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

Hussain Altammar¹

¹Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)

|May 13, 2026

Summary

Infill density significantly impacts impact localization in 3D-printed structures. Lower densities yield clearer signals, enabling accurate damage detection and structural health monitoring.

Keywords:

additive manufacturing fused deposition modeling genetic algorithm group velocity impact localization infill density

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Area of Science:

Additive Manufacturing
Structural Health Monitoring
Wave Propagation

Background:

Optimizing impact localization in 3D-printed structures is crucial for smart monitoring and damage detection.
Infill density is a key parameter influencing the mechanical behavior and wave propagation in additively manufactured components.

Purpose of the Study:

To investigate the effect of infill density on the accuracy of low-velocity impact localization in 3D-printed plates.
To analyze the relationship between infill density, signal characteristics, and wave propagation for structural health monitoring.

Main Methods:

Fabrication of 3D-printed cubic infill specimens at 30%, 50%, and 100% densities.
Impact testing with varying locations and magnitudes using two sensor network configurations.
Application of a genetic algorithm with continuous wavelet transform for simultaneous impact localization and group velocity determination.

Main Results:

Lower infill densities (30%) produced cleaner, low-frequency signals, acting as mechanical low-pass filters.
Higher infill densities (100%) supported complex wave propagation with higher energy and broader frequency content.
Group velocity increased with impact energy and infill density, ranging from 450 m/s (30% infill) to over 800 m/s (100% infill).
The genetic algorithm achieved robust localization with average errors below 6% across all densities.
Spatial probability mass functions showed tightly clustered predictions with uncertainties below 5%.

Conclusions:

Infill density is a critical factor influencing wave propagation and impact localization accuracy in 3D-printed structures.
The developed method demonstrates reliable performance for structural health monitoring of additively manufactured components.
Quantitative relationships between infill density and localization performance are established, aiding in the design of effective monitoring systems.