AI-Driven inverse design of 3D-printed continuous fiber composites using multiscale homogenization and dual neural networks

Accurately predicting the mechanical behavior of 3D-printed continuous fiber-reinforced composites remains a complex task due to their heterogeneous and anisotropic microstructures, which arise from fiber orientation, overlap, and spatial distribution. This study proposes a novel computational framework that combines multiscale homogenization and artificial intelligence to enable both predictive modeling and inverse design of composite materials. A finite element method (FEM) was employed to simulate 195 representative volume element (RVE) configurations, capturing various fiber overlaps, horizontal and vertical spacing parameters. The generated dataset was used to train a dual neural network architecture comprising a forward neural network (FNN) for property prediction and an inverse neural network (INN) for microstructural design. Advanced regularization, early stopping, batch normalization, and dropout techniques were applied to optimize model performance. The FNN model accurately predicted key mechanical properties including Young's modulus (E), shear modulus (G), and Poisson's ratio (v) with an average R² score above 92%. The INN enabled rapid inverse design of RVE configurations that satisfy targeted mechanical properties, achieving a mean prediction accuracy of 96.06% with response time under one second. Sensitivity analysis revealed that interlayer spacing parameters significantly affect material anisotropy and stiffness. The proposed hybrid FEM–AI framework demonstrates high accuracy and computational efficiency in both forward prediction and inverse microstructure design. It offers a scalable solution for the rapid development of custom-tailored composite materials and holds strong potential for high-performance applications in aerospace, automotive, and biomedical engineering.