Sound Of Porosity: Unlocking Local Acoustic Signatures Of Porosity In LPBF Via Low-Cost Sensing And Explainable AI
TL;DR
A low-cost, off-the-shelf ultrasonic microphone installed inside the build chamber is demonstrated as a practical alternative for capturing high-resolution acoustic signals during LPBF, and acoustic-based models achieve low-latency inference, suitable for in-process monitoring, paving the way for adaptive corrective control strategies.
Abstract
Conventional in-situ monitoring solutions—such as high-speed cameras, SWIR imaging, and acoustic emission sensors—are costly, data-intensive, and impractical for industrial deployment. In this work, we demonstrate a low-cost, off-the-shelf ultrasonic microphone installed inside the build chamber as a practical alternative for capturing high-resolution acoustic signals during LPBF. Using these signals, we benchmark multiple machine learning architectures for localized porosity prediction. Targets for lack-of-fusion and keyhole porosity are derived from volumetric X-ray Computed Tomography (XCT) analysis and carefully registered to process coordinates, enabling supervised learning at scanline granularity. Beyond performance evaluation, we conduct explainability analysis using feature attribution methods to uncover which acoustic signatures most strongly correlate with defect formation. Results show that acoustic-based models achieve low-latency inference, suitable for in-process monitoring, paving the way for adaptive corrective control strategies. This study establishes a new paradigm for practical, scalable LPBF quality assurance by combining low-cost sensing with interpretable AI.
