Mechanism and data dual-driven multimodal deep learning approach for constitutive relationship modeling of bearing steels
Authors: Bochun Liang, Cheng Ji, Xingyi Dai, Miaoyong Zhu
Accurate modeling of material constitutive relationships under compositional fluctuations poses significant challenges. Traditional mechanism-driven methods struggle to capture the complex nonlinear behavior of material properties as composition varies, while data-driven deep learning approaches, despite their high accuracy and robustness, lack strict constraints from physical metallurgical mechanisms, often leading to substantial prediction deviations. To address this critical issue, this study