Impact of micro and mesostructure on the fatigue crack growth in laser powder bed fusion fabricated AlSi10Mg
Source abstract
Abstract
Fatigue crack propagation in additive manufactured materials is often impacted by defects such as pores or lack of fusion and extensive research has been conducted to control pore formation and minimize their effect on fatigue life. The impact of process parameters on fatigue properties, however, has largely been overlooked. This study evaluates the effects of layer thickness, hatch spacing, scan strategy, and baseplate preheating on both the fatigue threshold and crack growth rates of laser powder bed fusion (LPBF) fabricated AlSi10Mg, tested in two orthogonal orientations. Five different LPBF process parameter sets were used and the resulting micro and mesostructure characteristics were correlated with the fatigue crack growth (FCG) behavior. Results indicate that the FCG rates are significantly more influenced by hatch spacing and baseplate preheating than layer thickness and scan strategy. Across all parameter sets, the threshold region is governed primarily by the solidification cells, with the cells acting as barriers to short-crack propagation. Steady state crack growth initiates when the cyclic plastic zone size approaches the cell size. In the Paris regime, smaller hatch spacing induces a distinct change in the slope of the crack growth curve as the plastic zone size approaches the melt pool mesostructure scale. Preheating the baseplate during LPBF impacts both the fatigue threshold and the Paris regime. Anisotropy was found to be minimal in both the threshold and Paris regimes. However, substantial anisotropy is observed in the fast crack growth regime, which is caused by the crack growth along the melt pool boundaries.
Source: openalex_inverted_index · Quality: baseline
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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