JP7620188B2 - Method and system for designing rubber compound - Google Patents

Description

The present invention relates to a method and system for designing rubber compounds, and more specifically, to a method and system for designing rubber compounds that can accurately and easily determine a rubber compound that meets the target rubber properties.

Vulcanized rubber is produced by vulcanizing unvulcanized rubber. Unvulcanized rubber is made by blending multiple types of compounding components in specific amounts. By varying the amounts of each compounding component, it is possible to change the rubber properties of the vulcanized rubber produced. In other words, to produce vulcanized rubber with the desired rubber properties, it is necessary to appropriately set the rubber compounding (the amount of each compounding component) according to the desired rubber properties.

Conventionally, to manufacture vulcanized rubber with the target rubber properties, for example, first, multiple types of unvulcanized rubber are molded with different rubber compositions, and then each unvulcanized rubber is vulcanized to manufacture vulcanized rubber. Next, the rubber properties of the multiple types of vulcanized rubber manufactured are measured to identify a vulcanized rubber with rubber properties that are close to the target rubber properties. By referring to the rubber composition of the identified vulcanized rubber, a rubber composition that matches the target rubber properties is identified. Identifying an appropriate rubber composition using this method requires a great deal of trial and error.

In recent years, a device and method have been proposed that uses machine learning to assist in the design of manufacturing recipes for products with specified characteristics (see, for example, Patent Document 1). The device and method proposed in Patent Document 1 are not specialized for rubber products, so rubber-specific considerations must be taken into account when applying them to rubber products. In addition, with this proposed device and method, there is a risk that the computational processing load will be excessive in order to accurately determine a rubber compounding that matches the target rubber physical properties, so it is necessary to reduce this load. Therefore, there is room for improvement in accurately and simply determining a rubber compounding that matches the target rubber physical properties.

JP 2019-86817 A

The object of the present invention is to provide a method and system for designing a rubber compound that can easily and accurately determine a rubber compound that meets the target rubber properties.

In order to achieve the above object, the rubber compounding design method of the present invention is characterized in that: compounding designation data in which a specified range of compounding amounts of each compounding component of a vulcanized rubber is set and target values of multiple types of rubber property data of the vulcanized rubber are input to a calculation device; the compounding amounts of each compounding component are specified by performing calculation processing by the calculation device based on the compounding designation data, and multiple candidates of compounding data that satisfy the specified range set by the compounding designation data are generated; using a predictive model constructed by machine learning, the calculation device performs calculation processing based on the compounding data and vulcanization condition data of each of the generated candidates to calculate each of the rubber property data of the target vulcanized rubber formed with the compounding data of each of the candidates and vulcanized based on the vulcanization condition data; and the compounding data of the target vulcanized rubber in which each of the calculated rubber property data is approximate to the target value is selected from each of the candidates.

The rubber compounding design system of the present invention has a calculation device, an input unit for inputting data to the calculation device, and an output unit connected to the calculation device for communication. In the rubber compounding design system, compounding designation data in which a specified range of the compounding amount of each compounding component of the vulcanized rubber is set, and target values of multiple types of rubber property data of the vulcanized rubber are input to the calculation device, and the compounding amount of each of the compounding components is specified by performing calculation processing by the calculation device based on the compounding designation data, and a plurality of candidates of compounding data that satisfy the specified range set by the compounding designation data are generated, and using a prediction model constructed by machine learning, the calculation device performs calculation processing based on the compounding data and vulcanization condition data of each of the generated candidates, and the rubber property data of each of the target vulcanized rubbers formed with the compounding data of each of the candidates and vulcanized based on the vulcanization condition data is calculated, and the compounding data of the target vulcanized rubber in which each of the calculated rubber property data is approximate to the target value is selected from each of the candidates and output by the output unit.

According to the present invention, the compounding amounts of each compounding component of the vulcanized rubber are specified based on the compounding specification data in which a specified range of the compounding amount of each compounding component of the vulcanized rubber is set, and multiple candidates for compounding data that satisfy the specified range are generated by the calculation processing by the arithmetic device. That is, the candidate compounding data is generated under a constraint condition that satisfies the specified range, so the burden of the calculation processing for generating the candidate compounding data and the calculation processing using the prediction model is reduced. In addition, by using the prediction model and performing calculation processing by the arithmetic device based on the compounding data and vulcanization condition data of each of the generated candidates, the rubber property data of each of the target vulcanized rubbers can be calculated with high accuracy. As a result, the compounding data of the target vulcanized rubber in which each of the rubber property data is highly accurately close to the target value can be easily grasped.

FIG. 1 is an explanatory diagram illustrating a rubber compound design system of the present invention. 1 is a flow diagram illustrating a procedure for determining compounding data of a target vulcanized rubber whose rubber physical property data approximates a target value.

The rubber compounding design method and system of the present invention will be specifically explained below based on the embodiment shown in the figure.

The rubber compounding design method of the present invention is carried out using a rubber compounding design system 1 of the present invention illustrated in Fig. 1. The vulcanized rubber G is produced by vulcanizing the unvulcanized rubber R. The vulcanized rubber G and the unvulcanized rubber R are formed by compounding a plurality of types of compounding components C ( _C1 , _C2 , ... _Cn ) in specific compounding amounts. The present invention has been devised and created in order to accurately and easily grasp the rubber compounding of the vulcanized rubber G (target vulcanized rubber Ga) having the target rubber properties, i.e., the compounding amounts of the respective compounding components C that form the vulcanized rubber G.

This design system 1 has a calculation device 2, an input unit 3, and an output unit 4. The calculation device 2, the input unit 3, and the output unit 4 are connected to each other so that they can communicate with each other via wires or wirelessly. In this embodiment, the design system 1 further has a measurement device 5. Data acquired by the measurement device 5 is input to the calculation device 2 through the input unit 3. The measurement device 5 may be independent and separate from the design system 1, and the data acquired by the measurement device 5 may be input to the calculation device 2.

The calculation device 2 receives and stores various data, and performs calculations using these data. Various computers can be used as the calculation device 2. Therefore, the calculation device 2 has a memory in which various data are stored, and a CPU that performs calculations.

The input unit 3 is an input means for inputting various data to the calculation device 2. Examples of the input unit 3 include a keyboard, a mouse, various terminal devices, and an interface provided in the calculation device 2.

The output unit 4 is an output means for displaying various data input to the calculation device 2 and the results of calculations (numerical values, tables, drawings, etc.) performed using the data. Examples of the output unit 4 include various monitors and printers.

The measuring device 5 is a means for measuring and acquiring rubber property data D2 of the vulcanized rubber G. Examples of the rubber property data D2 include various known rubber properties such as tan δ (loss factor), hardness, breaking strength, breaking elongation, 300% modulus, and specific gravity. In the present invention, multiple desired types of rubber property data D2 are used. The measuring device 5 uses a known device corresponding to the measurement of each rubber property data D2, and each rubber property data D2 is measured and acquired by a method conforming to a known method (such as the corresponding JIS standard).

In the present invention, a prediction model PM constructed by machine learning is used. This prediction model PM is a type of computer program for predicting multiple desired types of rubber property data D2 of a target vulcanized rubber Ga produced by vulcanizing a target unvulcanized rubber Ra under specified vulcanization conditions. An example of the procedure for constructing this prediction model PM will be described.

First, unvulcanized rubber R is molded based on compounding data D1, which sets the compounding amount of each compounding component C. At that time, many types of unvulcanized rubber R are molded with different compounding data D1. Different compounding data D1 means that the compounding amount of at least one type of each compounding component C is different. There are cases where the compounding amount of a specific compounding component C is zero, in which case the type of compounding component C used will be different.

Next, each molded unvulcanized rubber R is vulcanized under specified vulcanization conditions to produce vulcanized rubber G. In other words, many types of vulcanized rubber G with different compounding data D1 are produced. The compounding amount of each compounding component C affects the rubber property data D2 of the vulcanized rubber G, but the extent of this effect differs depending on the type and compounding amount of compounding component C.

Examples of vulcanization conditions include vulcanization temperature, vulcanization pressure, and vulcanization time. An index indicating these vulcanization conditions is the vulcanization condition data D3. The vulcanization conditions are set to appropriate conditions for the unvulcanized rubber R so that there is neither too much nor too little vulcanization. The vulcanization condition data D3 also affects the rubber physical property data D2. When manufacturing vulcanized rubber G at the laboratory level, for example, vulcanization can be performed under standardized vulcanization conditions, and the vulcanization conditions for all vulcanized rubbers G can be set to be constant. This makes it possible to substantially eliminate the influence of differences in the vulcanization condition data D3 on the rubber physical property data D2 of the vulcanized rubber G.

Next, the rubber property data D2 of each vulcanized rubber G produced is measured and obtained using a measuring device 5. From the many types of rubber property data D2 exemplified above, desired types of rubber property data D2 are measured and obtained.

Along with the desired multiple types of rubber property data D2 obtained from each manufactured vulcanized rubber G, compounding data D1 for each vulcanized rubber G and vulcanization condition data D3 for each vulcanized rubber G are prepared. Next, the rubber property data D2, compounding data D1, and vulcanization condition data D3 for each vulcanized rubber G are used as basic data (i.e., learning data) for predicting the rubber property data D2 of the target vulcanized rubber Ga. Then, a prediction model PM is constructed by machine learning using this learning data.

Specifically, the above data D1, D2, and D3 are input to the calculation device 2, and machine learning is performed using these data D1, D2, and D3. That is, the relationship between each rubber property data D2 and the compounding data D1 and the vulcanization condition data D3 is linked, and the influence of the compounding data D1 and the vulcanization condition data D3 on each rubber property data D2 of the vulcanized rubber G to be manufactured is analyzed and evaluated to construct the prediction model PM. In terms of the compounding data D1, the influence of the type and compounding amount of compounding component C on the rubber property data D2 is analyzed and evaluated.

The factors of the data D1 and D3 described above affect each other in a complex way, causing the rubber property data D2 of the vulcanized rubber G to change. Therefore, by using the data D1, D2, and D3 as learning data, it is possible to construct a prediction model PM by having an artificial intelligence (AI) learn the degree of difference in the data D2 (rubber property data) due to the combination of the data D1 and D3, the combination of each compounding component C in the data D1, the difference in the compounding amount of each compounding component C, and the characteristics of the data D2 in each case. Examples of machine learning methods include well-known methods such as deep learning using a neural network. The huge amount of data D1, D2, and D3 that has been accumulated in the past can be effectively used to construct this prediction model PM. The constructed prediction model PM is stored in the calculation device 2.

Next, an example of a procedure for determining the rubber compounding D1 of a target vulcanized rubber Ga having the target rubber properties will be described. That is, an example of a procedure for determining the compounding data D1 of a target vulcanized rubber Ga having rubber properties that satisfy the respective target values D2a set for the desired multiple types of rubber property data D2 will be described. In the following explanation, an example will be given in which the compounding data D1 of a conventional vulcanized rubber G in which the desired multiple types of rubber property data D2 do not satisfy the target values D2a is reviewed, and the compounding data D1 of a target vulcanized rubber Ga in which each rubber property data D2 satisfies the target value D2a is determined.

The main steps are as shown in FIG. 2. First, the necessary data is input to the calculation device 2 using the input unit 3. The data to be input is the compounding specification data Dx for the target vulcanized rubber Ga shown in Table 1 below, the target value D2a of the rubber property data D2 for the target vulcanized rubber Ga shown in Table 2 below, and the vulcanization condition data D3 for the target vulcanized rubber Ga.

As shown in Table 1, the compounding specification data Dx sets the specified range of compounding amounts for each compounding component C of the target vulcanized rubber Ga. In this example, a total of 11 types of compounding components C are set: two types of rubber components, SBR and BR, and carbon, zinc oxide, ..., and vulcanization accelerator B. Compounding components C are not limited to the types shown here, and types can be used as needed. The initial values in Table 1 are the compounding data for conventional vulcanized rubber G.

The compounding specification data Dx shown in Table 1, for the target vulcanized rubber Ga, is set to a variation of ±20 phr for each of SBR, BR, carbon, and aromatic oil from the compounding data (initial value) of conventional vulcanized rubber G. Therefore, the compounding amounts of SBR, BR, carbon, and aromatic oil in the target vulcanized rubber Ga are set to 30 phr or more and 70 phr or less. For other compounding components C such as zinc oxide, a variation of zero is set from the compounding data (initial value) of conventional vulcanized rubber G. Therefore, the compounding amounts of other compounding components C such as zinc oxide in the target vulcanized rubber Ga are set to the same compounding data (initial value) as conventional vulcanized rubber G.

In this way, in the present invention, the blending amount of each compounding component C of the target vulcanized rubber Ga is set by the blending specification data Dx. Therefore, depending on the purpose, it is possible to change only the blending amount of the desired compounding component C, or to set the blending amount of the desired compounding component C to zero and remove that compounding component C.

As shown in Table 2, the target value D2a of the rubber physical property data D2 is set for multiple desired types of rubber physical property data D2. The initial values in Table 2 are the physical property data D2 of conventional vulcanized rubber G. In this example, two types of rubber physical property data D2, tan δ at 60°C and hardness HS at 20°C, are set as the multiple desired types of rubber physical property data D2, but the present invention is not limited to this and any desired type of rubber physical property data D2 can be adopted, and the number of adopted rubber physical property data D2 can be three or more. In Table 2, the targets are set to change tan δ at 60°C from 0.2 to 0.18 and hardness HS at 20°C from 65 to 64.

The calculation device 2 performs calculation processing based on the inputted blending specification data Dx to generate multiple candidates for blending data D1 that satisfy the specified range set by the blending specification data Dx under specified conditions. That is, all combinations of blending components C that satisfy this specified range according to the specified conditions are generated as candidates for blending data D1. This specified condition is the pitch at which the fluctuation amount set for blending components C is changed (fluctuation amount change pitch). The blending amounts of BR, BR, carbon, and aroma oil, for which fluctuation amounts are set, are changed at a pitch of, for example, 1 phr, and all combinations of blending components C that satisfy the specified range set by the blending specification data Dx are generated as candidates for blending data D1.

The smaller this pitch is, the more combination data D1 will be generated as candidates. This is advantageous for grasping the desired combination data D1 with higher accuracy. On the other hand, if the pitch is made too small, the number of combination data D1 generated as candidates will be excessive, which will increase the burden on calculation processing. Therefore, the size of the pitch is set taking into consideration the improvement of accuracy and the reduction of the burden on calculation processing. For example, this pitch is set to about 0.5 to 4 phr.

This pitch can be the same for each compounding component C, or can be relatively large (small) for a specific compounding component C. For example, the pitch can be set smaller for compounding components C that affect the rubber physical property data D2 in smaller amounts, and larger for compounding components C that have no effect unless they are used in larger amounts. In other words, the magnitude of this pitch for each compounding component C can be set based on the extent to which that compounding component C affects the rubber physical property data D2.

Then, the compounding data D1 of each of the generated candidates and the vulcanization condition data D3 of the target vulcanized rubber Ga (the vulcanization condition data D3 when vulcanizing the target unvulcanized rubber Ra) are input to the prediction model PM, and a calculation process is performed to execute the prediction model PM. The prediction model PM calculates the rubber property data D2 of each of the target vulcanized rubber Ga formed with the compounding data D1 based on the input compounding data D1 and vulcanization condition data D3.

The calculated rubber physical property data D2 is compared with the target value D2a set for each data, and the calculation device 2 judges whether the data falls within the tolerance range set as being close. This tolerance range can be set arbitrarily, but is, for example, a range of about ±5% or ±2% of the target value D2a. From all the generated candidate compounding data D1, the compounding data D1 for which the rubber physical property data D2 is judged to be close to the target value D2a is selected by the calculation device 2. The compounding data D1 selected is not limited to one, and may be multiple.

As shown in Table 3 below, the compounding data D1 of the selected target vulcanized rubber Ga is output to the output 4. If the output unit 4 is a monitor, the compounding data D1 is displayed, and if the output unit 4 is a printer, the compounding data D1 is printed.

In Table 3, the amount of each compounding component C satisfies the specified range set by the compounding specification data Dx. Moreover, the target vulcanized rubber Ga formed with this compounding data D1 and vulcanized based on the vulcanization conditions of the input vulcanization condition data D3 is predicted to satisfy the target values D2a of each rubber property data D2 listed in Table 2.

That is, the data shown in Table 3 can be grasped as the compounding data D1 of the target vulcanized rubber Ga appropriate for changing the tan δ at 60°C from the initial value of 0.2 to the target value of 0.18, and for changing the hardness HS at 20°C from the initial value of 65 to 64. In this way, it is possible to grasp the compounding data D1 of the target vulcanized rubber Ga having rubber properties that satisfy the target value D2a that could not be obtained with the compounding data D1 of the conventional vulcanized rubber G. Note that the data shown in Tables 1 to 3 are data based on actual examples, and by using the present invention, it was possible to concisely and accurately grasp how to change the compounding data D1 of the conventional vulcanized rubber G in order to set the desired multiple types of rubber property data D2 to the target value D2a.

According to the present invention, the candidate compounding data D1 is generated under limited constraint conditions that satisfy the specified range set by the compounding specification data Dx, so the computational burden for generating the candidate compounding data D1 is small. In addition, the number of candidates for compounding data D1 from which rubber physical property data D2 is subsequently predicted is also limited, so the computational burden for this prediction is reduced.

In addition, by performing calculations using the above-mentioned prediction model PM based on the generated compounding data D1 of each candidate and the vulcanization condition data D3 of the target vulcanized rubber Ga, the rubber physical property data D2 of each target vulcanized rubber Ga can be calculated with high accuracy. As a result, it is possible to easily grasp the compounding data D1 of the target vulcanized rubber Ga in which each rubber physical property data D2 closely approximates the target value D2a with high accuracy.

When generating candidates for each of the compounding data D1, it is desirable to correct the total compounding amount of the rubber components in the compounding data D1 to 100 phr. In Table 3, the total compounding amount of the rubber components (SBR, BR) is corrected to 100 phr, and the compounding amounts of each of the other compounding components C are listed. By correcting the compounding data D1 in this way when generating candidates for the compounding data D1, it becomes easier to grasp the compounding amount of each compounding component C.

When there are multiple compounding data D1 for the target vulcanized rubber Ga, each of which is selected because its rubber physical property data D2 is determined to be close to the target value D2a, the compounding data D1 can be output by the output unit 4 in the following order:

For example, the selected compounding data D1 is output by the output unit 4 in the order of approximation of the rubber property data D2 of the target vulcanized rubber Ga formed by each compounding data D1 to the target value D2a for each type of rubber property data D2. In detail, each rubber property data D2 of the target vulcanized rubber Ga formed by each compounding data D1 is compared with the target value D2a for each type of rubber property data D2. The compounding data D1 of the target vulcanized rubber Ga having the rubber property data D2 with the smallest difference between the two compared is ranked first. The compounding data D1 of the target vulcanized rubber Ga having the rubber property data D2 with the second and third smallest difference between the two are ranked second and third, respectively. This ranking may change depending on the type of rubber property data D2. Each compounding data D1 is output by the output unit 4 in the order ranked in this way. By ranking in this way, it becomes easier to understand the superiority of each blend data D1 by using the degree of approximation of each type of rubber physical property data D2 to the target value D2a as an index.

Alternatively, each selected compounding data D1 is output by the output unit 4 in order of the degree of approximation of each rubber physical property data D2 of the target vulcanized rubber Ga formed with each compounding data D1 determined based on the priority weighting set for the type of rubber physical property data D2 to the target value D2a. For example, a case will be described in which a coefficient k1 with a large priority weighting is set for the rubber physical property data D2 of tan δ@60°C, and a coefficient k2 with a small priority weighting is set for the rubber physical property data D2 of hardness HC@20°C. The coefficients k1 and k2 mean that the higher the numerical value, the higher the priority. Then, when the rubber physical property data D2 of tan δ@60°C is an approximation coefficient n1 indicating the degree of approximation to the target value D2a, and the rubber physical property data D2 of hardness HC@20°C is an approximation coefficient n2 to the target value D2a, the evaluation of the degree of approximation of the target vulcanized rubber Ga having these rubber physical property data D2 is calculated by the coefficient k1 x approximation coefficient n1 + coefficient k2 x approximation coefficient n2. The larger the approximation coefficients n1 and n2 are, the closer they are to the target value D2a. Then, each selected compounding data D1 is ranked in order of the degree of approximation of the target vulcanized rubber Ga formed by each compounding data D1, and output by the output unit 4. By ranking in this way, it becomes easier to grasp the superiority of each compounding data D1 taking into account the weighting of the priority of each rubber physical property data D2.

The selected combination data D1 can also be output by the output unit 4 in order of lowest material cost per unit weight calculated based on each combination data D1. In detail, the cost per unit weight of each combination component C is grasped, and the material cost per unit weight of each combination data D1 is calculated based on the grasped cost of each combination component C and the combination amount of each combination component C in each combination data D1. Based on this calculation result, each combination data D1 is ranked and output to the output unit 4. By ranking in this way, it becomes easier to grasp the superiority of the material cost of each combination data D1.

Reference Signs List 1 Rubber compounding design system 2 Calculation device 3 Input section 4 Output section 5 Measurement device PM Prediction model D1 Compounding data Dx Compounding designation data D2 Rubber property data D2a Target value D3 Vulcanization condition data R Unvulcanized rubber Ra Target unvulcanized rubber G Vulcanized rubber Ga Target vulcanized rubber C (C1, C2, ... Cn) Compounding components

Claims (6)

Inputting into a calculation device blending designation data in which a designated range of blending amounts of each blending component of the vulcanized rubber is set, and target values of multiple types of rubber property data of the vulcanized rubber,
By performing a calculation process by the arithmetic device based on the blending designation data, the blending amounts of the respective blending components are specified, and a plurality of candidates for blending data that satisfy the designated range set by the blending designation data are generated;
A rubber compounding design method, characterized in that: using a predictive model constructed by machine learning, calculations are performed by the arithmetic device based on the generated compounding data and vulcanization condition data of each of the candidates, thereby calculating the rubber property data of each of the target vulcanized rubbers formed from the compounding data of each of the candidates and vulcanized based on the vulcanization condition data, and the compounding data of the target vulcanized rubber for which the calculated rubber property data of each of the candidates approximates the target value is selected from each of the candidates. The rubber compounding design method according to claim 1, wherein when a plurality of compounding data are selected, the selected compounding data are output by an output unit for each type of rubber physical property data in order of approximation of the rubber physical property data of the target vulcanized rubber formed with each compounding data to the target value. The rubber compounding design method according to claim 1, wherein when a plurality of compounding data are selected, the selected compounding data are output by an output unit in order of the degree of approximation of the rubber physical property data of the target vulcanized rubber formed with each compounding data, which is determined based on the priority weighting set for the type of rubber physical property data, to the target value. The rubber compounding design method according to claim 1, wherein when a plurality of compounding data are selected, each of the selected compounding data is output by an output unit in order of lowest material cost per unit weight calculated based on each of the compounding data. The rubber compounding design method according to any one of claims 1 to 4, wherein the total compounding amount of the rubber components in the compounding data is corrected to 100 phr when generating the candidates for the compounding data. A rubber compounding design system having a calculation device, an input unit for inputting data to the calculation device, and an output unit connected to the calculation device so as to be capable of communicating with the calculation device,
A compounding specification data in which a specified range of compounding amounts of each compounding component of the vulcanized rubber is set, and target values of a plurality of types of rubber property data of the vulcanized rubber are input to the arithmetic device,
The calculation device performs calculation processing based on the blending designation data to specify the blending amounts of the respective blending components, and generates a plurality of candidates for blending data that satisfy the designated range set by the blending designation data;
A rubber blend design system characterized in that, using a predictive model constructed by machine learning, the arithmetic device performs calculations based on the compounding data and vulcanization condition data of each of the generated candidates, thereby calculating the rubber property data of each of the target vulcanized rubbers formed from the compounding data of each of the candidates and vulcanized based on the vulcanization condition data, and the compounding data of the target vulcanized rubber for which the calculated rubber property data of each of the target vulcanized rubbers approximates the target value is selected from each of the candidates and output by the output unit.

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