An adaptive method of measuring the rake face wear of end mills based on image feature point set registration
An adaptive method of measuring the rake face wear of end mills based on image feature point set registration
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DOI：
10.1016/j.jmapro.2023.05.027
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发表时间：
2023-07
影响因子：
6.2
通讯作者：
Jianming Dou;Haiyan Dong;Jilin Zhang;Jiadong Meng;Yaping Tian;M. Pang;Wencui Luo;Chuangwen Xu
中科院分区：
文献类型：
--
作者：
Jianming Dou;Haiyan Dong;Jilin Zhang;Jiadong Meng;Yaping Tian;M. Pang;Wencui Luo;Chuangwen Xu
An acceptable tool wear evaluation and acquisition approach is critical for developing an accurate, rapid, and practical online tool wear prediction system. The one-dimensional crater wear depth is difficult to quantify, and relying solely on flank wear width is very simplistic. External conditions such as vibration and illumination have a large impact on two-dimensional wear area and three-dimensional volume. In practice, these are difficult to successfully characterize tool wear. This work proposes a two-dimensional rake face edge loss area as an additional indicator to assess milling cutter wear. An adaptive measurement approach based on 2D image feature point set matching is presented to suppress the area calculation error caused by image position offset and chips or built-up edges. 201 images were collected in two groups of cutting experiments for comparison with the proposed method and the other two methods, manual registration and non-registration. The results show that the proposed method's measurement values are closer to the actual values and have a higher measurement efficiency. Furthermore, the proposed indicator is sensitive to micro-scale changes to the blade edge, especially at the tip, and can be used as a supplement one to monitor tool wear more comprehensively.
