Prediction of Yield Surface of Single Crystal Copper from Discrete Dislocation Dynamics and Geometric Learning

Abstract

A yield surface of a material is a set of critical stress conditions beyond which macroscopic plastic deformation begins. For crystalline solids, plastic deformation occurs through the motion of dislocations, which can be captured by discrete dislocation dynamics (DDD) simulations. In this paper, we predict the yield surfaces and strain-hardening behaviors using DDD simulations and a geometric manifold learning approach. The yield surfaces in the three-dimensional space of plane stress are constructed for single-crystal copper subjected to uniaxial loading along the $[100]$ and $[110]$ directions, respectively. With increasing plastic deformation under $[100]$ loading, the yield surface expands nearly uniformly in all directions, corresponding to isotropic hardening. In contrast, under $[110]$ loading, latent hardening is observed, where the yield surface remains nearly unchanged in the orientations in the vicinity of the loading direction itself, but expands in other directions, resulting in an asymmetric shape. This difference in hardening behaviors is attributed to the different dislocation multiplication behaviors on various slip systems under the two loading conditions.