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

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