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Automatic Control and Computer Sciences

Erschienen in:

01.05.2020

Study of Tool Wear Monitoring Using Machine Vision

verfasst von: Ruitao Peng, Haolin Pang, Haojian Jiang, Yunbo Hu

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 3/2020

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Abstract

In order to improve tool utilization and reduce tool costs in milling processing, this paper presented a new approach to monitor tool wear status and replace tool in time by machine vision technology. A tool wear monitoring system was established. The wear images of the tool were obtained by a charge coupled device (CCD) camera, and the wear boundaries were established by image preprocessing, threshold segmentation and edge detection based on Canny operator and sub-pixel, then wear value of the tool was extracted. Milling experiments of GH4169 nickel-based superalloy were carried out. The wear values detected by the monitoring system were compared with that obtained by ultra-depth microscope. The results showed that the wear monitoring system had high detection accuracy and enabled on-machine monitoring of tool wear during milling process.

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Titel: Study of Tool Wear Monitoring Using Machine Vision
verfasst von: Ruitao Peng
Haolin Pang
Haojian Jiang
Yunbo Hu
Publikationsdatum: 01.05.2020
Verlag: Pleiades Publishing
Erschienen in: Automatic Control and Computer Sciences / Ausgabe 3/2020
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411620030062

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