Author:
Jentner R. M.,Tsai S. P.,Welle A.,Scholl S.,Srivastava K.,Best J. P.,Kirchlechner C.,Dehm G.
Abstract
AbstractDifferentiation of granular bainite and polygonal ferrite in high-strength low-alloy (HSLA) steels possesses a significant challenge, where both nanoindentation and chemical analyses do not achieve an adequate phase classification due to the similar mechanical and chemical properties of both constituents. Here, the kernel average misorientation from electron backscatter diffraction (EBSD) was implemented into a Matlab code to differentiate and quantify the microstructural constituents. Correlative electron channeling contrast imaging (ECCI) validated the automated phase classification results and was further employed to investigate the effect of the grain tolerance angle on classification. Moreover, ECCI investigations highlighted that the grain structure of HSLA steels can be subdivided into four grain categories. Each category contained a different nanohardness or substructure size that precluded a nanoindentation-based phase classification. Consequently, the automated EBSD classification approach based on local misorientation achieved a reliable result using a grain tolerance angle of 5°.
Graphical abstract
Funder
Max-Planck-Institut für Eisenforschung GmbH
Publisher
Springer Science and Business Media LLC
Subject
Mechanical Engineering,Mechanics of Materials,Condensed Matter Physics,General Materials Science
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献