4.8 Article

Predictive model for porosity in powder-bed fusion additive manufacturing at high beam energy regime

Journal

ADDITIVE MANUFACTURING
Volume 22, Issue -, Pages 817-822

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.addma.2018.05.042

Keywords

Porosity; Melt pool indentation; Selective laser melting; Ti6Al4V; Additive manufacturing

Funding

  1. Agency for Science, Technology and Research of Singapore (A*STAR)

Ask authors/readers for more resources

Process consistency and control are bottleneck issues to wider insertion of powder-bed fusion additive manufacturing in the industrial shopfloor. Of particular interest is the porosity of the components, which remains the limiting factor to high-cycle fatigue performance. Recent experiments have shown that, with increasing energy density, a surge in porosity is seen in selectively laser melted metals. In this high-energy density regime, porosity must originate from mechanisms that are different from the well-known incomplete melting in the low energy density regime. To shed light on this interesting phenomenon, this paper first discusses the mechanism of bubble formation in the melt pool and possible trapping during the solidification, and then formulates a predictive model for porosity in this regime. To compare with experimental results, we perform computer modeling and simulations which have been fully validated by experiments to determine the parameters in the model. We show that the model predictions are in good qualitative and quantitative agreement with the experimental measurements. Hence, the proposed model can be used as a tool to predict the porosity, and further to control and possibly reduce porosity in laser powder-bed fusion additive manufacturing, paving the way for its wider adoption in manufacturing shopfloors.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available