International Science Index


Optimizing Machine Vision System Setup Accuracy by Six-Sigma DMAIC Approach


Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use.

[1] Kumar R., Kulashekar P., Dhanasekar B., Ramamoorthy B. “Application of digital image magnification for surface roughness evaluation using machine vision.” International Journal of Machine Tools & Manufacture, 2005, 45: pp. 228-234.
[2] Rao A. R., “Future directions in industrial machine vision: a case study of semiconductor manufacturing applications”. Image and Vision Computing, 1996, 14: pp. 3-19.
[3] Jones R. E., Machine vision application. Mechatronics 1(4): 439-446, 1991.
[4] Derganc J., Likar B., Pernus F., “A machine vision system for measuring the eccentricity of bearings.” Computers in Industry, 2003, 50: pp. 103-111.
[5] Dhanasekar B., Ramamoorthy B., “Restoration of blurred images for surface roughness evaluation using machine vision.” Tribology International, 2010, 43: pp. 268-276.
[6] Jarvis J., “Research directions in industrial machine vision: a workshop summary.” Computer, 1982, 15(12): pp. 55-61.
[7] Sun T. H., Tseng C. C., Chen M. S., “Electric contacts inspection using machine vision.” Image and Vision Computing, 2010, 28: pp. 890-901.
[8] Thomas A. D. H., Rodd M. G., Holt J. D., “Neill C.J. Real-time industrial visual inspection: a review.” Real-Time Imaging, 1995, 1: pp. 139-158.
[9] Pfeifer T., Wiegers L., “Reliable tool wear monitoring by optimized image and illumination control in machine vision.” Measurement, 2000, 28: pp. 209-218.
[10] Golnabi H., Asadpourr A., “Design and application of industrial machine vision systems.” Robotics and Computer-Integrated Manufacturing, 2007, 23: pp. 630-637.
[11] Lahajnar F., Bernard R., Pernus F., Kovacic S., “Machine vision system for inspecting electric plates.” Computers in Industry, 2002, 47: pp. 113-122.
[12] Seulin R., Bonnot N., Merienne F., Gorria P., “Simulation process for the design and optimization of a machine vision system for specular surface inspection.” Proceeding SPIE 4576 Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II 129, 2002 doi:10.1117/12.455250.
[13] Sahoo A. K., Tiwari M. K., Mileham A. R., “Six sigma based approach to optimize radial forging operation variables.” Journal of materials processing technology, 2008, 202: pp. 125-126.
[14] Yeh D. Y., Cheng C. H., Chi M. L., “A modified two-tuple FLC model for evaluating the performance of SCM: By the Six Sigma DMAIC process.” Applied Soft Computing, 2007, 7: pp. 1027-1034.
[15] Rohini R., Mallikarjun. J., “Six Sigma: Improving the Quality of Operation Theatre.” Procedia-Social and Behavioral Sciences, 2011, 25: pp.273-280.
[16] Taguchi G., Sayed M. E., Hsaing C., “Quality Engineering and Quality Systems.” McGraw-Hill, NY, 1989.
[17] Maghsoodloo S., Ozdemir G., Jordan V., Huang C. H., “Strengths and Limitations of Taguchi’s Contributions to Quality, Manufacturing, and Process Engineering.” Journal of Manufacturing Systems, 2004, 23(2): pp. 73-126.
[18] Zhang J. Z., Chen J. C., Kirby E. D., “Surface roughness optimization in an end-milling operation using the Taguchi design method.” Journal of Materials Processing Technology, 2007, 184: pp. 233-239.
[19] Antony, J., Knowles, G., Roberts, P., “Gauge capability analysis: classical versus ANOVA.” Quality Assurance: Good Practice, Regulation and Law, 1998, 6: pp. 173-181.
[20] Cheng S. W., Ye K. Q., “Geometric Isomorphism and Minimum Aberration for Factorial Designs with Quantitative Factors.” The Annals of Statistics, 2004, 32 (5): pp. 2168-2185.
[21] Moshat S., Datta S., Bandyopadhyay A., Pal P. K., “Optimization of CNC end milling process parameters using PCA-based Taguchi method.” International Journal of Engineering, Science and Technology, 2010, 2(1): pp. 92-102.
[22] Lentner M. BishoP T., “Experimental design and analysis (2nd edition)”, Blacksburg, VA 24063: Valley Book Company, 1993, ISBN 0-9616255-2-X.