A comprehensive investigation on the performance of reconstruction of noncircular fiber-representative volume elements in unidirectional composites using diffusion generative models

Abstract

This study employs diffusion generative models to reconstruct random representative volume elements (RVEs) of unidirectional composites with noncircular fibers. Microscope images of these composites were prepared and trained with denoising diffusion probabilistic model (DDPM), denoising diffusion implicit model (DDIM), progressive distillation diffusion model (PDDM), and deep convolutional generative adversarial network (DCGAN). Hyperparameter tuning was performed for both DDPM and PDDM, and the generated RVE images were evaluated using the two-point correlation function (TPCF), Fréchet Inception Distance (FID), and computational cost. Furthermore, finite element (FE) models were generated using these images, and FE simulations were conducted considering interfacial debonding behavior. The resulting stress and strain curves from these simulations were compared. The results show that DDPM demonstrated the best performance in final image quality, while PDDM maintained stable performance from the early stages of training. Additionally, both models exhibited excellent agreement with the original images, indicating high quality, diversity, and resemblance.