"Current Methods of Enhancing Print Quality for Nb Based Materials in LPBF Through Analytical Parameter Optimization" 

Dina Khattab, MSE PhD Candidate 

Advisors: Professors Michael Titus and Paul Mort

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ABSTRACT

Additive manufacturing (AM) is an advanced manufacturing process that can produce parts with high yield and dimensional accuracy. One challenge in AM is that it has multiple independent parameters that could be changed, resulting in a vast design space. The optimal parameters are determined through trial and error, resulting in costly and time-consuming procedures. Therefore, processing maps can be used as an effective parameter selection method. Processing maps include possible defect regions and an optimal region defined through dimensionless Π numbers. The dimensionless numbers take into account process and material parameters, which are then compared to experimental data to determine an optimal threshold. Two optimal thresholds were examined and proved to be unsuitable for predicting the relative densities of Nb parts. Optimal thresholds based on experimental results of non-refractory materials yielded more accurate predictions than thresholds based on refractory materials such as tungsten and molybdenum. Variations in thermophysical properties and melt pool fluidity affect convection which influences interlayer bonding and densification. Marangoni convection controls fluid and heat flow, which depends on the variations in surface tension and thermal gradients across the melt pool. Niobium’s low surface tension sensitivity coefficient decreases convection and melt pool fluidity, resulting in higher porosity compared to SS316L. Increasing the temperature gradient inside the melt pool using higher energy density will improve its fluidity and the parts’ relative density.