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With a growing demand for high-quality fabrication, the interest in real-timeprocess and defect monitoring of laser powder bed fusion (LPBF) has increased,leading manufacturers to incorporate a variety of online sensing methodsincluding acoustic sensing, photodiode sensing, and high-speed imaging.However, real-time acquisition of high-resolution melt pool images inparticular remains computationally demanding in practice due to the highvariability of melt pool morphologies and the limitation of data caching andtransfer, making it challenging to detect the local lack-of-fusion (LOF) defectoccurrences. In this work, we propose a new acoustic and thermalinformation-based monitoring method that can robustly infer critical LPBF meltpool morphological features in image forms and detect spatially-dependent LOFdefects within a short period. We utilize wavelet scalogram matrices ofacoustic and photodiode clip data to identify and predict highly time-resolved(within a 1.0 ms window) visual melt pool characteristics via a well-traineddata-driven pipeline. With merely the acoustic and photodiode-collected thermalemission data as the input, the proposed pipeline enables data-driven inferenceand tracking of highly variable melt pool visual characteristics with$R^2=0.8$. We subsequently validate our proposed approach to infer local LOFdefects between two adjacent scanlines, showing that our proposed approach canoutperform our selected baseline theoretical model based on previousliterature. Revealing the physical correlation between airborne acousticemission, thermal emission, and melt pool morphology, our work demonstrates thefeasibility of creating an efficient and cost-effective acoustic- andthermal-based approach to facilitate online visual melt pool characterizationand LOF defect detection.

Inference of highly time-resolved melt pool visual characteristics and  spatially-dependent lack-of-fusion defects in laser powder bed fusion using  acoustic and thermal emission data