Engineering materials processed using additive manufacturing (AM) techniques such as laser powder bed fusion (LPBF) often exhibit unique microstructures and defects that must be controlled to obtain peak performance in mechanical properties and as such a level of damage-tolerance that cannot be achieved in cast alloys. However, our understanding of how processing conditions control micro- and mesostructure and, in turn, mechanical performance, particularly regarding failure resistance, is weak. Furthermore, heat treatments that have been designed to achieve peak performance in cast alloys are often not optimized for alloys that have been processed using AM techniques. Here, we report our work on the effect of processing parameters such as layer thickness, hatch spacing, and scan strategy on crack resistance curve (R-curve) behavior in different orientations of LPBF-processed AlSi10Mg and correlate mechanical performance with meso- and microstructural features such as melt pool arrangement, cell morphology, grain size, grain orientation, and texture. Compared to that we show how heat-treatments impact fracture resistance as well as their anisotropy in two orthogonal orientations in an LPBF-processed 18Ni-300 maraging steel.
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A melt pool geometry-based approach is developed to predict surface roughness in metal additively manufactured parts for a range of processing parameters. It is shown that surface roughness on a particular facet can be estimated by stacking melt pools along the facet, extracting their outer contour and applying the necessary transformations. To be able to predict surface quality of various processing parameters in a reasonable time frame, a machine learning framework is developed. This framework is trained over melt pool data generated by high-fidelity FE simulations.
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