Development of an Artificial Neural Network-Based Tool for Predicting Failures in Composite Laminate Structures

Abstract

Composite materials are widely used in aerospace, automotive, biomedical, and renewable energy sectors due to their high strength-to-weight ratio and design flexibility. However, their anisotropic and layered nature makes structural analysis and failure prediction challenging. Traditional methods require solving complex interlaminar stress–strain equations, demanding significant computational resources. This paper presents a bio-inspired machine learning approach, based on human reasoning, to accelerate predictions and reduce dependence on computationally intensive Finite Element Analysis (FEA). An artificial neural network model was developed to rapidly estimate key parameters—laminate thickness, total weight, maximum stress, displacement, deformation, and failure criteria—based on stacking sequence and geometry for a desired load case. Although validated using a specific composite beam, the methodology demonstrates potential for broader use in rapid structural assessment, with prediction deviations under 15% compared to FEA results. The time savings are particularly significant—while conventional FEA can take several hours or even days, the ANN model delivers accurate predictions within seconds. The approach significantly reduces computational time while maintaining precision. Moreover, with further refinement, this logic-driven model could be effectively applied to aircraft maintenance, enabling faster decision-making and improved structural reliability assessment.