journal article·2025-12Open access

Enhancing fatigue performance of austenitic stainless steel via warm surface severe plastic deformation using surface mechanical attrition treatment

Y Austernaud, M Novelli, T Grosdidier, P Bocher

Abstract

The effects of warm Surface Severe Plastic Deformation (SSPD) performed via Surface Mechanical Attrition Treatment (SMAT) on the microstructure, hardness, and residual stress gradients, as well as the resulting fatigue properties of a 316L austenitic stainless steel were investigated. Machined samples were ultrasonically shot peened for 10 min at Room Temperature (RT), 523 K, and 773 K before undergoing rotating-bending fatigue tests to determine the endurance limit. The RT-SMATed sample, for which machining grooves are removed by the shot impacts, showed a superior fatigue limit endurance than machined samples (+25 %), with subsurface nucleation sites. The 523 K peened samples revealed a similar fatigue limit endurance accompanied by the same type of subsurface crack nucleation. Due to the increased roughness and expansion of surface stress raisers by pile-ups and surface oxidation, the nucleation of the fatigue cracks occurred at the extreme surface when SMAT was done at 773 K. Despite the surface nucleation, SMAT carried out at 773 K provided a superior endurance limit (+15 % compared to RT-SMAT). This improvement was attributed to the restored microstructure formed under 773 K peening, which stabilizes the introduced compressive residual stress, and to the deeper and lower tensile peak induced by warm SMAT. To support the interpretation of fatigue behaviour under varying mean stress conditions, a Goodman analysis was conducted, confirming the beneficial role of compressive residual stress introduced by warm peening on endurance limit improvement.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Surface Treatment and Residual Stress · Erosion and Abrasive Machining · Metal Alloys Wear and Properties

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

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