Application of Artificial Neural Network in Impact and Crashworthiness: A Review

The paper presents an extensive review of the application of machine learning (ML) techniques, especially artificial neural networks (ANNs), in the realm of crashworthiness, hypervelocity impacts, and inverse analysis across various structural and aerospace domains. It delves into the prediction of stiffness matrices, contact stiffness, impact forces, and failure conditions, showcasing the potential of ML to streamline complex structural analysis processes. While the review highlights the predictive accuracies and computational efficiencies achieved by these methods, it also underscores the need for a more critical evaluation of model robustness, uncertainties, and real-world applicability. The review offers valuable insights into how ML can transform inverse analysis yet emphasizes the importance of addressing challenges and limitations to ensure the reliability and practicality of these innovative approaches.