VISION-BASED APPROACH TO END MILL TOOL MONITORING

The paper is concerned about the system of automatic detecting of wear and damages of the end mill tool by the use of computer vision. By using the algorithms developed for image segmentation and by an innovative approach to extraction of features describing individual end mill tool tooth the information representing significant features of the individual tool tooth is effectively gained from the captured image. The proposed approach to feature extraction is robust and independent of the scale and rotation of the end mill tool in the image. The features vectors have been classified by two approaches, i.e., k-nearest neighbour algorithm and artificial neural network. Both approaches have been tested by the test base of tool images and the results mutually compared. The classification has been validated by 10-fold cross-validation method. The best precision of classification (92.63 %) has been reached by the use of artificial neural network. Simulation results have confirmed that the proposed approach can improve monitoring of tool wear and damages and, consequently, the effectiveness and reliability of CNC milling machine tool. (Received in August 2014, accepted in May 2015. This paper was with the authors 1 month for 1 revision.)

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