Process variation in Laser Powder Bed Fusion of Ti-6Al-4V

In this work, a concept of using surface roughness data as an evaluation tool of the process variation in a commercial Laser Powder Bed Fusion (L-PBF) machine is demonstrated. The interactive effects of powder recoating, spatter generation, gas flow and heat transfer are responsible for the intra-build quality inconsistency of the L-PBF process. Novel specimens and experiments were designed to investigate how surface roughness varies across the build volume and with the progression of a build. The variation in roughness has a clear and repeatable pattern due to the strong impact of the orientation of inclined surface to the laser origin. The effects of other factors such as exposure sequence of specimens, build height, and recoating process are less prominent and are difficult to isolate. A neural network regression model was built upon the large dataset in measured Ra values. The neural network model was applied to predict distribution of roughness within the build volume under hypothetical processing conditions. Connections between the predicted variation in roughness and underlying physical mechanisms are discussed. The present work has value for machine qualification and modifications which lead to the manufacturing of parts with better consistency in quality. The detailed variation observed in surface roughness can be used as a reference for designing experiments to optimise processing parameters in order to minimise the roughness of inclined surfaces