Effect of Defect Variability in Aluminum Alloys on Ultrasonic Fatigue Performance Across Additive Manufacturing Platforms

Optimization of process parameters is a critical step toward realization of component builds across multiple additive manufacturing (AM) platforms and broadening the deployment of AM processes. The main focus of the present work is the quantification of defect variability and fatigue performance of builds across multiple laser powder bed fusion (L-PBF) equipment. This study of variability is predicated upon the hypothesis that, given process variables yielding a similar percentage/type of porosity based on process maps for multiple AM platforms, the defect size distribution across them should be similar. Aluminum alloy (AlSi10Mg) specimens of cylindrical geometry built on three different L-PBF machines are characterized using computed tomography. The distributions of defect size are compared using Kolmogorov-Smirnov tests. Ultrasonic fatigue testing is used to quantify fatigue properties of the builds from the different L-PBF machines under fully reversed loading conditions. Finally, fatigue performance correlated to the defect distribution in builds across the three platforms.