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 testing in field materials science is through use metallographic study. This technological boon study opens new gateways for characterization image processing technologies. Image segmentation, edge detection, roughly estimating grain size are three main goals processing. The objective this was determine EN9 steel by applying different clustering techniques textured data, collected from specimens normalized annealed condition. In order average when seen with a metallurgical microscope, we blend ideas various hierarchical methodologies material characteristics.