Machine learning approach to evaluating impact behavior in fabric-laminated composite materials

Abstract

Fabric-layered composites play a crucial role in safety and surveillance applications, making it imperative to accurately predict their impact behavior. This research focuses on creating a machine-learning model to predict the impact behavior of fabric-stacked composites, specifically carbon and Kevlar fabrics. Low-velocity impact tests were performed with varying parameters, and the impact energy and laminate thickness information were used to train machine-learning models to predict impact properties such as impact force, displacement, and absorbed energy. It was observed that the impact force increased by 118.5 % in carbon-laminated fibers and 175.8 % in Kevlar-laminated fibers, while the hybrid layer showed a 101.4 % increase upon impact from 16J. Displacement can affect the stability of the layered structure; thus, a similar stacking sequence is less stable than the hybrid laminated structure. In terms of absorbed energy, as the laminated layers increase, carbon fiber laminate absorbs 4.8 times more energy, and Kevlar fiber and hybrid laminated structures absorb 3 times more energy at higher impact energy. Furthermore, four machine learning models were used to investigate the impact behavior of identical and mixed-layered laminated composites. The impact force and displacement were predicted with higher accuracy using the polynomial regression model, achieving 80 % and 89 % accuracy, respectively. The support vector machine predicted the absorbed energy with approximately 96 % accuracy. In continuing, the experimental results closely matched the predictions made by the other machine learning models utilized in this study. Additionally, the importance of distinctive features and their influence on the performance of the machine learning-based model were interpreted using transposed features of importance and dependency plots. Various failure modes in fabric laminated composites were also identified, providing insights to enhance the performance of stacked fiber materials.