A virtual sensing approach for monitoring melt-pool dimensions using high speed coaxial imaging during laser powder bed fusion of metals

Abstract

Abstract Metal parts produced by Laser Powder Bed Fusion (L-PBF) are frequently used for demanding applications. To meet stringent safety and certification requirements, a better understanding of melt-pool behavior and stability during processing is desired. This work presents a novel, fast and economically feasible virtual sensing approach for accurate estimation of melt-pool depth and width during L-PBF of metals. In a first step, the melt-pool width is determined by GPU-based processing of images from a high-speed coaxial camera monitoring system. In a second step, a physics-based analytical model is used to calculate the melt-pool depth-to-width ratio from the processing conditions and material properties. In a third and last step, the results from the first two steps are combined to estimate the melt-pool depth. Experimental validation of these predicted melt-pool dimensions is performed on 316L SS single layer strips that are consecutively produced, cross-sectioned, polished and etched to reveal the actual melt-pool boundaries. The results indicate an average relative error on the predicted melt-pool depth of 9.9% and 2.8% for the full L-PBF parameter range and for the optimal parameter range respectively. This gives confidence in the predictive capabilities of a virtual sensing approach using coaxial camera images for the assessment of the melt-pool depth and process stability.