A computer vision-based fast approach to drilling tool condition monitoring

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 220, Issue 9, 2006, Pages 1409-1415

  Atli, A Volkan ^a   Urhan, O ^a   Erturk S ^a   Sonmez M ^a  

^a KOCAELI UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

CAMERAS; CHARGE COUPLED DEVICES; COMPUTER VISION; CONDITION MONITORING; EDGE DETECTION; WEAR OF MATERIALS;

DEVIATION FROM LINEARITY (DEFROL); DRILLING TOOLS; TOOL WEARS;

DRILLING EQUIPMENT;

EID: 34247130049     PISSN: 09544054     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544054JEM412     Document Type: Article

References (20)

1. Thasty, J. and Smith, S. Current trends in high-speed machining. J. Mjg Sci. Engng, ASME, 1997, (1119), 664-666.
2. Kim, J.-H., Moon, D.-K., Lee, D.-W., Kim, J.-S., Kang, M.-C., and Kim, K.-H. Tool wear measuring technique on the machine using CCD and exclusive jig. J. Mater. Proc. Technol., 2002, (131), 668-674.
3. Konig, W., Langhammer, K., and Schemmel, H. U. Correlation between cutting force components and tool wear. Ann. CIRP, 1972, 19-20.
4. Lindstrom, B. and Lindberg, B. Measurement of dynamic cutting forces in the cutting process, a new sensor for in-process measurement. In Proceedings of the 24th International Machine Tool Design and Research Conference, 1983, pp. 137-147.
5. Takeyama, H., Doi, Y., Mitsoka, T., and Sekiguchi, H. Sensors of tool life for optimisation of machining. In Proceedings of the 8th International Machine Tool Design and Research Conference, 1967, pp. 191-208.
6. Stoferle, T. H. and Bellmann, B. Continuous measuring of flank wear. In Proceedings of the 16th International Machine Tool Design and Research Conference, 1975, pp. 573-578.
7. Kurada, S. and Bradley, A. A review of machine vision sensors for tool condition monitoring. Computers in Ind., 1997, (34), 55-72.
8. Tsai, D-M., Chen, J-J., and Chen, J-F. A vision system for surface roughness assessment using neural network. Int. J. Advanced Mfg Technol., 1998, (14), 412-422.
9. Mannan, M. A., Kassim, A. A., and Jing, M. Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recognition Lett., 2000, (21), 969-979.
10. Bradley, C. and Wong, Y. S. Surface texture indicators of tool wear - a machine vision approach. Int. J. Advanced Mfg Technol., 2001, (17), 435-443.
11. Mannan, M. A. and Kassim, A. A. Texture analysis using fractals for tool wear monitoring. In Proceedings of IEEE International Conference on Image processing, 2002, 105-108.
12. Sortino, M. Application of statistical filtering for optical detection of tool wear. Int. J. Mach. Tools Mfe, 2003. (43), 493-497.
13. Mannan, M. A., Mian, Z., and Kassim, A. A. Tool wear monitoring using a fast Hough transform of images of machined surfaces. Mach. Vision Applic., 2004, (15), 156-163.
14. Jurkovic, J., Korosec, M., and Kopac, J. New approach in tool wear measuring technique using CCD vision system. Int. J. Mach. Tools Mfe., 2005, (45), 1023-1030.
15. Yang, M.-Y. and Kwon, O.-D. A tool condition recognition system using image processing. Control Engng Practice, 1998, (6), 1389-1395.
16. Lanzetta, M. A new flexible high-resolution vision sensor for tool condition monitoring. J. Mater. Processing Technol., 2002, (119), 73-82.
17. Kerr, D., Pengilley, J., and Garwood, R. Assessment and visualization of machine tool wear using computer vision. Int. J. Advanced Mfg Technol., 2006, (28), 781-791.
18. Wang, W., Wong, Y. S., and Hong, G. S. Flank wear measurement by successive image analysis. Computers in Ind., 2005, (56), 816-830.
19. Reborn, A. G., Jiang, J., and Orban, P. E. State-of-the-art methods and results in tool condition monitoring: a review. Int. J. Advanced Mfg Technol., 2005, (26), 693-710.
20. Canny, J. A computational approach to edge detection. IEEE Trans., on Pattern Analysis and Mach. Intell., 1986, (8), 679-698.