Phase-field simulation of austenite growth behavior: Insights into the austenite memory phenomenon

Pengcheng Song, Yanzhou Ji, Lei Chen, Wenbo Liu, Chi Zhang, Long Qing Chen, Zhigang Yang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Austenite memory phenomenon impedes the application of reverse austenitic transformation to refine grains in steels. In this work, a phase-field model is employed to understand the austenite memory mechanism in terms of austenite growth behaviors under different mechanical boundary conditions, using the Fe-23Ni (wt.%) alloy as an example. The effect of defects formed during martensitic transformation on reverse austenitic transformation is considered by introducing a "stored energy" term. Kurdjumov-Sachs (K-S) variants of each phase are divided into three groups based on the crystallography analysis. Results show that different combinations of mechanical boundary conditions during the austenite → martensite → austenite transformation cycle have different effects on the austenite memory phenomenon, which can be attributed to the minimization of strain energy induced by phase transformations, as well as the inhomogeneous distribution of stored energy (energy of defects).

Original languageEnglish (US)
Pages (from-to)139-150
Number of pages12
JournalComputational Materials Science
Volume117
DOIs
StatePublished - May 1 2016

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Chemistry(all)
  • Materials Science(all)
  • Mechanics of Materials
  • Physics and Astronomy(all)
  • Computational Mathematics

Fingerprint

Dive into the research topics of 'Phase-field simulation of austenite growth behavior: Insights into the austenite memory phenomenon'. Together they form a unique fingerprint.

Cite this