Characterizing the effect of residual stresses on high cycle fatigue (HCF) with induction heating treated stainless steel specimens

Autor: Jacques Lanteigne, Carlo Baillargeon, Daniel Paquet, Marie Bernard
Rok vydání: 2014
Předmět:
Zdroj: International Journal of Fatigue. 59:90-101
ISSN: 0142-1123
DOI: 10.1016/j.ijfatigue.2013.09.011
Popis: A new method for introducing a predetermined amount of residual stresses in stainless steel thick-walled hollow fatigue test specimens was developed by the authors [1] using high frequency induction heating. The advantage of the proposed method over more traditional approaches is to avoid any change in other important fatigue parameters, i.e. surface roughness, geometry, and microstructure, while introducing the residual stresses. The last point only holds if the material under study does not undergo any phase transformation within the range of temperatures and time exposures reached during the heat treatment. In this paper, the effect of residual stresses on high cycle fatigue (HCF) life of annealed AISI 304L stainless steel is investigated by introducing a residual stress field in thick-walled hollow fatigue specimens and by comparing the fatigue life obtained with the reference S – N curve. For the particular case studied, a surprising observation is made. Introducing tensile residual stresses beneath the surface of hollow fatigue specimens using the method proposed by Paquet et al. [1] leads to improved HCF lives. Validity of this result is confirmed by a statistical analysis. Residual stresses were analyzed by the X-ray diffraction (XRD) technique to rationalize this experimental result. The increase in fatigue life is explained by residual stresses evolution within the specimen cross section during the fatigue test, leading to a build up of compressive residual stresses beneath its surface. This is a clear demonstration that assimilating residual stresses resulting from fabrication processes to superimposed static mean stresses can lead to considerable errors in fatigue life predictions.
Databáze: OpenAIRE