AI for Learning Deformation Behavior of a Material: Predicting Stress-Strain Curves 4000x Faster Than Simulations

Yuwei Mao, Shahriyar Keshavarz, Vishu Gupta, Andrew C. E. Reid, Wei-keng Liao, Alok N. Choudhary, Ankit Agrawal

Abstract: Stress-strain curves are important representations of a given material's mechanical properties, which depend primarily on the orientation of the individual crystals in the microstructure. Generating stress-strain curves from numerical methods such as the crystal plasticity finite element (CPFE) simulations is computationally intensive. As a result, it is difficult to generate complete stress-strain curves for all possible orientations of a material. In this work, we propose a bilinear stress-strain curve prediction framework for metallic alloys by integrating supervised and unsupervised deep learning methods via transfer learning principles. As a specific case-study, we focus on predicting stress-strain curves of Nickel (Ni)-based superalloys that have important applications in aerospace industry. Using a small training set of just 100 complete stress-strain curves (4,000 strain steps each) of different orientations generated by CPFE simulation code, we were able to build a model that could accurately predict stress-strain curves (<2 % error) using simple features that could be obtained by running the CPFE simulation for just a single strain step. The proposed model can thus predict the complete stress-strain curve for a given orientation of Ni-based superalloys in a fraction of a second, which amounts to a speedup of over 4000x as compared to the simulation.