Utilizing Machine Learning for Metallographic Compression Algorithm in Power Grid Metal Materials Research.

With the advancement of science and technology, metallographic research on metal materials has made remarkable progress. In this paper, the authors processed metallographic images using machine learning algorithms to classify the different elements in the images. The classification results divide the elements into three categories, which are metal elements, non-metal elements and metal-nonmetal combination elements. On the basis of this research, a metallographic compression algorithm based on adaptive threshold is also explored, and grayscale experiments are carried out on carbon steel samples. The recognition rate is 98.3%, and the compression rate is 1.06. Experimental results verify the effectiveness of the algorithm in processing metallographic compressed images of metal materials in power grids. [ABSTRACT FROM AUTHOR]