Currently, the laser powder bed fusion (L-PBF) process cannot offer a reproducible and predefined quality of the processed parts. Recent research on process monitoring focuses strongly on integrated optical measurement technology. Besides optical sensors, acoustic sensors also seem promising. Previous studies have shown the potential of analyzing structure-borne and air-borne acoustic emissions in laser welding. Only a few works evaluate the potential that lies in the usage during the L-PBF process.
This work shows how the approach to structure-borne acoustic process monitoring can be elaborated by correlating acoustic signals to statistical values indicating part quality. Density measurements according to Archimedes’ principle are used to label the layer-based acoustic data and to measure the quality. The data set is then treated as a classification problem while investigating the applicability of existing artificial neural network algorithms to match acoustic data with density measurements. Furthermore, this work investigates the transferability of the approach to more complex specimens.