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Fast Evaluation of Microstructure-Property Relation in Duplex Alloys Using SEM Images

Published online by Cambridge University Press:  02 January 2019

Thantip S. Krasienapibal Open the ORCID record for Thantip S. Krasienapibal [Opens in a new window] ,
Yasuhiro Shirasaki ,
Momoyo Enyama ,
Akiko Kagatsume ,
Minseok Park  and
Sayaka Tanimoto
Thantip S. Krasienapibal*
Affiliation:
Research and Development Group, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo, 185-8601, Japan
Yasuhiro Shirasaki
Affiliation:
Research and Development Group, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo, 185-8601, Japan
Momoyo Enyama
Affiliation:
Research and Development Group, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo, 185-8601, Japan
Akiko Kagatsume
Affiliation:
Research and Development Group, Hitachi Ltd.,832-2, Horiguchi, Hitachinaka-shi, Ibaraki, 312-0034, Japan
Minseok Park
Affiliation:
Research and Development Group, Hitachi Ltd., 7-1-1, Omika, Hitachi-shi, Ibaraki, 319-1221, Japan
Sayaka Tanimoto
Affiliation:
Research and Development Group, Hitachi Ltd., 1-280, Higashi-koigakubo, Kokubunji-shi, Tokyo, 185-8601, Japan
*
*(Email: [email protected])
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Abstract

To shorten the time for designing and developing new materials, fast evaluation of microstructure-property relation is required. In this research, we applied machine learning to perform crystal phase segmentation and extracted microstructure features of Cr-based duplex alloys from the scanning electron microscope (SEM) images. The results show that the accuracy of crystal phase segmentation was improved when the backscattered electron (BSE) images with controlled channeling contrast was used. This indicates that measurement conditions of the BSE images are important for obtaining high segmentation accuracy. The segmented images were used for the extraction of microstructure features. With the extracted features, the reliable prediction of mechanical properties of Cr-based duplex alloys was verified with a prediction error less than 10%. We demonstrated a method of using microstructure features extracted from SEM images for fast evaluation of microstructure-property relation.

Keywords

alloyscanning electron microscopy (SEM)microstructure
Type
Articles
Information
MRS Advances , Volume 4 , Issue 19: Broader Impact/Machine Learning and Robotics , 2019 , pp. 1101 - 1107
Copyright
Copyright © Materials Research Society 2018 

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References

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