Automatic Grain Size Detection and Classification of Metals

Abstract

Metallography is the study of the microstructure of metallic alloys. The integrity of metals in vital in the case of any manufacturing industry as the durability is dependent on quality of metal. Microscopic analysis is essential part of modern manufacturing process. Micro-structural Inclusions measurement is routinely required process for maintaining quality. The most important physical properties of particulate samples are particle size and grain size. The diameter of individual grains of sediments is called as grain size. Gran size determination is actually depends upon the manual process which leading to time-consuming and errors are occur at the time of determination. The main objective to propose this system is to automatically analyze the material according to their size/shape and grain sizes. Fuzzy logic model is a classifier used to detect edges and corners of grains available in the input image. The system categories the metal using its grain size. The obtained results are compared with ASTM standards and other reported results in survey. The proposed system is useful in metal industry which are depends upon size and shape of the grains.