University of Oulu

Babu, S.R.; Davis, T.P.; Haas, T.; Jarvenpää, A.; Kömi, J.; Porter, D. Image Processing Tool Quantifying Auto-Tempered Carbides in As-Quenched Low Carbon Martensitic Steels. Metals 2020, 10, 171.

Image processing tool quantifying auto-tempered carbides in as-quenched low carbon martensitic steels

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Author: Ramesh Babu, Shashank1; Davis, Thomas Paul2; Haas, Tim3;
Organizations: 1Materials and Mechanical Engineering, Centre for Advanced Steels Research, University of Oulu, 90014 Oulun, Finland
2Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
3Department for Industrial Furnaces and Heat Engineering, RWTH Aachen University, Kopernikusstr. 10, 52074 Aachen, Germany
4Kerttu Saalasti Institute, University of Oulu, Pajatie 5, FI-85500 Nivala, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.1 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2020
Publish Date: 2020-01-28


As-quenched low-carbon martensitic steels (<0.2 wt.% C) contain auto-tempered carbides. Auto-tempering improves the work hardening and upper-shelf impact energy; however, an efficient characterization method to determine the degree of auto-tempering has not been available. This paper demonstrates an efficient image processing tool that calculates the relative auto-tempered carbide fraction by analyzing scanning electron microscope micrographs. By the process of image segmentation, the qualitative volume fraction of auto-tempered carbides can be determined, and an associated color map produced, which distinguished the levels of auto-tempering. This image processing tool could become useful for the optimization of new low-carbon steel’s mechanical properties.

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Series: Metals
ISSN: 2075-4701
ISSN-E: 2075-4701
ISSN-L: 2075-4701
Volume: 10
Issue: 2
Article number: 171
DOI: 10.3390/met10020171
Type of Publication: A1 Journal article – refereed
Field of Science: 216 Materials engineering
Funding: The authors are grateful for financial support from the European Commission under grant number 675715‐MIMESIS‐H2020‐MSCA‐ITN‐2015, which is a part of the Marie Sklodowska-Curie Innovative Training Networks European Industrial Doctorate Programme. T.P. Davis is funded by the Clarendon Scholarship from the University of Oxford and United Kingdom’s Engineering and Physical Sciences Research Council Fusion Centre for Doctorial Training [EP/L01663X/1].
EU Grant Number: (675715) MIMESIS - Mathematics and Materials Science for Steel Production and Manufacturing
Copyright information: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.