本团队龚攀副教授和硕士生汪壮等在Intermetallics发表研究论文。

摘要：Understanding the deformation behavior of materials is critical for the effective manufacturing of formed parts. The thermoplastic deformation behavior (TDB) of bulk metallic glass composites (BMGCs) is influenced by numerous process parameters, and the complex nonlinear interactions present significant challenges for accurate description using phenomenological or physics-based constitutive models. The success of machine learning (ML) in various fields suggests its potential to address such complexities in materials science. In this study, we apply four ML regression algorithms to predict the TDB of (Zr55Cu30Al10Ni5)94Ta6 BMGCs. Our results demonstrate that the Extra Trees algorithm provides the most accurate predictions, with an analysis of its performance based on its underlying principles and the material's deformation behavior. Using the well-trained model, we generate a strain rate sensitivity index contour plot, revealing the transition between Newtonian and non-Newtonian rheological regime.