97
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Intensified texture of Zr702 sheet after slow cooling from near β-transus temperature

, , , ORCID Icon, , , , ORCID Icon & show all
Pages 1822-1830 | Received 25 Apr 2019, Accepted 27 Jul 2019, Published online: 12 Aug 2019
 

ABSTRACT

Microstructures and textures of a Zr702 sheet subjected to slow cooling (air cooling (AC) and furnace cooling (FC)) from a near β-transus temperature (980°C) were characterised by electron channelling contrast imaging and electron backscatter diffraction techniques. Results show that textural intensities of both the AC and the FC are markedly higher than that of the initial specimen and the FC specimen owns the strongest texture. After both the heat treatments, the initial bimodal basal textural features are retained with the recrystallisation textural component (0°, 30°, 30°) becoming dominant but the deformation textural component (0°, 30°, 0/60°) largely weakened. The textural intensification is attributed to strong variant selection during the β → α phase transformation and slow cooling-induced sufficient growth of residual prior α grains.

Acknowledgements

Ms. Tingting Wang is acknowledged for providing assistance in post-processing EBSD data. The reviewers of this paper are also gratefully appreciated for their critical comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work is financially supported by the Fundamental and Cutting-Edge Research Plan of Chongqing [grant number cstc2018jcyjAX0299 and cstc2017jcyjAX0114], the National Natural Science Foundation of China [grant number 51601165] and the fund of the State Key Laboratory of Solidification Processing in NPU [grant number SKLSP201920].

Log in via your institution

Log in to Taylor & Francis Online

There are no offers available at the current time.

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.