Passivation classification of a continuous polishing machine disk based on the Adaboost algorithm
LI Zelin, LIU Chengying
Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipment and Control, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Abstract：This paper describes a classification method based on a gray-level co-occurrence matrix (GLCM) and the Adaboost algorithm for monitoring passivation of a continuously polishing machine disk. The texture image features of a continuously polishing machine disk are derived from a GLCM operator of a disk photograph. Four second-order statistics of the GLCM are input into a Adaboost classifier for training so that the classifier can then identify if the disk has been passivated. Tests show that the classification accuracy is best when the GLCM point-to-point distance is 11 and the GLCM gray level is 16. The image classification accuracy is 98.3%, which is 9.5% higher than that of the LBP algorithm and 2.08% higher than that of the PNN algorithm.
李泽林, 刘成颖. 基于Adaboost算法的环抛机盘面钝化程度分类[J]. 清华大学学报（自然科学版）, 2021, 61(9): 986-993.
LI Zelin, LIU Chengying. Passivation classification of a continuous polishing machine disk based on the Adaboost algorithm. Journal of Tsinghua University(Science and Technology), 2021, 61(9): 986-993.
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2021, Vol. 61 
