Deep learning driven framework for optimization of polycrystalline microstructures under competing strength requirements

Abstract

Computational design of polycrystalline microstructures for enhanced mechanical performance requires repeated high-fidelity simulations over stochastic morphologies, rendering conventional crystal plasticity finite element (CPFE) approaches prohibitively expensive for optimization. Here, we develop a deep learning-driven framework for optimizing polycrystalline microstructures under competing quasi-static and dynamic performance requirements. A 3D U-Net surrogate maps polycrystalline copper microstructures directly to full-field velocity histories from plate-impact simulations, preserving the spatial and temporal resolution needed to evaluate dynamic performance and interrogate underlying wave interactions and failure mechanisms. The surrogate is coupled with stochastic microstructure generation and derivative-free optimization to efficiently navigate the design space. To monitor surrogate reliability during optimization, we introduce a distribution-consistent reliability region (DCRR) based on maximum mean discrepancy (MMD) to quantify distributional shift from the training data. The framework is demonstrated for multiple design problems, including constrained optimization of spall and yield strengths and inverse design. The optimal designs are validated using CPFE simulations with less than 3% prediction error across all optimization cases, while the surrogate-driven optimization reduces cumulative evaluation time by approximately four orders of magnitude compared with direct CPFE-based optimization. Although demonstrated here for the design of polycrystalline microstructures targeting spall and yield strengths, the framework is broadly applicable to optimization problems where high-fidelity full-field simulations are prohibitively expensive but spatially and temporally resolved response information is essential for evaluating, interpreting, and optimizing candidate designs.