Abstract: The purpose of this paper is to report recent results demonstrating feasibility of active monitoring and fault probability estimation in the Selective Laser Melting (SLM) process demonstrated on a Renishaw AM250 machine, through analysis of layer-by-layer surface profile data of Fe3Si powder. The data was collected in-situ during the metal additive manufacturing of a Heat Exchanger section, comprised of a series of conformal channels. A shallow artificial neural net (ANN) was trained with high-resolution powder bed surface height data from a laser profilometer and then linked to post-print CT scans which provided the truth-data labelling of each site as faulty or nominal. Various measures of accuracy and performance demonstrate excellent performance of the ANN, suggesting that the neural net is capable of discovering strong correlations between surface roughness characteristics and the presence and size of faults. These developments carry the potential of active monitoring to become a future component of real-time control systems in SLM processes.
Authors: Benjamin S. Terry, Brandon Baucher, Anil Chaudhury, and Subhadeep Chakraborty
Keywords: In-Situ monitoring, Selective Laser Melting, Machine Learning, Image Processing