Mechanical Performance Optimization of Carbon Fiber Reinforced Composites using Finite Element Analysis and Machine Learning

Abstract

The mechanical performance of Carbon Fiber Reinforced Composites is inherently  governed by complex microstructural parameters, including fiber orientation, volume fraction,  and interfacial bonding characteristics. Traditional iterative experimental approaches to optimize  these materials are resource-intensive and time-consuming. This paper presents a novel integrated  framework that synergizes high-fidelity Finite Element Analysis with advanced Machine Learning  algorithms to accelerate the design and optimization process of composite materials. By utilizing  Representative Volume Elements to generate a comprehensive dataset of stress-strain responses  under various loading conditions, we train a deep neural network to predict mechanical properties  with high accuracy. Subsequently, this surrogate model is embedded within a genetic algorithm to  identify optimal microstructural configurations that maximize tensile strength while minimizing  weight. Our results demonstrate that the machine learning-assisted approach reduces  computational time by several orders of magnitude compared to direct numerical simulation loops  while maintaining a prediction error margin below two percent. This methodology provides a  robust pathway for the rapid prototyping of high-performance composite structures in aerospace  and automotive applications.