Plasticity encoding and mapping during elementary loading for accelerated mechanical properties prediction

Encoding metal plasticity captured from high-resolution digital image correlation (HR-DIC) is 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 small regions are spatially mapped across large fields. These 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.