Abstract
This study presents an innovative deep learning-based surrogate model for the Crystal Plasticity Finite Element (CPFE) method, fundamentally transforming the generation of mechanical properties such as stress-strain curves in the study of crystal plasticity. Stress-strain curves are pivotal in understanding material deformation, elucidating the intricate relationship between a material's structure and its properties. Traditional CPFE methods, though thorough in their analysis, face significant computational challenges, largely due to the complexity of the crystal plasticity framework. The proposed model circumvents this bottleneck by utilizing an autoencoder architecture to learn intermediate data representations, which are then used to predict the plastic component of deformation. This predicted plastic component serves as a foundation for computing stress-strain curves, effectively bypassing the most time-intensive aspect of traditional CPFE methods, the plasticity self-consistency procedure (achieving a 29.3x speed increase without compromising accuracy).
Published Version
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