The additive manufacturing (AM) process induces high uncertainty in the mechanical properties of 3D-printed parts, which represents one of the main barriers for a wider AM processes adoption. To address this problem, a new time-efficient microstructure prediction algorithm was proposed in this study for the laser powder bed fusion (LPBF) process. Based on a combination of the melt pool modeling and the design of experiment approaches, this algorithm was used to predict the microstructure (grain size/aspect ratio) of materials processed by an EOS M280 LPBF system, including Iron and IN625 alloys. This approach was successfully validated using experimental and literature data, thus demonstrating its potential efficiency for the optimization of different LPBF powders and systems.
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