System and Method for Micromechanics Simulation

Heterogeneous composite materials, such as ceramic matrix composites (CMCs), are increasingly employed in high-temperature and extreme-environment applications due to their superior specific strength, thermal stability, and damage tolerance. The macroscopic response of these materials is governed by complex interactions among multiple constituents, microstructural architectures, and defect populations that span a wide range of length scales. Variability arising from manufacturing processes, constituent properties, and inherent defects plays a critical role in determining stiffness, strength, and failure mechanisms. As a result, reliable prediction of composite performance requires modeling frameworks that account for both material heterogeneity and stochastic variability across length scales (micro-, meso-, and macroscale).

 

Researchers at Arizona State University have developed an integrated deep learning and micromechanical framework leveraging generative models and uncertainty-aware, physics-based constraints for accurate reconstruction and thermomechanical simulation of high-performance composites. The technology combines conditional discrete diffusion-based models and generative adversarial networks (GANs) to create stochastic representative volume elements (SRVEs) that represent fiber tow microstructures, enabling high-fidelity simulations of the time-dependent behavior of composites such as C/SiC and SiC/SiC CMCs. By integrating semantic segmentation, image regression, periodic synthetic microstructures, and micromechanics simulations, the framework captures microstructural intrinsic variability and predicts material responses including porosity effects and creep behavior, under a range of loading and environmental conditions. The technology is not limited to CMCs and has also been recently tested on other composite systems, including high-temperature polymer matrix composites (HTPMCs).

 

While deep generative models have shown success in constructing realistic microstructures across material systems, our framework incorporates descriptor-constrained approaches that generate an ensemble of SRVEs, accurately capturing intrinsic microstructural variability from limited laboratory data while accelerating training convergence.

 

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