Plasticity Encoding and Mapping during Elementary Loading for Accelerated Mechanical Properties Prediction

Abstract

Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of metallic materials. To capture the spatial heterogeneity of plasticity that develops in metals, latent space features describing plasticity of a small region are spatially mapped across a large field of view while maintaining the same spatial relationships as the experimental measurements. Latent space feature maps capture the complexity and heterogeneity of metal plasticity as a low-dimensional representation. These feature maps are then used to train a convolutional neural network-based model to predict monotonic and cyclic macroscopic properties. The approach is demonstrated on a large set of face-centered cubic metals, enabling rapid and accurate property prediction. The effects of hyperparameters and training strategies are analyzed, and the extension of the proposed approach to a broader range of metallic materials and loading conditions is discussed.