Porosity management and control in powder bed fusion process through process-quality interactions

Abstract

Laser Powder bed fusion (L-PBF) has gained much attention for its ability to manufacture high-precision and high-complexity metal components for the aircraft and automobile industries. However, to a certain degree, the print quality (shape, GD&T, mechanical property) is difficult to predict and control due to the multi-variant processability. Recent research predominantly focuses on building a semantic/qualitative process relationship from the single/a few process parameters on the ultimate print qualities, such as thermal distortion failure/defect probabilities. However, such semantic/qualitative process relationships cannot reflect specific governing effects from process variables and provide an optimal selection of the parameters to ensure the build quality. Therefore, there is a strong need to develop a quantitative model within a process network and achieve a constant quality level by controlling the process parameter set. To address the aforementioned challenges, this research focuses on developing a multi-dimensional process-quality interaction model to predict and control the porosity of Titanium 6Al-4V in L-PBF. The process network is first derived from existing literature composing the potential, influential process parameters that can deviate the porosity level, such as laser power and scanning speed. Then the quantitative model is built based on a multi-dimensional quantitative modeling technique from a collection of experimental data. The multi-dimensional modeling provides an architecture that replaces the artificial neural network (ANN) or regression model to depict the mathematical relationship. The established quantitative process models analyze the intercorrelated effects from process parameters to the porosity. The printable zone can also be derived from such a quantitative model and visualized through a multi-dimension variable-control response graph to optimize the selection of process parameters. This would result in a comprehensive understanding of selecting the process parameters according to the desired porosity level and pave the path to fully control the metal L-PBF print qualities by adjusting the process variables. The experimental data also validate the proposed quantitative model to demonstrate its effectiveness and correctness.