Study of Microstructural Variability of Fiber Reinforced Composites Using Multi-Scale Modeling and Machine Learning

Abstract

This paper presents an overview of research conducted to track the variation in fiber reinforced composites at the microscale induced from manufacturing, and determine the effect composite properties. For this effort, the microscale was first quantified using statistical descriptors. These descriptors are needed in order to determine statistical equivalency of artificially generated microstructures. The artificially generated microstructures have been made using a combination of randomly distributed fibers and simulations. Numerical parameters of the simulation can be manipulated to produce different features found in common microstructures such as fiber clusters and matrix-rich regions. Finally, reduced order structural models were used to quantify the variation in the response due to variations at the microstructure. Meso-scale simulations utilize integration point variation in material properties to simulate the overall variation in the response. Machine learning can be used to replace models at the microscale level and capture variation without having to link multi-scale models.