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Dataset for machine learning of microstructures for 9% Cr steels.

Authors :
Rozman KA
Doğan ÖN
Chinn R
Jablonksi PD
Detrois M
Gao MC
Source :
Data in brief [Data Brief] 2022 Oct 29; Vol. 45, pp. 108714. Date of Electronic Publication: 2022 Oct 29 (Print Publication: 2022).
Publication Year :
2022

Abstract

The microstructure of steel greatly influences the mechanical properties. For 9 wt% Cr steels, which are widely used in the power generation industry, the steels have a ferritic and martensitic microstructure which can be altered by heat treating and chemical composition variations. Fully martensitic steels typically having high yield strengths but low ductility. Tempering can reduce the amount of martensite in the steel lowering the yield strength but increasing the ductility of the alloy. Alloying can alter the time required for a martensitic transformation. In authors' previously published research, the authors used machine learning methodology to predict room temperature tensile properties from scanning electron microscopy (SEM) images of the initial steel microstructures from a wide range of steel compositions. This data-in-brief supplies the raw image files and the associated tensile properties for the authors' previously published research utilized to predict tensile properties of steels [1].<br />Competing Interests: Please tick the appropriate statement below and declare any financial interests/personal relationships which may affect your work in the box below. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Please declare any financial interests/personal relationships which may be considered as potential competing interests here.<br /> (© 2022 The Authors. Published by Elsevier Inc.)

Details

Language :
English
ISSN :
2352-3409
Volume :
45
Database :
MEDLINE
Journal :
Data in brief
Publication Type :
Academic Journal
Accession number :
36425963
Full Text :
https://doi.org/10.1016/j.dib.2022.108714