Prediction of mechanical properties of elastomeric materials using neural networks

作     者：Fomin, S. V. Rostovtsev, V. S. Meltsov, V. U. Shirokova, E. S. 

作者机构：Vyatka State Univ Kirov Russia 

出 版 物：《MATERIALS PHYSICS AND MECHANICS》 (Mater. Phys. Mech.)

年 卷 期：2023年第51卷第5期

页      面：66-78页

核心收录：

学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0702[理学-物理学] 

主　　题：elastomers rubbers physical and mechanical properties convolutional neural network hyperparameter optimization neural network technologies python language keras library 

摘      要：The article is devoted to the use of neural networks for predicting the mechanical properties of rubber. Rubbers include, as a rule, more than one and a half dozen components. Each of the components has a complex and ambiguous effect on the complex of material properties. When developing new compositions, this significantly complicates and lengthens the solution of material science problems by traditional methods of composition selection. These problems can be effectively solved using machine learning techniques. The authors have developed approaches to the use of neural networks for predicting the mechanical properties of rubber from a known composition. In this article, neural network models have been created and optimized, which make it possible to predict the mechanical properties of elastomeric materials with high accuracy.