Physics-Informed Framework for Impact Identification in Aerospace Composites

Abstract

This paper introduces a novel physics-informed impact identification (Phy-ID) framework. The proposed method integrates observational, inductive, and learning biases to combine physical knowledge with data-driven inference in a unified modelling strategy, achieving physically consistent and numerically stable impact identification. The physics-informed approach structures the input space using physics-based energy indicators, constrains admissible solutions via architectural design, and enforces governing relations via hybrid loss formulations. Together, these mechanisms limit non-physical solutions and stabilise inference under degraded measurement conditions. A disjoint inference formulation is used as a representative use case to demonstrate the framework capabilities, in which impact velocity and impactor mass are inferred through decoupled surrogate models, and impact energy is computed by enforcing kinetic energy consistency. Experimental evaluations show mean absolute percentage errors below 8% for inferred impact velocity and impactor mass and below 10% for impact energy. Additional analyses confirm stable performance under reduced data availability and increased measurement noise, as well as generalisation for out-of-distribution cases across pristine and damaged regimes when damaged responses are included in training. These results indicate that the systematic integration of physics-informed biases enables reliable, physically consistent, and data-efficient impact identification, highlighting the potential of the approach for practical monitoring systems.

Figures (12)

• Figure 1: General framework of Physics-Informed Impact Identification (Phy-ID), showing how observational, inductive, and learning biases are combined in a structured integration step to achieve physics-informed impact energy estimation.
• Figure 2: High-level overview of the sequential disjoint training process: the displacement network (top) generates a velocity estimate for the impactor mass network (bottom), which in turn produces a mass estimate fed back to the displacement model.
• Figure 3: Parallel coordinate plots showing the performance of the displacement and mass networks across hyperparameter combinations. ($n_h$: fully connected layer size; $L_h$: number of fully connected layers; lr: learning rate; and $\sigma$: activation functions.)
• Figure 4: Schematic impact test configuration; $\bullet$ piezoelectric sensors (PZT) and $\star$ Impact Locations (IE): (a) drop-tower assembly with impact height ($h$) set by impact energy; (b) stiffened thermoplastic composite structure ($1600 \times 370$ mm) with varying thicknesses $t_1 = 8.26$ mm, $t_2 = 6.30$ mm, and $t_3 = 5.46$ mm; (c) experimental set-up, with stiffeners at the bottom-side of the panel. Adapted from marinho2025evaluatingmarinho2024impact.
• Figure 5: Phased array ultrasonic scans for different impact locations acquired after the impact events that precede and produce damage, highlighting the impact energy associated with damage onset. Each scan shows backwall attenuation, reflection view and cross-sectional view.