Additive manufacturing by Powder Bed Fusion using a Laser Beam on Metals (PBF-LB/M) enables unprecedented design freedom but remains limited by defect formation that stems from unstable melt pool dynamics. Current monitoring approaches often depend on machine learning, which can obscure the underlying physics and complicate industrial deployment. Here, a direct acoustic emission-based methodology is introduced that captures sound signatures of conduction-keyhole transitions and keyhole collapse. Using acoustic emission measurements validated by operando synchrotron X-ray imaging, a series of envelope-based indicators are established that robustly distinguish stable and unstable regimes in 316 L steel and Ti6Al4V under continuous and pulsed lasers. This physics-driven framework provides transparent, localized regime prediction, paving the way for more reliable and industrially scalable monitoring solutions in metal additive manufacturing.
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Nasab et al. (2026) studied this question.
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