Understanding the creep behaviors and mechanisms of Mg-Gd-Zn alloys via machine learning - Shuxia Ouyang; Xiaobing Hu; Qingfeng Wu; Jeong Ah Lee; Jae Heung Lee; Chenjin Zhang; Chunhui Wang; Hyoung Seop Kim; Guangyu Yang; Wanqi Jie (Journal of Magnesium and Alloys)
Mg-Gd-Zn based alloys have better creep resistance than other Mg alloys and attract more attention at elevated temperatures. However, the multiple alloying elements and various heat treatment conditions, combined with complex microstructural evolution during creep tests, bring great challenges in understanding and predicting creep behaviors. In this study, we proposed to predict the creep properties and reveal the creep mechanisms of Mg-Gd-Zn based alloys by machine learning. On the one hand, the minimum creep rates were effectively predicted by using a support vector regression model. The complex and nonmonotonic effects of test temperature, test stress, alloying elements, and heat treatment conditions on the creep properties were revealed. On the other hand, the creep stress exponents and creep activation energies were calculated by machine learning to analyze the variation of creep mechanisms, based on which the constitutive equations of Mg-Gd-Zn based alloys were obtained. This study introduces an efficient method to comprehend creep behaviors through machine learning, offering valuable insights for the future design and selection of Mg alloys.