journal article·2025-12

Prevailing effect of residual stresses and defects on the fatigue strength of net-shape parts produced with Laser Powder Bed Fusion (L-PBF) 316L stainless steel

Marion Auffray, Franck Morel, Linamaria Gallegos Mayorga, Etienne Pessard, Thierry Baffie, Pierre Merot, Paul Buttin

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Abstract

Laser Powder Bed Fusion (L-PBF) additive manufacturing enables the production of complex-shaped parts with high mechanical resistance. Such components exhibit a multi-scale microstructure, internal, sub-surface, and surface defects, a rough surface finish and a residual stress gradient in the as-built net-shape condition. All of these factors can alter the fatigue behaviour. This study aims to improve the understanding of the combined effect of various surface parameters on the fatigue behaviour of L-PBF 316L stainless steel by conducting an extensive experimental campaign. Uni-axial fatigue tests were carried out on six batches having an as-built or heat-treated microstructure and a net-shape, pre-corroded net-shape, single-defect net-shape or polished surface condition. Results were compared with data from the literature: as-built polished, pre-corroded polished, or single-defect polished specimens. Studied defects were process-induced (e.g. lack of fusion, spatter, gas pore) and artificial (i.e. corrosion pit, electric discharge machined defect). Their sizes ranged from 10 to 700 μm. The residual stresses gradients were characterized by X-ray diffraction. A Kitagawa–Takahashi diagram was used to illustrate the effects of the various parameters on the fatigue behaviour. Residual stresses and defects were the most influential factors on the fatigue strength of net-shape specimens over surface condition and sub-surface microstructure.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Metal Alloys Wear and Properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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