Fatigue properties of AlSi10Mg produced by Additive Layer Manufacturing
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Abstract
Abstract This work shows the impact of microstructure and defect on the fatigue life of an AlSi10Mg manufactured by Additive Layer Manufacturing (ALM). Samples were manufactured via a laser powder-bed process: two configurations (0° and 90°) are considered in order to evaluate the impact of the building direction on fatigue properties. 3D X-Ray tomography was used to characterize the defect population. The microstructure was characterized by considering four parameters: melt-pools, crystallographic grains, dendritic structure and precipitates. The fatigue properties were determined by establishing S-N curves for machined samples, with and without T6 heat-treatment, at R = −1 under tensile loading. The size of the defect responsible for the fatigue failure was determined in each sample so as to establish a relationship between the fatigue limit and the defect size using Kitagawa-type diagrams. In order to study a broader range of defect size, artificial defects were introduced using electro-discharge machining. The following observations are made: (i) after heat-treatment, the boundaries of melt-pools and the dendritic structure are not visible. Si is organized into pure precipitates homogeneously distributed over space and intermetallic Fe based compounds are observed in the form of needles; (ii) The impact of building direction on fatigue life is seen only after T6 heat treatment; (iii) An improvement of the fatigue resistance is observed after T6, in spite of the presence of intermetallic needles; (iv) The fatigue limit is controlled by the defect size both before and after T6 heat treatment, and it seems that the influence of T6 decreases as the defect size increases.
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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