Physically Constrained 3d Diffusion for Inverse Design of Fiber-Reinforced Polymer Composite Materials

Abstract

Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for unlocking their full potential and expanding their applications across various industries. The inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with specific nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. Although the vanilla PC3D_Diffusion can generate visually appealing results, less than 10% of FRPCs generated by the vanilla model are collision-free, meaning the fibers do not intersect. To address this, we propose a loss-guided, learning-free approach that enforces physical constraints during generation. To train the model, 1.35 million FRPCs were generated, and their corresponding stress-strain curves were calculated. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.

Keywords: AI enabled inverse composite design, design for nonlinear stress-strain response, physically constrained diffusion model, fiber-reinforced polymer composites, machine learning