Title: A method for surface wear detection of machined parts based on image processing

Authors: Weilin Zeng; Weizhao Guo; Jiang Qiu; Hong Wen

Addresses: School of Mechanical Engineering, Jiangxi Vocational College of Industry and Engineering, Pingxiang, 337000, China ' School of Mechanical Engineering, Jiangxi Vocational College of Industry and Engineering, Pingxiang, 337000, China ' School of Mechanical Engineering, Jiangxi Vocational College of Industry and Engineering, Pingxiang, 337000, China ' School of Mechanical Engineering, Jiangxi Vocational College of Industry and Engineering, Pingxiang, 337000, China

Abstract: In order to reduce the error in the surface wear detection of machined parts, improve the detection accuracy of the failure degree of parts and shorten the detection time, a method of surface wear detection of machined parts based on image processing is designed. Firstly, the wear mechanism is analysed and the mathematical model of wear law is established. Then, the relationship model of surface wear of machined parts is analysed to determine the range of wear data to be detected. Finally, three components are used to determine the grey level image of part surface wear, calculate the pixel value of the maximum grey level image, and then binary processing is carried out. The noise is removed by means of the mean filtering algorithm. Finally, the wear detection of machined parts is realised by calculating the weighted mean. The results show that the wear detection error of the proposed method is low and has credibility.

Keywords: image processing; machined parts; surface wear; limit value; mean filtering; binarisation.

DOI: 10.1504/IJMTM.2025.144116

International Journal of Manufacturing Technology and Management, 2025 Vol.39 No.1/2, pp.137 - 151

Received: 30 Dec 2022
Accepted: 21 Mar 2023
Published online: 28 Jan 2025 *

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