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

Abstract

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. This technological boon in the field of metallographic study opens new gateways 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 study 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, we blend the ideas of image processing with various hierarchical clustering methodologies to study material characteristics.