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The formation of a melt pool, during an additive manufacturing process, is a complex multi-physical process. Apparent factors, contributing to its complexity, are among others: the high temperatures, the existence of the material in multiple physical states and the involved high dynamics. However, it is a challenge to have full real-time control over all these factors. In this contribution, we will present our results and shed light, on the basis of measurements with a scanning laser doppler vibrometer, on how the vibrational behavior of the melt pool relates to its penetration depth into the substrate.

The MiCLAD machine designed at the VUB, Belgium, allows for closed-loop controlled laser metal deposition including various in-situ optical based measurement systems. These integrated sensors collect information on deposition geometry and temperature during the building process. Hence, each cubic millimeter of material that is either added or removed is mapped to its digital twin with a millisecond temporal resolution in the machines database. This paper introduces the platform and its capabilities by focusing on the procedure of obtaining the necessary training data for the future application of machine learning algorithms, with the goal of controlling the geometry and temperature history during additive manufacturing.