Generative neural networks for synthetic PBX microstructures with varying levels of damage to evaluate shock sensitivity through meso-scale simulations*

Damage in energetic material (EM) microstructures has the potential to significantly impact the performance of critical national security and safety devices. Understanding and modeling microstructural damage and its effects under various loading conditions requires multi-scale computational models. A significant bottleneck for computational modeling efforts is the paucity of microstructural images at various levels of damage. Here we present HEDS (Heterogeneous Energetic Damage Simulator), a versatile tool designed for generating varying levels of damage within microstructure images of a specific plastic bonded explosive (PBX). The HEDS workflow commences with the preprocessing of available PBX micrographs. Then, leveraging machine learning techniques, HEDS not only removes damage from existing PBX images but also systematically reintroduces damage at different levels or volume fractions, enabling the study of material responses at various damage levels. Furthermore, HEDS offers the capability to create synthetic microstructures, addressing the challenge of limited availability of real microstructure images. HEDS comprises three distinct machine learning models: 1) Microstructure Generation (Diffusion model): enables the generation of a stochastic ensemble of entirely new synthetic microstructures, which is valuable for addressing data scarcity issues in sourcing microstructure images; 2) Image Inpainting (U-Net): this model removes all damage from a given microstructure, creating an undamaged (inpainted) reference; 3) Damage Reintroduction (CycleGAN): reintroduces realistic damage at varying volume fractions into the in-painted microstructure, allowing for controlled assessments of sensitivity to shock loading;. By progressively reintroducing damage into the inpainted microstructure, our framework facilitates a comprehensive analysis of PBX behavior under different levels of damage. HEDS is a useful tool for advancing our understanding of energetic materials and enhancing the safety and effectiveness of related applications.