Application of Hierarchical Clustering Approach for Prediction of Grain Size in Heat-Treated EN9 Steel

One of the simplest, most popular, and productive ways to conduct research and testing in the field of materials science is through the use of metallographic study. Technological boon in the field of metallographic study, opens new gateway for materials characterization through image processing tech...

Full description

Saved in:
Bibliographic Details
Published inECS Advances Vol. 2; no. 2; pp. 21001 - 21006
Main Authors Sahoo, Tapasmini, Panda, Susmita, Biswal, Sweta Rani
Format Journal Article
LanguageEnglish
Published IOP Publishing 01.06.2023
Online AccessGet full text

Cover

More Information
Summary:One of the simplest, most popular, and productive ways to conduct research and testing in the field of materials science is through the use of metallographic study. Technological boon in the field of metallographic study, opens new gateway for materials characterization through image processing technologies. Image segmentation, edge detection, and roughly estimating grain size are the three main goals of metallographic image processing. The objective of this paper was to determine the grain size of EN9 steel by applying different clustering techniques to the image textured data, collected from EN9 steel metallographic specimens in normalized and annealed condition. In order to determine the average grain size in EN9 steel specimens when seen with a metallurgical microscope, this article blends the ideas of image processing with various hierarchical clustering methodologies to study material characteristics.
Bibliography:ECSA-100082.R1
ISSN:2754-2734
2754-2734
DOI:10.1149/2754-2734/acbb1a