Efficient Multiscale Simulations of Incremental Sheet Forming Using Machine Learning Surrogate Models for Crystal Plasticity

Abstract Multiscale crystal plasticity modeling of metal forming offers potential for effective design processes that consider microstructural evolution during significant plastic deformation. However, conventional multiscale methods for forming are expensive due to complex loading conditions and high-cost microscale models, making them challenging to apply in practice. These methods can be significantly accelerated through use of low-cost machine learning surrogate models for the microscale response. While these techniques have been demonstrated for simple load cases, they have not yet been demonstrated for manufacturing applications with full-field texture results. In this work, we develop efficient multiscale simulation workflows for manufacturing through single-point incremental forming using recurrent neural networks for constitutive response and texture evolution of a crystal plasticity model. This approach achieves up to a 63.6x increase in speed compared to conventional techniques and is demonstrated for an aluminum alloy with two unique forming paths. These workflows yield consistent trends between forming force and thickness variation, texture results in agreement with ground truth models, and permit extraction of the full-field texture evolution over the entire formed part. This enables efficient multiscale trade studies and optimization of local microstructures in industrial forming applications.