Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials  

作　　者：Petr Opela Josef Walek Jaromír Kopecek 

机构地区：[1]Department of Metallurgical Technologies,Faculty of Materials Science and Technology,VSB–Technical University of Ostrava,Ostrava,70800,Czech Republic [2]FZU—Institute of Physics of the Czech Academy of Sciences,Prague,18200,Czech Republic

出　　处：《Computer Modeling in Engineering & Sciences》2025年第1期713-732,共20页工程与科学中的计算机建模（英文）

基　　金：supported by the SP2024/089 Project by the Faculty of Materials Science and Technology,VˇSB-Technical University of Ostrava.

摘　　要：In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

关 键 词：Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior 

分 类 号：TG14[一般工业技术—材料科学与工程] TP3[金属学及工艺—金属材料]