Energy estimation is critical to impact identification on aerospace composites, where low-velocity impacts can induce internal damage that is undetectable at the surface. Data sparsity, signal noise, complex feature interdependencies, non-linear dynamics, massive design spaces, and the ill-posed nature of the inverse problem often constrain current methodologies for energy prediction. Machine learning enriched with prior knowledge is a promising direction for overcoming these constraints. Prior knowledge can be incorporated by acting on the input space, where the choice of data representation directly influences how effectively the model relates measured signals to impact energy. Despite its importance, the selection of effective features lacks a systematic procedure, with no consensus on how to choose among the many candidate descriptors available. The present study addresses that gap through a structured workflow that designs the input space using domain knowledge. Features are extracted from the time, frequency, and time-frequency domains, then filtered for statistical significance, correlation, dimensionality reduction, and robustness to noise. Exploratory data analysis further relates the retained descriptors to the dynamics. The resulting indicators form the input space for a fully connected neural network, which is trained and validated on experimental data from multiple impact scenarios, including pristine and damaged states. The model reduces the prediction error by a factor of three relative to conventional time-series techniques and purely data-driven baselines, while every retained or discarded descriptor remains traceable to the aspect it describes. Overall, the framework advances predictive performance, interpretability, and diagnostic confidence by embedding domain knowledge through targeted feature selection.

翻译：暂无翻译