Optimizing in-situ monitoring for laser powder bed fusion process: Deciphering acoustic emission and sensor sensitivity with explainable machine learning

Metal-based Laser Powder Bed Fusion (LPBF) has made fabricating intricate components easier. Yet, assessing part quality is inefficient, relying on costly Computed Tomography (CT) scans or time-consuming destructive tests. Also, intermittent inspection of layers also hampers machine productivity. The Additive Manufacturing (AM) field explores real-time quality monitoring using sensor signatures and Machine Learning (ML) to tackle this. One such approach is sensing airborne Acoustic Emissions (AE) from process zone perturbations and comprehending flaw formation for monitoring the LPBF process. This study emphasizes the importance of selecting airborne AE sensors for accurately classifying LPBF dynamics in 316 L, utilizing a flat response sensor to capture AE's during three regimes: Lack of Fusion, conduction mode, and keyhole. To comprehensively under-stand AE from a broad process space, the data was collected for two different 316 L stainless steel powder distributions (> 45 mu m and < 45 mu m) using two different parameter sets. Frequency analysis unveiled distinct LPBF dynamics as dominant and correlated in specific frequency ranges. Empirical Mode Decomposition was used to examine the periodicity of AE signals by separating them into constituent signals for comparison. Transformed AE signals were trained to distinguish regimes using ML classifiers (Convolutional Neural Networks, eXtreme Gradient Boosting, and Support Vector Machines). Sensitivity analysis using saliency maps and feature importance scores identified frequency information below 40 kHz relevant for decision-making. This study highlights interpretable machine learning's potential to identify critical frequency ranges for distinguishing LPBF regimes and underscores the importance of sensor selection for enhanced process monitoring.