Optimisation of Shutter Speed in Machine Vision Technique for Monitoring Grinding Wheel Loading

Abstract:

Grinding is an abrasive machining process which controls the surface finish of the materials in production of industrial components at the end stage. During the grinding process, wheel gets loaded with swarf and this loading will affect the cutting ability of the wheel and also the surface finish of the job. Hence the grinding wheel is to be dressed at appropriate time intervals and for necessary time duration. At present, the monitoring of grinding wheel loading and dressing is done by the operator i.e., the frequency of dressing and duration of dressing is determined by the operator based on his experience. Hence there arises a need to develop a systematic approach to this problem which can monitor the process and if not accurately should at least produce a realistic estimate of the percentage of wheel loading, frequency of dressing and duration of dressing. In machine vision system, Shutter speed is an important factor which will affect the image quality. Hence, in this work, attempts are made to monitor wheel loading using a low cost-high speed camera with variable shutter speed and the shutter speed is optimized for the better image quality.

• Periodical: Applied Mechanics and Materials (Volumes 766-767)
• Pages: 878-883
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.766-767.878
• Online since: June 2015
• Authors: S. Ragavanantham*, S. Sampath Kumar, M.S. Shyam
• Keywords: Global Thresholding, HCHCR Steel, Machine Vision, Shutter Speed, Silicon Carbide (SiC), Wheel Loading

References

[1] Chen Zhang and Jilin Zhang (2013), On-line tool wear measurement for ball-end milling cutter based on machine vision, Computers in Industry 64 (2013) 708–719.

DOI: 10.1016/j.compind.2013.03.010

[2] D C D Ogoamanam, H. Raafat and S. M. Tabown (1993), A Machine Vision system for wear monitoring and breakage detection of single point cutting tool, Computers ind. Engng Vol 26, No 3, pp.575-598(1994).

DOI: 10.1016/0360-8352(94)90052-3

[3] J. Jurkovic, M. Korosec, and J. Kopac (2005), New approach in tool wear measuring technique using CCD vision system, International Journal of Machine Tools &Manufacture 45 (2005) 1023–1030.

DOI: 10.1016/j.ijmachtools.2004.11.030

[4] S. Kurada and C. Bradley (1996), A review of machine vision sensors for tool condition monitoring, Computers in Industry 34 (1997) 55-72.

DOI: 10.1016/s0166-3615(96)00075-9

[5] S. Ragavanantham, S. Sampath Kumar and Vipin Gopan (2013), Condition monitoring of Grinding process through Machine Vision technique, International Conference on Machine Vision and Image Processing (2012).

DOI: 10.1109/mvip.2012.6428789

[6] T. Pfeifer and L. Wiegers (2000), Reliable tool wear monitoring by optimized image and illumination control in machine vision, Measurement 28 (2000) 209-218.

DOI: 10.1016/s0263-2241(00)00014-2