Fracture resistance of AlSi10Mg fabricated by laser powder bed fusion
Source abstract
Abstract
Abstract The combination of refined microstructures (induced by rapid cooling) and melt pool-induced mesostructures in AlSi10Mg fabricated using laser powder bed fusion (LPBF) – a widely used additive manufacturing technique – impart high strength and fracture toughness. Further exploitation of such property combinations requires a detailed understanding of how the processing conditions control the micro- and mesostructures and, in turn, the mechanical performance, especially regarding fracture resistance. Towards this end, the crack resistance curve (R-curve) behavior in different orientations of LPBF-fabricated AlSi10Mg alloys processed with different layer thickness, hatch spacing, and scan strategies was evaluated and correlated with micro- and mesostructural features such as grain size and grain orientation, texture, cell morphology, and melt pool arrangement. Results show a strong anisotropy in both tensile stress-strain behavior and fracture toughness with layer thickness and hatch spacing controlling strength and scan strategy dictating fracture resistance. In terms of tensile stress-strain behavior, the arrangement of melt pool boundaries with respect to loading direction results in anisotropy in ductility whereas strength is controlled by grain size and cellular structure. In case of fracture toughness, measurements show that failure is dominated primarily by melt pool morphology and hence the mesostructure that is controlled by scan strategy. They furthermore reveal, that, despite the pronounced anisotropy in the R-curve behavior the presence of such mesostructure enables a level of damage-tolerance in AlSi10Mg that cannot be achieved in a cast alloy.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · Additive Manufacturing and 3D Printing Technologies · High Entropy Alloys Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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