Determination of opening stresses for railway steel under low cycle fatigue using digital image correlation

Ans Al Rashid*, Ramsha Imran, Muhammad Yasir Khalid

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

Crack closure phenomenon is important to study as it provides an estimation to fatigue life of the components. It becomes even more complex under low cycle fatigue (LCF), since under LCF high amount of plasticity is induced within the material near notches or defects, as a result the assumptions used by linear elastic fracture mechanics (LEFM) approach become invalid. Evaluation of opening stresses for mechanical components undergoing LCF phenomenon requires a robust methodology to correctly predict the fatigue life. In this study, an experimental campaign was carried out for determination of opening stresses of railway steels (25CrMo4 and 30NiCrMoV12) subjected to LCF using digital image correlation (DIC) technique. The concept of crack opening displacement (COD) was used for the analysis. Two different methodologies were introduced to analyze experimental data for the identification of opening levels. Experimental results were then compared with crack closure prediction model, Newman model. Results from Newman model agreed well with the experimental analysis. Newman model provided very good prediction for strain ratio Rε = −1, however, for the materials undergoing strain ratio Rε = 0, stress ratio must be considered rather than strain ratio, because Newman model can't predict stress relaxation behaviour.

Original languageEnglish
Article number102601
JournalTheoretical and Applied Fracture Mechanics
Volume108
DOIs
Publication statusPublished - Aug 2020

Keywords

  • Crack closure
  • Crack propagation
  • Digital image correlation
  • Low cycle fatigue
  • Non-contact testing
  • Opening stresses

Fingerprint

Dive into the research topics of 'Determination of opening stresses for railway steel under low cycle fatigue using digital image correlation'. Together they form a unique fingerprint.

Cite this