Advanced Meta-Modeling framework combining Machine Learning and Model Order Reduction towards real-time virtual testing of woven composite laminates in nonlinear regime

This paper presents an advanced meta-modeling framework that efficiently combines Machine Learning and Model Order Reduction (MOR) techniques for real-time virtual testing of woven composite materials. The framework is specifically designed to develop a multiparametric solution capable of accurately predicting the macroscopic nonlinear stress-strain curves of woven composite laminates submitted to loading-unloading paths. It takes into account five key microstructural parameters: yarn weft width, yarn warp width, yarn spacing, fabric thickness as well as the reinforcement orientation. The methodology employs the Proper Orthogonal Decomposition (POD) technique to decompose the stress-strain curves, extracting principal features that effectively characterize the overall composite's response. Subsequently, a Random Forest machine learning model is applied to interpolate these features across the microstructural parameter space, allowing for rapid retrieval of corresponding features for any new laminate configuration in the nonlinear regime. A key advantages of this approach is its capacity to dynamically generate extensive virtual test databases, in realtime, across a wide range of composite laminate configurations. This capability provides a comprehensive and efficient tool for analyzing and optimizing composite performance while substantially reducing both experimental and computational costs. Furthermore, to enhance usability for engineers and researchers, this multiparametric solution has been integrated into a user-friendly Graphical User Interface (GUI) application. This GUI empowers users to easily explore various laminate configurations, visualize results, and conduct virtual testing, establishing the framework as a powerful tool for real-time virtual testing and in-depth analysis of microstructural effects on composite materials.