Prediction of fatigue limit of induction surface hardened 1.05Cr-0.23Mo steel alloy using extreme value statistics

Byoung-Ho Choi, Sam Hong Song

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Fatigue characteristics of the surface hardened steel are different from that of normal steel, so the prediction of the fatigue limit of surface hardened steel is very complicated. In this paper, specimens are tested using rotary bending, and the surface of 1.05Cr-0.23Mo steel alloy is hardened by induction surface hardening. Variation of the distribution of microvickers hardness and residual stress is discussed, and the difference of S-N diagram between surface hardened steel and unhardened steel is examined. The maximum defect size of surface hardened specimen is calculated by the extreme value statistics to predict conservative fatigue limit. Actual shape of defect in the specimen is three dimensional, so a conversion method from 2D to 3D defect size based on examination volume and inclusion size is used to predict statistical maximum defect size. The predicted results can be defined as a lower fatigue limit which may be useful to predict conservative fatigue limit of surface hardened specimen.

Original languageEnglish
Pages (from-to)5427-5433
Number of pages7
JournalJournal of Materials Science
Volume40
Issue number20
DOIs
Publication statusPublished - 2005 Oct 1
Externally publishedYes

Fingerprint

Alloy steel
induction
Steel
Statistics
statistics
steels
Fatigue of materials
predictions
Defects
defects
S-N diagrams
hardening
residual stress
Hardening
Residual stresses
hardness
examination
Hardness
inclusions

ASJC Scopus subject areas

  • Materials Science(all)
  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics

Cite this

Prediction of fatigue limit of induction surface hardened 1.05Cr-0.23Mo steel alloy using extreme value statistics. / Choi, Byoung-Ho; Song, Sam Hong.

In: Journal of Materials Science, Vol. 40, No. 20, 01.10.2005, p. 5427-5433.

Research output: Contribution to journalArticle

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