Data-driven keyhole pore detection in laser powder bed fusion: Integrating process insights with X-CT

Abstract

Industries are increasingly adopting metallic laser powder bed fusion (PBF-LB/M) to fabricate complex components not achievable via traditional manufacturing. However, enhancing productivity by increasing laser energy density can lead to keyhole formation, causing pore defects from unstable vapor depressions in the melt pool. This study presents a data-driven surrogate model by correlating in-situ melt pool monitoring images with keyhole pores identified via X-ray computed tomography (X-CT). A significant challenge addressed is the spatial-temporal misalignment between in-process monitoring signals and final pore locations, as gas bubbles can migrate within the liquid melt pool before solidification. To mitigate this, a many-to-one approach is introduced, linking multiple melt pool frames to each defect location to incorporate the full thermal history relevant to defect formation. Twelve scan tracks were fabricated atop a Ti6Al4V cuboid, inducing keyhole pores by halving the scan speed or doubling the laser power, shifting from conduction mode to unstable keyholing mode. Experimental results demonstrate that a Random Forest model (Type I) utilizing physics-informed melt pool features outperforms a deep learning-based ResNet-LSTM model (Type II), achieving superior predictive accuracy (AUROC = 0.95, AUPRC = 0.92) and requiring notably less computational resources (7.5 times faster training and 27.6 times faster prediction). The key findings emphasize that leveraging physics-informed features and thermal history effects not only enhances prediction accuracy but also provides interpretability and computational efficiency, making this approach particularly suitable for future in-situ defect detection and qualify-as-you-build process control in PBF-LB/M.