Defect structure process maps for laser powder bed fusion additive manufacturing

Jerard V.Gordon, Sneha P.Narra, Ross W.Cunningham, He Liu, Hangman Chen, Robert M.Suter, Jack L.Beuth, Anthony D.Rollett - Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, USA / Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, USA / Next Manufacturing Center, Carnegie Mellon University, Pittsburgh, USA / Department of Physics, Carnegie Mellon University, Pittsburgh, USA

Accurate detection, characterization, and prediction of defects has great potential for immediate impact in the production of fully-dense and defect free metal additive manufacturing (AM) builds. Accordingly, this paper presents Defect Structure Process Maps (DSPMs) as a means of quantifying the role of porosity as an exemplary defect structure in powder bed printed materials. Synchrotron-based micro-computed tomography (μSXCT) was used to demonstrate that metal AM defects follow predictable trends within processing parameter space for laser powder bed fusion (LPBF) materials.

How Amira-Avizo Software is used

Three-dimensional μSXCT volumes were reconstructed using the TomoPy and AVIZO by FEI™ 9.1.1 software. Pore morphology (i.e. « spherical » versus « non-spherical » designations) was determined using the ‘anisotropy’ function in Avizo 9 with a value of 0.5 being the cutoff for « spherical ». […] Porosity within the powder particles was identified as void regions that are surrounded by particle voxels in 3-D space. Finally, these binary data sets were well handled by the commercial software, AVIZO for statistical analysis. Statistical information including powder size, porosity distribution, and morphology were extracted from these volumetric data sets.