TU Darmstadt, Fachgebiet SAM
Additive manufacturing such as Powder Bed Fusion with Laser Beam (PBF-LB) is gaining attention in the producing industry. Monitoring the quality of the PBF-LB products in situ is crucial for ensuring that the desired product criteria are met. Currently, the state of the research focuses on optical process monitoring using CCD cameras, photodiodes, high-speed cameras, and pyrometers. A disadvantage of these approaches is that knowledge can only be extracted from the last manufactured layer, whereas defects such as cracks or warpage in deeper layers can remain hidden. A way to extend such monitoring systems is the use of microphones to analyze the sound pressure generated by PBF-LB. We describe possible defects in PBF-LB and how to detect them with ultrasonic microphones. The experimental setup is optimized regarding the acoustic conditions in the manufacturing chamber and the Signal-to-Noise ratio. The optimized setup is exemplified as used in the experiments in the project “Development of machine learning algorithms on the basis of virtual sound data for lightweight construction for quality assurance in additive manufacturing” (ML-S-LeAF). Finally, an outline is given for future work.
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