Passivation classification of a continuous polishing machine disk based on the Adaboost algorithm

LI Zelin; LIU Chengying

doi:10.16511/j.cnki.qhdxxb.2020.25.042

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Journal of Tsinghua University(Science and Technology) ›› 2021, Vol. 61 ›› Issue (9) : 986-993. DOI: 10.16511/j.cnki.qhdxxb.2020.25.042

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Passivation classification of a continuous polishing machine disk based on the Adaboost algorithm

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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.

Key words

continuous polishing / passivation / gray-level co-occurrence matrix (GLCM) / Adaboost algorithm

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LI Zelin, LIU Chengying. Passivation classification of a continuous polishing machine disk based on the Adaboost algorithm[J]. Journal of Tsinghua University(Science and Technology). 2021, 61(9): 986-993 https://doi.org/10.16511/j.cnki.qhdxxb.2020.25.042

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