The Role of Infill Density in Impact Localization for Additively Manufactured Structures

The optimization of impact localization in 3D-printed structures is critical for their application in smart monitoring and damage detection systems. This study investigates the influence of infill density on the accuracy of low-velocity impact localization in 3D-printed plates. Specimens with cubic infill patterns and varying densities (30%, 50%, and 100%) were fabricated and subjected to impacts with varying locations and magnitudes using two different sensor network configurations. A genetic algorithm integrated with continuous wavelet transform was employed to simultaneously determine impact coordinates and group velocity. Key findings reveal that lower infill structures act as mechanical low-pass filters, producing clean and low-frequency signals, while higher densities support complex wave propagation with higher energy and broader frequency content. The dominant frequency of first arrival shifts toward lower values with increasing impact energy across all densities. Group velocity increases with both impact energy and infill density. For 30% infill, it averages around 450 m/s, while for 100% infill it exceeds 800 m/s. The genetic algorithm demonstrated robust performance across all experimental conditions, simultaneously estimating impact coordinates and group velocity with average errors below 6% for all infill densities. Spatial probability mass functions revealed tightly clustered predictions around true impact locations, with maximum probabilities reaching 68% and uncertainties below 5%. Computational efficiency varied modestly with infill density. These findings provide quantitative relationships between infill density, wave propagation characteristics, and localization performance for designing a reliable structural health monitoring of additively manufactured structures.