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Part A: Materials Science

Experimental and theoretical investigation of crystallography and variant selection during massive transformation in Zr alloy

, , , , , & show all
Pages 1163-1183 | Received 28 May 2018, Accepted 12 Dec 2018, Published online: 18 Feb 2019
 

ABSTRACT

Massive transformation, as a non-conventional solid-state phase transformation mode, is scarcely observed in metals with hexagonal closed packed (HCP) structure, especially in Zr and its alloys. In this study, however, we report the massive transformation in a Zr-1.0Cr-0.4Fe alloy after conventional β-quenching. It is shown that the necessary condition to induce the occurrence of massive transformation requires an appropriate composition and cooling rate of the alloy to be simultaneously within reasonable ranges. We combine the electron backscatter diffraction (EBSD) and crystallographic reconstruction techniques to systematically assess the orientation relationship between massive grain (αm) and its β parent grain. It is demonstrated that, similar to martensitic transformation, the orientation between αm and β parent grain during massive transformation satisfies Burgers orientation relationship, i.e. {110}β{0001}αm and <111>β<112¯0>αm. Furthermore, a statistical analysis of EBSD data shows that variant selection occurs during massive transformation due to pre-existing β-β grain boundary. Based on mathematical theory and crystallographic calculations, we further explore the detailed mechanisms of variant selection during massive transformation.

Acknowledgements

We would like to thank Dr. R.P. Shi in Department of Materials Science and Engineering, The Ohio State University for fruitful discussions on this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

We express sincere thanks to the support by the National Natural Science Foundation of China [grant number 51531005, 51421001, 51501021 and 51371202] and the Fundamental Research Funds for the Central Universities [grant number 106112017CDJQJ138803]. KLM acknowledges support from US National Science Foundation through [grant number CMMI-1727237].

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