Airborne acoustic emission enables sub-scanline keyhole porosity quantification and effective process characterization for metallic laser powder bed fusion

TL;DR

This paper introduces an airborne acoustic emission-based method for high-resolution, real-time quantification of keyhole porosity in laser powder bed fusion, combining experimental data and neural networks for process monitoring.

Contribution

It presents a novel integrated framework using airborne AE and deep learning to quantify and locate keyhole porosity during LPBF, enabling noninvasive, rapid defect detection.

Findings

AE signals correlate with porosity severity.

Neural network predicts porosity metric with R-squared > 0.8.

Frequency analysis identifies key AE bands for defect detection.

Abstract

Keyhole-induced (KH) porosity, which arises from unstable vapor cavity dynamics under excessive laser energy input, remains a significant challenge in laser powder bed fusion (LPBF). This study presents an integrated experimental and data-driven framework using airborne acoustic emission (AE) to achieve high-resolution quantification of KH porosity. Experiments conducted on an LPBF system involved in situ acquisition of airborne AE and ex situ porosity imaging via X-ray computed tomography (XCT), synchronized spatiotemporally through photodiode signals with submillisecond precision. We introduce KHLineNum, a spatially resolved porosity metric defined as the number of KH pores per unit scan length, which serves as a physically meaningful indicator of the severity of KH porosity in geometries and scanning strategies. Using AE scalogram data and scan speed, we trained a lightweight convolutional neural network to predict KHLineNum with millisecond-scale temporal resolution, achieving an R-squared value exceeding 0.8. Subsequent analysis identified the 35-45 kHz frequency band of AE as particularly informative, consistent with known KH oscillations. Beyond defect quantification, the framework also enables AE-driven direct inference of KH regime boundaries on the power-velocity process map, offering a noninvasive and scalable component to labor-intensive post-process techniques such as XCT. We believe this framework advances AE-based monitoring in LPBF, providing a pathway toward improved quantifiable defect detection and process control.