Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites

Abstract

Fiber-reinforced composite materials are integral to aerospace, automotive, and military industries. In manufacturing, these composites are subjected to certain curing cycles, which are known to have a significant impact on the mechanical properties of the material. Many studies have focused on predicting these mechanical properties of composites, but environmental conditions and curing cycles are often not considered. In this work, supervised machine learning techniques are applied to experimental data obtained from the National Center for Advanced Materials Performance (NCAMP) for various unidirectional carbon fiber laminates to predict the mechanical properties of composite materials. These techniques holistically consider the effects of environmental conditions and curing cycles, factors frequently overlooked in analytical approaches. Results show that recurrent neural network models can accurately predict the modulus of these materials, achieving R² values up to 0.98. This work establishes a statistical framework to analyze complex empirical data for advanced materials design.