JP-7856765-B2 - Machine learning-based prediction method for developing composite materials for tire tread compounds

Inventors

• パオロ パチョッタ
• アンドレア グイディ
• ロベルト ロンバルディ

Assignees

• ブリヂストン ヨーロッパ エヌブイ／エスエイ

Dates

Publication Date

20260511

Application Date

20221129

Priority Date

20211129

Claims (7)

1. A method performed using a computer to predict the viscoelastic or processability properties of a composite material to be tested for the manufacture of a tire tread compound, a) A step of preparing a database of raw data to be used as a reference, i.e., a primary dataset including recipes for existing composite materials and corresponding known viscoelastic or processability properties, b) A step of preprocessing the primary dataset, i. A step of integrating one or more characteristic parameters in the primary dataset, wherein the one or more characteristic parameters are as follows: - Gini coefficient for each recipe - Mixing category for each recipe - Type of application for each recipe - Total amount of material for each recipe - Proportion of components for each recipe - Grouping by Reuven method for the composite material recipes - Grouping by K-means method for the composite material recipes, and - Selected from data whose dimensions have been reduced through an autoencoder applied to the primary dataset formed by the composite material recipes, thereby obtaining an expanded dataset, the integration step, ii. A transformation step of the extended dataset, wherein one or more transformation functions are applied to the components and/or numerical characteristic parameters of the extended dataset, the one or more transformation functions being: The transformation step is selected from 1. B-spline smoothing, 2. Box-Cox transformation, and 3. scaling transformation, thereby obtaining the transformed dataset. The preprocessing step, which is performed by integrating one or more characteristic parameters in the primary dataset, includes the integrating step of all the characteristic parameters shown in step i. c) A step of training a machine learning-based algorithm using the data from the transformed dataset, d) Applying the algorithm trained according to step c) to a set of data that has been preprocessed according to step b) and represents the recipe of the composite material to be tested, in order to predict the viscoelastic or processability properties of the composite material to be tested, A method that includes [a certain feature].
2. A method according to claim 1, wherein the viscoelastic or processability characteristics include torque ML and MH, T10, T50 and T90, scorch time (Ts), vulcanized and unvulcanized shear modulus G', and tan under imposed conditions.
3. The method according to claim 1, wherein the step of transforming the extended dataset includes the step of applying all of the transformation functions shown in ii.
4. The method according to claim 3, wherein the conversion function shown in ii. is executed sequentially.
5. The method according to claim 4, wherein the conversion function shown in ii. is executed in the order shown (1., 2., 3.).
6. A method according to claim 1, wherein the machine learning-based algorithm is configured to apply a mixed linear model.
7. A composite material analyzer (RPA) configured to perform the method described in any one of claims 1 to 6.

Description

This invention relates to a method for predicting the viscoelastic or processability properties of rubber compounds before, during, and after vulcanization. This method is based on machine learning and is therefore implemented by computer for the development of composite materials for tire tread compounds. This invention relates to the determination of the composition of rubber compounds used in the field of tire manufacturing, particularly in the manufacture of tire treads. RPA (Rubber Process Analyzer) is an advanced dynamic mechanical rheology testing device that is generally available in all factories. The Rubber Process Analyzer (RPA), as an advanced dynamic mechanical rheology testing device, is generally available in all plants to monitor the manufacturing parameters of composite materials at each step of the process. In fact, the workability of composite materials is determined by specific ranges of pre- and post-curing rheometric curves and shear modulus descriptors defined during the development phase (e.g., ML and MH torques, T10, T50 and T90, scorch time, vulcanized and unvulcanized shear moduli G' and tan under constant load stress conditions). These properties are ensured by the characteristics of the recipe used in the composite material, particularly the components, their quantities, and the specific synergistic effects established between two or more of them. Refer to the attached diagram. This is a schematic block diagram of the training steps of the machine learning and characteristic prediction algorithm according to the present invention. This is a schematic block diagram of the training steps of the machine learning and characteristic prediction algorithm according to the present invention. This graph is useful for verifying the time required for the vulcanization torque to reach a predetermined increase. [Theoretical background] Polymer matrix composites are unique materials that exhibit characteristic elastic and viscous responses when subjected to stress. Predicting the rheometric curve of a composite material based on one of its fundamental parameters (e.g., torque ML and MH, T10, T50 and T90, scorch time, vulcanized and unvulcanized shear modulus G', and tan under fixed shear conditions) is fundamental to determining and evaluating the workability of the composite material from mixing to extrusion and vulcanization steps, and to avoid problems such as mixer downtime, defects in extruded products, press clogging during vulcanization, or under-vulcanization/over-vulcanization. Processability is evaluated by performing rheometric tests in multiple steps. Since some of the process parameters tested in these plants have recently been correlated with performance parameters through specific evaluations, their prediction has become even more important for estimating plant performance variability. Such evaluations require multiple laboratory tests to arrive at the validation of the composite material, which is time-consuming and resource-intensive. On the other hand, using digital prediction devices makes the following possible: - Reduction of operating costs (raw materials, labor costs, etc.). - Optimization of workload and quality in the testing laboratory (allowing personnel to concentrate on other activities). - Shorten the time to market for new products. Therefore, potential end-users are all engineers and laboratory professionals who could potentially benefit from this device. As anticipated in the paragraph above, processability testing equipment can also be used in factories to monitor composite materials to ensure they meet quality standards. The present invention can also be extended and released to factories as end-users, allowing factory technical services to significantly reduce the time required to evaluate changes in formulation properties during formulation development or simply improve processability in research and development, addressing potential problems in factories where downtime/loss is limited. The Rubber Process Analyzer (RPA) is a valuable machine designed to measure the viscoelastic or processability properties of polymers and composites before, during, and after vulcanization. Vulcanization properties can be determined by measuring them as a function of time and temperature. Tests can be performed under different conditions depending on the required test method, and G' and tand measurements can be continuously recorded as a function of time and/or strain applied by periodic torque at different shear rates. Some of the outputs from each test have been selected within a range based on their cardinality in the dataset and their importance to engineers in evaluating machinability. 1. ML, low torque of the vulcanization curve, and modulus of elasticity of the green (unvulcanized) compound. 2. MH, the maximum torque of the vulcanization curve, or the torque when the curve rises to a plateau, and the modulus of elasticity of the vulcanization compound. 3. T10, T50, a