Abstract
The development of the tool wear monitoring system by machining processes has been well recognized in the machine industry mainly due to the growing demand for product quality and improved productivity. For this, artificial vision systems have been used as a measurement tool in various application areas. Thus, the objective was to develop a system automatic based in the image processing to identify and measure flank wear in machining tool. Image processing techniques, discriminant function, to identify insert breakage, and Hough Transform, to find the flank wear profile, achieved over accuracy. Consequently, 94.3% to identify between worn and broken insert and 0.04 mm mistake in measurement of flank wear width compared with microscope.
Highlights
A criterion widely used to replace cutting tools is the measurement of the maximum flank wear
The development of the tool wear monitoring system by machining processes has been well recognized in the machine industry mainly due to the growing demand for product quality and improved productivity
Artificial vision systems have been used as a measurement tool in various application areas
Summary
A criterion widely used to replace cutting tools is the measurement of the maximum flank wear. Bhushan 2013 used tool wear in turning operation as a set of the machining parameters to satisfy the objectives of the minimum flank and crater wear, the maximum metal removal rate. The regression models developed for the minimum tool wear and the maximum metal removal were used for finding the multi response optimization solutions. The multi-objective optimization resulted in a cutting speed of 210 m/min, a feed of 0.16 mm/rev, a depth of cut of 0.42 mm, and a nose radius of 0.40 mm. These machining conditions are expected to respond with the minimum tool wear and maximum metal removal. The aim is to develop an automatic system based on image processing to identify and measure tool wear
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