A data-driven hybrid method combining experiments, finite element modeling and machine learning for impact response prediction of TPU composites

Purpose – This study aims to develop a robust and generalizable hybrid strategy for investigating impact behaviors of high-performance composites, significantly advancing composite impact engineering and demonstrating strong potential for applications in protective and structural systems. Design/methodology/approach – With the hybrid framework that integrates experimental testing, finite element (FE) modeling and machine learning (ML), the study on the low-velocity impact behavior of Kevlar fiber-reinforced composites with thermoplastic polyurethane matrix was carried out: with the validation of the FE model by experiments, the numerical model was used to produce data to train the ML methods. Findings – The best-performing Levenberg–Marquardt artificial neural network model achieved excellent agreement with FE simulation data, yielding a correlation coefficient R > 0.98 and a low mean squared error, which was also proven through experimental validation with satisfactory accuracy. Originality/value – In the current work, the combined method with the FE model, experiments and ML was developed for low-velocity impact of thermoplastic composite materials. The damage process was investigated, while the accuracy of the proposed methodology was verified when compared to experimental outcomes.