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Quantification of grain boundary connectivity for predicting intergranular corrosion resistance in BFe10-1-1 copper–nickel alloy

Published online by Cambridge University Press:  22 October 2018

Yinghui Zhang ,
Xingyu Feng Open the ORCID record for Xingyu Feng [Opens in a new window] ,
Chunmei Song ,
Hang Wang ,
Bin Yang  and
Zhigang Wang
Yinghui Zhang
Affiliation:
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Xingyu Feng*
Affiliation:
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Chunmei Song
Affiliation:
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Hang Wang
Affiliation:
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Bin Yang
Affiliation:
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
Zhigang Wang*
Affiliation:
School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
*
Address all correspondence to Xingyu Feng at fxy_105831@163.com and Zhigang Wang at wzgang2008cn@163.com
Address all correspondence to Xingyu Feng at fxy_105831@163.com and Zhigang Wang at wzgang2008cn@163.com
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Abstract

We investigated the connectivity of high-energy random grain boundaries through fractal analyses of specimens with different grain boundary (GB) microstructures in BFe10-1-1 copper–nickel alloy. It was found that the profile of maximum random boundary network possesses a fractal nature and more than one fractal dimension can exist. The fraction of special boundaries and grain size homogeneity can play an important role on GB character distribution. Here, GB microstructures are combined with quantitative materials structure–property relationship models to predict intergranular corrosion properties. The experimental results are accurately consistent with the theoretical predictions.

Type
Research Letters
Information
MRS Communications , Volume 9 , Issue 1 , March 2019 , pp. 251 - 257
Copyright
Copyright © Materials Research Society 2018 

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