Classification of Copper Alloys Microstructure using Image Processing and Neural Network

The most important aspect of any engineering material is its structure. The methods used to accurately determine the material microstructures is a very time-consuming process, causes operator fatigue, and it is prone to human errors and inconsistency. There are two computational approaches, a feature features and a neural network algorithm, are used separately for classifying and detection of surface textures in the field of remote sensing, science, medicine, journalism, advertising, design, education and entertainment. In this paper, a combination of the two approaches has been utilized to classify and to detect copper and copper alloys microstructure using image process, texture features and neural network. The overall average discrimination rate results from the combined approaches are about 97.6%. This paper offers a reliable basis for the classification and characterization of microscopic images by image processing and neural network. [Ossama B. Abouelatta. Classification of Copper Alloys Microstructure using Image Processing and Neural Network. J Am Sci 2013;9(6):213-223]. (ISSN: 1545-1003). http://www.jofamericanscience.org. 25