TY  - EJOU
AU  - Opěla, Petr 
AU  - Walek, Josef 
AU  - Kopeček, Jaromír 

TI  - Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 142
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Machine learning; Gaussian process regression; artificial neural networks; support vector machine; hot deformation behavior

DO  - 10.32604/cmes.2024.055219