TU Darmstadt, Fachgebiet SAM
Abstract:
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.