Online Measurement of Machining Tool Wear Based on Machine Vision

doi:10.16183/j.cnki.jsjtu.2020.083

Abstract

Abstract:

In order to solve the problems of tool wear measurement in actual production, such as manual operation and shutdown detection, a machining tool wear measurement system based on machine vision is developed in this paper. First, the Otsu segmentation algorithm based on Laplacian edge information is proposed to binarize the images. Then, through rough positioning by morphology-based Canny operator edge detection and image registration, the tool wear area is extracted effectively. Finally, sub-pixel edge detection based on Zernike moment is used to improve the measurement accuracy while the principal curve method is used to fit sub-pixel edge points so as to obtain the smooth edge curve and realize the online measurement of tool wear. In real machining process, the tool wear test results show that the system has a high degree of automation and a rapid running speed. Moreover, its measurement accuracy can reach micron level. This system can be effectively applied to real-time monitoring of tool wear in industry.

Key words: machining tool, wear measurement, machine vision, image segmentation, edge detection

CLC Number:

TG71
TP391.4

ZHOU Junjie, YU Jianbo. Online Measurement of Machining Tool Wear Based on Machine Vision[J]. Journal of Shanghai Jiao Tong University, 2021, 55(6): 741-749.

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方法	b₀/μm	ε₀/%
Otsu分割算法	471.32	8.65
基于Laplacian算子边缘信息的Otsu分割算法	545.74	5.77
人工测量	515.97	0

边缘检测方法	t_s/s
拟合法亚像素边缘检测	1.722
插值法亚像素边缘检测	1.241
Zernike矩法亚像素边缘检测	0.818

边缘检测方法	b_E/μm	ε_E/%
拟合法亚像素边缘检测	396.90	23.08
插值法亚像素边缘检测	542.56	5.15
Zernike矩法亚像素边缘检测	545.74	5.77
Canny算子边缘检测	555.66	7.69
人工测量	515.97	0

刀具图示	b₁/μm	b₂/μm	ε/%
①	0	0	0
②	396.90	385.99	2.75
③	396.90	406.82	2.50
④	436.59	447.11	2.41
⑤	476.28	505.06	6.04
⑥	515.97	545.74	5.77

刀具磨损测量各步骤	t_p/s
图像预处理	0.271
图像二值化及边缘初定位	0.470
亚像素边缘精定位及边缘拟合	0.818
磨损量计算	0.079
合计	1.638