Ming Tang, PhD Candidate, Carnegie Mellon University
Petrus Pistorius PhD, POSCO Professor of Materials Science and Engineering, Carnegie Mellon University
The fatigue strength of metals is critical for many industrial applications and usually dictated by the internal defects when present. However, there exists only a handful of investigations into the prediction of fatigue life for additive manufactured materials. In this work, a pore-based fatigue strength prediction approach was studied and validated with AlSi10Mg cylinders fabricated by selective laser melting. The porosity in as-built samples was analyzed by 2D metallography and 3D x-ray tomographic study. Porosity characteristics, including total volume fraction, maximum size, aspect ratio, and relative location to the sample surface, were incorporated in the prediction. It is found that largest possible size based on the size distribution of the porosity can be used as an indicator of the fatigue life. Such pore-based prediction provides insights into improving fatigue resistance by adjusting processing parameters.