Machine vision monitoring of tool wear

Yoke-San Wong; Wai Keong Yuen; Kim Seng Lee; Colin H. Bradley

doi:10.1117/12.332791

17 December 1998 Machine vision monitoring of tool wear

Yoke-San Wong, Wai Keong Yuen, Kim Seng Lee, Colin H. Bradley

Proceedings Volume 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics; (1998) https://doi.org/10.1117/12.332791
Event: Photonics East (ISAM, VVDC, IEMB), 1998, Boston, MA, United States

Abstract

Automated tool condition monitoring is an enabling technology in the push to develop fully unmanned machining centers. If this goal can be achieved across a broad range of machine tools, then researchers have assisted industry in moving one step closer to attaining truly flexible manufacturing work cells. Recent advances in the field of image processing technology have led to experimentation with machine vision as a potential means of directly evaluating tool condition. In this work, a machine vision system is employed that permits direct milling inset wear measurement to be accomplished in-cycle. The system is characterized by measurement flexibility, good spatial resolution and high accuracy. The flank wear monitoring system consists of an illumination source, CCD camera and high-resolution microscope lens. the extent of flank wear on the milling inserts was measured using the vision system and an image- processing algorithm. Two vision-based parameters were developed and their efficacy in directly quantifying inset flank were was compared with measurements on a traditional toolmaker's microscope.

Citation Download Citation

Yoke-San Wong, Wai Keong Yuen, Kim Seng Lee, and Colin H. Bradley "Machine vision monitoring of tool wear", Proc. SPIE 3518, Sensors and Controls for Intelligent Machining, Agile Manufacturing, and Mechatronics, (17 December 1998); https://doi.org/10.1117/12.332791

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