Prediction of grain boundary of a composite microstructure using digital image processing: A comparative study

In recent years, due to its balance mechanical and tribological properties, hybrid materials have occupied a dominant position in the automotive industry and aerospace industry. However, in the study of mechanical and tribological properties, it is necessary to perform metallographic characterization of hybrid composites. The discovery of digital image processing technique as a next generation technology for metallographic characterization results in time saving and error free solution. This automation technology includes multiple stages, such as image enhancement. Perform spatial filtering to remove noise, edge detection, and watershed conversion. One of the key factors of digital image processing technique is the application of statistical analysis in edge detection stage. This signify, edge detection stage plays the crucial role in digital image processing technique which provide a good quality microstructure from an unfavorable image. As per our comparative study between the edge operators like Sobel operator, Robert operator, Prewitt operator and Canny operator, we recommend the results of canny edge detectors for watershed transformation to calculate the grain edge to find the average grain size and results of statistical analysis method validate the same. The results also confirmed the formation of distinct edge of grain after watershed transformation. (C) 2019 Elsevier Ltd. All rights reserved.