a molding process to form a green tire. This green tire is then vulcanized to manufacture the tire.

[0003] It has been proposed that in the tire manufacturing process, quality information for each tire component formed to the individual size of one tire is acquired, and the manufacturing conditions are adjusted based on a comparison between the quality information for the tire component of the individual size used when forming a green tire and a target value for that quality to manufacture the tire (see Patent Document 1). This proposed tire manufacturing method makes it possible to quickly optimize the manufacturing conditions for each tire based on a comparison between the quality information for the tire component of the individual size and a target value for that quality, which is beneficial for manufacturing tires with target performance. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2023-34783 Summary of the Invention [Problem to be solved by the invention]

[0005] Tire manufacturing involves various processing steps and uses various tire components, but the processing conditions in each processing step and the contribution of each tire component to tire performance are not fully understood. Therefore, there is room for improvement in estimating the performance of manufactured tires with high accuracy. For example, by prioritizing improvements in processing conditions and tire components with high contributions over processing conditions and tire components with low contributions, tire performance can be changed more significantly, which generally makes it easier to manufacture tires with target performance. Therefore, there is room for improvement in more reliably manufacturing tires with target performance.

[0006] An object of the present invention is to provide a tire manufacturing method and manufacturing system that can estimate the performance of a tire to be manufactured with higher accuracy and that can more reliably manufacture tires having target performance based on the estimation results. [Means for solving the problem]

[0007] In order to achieve the above-mentioned object, the tire manufacturing method of the present invention involves integrating multiple types of tire components, including tire components formed by cutting a long body into individual sizes for one tire, to form a green tire, and vulcanizing this green tire to manufacture a tire, and is characterized in that tire performance data indicating predetermined tire performance of each manufactured tire is used as a dependent variable, and quality information data indicating predetermined quality of the tire components of the individual sizes used in the manufacture of each tire and processing condition data indicating conditions for predetermined processing are used as explanatory variables, and the contribution of the predetermined quality information data and the processing condition data to the predetermined tire performance is grasped in advance based on the relationship between the explanatory variables and the dependent variables calculated by machine learning using a computing device.

[0008] The tire manufacturing system of the present invention comprises component manufacturing equipment for manufacturing each of a plurality of types of tire components, molding equipment for integrating the plurality of types of tire components to form green tires, and vulcanization equipment for vulcanizing the green tires, and each of the component manufacturing equipment includes a long body manufacturing machine for manufacturing long bodies and a cutting machine for cutting the long bodies to manufacture the tire components formed into individual sizes for one tire.The tire manufacturing system has a calculation device that uses tire performance data indicating predetermined tire performance of each of the manufactured tires as a dependent variable, and calculates the contribution of the predetermined quality information data and the processing condition data to the predetermined tire performance based on the relationship between the dependent variables and the explanatory variables calculated by machine learning using quality information data indicating a predetermined quality for the tire components of the individual sizes used in the manufacture of each of the tires and processing condition data for the predetermined processing, and controls the predetermined quality and the conditions for the predetermined processing for the tire components of the individual sizes used in the tire to be manufactured according to the previously determined contribution so that the predetermined tire performance approaches a set target value. [Effects of the Invention]

[0009] According to the present invention, the contributions of the predetermined quality information data and the processing condition data to the predetermined tire performance are determined in advance based on the relationship between the explanatory variables and the objective variables, thereby enabling the detailed contributions to be determined for each tire. Therefore, using the determined contributions and the quality information data and processing condition data for the tire components of the individual sizes used in the tire to be manufactured is advantageous for estimating the predetermined tire performance of the tire to be manufactured with higher accuracy. Furthermore, controlling the conditions for the predetermined quality and the predetermined processing for the tire components of the individual sizes used in the tire to be manufactured in accordance with the contributions is advantageous for more reliably manufacturing tires whose predetermined tire performance approaches target values. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is an explanatory diagram illustrating an example of the overall concept of a tire manufacturing method and manufacturing system; [Figure 2] 1 is an illustration illustrating an embodiment of a tire manufacturing system. [Figure 3] 3 is an explanatory diagram illustrating an enlarged example of a part of the manufacturing system of FIG. 2. [Figure 4] FIG. 4 is an explanatory diagram illustrating FIG. 3 in a plan view. [Figure 5] FIG. 10 is an explanatory diagram illustrating a state in which the identifier is read and tire-specific information is acquired. [Figure 6] FIG. 3 is an explanatory diagram showing an example of a tire manufacturing flow using the manufacturing system of FIG. 2. DETAILED DESCRIPTION OF THE INVENTION

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A tire manufacturing method and manufacturing system according to the present invention will be described below based on the embodiments shown in the drawings.

[0012] 1 , the tire manufacturing method and manufacturing system of the present invention use tire performance data TP indicating predetermined tire performance of each manufactured tire T, quality information data QL indicating predetermined quality of tire components E1 of individual sizes for one tire used in manufacturing each tire T, and processing condition data Pc indicating conditions for predetermined processing of the tire components E1. The tire performance data TP, quality information data QL, and processing condition data Pc are input to a calculation device 9, which calculates the contribution Ct of the quality information data QL and processing condition data Pc to the predetermined tire performance (tire performance data TP). Various known computers can be used as the calculation device 9.

[0013] Examples of the tire performance data TP include data on the uniformity performance, rolling resistance performance, running quietness performance, wear resistance performance, braking performance, grip performance, and durability performance of the tire T. From among these, desired tire performance data TP is selected. This tire performance data TP may be obtained for each tire T under desired common measurement conditions using various known methods. Other known various tire performance data TP may also be used.

[0014] Specifically, the uniformity performance of tire T is measured, for example, by measuring radial force variation (RFV) in accordance with the method specified in JASO C607, "Test Method for Uniformity of Automobile Tires." Other measurements, such as lateral force variation (LFV) and tractive force variation (TFV), can also be used. The rolling resistance performance of tire T is measured, for example, by measuring rolling resistance under specified conditions in accordance with ISO 28580 using a known indoor drum testing machine. The quietness performance of tire T is measured by mounting tire T on a specified wheel, inflating it to a specified air pressure, and running the vehicle on a road noise measurement surface at a specified speed (e.g., 50 km / h) to collect road noise. The collected road noise is then subjected to frequency analysis, and the sound level (dB) at 315 Hz is used as a representative value of mid-frequency road noise, for example.

[0015] Examples of the tire component E1 include tread rubber, side rubber, inner liner, etc. made of unvulcanized rubber R, and carcass material, belt material, etc., which are composites of unvulcanized rubber R and reinforcing cords. Quality information data QL and processing condition data Pc for these various tire components E1 are input to the calculation device 9.

[0016] Examples of the quality information data QL indicating a predetermined quality of the tire component E1 include the mass of the tire component E1, the cross-sectional shape, and the degree of waviness in the thickness direction of the surface (e.g., arithmetic mean waviness). It is desirable to include at least one of these quality information data QL. Other quality information data QL that can be used include material properties such as viscoelasticity and tensile strength of the raw materials supplied to the tire component E1.

[0017] Examples of processing condition data Pc for a predetermined process on the tire component E1 include the molding temperature and molding speed when the tire component E1 is molded, and the vulcanization temperature and vulcanization pressure when the tire component E1 is vulcanized. It is desirable to include at least one of these processing condition data Pc. Other processing condition data Pc that can be used include time management data such as the lead time and production time of the tire component E1, and temperature management data such as machine temperature control settings, atmospheric temperature and humidity during the process, and the temperature of the raw materials used.

[0018] More specifically, when the tire component E1 is an extruded material (rolled material), the molding temperature when the tire component E1 is molded is the extrusion temperature (rolling temperature), the molding speed when the tire component E1 is molded is the extrusion speed (rolling speed), and the vulcanization temperature when the tire component E1 is vulcanized is the vulcanization temperature (maximum temperature) in the vulcanization process.

[0019] Although it is preferable to use the tire performance data TP, quality information data QL, and processing condition data Pc for each tire with the same specifications, data for tires with slightly different specifications can also be used together if they are considered to be generally equivalent. If the only tire specification that differs substantially is the tire width, the tire specifications can be considered equivalent if the difference in tire width is within 10 mm, for example. If the only tire specification that differs substantially is the rim diameter, the tire specifications can be considered equivalent if the difference in rim diameter is within 1 inch, for example.

[0020] The calculation device 9 performs machine learning using the tire performance data TP as the dependent variable and the quality information data QL and processing condition data Pc as the dependent variable, thereby calculating the relationship between the dependent variable and the explanatory variable (calculating the degree of correlation). From the calculation results, the contribution Ct of the quality information data QL and processing condition data Pc to a predetermined tire performance is calculated, and an estimation model EM is generated. Various known methods can be used for machine learning by the calculation device 9. For example, deep learning using a neural network is used.

[0021] This contribution Ct is the degree of influence that the quality information data QL and processing condition data Pc have on a predetermined tire performance (tire performance data TP), and indicates the degree of correlation with the predetermined tire performance (tire performance data TP). The larger the value of the contribution Ct, the greater the change in the predetermined tire performance that the quality information data QL and processing condition data Pc have. For example, quality information data QL (processing condition data Pc) with a large contribution Ct will cause a greater change in the predetermined tire performance than quality information data QL (processing condition data Pc) with a small contribution Ct, even if the degree of change in the data itself is the same. Therefore, to make a large change in the predetermined tire performance, controlling quality information data QL (processing condition data Pc) with a larger contribution Ct generally allows for faster and easier adjustment. However, to make a small change in the predetermined tire performance, controlling quality information data QL (processing condition data Pc) with a smaller contribution Ct generally allows for more precise adjustment.

[0022] In addition to the contribution Ct of each piece of quality information data QL (processing condition data Pc) to a predetermined tire performance, the contribution Ct of a combination of, for example, two or three types of multiple quality information data QL (processing condition data Pc) to a predetermined tire performance may also be calculated.

[0023] The estimation model EM generated by the calculation device 9 receives the quality information data QL and the processing condition data Pc as input data and receives the tire performance data TP as output data. Therefore, by inputting the quality information data QL and the processing condition data Pc of the tire component E1 used in the tire to be manufactured into the estimation model EM, it is possible to obtain an estimated value of predetermined tire performance (tire performance data TP) of the tire T to be manufactured.

[0024] In the embodiment of the tire manufacturing system 1 illustrated in Fig. 2, a green tire G is formed by integrating multiple types of tire components E (E1, E2, E3, E4, ...), and the green tire G is vulcanized to manufacture a tire T. The multiple types of tire components E include a tire component E1 formed by cutting a long body L into individual sizes for one tire.

[0025] In addition to the above-mentioned arithmetic device 9, the manufacturing system 1 includes component manufacturing equipment (such as a kneader 11 and an extruder 12) for manufacturing each of a plurality of types of tire components E, molding equipment (such as a molding machine 15) for integrating the tire components E to form a green tire G, and vulcanization equipment (such as a vulcanizer 16) for vulcanizing the green tire G. Various known types of component manufacturing equipment, molding equipment, and vulcanization equipment can be used. The operations of the component manufacturing equipment (such as the kneader 11 and the extruder 12), molding equipment (such as the molding machine 15), and vulcanization equipment (such as the vulcanizer 16) can be controlled by the arithmetic device 9.

[0026] The manufacturing system 1 further includes a marking machine 2, measuring machines 4 (4a, 4b, 4c), a calculation unit 5, and a specific mark reader 6. Since each manufactured tire T has an identifier 8 attached thereto, the manufacturing system 1 also includes an identifier reader 7 that reads the identifier 8 (written information or stored information).

[0027] The elongated body L is, for example, a component molded (manufactured) by extruding unvulcanized rubber R, or a component molded (manufactured) by rolling. Specific examples of tire component E1 are as exemplified above, but even the various components described above may be manufactured by a method other than cutting the elongated body L into individual pieces of the size of a single tire. In that case, the tire component E is not the tire component E1, but other tire components E2, E3, and E4. The bead component is a tire component E other than the tire component E1.

[0028] The manufacturing equipment for the tire component E1 includes a long body manufacturing machine 12 that manufactures the long body L, and a cutting machine 14a that manufactures the tire component E1 by cutting the long body L. In this embodiment, a rubber extrusion is used as the tire component E1, so the manufacturing equipment for the tire component E1 includes a kneading machine 11 that supplies kneaded unvulcanized rubber R to the long body manufacturing machine 12, and the long body manufacturing machine 12 is an extruder 12 for the unvulcanized rubber R. If the tire component E1 is a rolled material, the long body manufacturing machine 12 is a rolling device for the unvulcanized rubber R.

[0029] The molding facility includes a molding machine 15 having a molding drum 15a. The molding facility is also appropriately equipped with a supply mechanism (such as a conveying mechanism 13) that supplies each tire component E to the molding machine 15, and other necessary equipment.

[0030] The vulcanization equipment includes a vulcanization device 16 to which a vulcanization mold 17 is attached. The vulcanization equipment is appropriately equipped with a feeding mechanism for feeding a green tire G into the vulcanization device 16, an unloading mechanism for unloading the manufactured tire T from the vulcanization device 16, and other necessary equipment.

[0031] When the elongated body L is manufactured, the marking machine 2 marks the elongated body L with a position specifying mark 3 in each range corresponding to the individual size of one tire (i.e., the length of one tire component E1). For example, the marking machine 2 can be a device that marks the position specifying mark 3 by applying ink or the like to the surface of the elongated body L by spraying, transferring, or the like. The marking machine 2 can be used to mark the elongated body L as it is being transported. It is sufficient to mark at least one position specifying mark 3 in each range corresponding to the individual size of one tire on the elongated body L. The position specifying mark 3 can be any mark that can identify the position on the elongated body L where it is marked, and various letters, numbers, symbols, two-dimensional codes, or combinations thereof can be used.

[0032] When the elongated body L is manufactured, the measuring machine 4 acquires quality information data QL and processing condition data Pc indicating the predetermined quality of the elongated body L in each range marked with the position identification mark 3. The mass, cross-sectional shape, and waviness in the thickness direction of the surface of the elongated body L at the time of manufacturing have a relatively large effect on the predetermined tire performance of the tire T. Therefore, the types of quality information data QL for tire components E1 of individual sizes preferably include at least one of these types, and more preferably include two or all types. The acquired quality information data QL may include any other required types. A known measuring machine 4 suitable for acquiring each quality information data QL may be used.

[0033] The molding temperature (extrusion temperature and rolling temperature), molding speed (extrusion speed and rolling speed), and vulcanization temperature (described later) during the production of the elongated body L have a relatively large effect on the predetermined tire performance of the tire T. Therefore, the types of processing condition data Pc for tire components E1 of individual sizes preferably include at least one of these types, and more preferably include two or all of these types. The processing condition data Pc to be acquired can also include any other necessary types. Known measuring devices 4, detection sensors, etc. suitable for acquiring each processing condition data Pc can be used.

[0034] The processing condition data Pc and quality information data QL acquired by the measuring device 4 are linked to the position identification marks 3 affixed to the ranges where the processing condition data Pc and quality information data QL were acquired and stored in the calculation unit 5. A known computer is used as the calculation unit 5.

[0035] The specific mark reader 6 reads the position identification mark 3 attached to the tire component E1 used when various tire components E including the tire component E1 are integrated to form a green tire G. The specific mark reader 6 is also used to read the position identification mark 3 attached to the surface of the elongated body L when it is manufactured. As the specific mark reader 6, for example, a known scanner equipped with a digital camera or the like that acquires image data can be used. The specific mark reader 6 can be used to read the position identification mark 3 attached to the elongated body L (tire component E1) being transported.

[0036] The identifier 8 may be a barcode, a two-dimensional code, an IC tag (RFID tag), or the like, and the identifier reader 7 may be a known scanner that reads these identifiers 8.

[0037] The calculation device 9 stores the unique information D of the tire T and the identifier 8 attached to the tire T in association with each other. In this embodiment, the calculation device 9 and the calculation unit 5 are configured as separate computers that are connected to each other so as to be able to communicate with each other. However, the calculation unit 5 may also be configured as being integrated into the calculation device 9.

[0038] As illustrated in FIGS. 3 and 4, in this manufacturing system 1, processing condition data Pc and predetermined quality information data QL of the elongated body L when unvulcanized rubber R is extruded from an extruder 12 and manufactured into the elongated body L are acquired by measuring machines 4a, 4b, and 4c. The measuring machine 4a detects the temperature of the elongated body L, the measuring machine 4b acquires the cross-sectional shape of the elongated body L, and the measuring machine 4c acquires the mass of the elongated body L, and are arranged in this order in the conveying direction of the elongated body L. The mark applicator 2 is arranged between the measuring machines 4a and 4b, and the specific mark reader 6 is arranged downstream of the measuring machine 4c (on the right side in FIGS. 2 and 3). The arrangement order of the measuring machines 4a, 4b, and 4c and the mark applicator 2 is not particularly limited and can be set arbitrarily. The specific mark reader 6 is arranged downstream of the mark applicator 2. The measuring machines 4a, 4b, and 4c can be arranged upstream or downstream of the specific mark reader 6.

[0039] A known non-contact temperature sensor or the like can be used as the measuring device 4a that detects the temperature of the elongated body L (tire component E1). A known non-contact profile sensor or the like can be used as the measuring device 4b that acquires the cross-sectional shape of the elongated body L (tire component E1). The measuring device 4b acquires the cross-sectional shape of the elongated body L (tire component E1) by irradiating the surface of the elongated body L with laser light or the like and reflecting it. A known weighing scale or the like that is installed on the underside of the conveying mechanism 13 and can continuously measure the mass of the elongated body L being conveyed can be used as the measuring device 4c that acquires the mass of the elongated body L (tire component E1). A known speed sensor can be used to detect the extrusion speed of the elongated body L (tire component E1), and a known temperature sensor can be used to detect the vulcanization temperature of the tire component E1 in the vulcanization device 16. A known height sensor can be used to acquire the waviness of the surface of the elongated body L (tire component E1).

[0040] Next, the procedure of the tire manufacturing method of the present invention using the manufacturing system 1 will be described using as an example a case where the long body L is manufactured by extruding unvulcanized rubber R.

[0041] As shown in Fig. 2, a kneader 11 kneads a plurality of types of raw materials M (raw rubber, various compounding agents) to produce unvulcanized rubber R. A Banbury mixer or the like is used as the kneader 11. The produced unvulcanized rubber R is fed into an extruder 12 for the next process by a transport mechanism 13 such as a belt conveyor.

[0042] The extruder 12 brings the unvulcanized rubber R to an appropriate viscosity, extrudes it from a nozzle at the tip into a predetermined cross-sectional shape, and forms it into a long body L. The unvulcanized rubber R can be made into a sheet-like long body L using a rolling device, or a sheet-like long body L can be made by integrating the unvulcanized rubber R with a large number of aligned reinforcing cords. The manufactured long body L is transported to a stock means 14 by a transport mechanism 13 such as a belt conveyor arranged in front of the extruder 12. The transport speed of the long body L is constantly monitored by a calculation device 9.

[0043] The stock means 14 temporarily stores the long body L until it is needed in the next process. For example, a winding drum device that winds up the long body L together with the liner is used as the stock means 14. In the case of a direct extrusion / molding line in which the manufactured long body L is used immediately in the next process, the stock means 14 is not necessary.

[0044] As illustrated in FIGS. 3 and 4, in the conveyance section from the extruder 12 to the storage means 14, processing condition data Pc (extrusion temperature) for a predetermined processing of the conveyed long body L is acquired by a measuring device 4a, and predetermined quality information data QL (cross-sectional shape, mass) is acquired by measuring devices 4b and 4c. The processing condition data Pc and quality information data QL acquired by each measuring device 4 are input to and stored in a calculation unit 5. On the surface of the conveyed long body L, a marking device 2 sequentially applies position identification marks 3 to each range of the long body L corresponding to the individual size of a single tire. The position identification marks 3 are preferably applied to the longitudinal center (center in the conveyance direction) of the range corresponding to the individual size of a single tire. A specific mark reader 6 sequentially reads the position identification marks 3 applied to the conveyed long body L, and the read position identification marks 3 (notation information) are input to and stored in the calculation unit 5.

[0045] The arrangement (distance between) each measuring machine 4 and specific mark reader 6 is known, and the transport speed of the long body L is also known. Therefore, the time at which the position specifying mark 3 read by the specific mark reader 6 passed each measuring machine 4 is determined. In other words, it is determined how long before each position specifying mark 3 was read by the specific mark reader 6, the processing condition data Pc and quality information data QL for the range to which the position specifying mark 3 was attached were acquired by the measuring machine 4.

[0046] Therefore, the calculation unit 5 associates and stores the processing condition data Pc and quality information data QL of the elongated body L acquired in the range where each position identification mark 3 is attached with each position identification mark 3. If the specification mark reader 6 is located upstream of the measuring device 4, it becomes clear how much later than the time when each position identification mark 3 is read by the specification mark reader 6 the processing condition data Pc and quality information data QL of the range where each position identification mark 3 is attached are acquired by the measuring device 4. Therefore, in this case as well, the processing condition data Pc and quality information data QL of the elongated body L acquired in the range where each position identification mark 3 is attached with each position identification mark 3 are associated and stored in the calculation unit 5.

[0047] Next, the long body L stored in the stock means 14 is unwound from the stock means 14 and transported to the molding machine 15 by a transport mechanism 13 such as a belt conveyor arranged in front. In the molding machine 15, various tire components E including the tire component E1 are integrated on a molding drum 15a to form a green tire G.

[0048] Before being supplied to the molding machine 15, the long body L is cut by, for example, a cutting machine 14a on the conveying mechanism 13 to form individual tire components E1 each corresponding to one tire. A position identification mark 3 is attached to the surface of each formed tire component E1. When each tire component E1 is to be used in the molding machine 15, the position identification mark 3 attached to that tire component E1 is read by a specific mark reader 6.

[0049] Each green tire G to be molded is provided with an identifier 8 for distinguishing it from other green tires G. Each identifier 8 (notation information or stored information) is input to and stored in a computing device 9. The computing device 9 also stores various pieces of unique information D for each green tire G linked to the identifier 8. Examples of the unique information D include the product number, size, manufacturing date and time, manufacturing location, manufacturing lot, product numbers of each tire component E used, processing conditions, and quality information of the tire T to be manufactured.

[0050] As illustrated in FIG. 2 , the position identification marks 3 of the tire components E1 used in building each green tire G are read by the identification mark reader 6 when the green tire G is built. The position identification marks 3 stored in the calculation unit 5 are then linked to the identifiers 8 affixed to the green tires G in which the tire components E1 were used and stored in the calculation device 9. Each position identification mark 3 stored in the calculation unit 5 is linked to the acquired processing condition data Pc and quality information data QL. Meanwhile, each identifier 8 stored in the calculation device 9 is linked to the unique information D. Therefore, by linking the position identification marks 3 to the identifiers 8, the processing condition data Pc and quality information data QL associated with the position identification marks 3 are acquired as the processing condition data Pc and quality information data QL for the tire component E1 of an individual size to which the position identification marks 3 are affixed, and can be included as the unique information D of the green tire G (tire T) to which the identifiers 8 are affixed.

[0051] Next, the green tire G with the identifier 8 attached thereto is vulcanized by a vulcanization device 16 to produce a tire T that is shaped into a predetermined shape by a vulcanization mold 17. In the vulcanization device 16, the vulcanization temperature (maximum temperature) of the green tire G is detected by a temperature sensor, and the detected temperature is linked to the identifier 8 attached to the green tire G as processing condition data Pc of the tire component E1 used in the green tire G and stored in the calculation device 9. The identifier 8 is still attached to the vulcanized tire T that has been produced.

[0052] 5, an identifier 8 is read by an identifier reader 7, and the read identifier 8 is input to a calculation device 9. Since the calculation device 9 associates the identifier 8 with the unique information D of the tire T, the calculation device 9 can display the associated unique information D on, for example, a monitor 10 based on the input identifier 8.

[0053] Therefore, the quality information data QL at the time when the elongated body L was manufactured before the tire components E1 were cut into individual units can also be obtained with higher accuracy. That is, along with various unique information D of the tire T, the quality information data QL at the time of manufacturing the elongated body L that will become the tire component E1 can be grasped with higher accuracy in individual size units for one tire.

[0054] The unique information D of the tire T may include quality information of each tire component E including the above-mentioned quality information data QL, manufacturing conditions of each tire component E including the above-mentioned processing condition data Pc, molding conditions for the green tire G, and data on vulcanization conditions. This makes it possible to quickly and accurately grasp the quality information and manufacturing condition data for a series of processes from upstream to downstream in the manufacture of the tire T by reading the identifier 8 attached to the tire T with the identifier reader 7. This further improves the traceability of tire quality.

[0055] In this way, tire performance data TP, which acquires predetermined tire performance of a large number of tires T manufactured with the same specifications or tires that can be considered to have equivalent specifications, is input to a calculation device 9 as a response variable, as illustrated in FIG. 1. In addition, quality information data QL indicating predetermined quality for tire components E1 of individual sizes used in manufacturing a large number of tires T manufactured with the same specifications or tires that can be considered to have equivalent specifications, and processing condition data Pc indicating conditions for predetermined processing, are input as explanatory variables. The calculation device 9 performs machine learning on the input data to calculate the relationship between the explanatory variables and the response variable. Based on this relationship, the calculation device 9 calculates the contribution Ct of the quality information data QL and the processing condition data Pc to the predetermined tire performance, and also generates an estimation model EM based on this relationship.

[0056] In detail, according to the procedure illustrated in FIG. 6, the manufacturing system 1 controls the predetermined quality (quality information data QL) and the conditions for predetermined processing (processing condition data Pc) for the tire component E1 of an individual size to be used in the tire T to be manufactured thereafter, in accordance with the previously determined contribution Ct, so that the predetermined tire performance (tire performance data TP) approaches the set target value.

[0057] Therefore, an estimation model EM generated based on the calculated contribution Ct is used to estimate predetermined tire performance (tire performance data TP) of the tire T to be manufactured. The estimation model EM calculates an estimated value of the predetermined tire performance (tire performance data TP) of the tire T to be estimated by inputting quality information data QL and processing condition data Pc about tire components E1 of individual sizes used in the tire T to be estimated and performing arithmetic processing.

[0058] According to this manufacturing system 1, it is possible to grasp the detailed contribution degree Ct of the tire component E1 for each tire T. As a result, by using the grasped contribution degree Ct and the quality information data QL and processing condition data Pc for the tire component E1 of an individual size used in the tire T to be manufactured, it is advantageous to estimate with higher accuracy the predetermined tire performance (tire performance data TP) of the tire T to be manufactured.

[0059] Here, it is also possible to generate an estimation model EM by selecting only one or more (approximately two) types of data in descending order of contribution Ct from among the quality information data QL and the processing condition data Pc. Then, the types of data selected for generating the estimation model EM are input to the estimation model EM and subjected to arithmetic processing to calculate an estimated value of predetermined tire performance (tire performance data TP) of the tire T to be estimated. In this way, the estimation model EM is used to estimate the predetermined tire performance of the tire T to be estimated.

[0060] For example, among the predetermined tire performances of the tire T, the contribution Ct of the various quality information data QL and processing condition data Pc about the tread rubber E1 is high for the uniformity performance, rolling resistance performance, and running quietness performance. Therefore, the estimation model EM can be generated using the various quality information data QL and processing condition data Pc about the tread rubber E1. Then, the various quality information data QL and processing condition data Pc about the tread rubber E1 used in the tire T to be manufactured can be input into the estimation model EM to estimate the uniformity performance, rolling resistance performance, running quietness performance, and the like of the tire T to be manufactured. In this way, by selecting and using only one or more types (about two types) of data in descending order of contribution Ct, it is possible to reduce the load on the calculation processing of the calculation device 9 while suppressing a decrease in estimation accuracy.

[0061] Next, the target value of the predetermined tire performance (tire performance data TP) is compared with the estimated value for the predetermined tire performance (tire performance data TP) of the tire T to be estimated by the estimation model EM. If the two are close to each other, that is, if the estimated value is within an allowable range of the target value, the values ​​of the quality information data QL and processing condition data Pc used in the estimation are determined as the predetermined quality reference value and the predetermined processing condition reference value, respectively, for the tire component E1 of an individual size used in the tire T to be manufactured with the predetermined tire performance set to the target value.

[0062] If the estimated value and the target value are not close to each other, that is, if the estimated value is outside the allowable range of the target value, at least one of the quality information data QL and the processing condition data Pc for the tire component E1 of individual size is set to a different value and input into the estimation model EM, and an estimated value of the predetermined tire performance (tire performance data TP) of the tire T to be estimated is calculated. In this way, calculation processing is performed until the estimated value and the target value become close to each other.

[0063] Next, when manufacturing a tire T whose predetermined tire performance is set to this target value, the corresponding manufacturing equipment (kneader 11, extruder 12, molding machine 15, vulcanizer 16, etc.) is controlled so that the predetermined quality (quality information data QL) and the predetermined processing conditions (processing condition data Pc) of the tire component E1 of individual size become the determined respective reference values. In this way, the manufacturing system 1 manufactures a tire T whose predetermined tire performance (tire performance data TP) is close to the target value, i.e., within an allowable range of the target value.

[0064] According to this manufacturing system 1, by utilizing the calculated contribution Ct, predetermined tire performance (tire performance data TP) of the tire T to be manufactured can be estimated with high accuracy. Accordingly, the reference values ​​of the quality information data QL and the processing condition data Pc for the tire component E1 of the individual size used for the tire T to be manufactured are appropriately determined. Therefore, by controlling the quality information data QL and the processing condition data Pc for the tire component E1 of the individual size used for the tire T to be manufactured so that they approach the determined reference values, it is advantageous to more reliably manufacture a tire T whose predetermined tire performance (tire performance data TP) approaches a target value.

[0065] It is also possible to acquire, as the quality information data QL, the physical property values ​​of the raw material M used for the tire component E1 of an individual size when it is supplied to the kneader 11. Alternatively, it is also possible to acquire, as the processing condition data Pc, the temperature when the raw material M used for the tire component E1 of an individual size is supplied to the kneader 11, the kneading time in the kneading step, the rotor rotation speed, and the like. These pieces of data can also be used to generate the estimation model EM and to estimate predetermined tire performance (tire performance data TP) of the tire T to be manufactured.

[0066] The present invention is not limited to pneumatic tires, and can be used when manufacturing various other types of tires T.

[0067] The present disclosure includes the following inventions. Invention 1: A tire manufacturing method in which a green tire is manufactured by integrating a plurality of types of tire components, including tire components formed by cutting a long body into individual sizes for one tire, and vulcanizing the green tire, A tire manufacturing method in which tire performance data indicating predetermined tire performance of each manufactured tire is used as a dependent variable, and quality information data indicating predetermined quality of the tire components of the individual sizes used in manufacturing each of the tires and processing condition data indicating conditions for predetermined processing are used as explanatory variables, and the degree of contribution of the predetermined quality information data and the processing condition data to the predetermined tire performance is grasped in advance based on the relationship between the explanatory variables and the dependent variables calculated by machine learning using a computing device. Invention 2: The tire manufacturing method according to Invention 1, wherein an estimation model is generated based on the calculated relationship between the explanatory variable and the objective variable, and the quality information data and the processing condition data for the tire component of the individual size used in the tire to be estimated are input to the estimation model and subjected to calculation processing, thereby estimating the predetermined tire performance of the tire to be estimated. Invention 3: The tire manufacturing method according to Invention 2, in which only one or more types of data in descending order of contribution are selected from the predetermined quality information data and the processing condition data to generate the estimation model, and the data of the types selected for generating the estimation model for the tire components of the individual sizes used in the tire to be estimated are input into the estimation model and subjected to arithmetic processing, thereby estimating the predetermined tire performance of the tire to be estimated. Invention 4: The method for manufacturing a tire according to Invention 2 or 3, further comprising determining, based on a comparison between the target value of the predetermined tire performance and an estimated value for the predetermined tire performance of the tire to be estimated by the estimation model, the predetermined standard value of quality and the predetermined standard value of conditions for processing for the tire component of the individual size used in a tire to be manufactured with the predetermined tire performance set to the target value, and controlling the predetermined quality and the predetermined standard value for conditions for processing so as to approach the determined standard value when manufacturing a tire with the predetermined tire performance set to the target value. Invention 5: When the elongated body is manufactured, a position identification mark is attached to each range of the elongated body corresponding to the individual size of one tire, and quality information data indicating the predetermined quality of the elongated body in each range where the position identification mark is attached is acquired, and processing condition data for the range of the elongated body where the position identification mark is attached is linked to the position identification mark together with the acquired quality information data and stored in a storage unit. 5. The tire manufacturing method according to any one of Inventions 1 to 4, wherein, when molding the green tire, the position identification marks attached to the tire components formed to the individual sizes used for this molding are read, and the quality information data linked to the position identification marks are acquired as the quality information data for the tire components of the individual sizes, and the processing condition data linked to the position identification marks are acquired as the processing condition data for the tire components of the individual sizes. Invention 6: The tire manufacturing method according to invention 5, wherein the quality information data indicating the predetermined quality of the tire components and the processing condition data indicating the conditions for processing the predetermined quality are included in the unique information of the manufactured tire and stored in the calculation unit, an identifier linked to this unique information is provided on the tire, and the unique information can be obtained by reading the identifier with an identifier reader. Invention 7: The method for manufacturing a tire according to any one of Inventions 1 to 6, wherein the long body is a component formed by extruding or rolling unvulcanized rubber, the predetermined quality of the tire component includes at least one of the mass, cross-sectional shape, and surface waviness of the tire component, and the processing conditions for the predetermined processing of the tire component include at least one of the molding temperature, molding speed, and vulcanization temperature when the tire component is molded. Invention 8: 8. The method for manufacturing a tire according to any one of Inventions 1 to 7, wherein the predetermined tire performance includes at least one of uniformity performance, rolling resistance performance, wear resistance performance, steering stability performance, and running quietness performance. Invention 9: A tire manufacturing system including component manufacturing equipment for manufacturing each of a plurality of types of tire components, molding equipment for integrating the plurality of types of tire components to form a green tire, and vulcanization equipment for vulcanizing the green tire, wherein each of the component manufacturing equipment includes a long body manufacturing machine for manufacturing a long body, and a cutting machine for cutting the long body to manufacture the tire components formed into individual sizes for one tire, a calculation device that calculates the degree of contribution of the predetermined quality information data and the processing condition data to the predetermined tire performance based on a relationship between the explanatory variables and the calculated explanatory variables, the relationship being calculated by machine learning using tire performance data that indicates predetermined tire performance of each of the manufactured tires as a response variable and quality information data that indicates predetermined quality of the tire components of the individual sizes used in manufacturing each of the tires and processing condition data for predetermined processing as explanatory variables; A tire manufacturing system in which conditions for the specified quality and the specified processing of the tire components of the individual sizes used in the tire to be manufactured are controlled according to the previously determined contribution so that the specified tire performance approaches a set target value. Invention 10: a marking machine that, when the long body is manufactured, marks a position identification mark on the long body in each range corresponding to the individual size of one tire; a measuring machine that acquires quality information data that indicates the predetermined quality of the long body in each range where the position identification mark is attached; a storage unit that stores the quality information data acquired by the measuring machine as well as processing condition data for the range where the position identification mark is attached on the long body, linking the data to the position identification mark; and a specific mark reader that, when the green tire is molded, reads the position identification mark attached to the tire component formed to the individual size used in this molding, The tire manufacturing system according to invention 9, wherein the position identification mark attached to the tire component formed to the individual size used when forming the green tire is read by the specific mark reader, so that the quality information data linked to the position identification mark is acquired as the quality information data for the tire component of the individual size, and the processing condition data linked to this position identification mark is acquired as the processing condition data for the tire component of the individual size. [Explanation of symbols]

[0068] 1. Manufacturing System 2 Marking machine 3 Location mark 4(4a, 4b, 4c) Measuring machine 5 Arithmetic section 6 Specific Mark Reader 7 Identification device reader 8 Identification Body 9 Arithmetic unit 10 monitors 11 Kneader 12 Extruder (long body manufacturing machine) 13 Conveyor mechanism 14 Stocking Methods 14a cutting machine 15 Molding machine 15a Forming drum 16 Vulcanization equipment 17 Vulcanization mold G Green Tire T Vulcanized tires L Long body E (E1, E2, E3, E4, ...) Tire components R Unvulcanized rubber M Raw materials D-specific information TP tire performance data QL Long body (tire component E1) quality information data Pc Processing condition data for long body (tire component E1) EM estimation model Ct contribution

Claims

1. A tire manufacturing method in which a green tire is manufactured by integrating a plurality of types of tire components, including tire components formed by cutting a long body into individual sizes for one tire, and vulcanizing the green tire, A tire manufacturing method in which tire performance data indicating predetermined tire performance of each manufactured tire is used as a dependent variable, and quality information data indicating predetermined quality of the tire components of the individual sizes used in manufacturing each of the tires and processing condition data indicating conditions for predetermined processing are used as explanatory variables, and the degree of contribution of the predetermined quality information data and the processing condition data to the predetermined tire performance is grasped in advance based on the relationship between the explanatory variables and the dependent variables calculated by machine learning using a computing device.

2. 2. The tire manufacturing method according to claim 1, wherein an estimation model is generated based on the calculated relationship between the explanatory variable and the objective variable, and the quality information data and the processing condition data for the tire component of the individual size used in the tire to be estimated are input to the estimation model and subjected to arithmetic processing, thereby estimating the predetermined tire performance of the tire to be estimated.

3. 3. The tire manufacturing method according to claim 2, wherein the estimation model is generated by selecting only one or more types of data from the predetermined quality information data and the processing condition data in descending order of contribution, and the data of the types selected for generating the estimation model for the tire component of the individual size used in the tire to be estimated is input into the estimation model and subjected to arithmetic processing, thereby estimating the predetermined tire performance of the tire to be estimated.

4. 4. The method for manufacturing a tire according to claim 2, further comprising: determining, based on a comparison between a target value of the predetermined tire performance and an estimated value for the predetermined tire performance of the tire to be estimated by the estimation model, a reference value of the predetermined quality and a reference value of the predetermined processing conditions for the tire component of the individual size used in a tire to be manufactured with the predetermined tire performance set to the target value; and controlling, when manufacturing a tire with the predetermined tire performance set to the target value, the predetermined quality and the predetermined processing conditions to approach the determined reference values, respectively.

5. When the elongated body is manufactured, a position identification mark is attached to each range of the elongated body corresponding to the individual size of one tire, and quality information data indicating the predetermined quality of the elongated body in each range where the position identification mark is attached is acquired, and processing condition data for the range of the elongated body where the position identification mark is attached is linked to the position identification mark together with the acquired quality information data and stored in a storage unit.

4. The tire manufacturing method according to claim 1, wherein, when molding the green tire, the position identification marks affixed to the tire components formed to the individual sizes used for this molding are read, and the quality information data linked to the position identification marks is acquired as the quality information data for the tire components of the individual sizes, and the processing condition data linked to the position identification marks is acquired as the processing condition data for the tire components of the individual sizes.

6. 6. A method for manufacturing a tire as described in claim 5, wherein the quality information data indicating the specified quality of the tire components and the processing condition data indicating the conditions for processing the specified components are included in unique information of the manufactured tire and stored in the calculation unit, an identifier linked to this unique information is provided on the tire, and the unique information can be obtained by reading the identifier with an identifier reader.

7. 4. The method for manufacturing a tire according to claim 1, wherein the elongated body is a component formed by extruding or rolling unvulcanized rubber, the predetermined quality of the tire component includes at least one of the mass, cross-sectional shape, and surface waviness of the tire component, and the processing conditions for the predetermined processing of the tire component include at least one of the molding temperature, molding speed, and vulcanization temperature when the tire component is molded.

8. The method for manufacturing a tire according to any one of claims 1 to 3, wherein the predetermined tire performance includes at least one of uniformity performance, rolling resistance performance, and running quietness performance.

9. A tire manufacturing system including component manufacturing equipment for manufacturing each of a plurality of types of tire components, molding equipment for integrating the plurality of types of tire components to form a green tire, and vulcanization equipment for vulcanizing the green tire, wherein each of the component manufacturing equipment includes a long body manufacturing machine for manufacturing a long body, and a cutting machine for cutting the long body to manufacture the tire components formed into individual sizes for one tire, a calculation device that calculates the degree of contribution of the predetermined quality information data and the processing condition data to the predetermined tire performance based on a relationship between the explanatory variables and the calculated explanatory variables, the relationship being calculated by machine learning using tire performance data that indicates predetermined tire performance of each of the manufactured tires as a response variable and quality information data that indicates predetermined quality of the tire components of the individual sizes used in manufacturing each of the tires and processing condition data for predetermined processing as explanatory variables; A tire manufacturing system in which conditions for the predetermined quality and the predetermined processing of the tire components of the individual sizes used in the tire to be manufactured are controlled according to the previously determined contribution so that the predetermined tire performance approaches a set target value.

10. a marking machine that, when the long body is manufactured, marks a position specifying mark on the long body in each range corresponding to the individual size of one tire; a measuring machine that acquires quality information data that indicates the predetermined quality of the long body in the range where each of the position specifying marks is marked; a storage unit that stores the quality information data acquired by the measuring machine as well as processing condition data for the range where the position specifying mark is marked on the long body, linking the data to the position specifying mark; and a specific mark reader that, when the green tire is molded, reads the position specifying mark that is marked on the tire member that has been formed to the individual size used in this molding, 10. The tire manufacturing system according to claim 9, wherein when molding the green tire, the position identification mark attached to the tire component formed to the individual size used is read by the specific mark reader, so that the quality information data linked to the position identification mark is acquired as the quality information data for the tire component of the individual size, and the processing condition data linked to this position identification mark is acquired as the processing condition data for the tire component of the individual size.

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