Machine learning-assisted investigation of anisotropic elasticity in metallic alloys

This study aims to develop a machine learning (ML)-based method for predicting the elasticity tensor of anisotropic metallic alloys with 21 independent constants. In this respect, the elastic moduli of a batch of additive-manufactured specimens with various orientations, determined by Tait-Bryan angles, are utilized as input data, while the anisotropic elastic tensor are targeted for prediction. Our primary findings indicate the efficacy of the radial basis function neural network (RBFNN) as a robust ML model for elasticity tensor prediction. Notably, the model demonstrates slightly higher efficiency in predicting elasticity tensor components aligned with principal axes compared to off-diagonal components. Moreover, with increasing anisotropy, the model assigns greater contribution to shear and orthotropic components, optimizing prediction performance. Additionally, a case study using Inconel 718 further illustrates the model's effectiveness in capturing the elasticity tensor. Importantly, it is suggested that the proposed ML model offers advantages over experimental methods, such as impulse excitation-based procedures, and iterative numerical simulations, by reducing sample preparation requirements and saving time.