{"title":"Optimizing Machine Vision System Setup Accuracy by Six-Sigma DMAIC Approach","authors":"Joseph C. Chen","volume":126,"journal":"International Journal of Industrial and Manufacturing Engineering","pagesStart":1175,"pagesEnd":1185,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10007194","abstract":"
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.<\/p>\r\n","references":"[1]\tKumar R., Kulashekar P., Dhanasekar B., Ramamoorthy B. \u201cApplication of digital image magnification for surface roughness evaluation using machine vision.\u201d International Journal of Machine Tools & Manufacture, 2005, 45: pp. 228-234.\r\n[2]\tRao A. R., \u201cFuture directions in industrial machine vision: a case study of semiconductor manufacturing applications\u201d. Image and Vision Computing, 1996, 14: pp. 3-19.\r\n[3]\tJones R. E., Machine vision application. Mechatronics 1(4): 439-446, 1991.\r\n[4]\tDerganc J., Likar B., Pernus F., \u201cA machine vision system for measuring the eccentricity of bearings.\u201d Computers in Industry, 2003, 50: pp. 103-111.\r\n[5]\tDhanasekar B., Ramamoorthy B., \u201cRestoration of blurred images for surface roughness evaluation using machine vision.\u201d Tribology International, 2010, 43: pp. 268-276.\r\n[6]\tJarvis J., \u201cResearch directions in industrial machine vision: a workshop summary.\u201d Computer, 1982, 15(12): pp. 55-61.\r\n[7]\tSun T. H., Tseng C. C., Chen M. S., \u201cElectric contacts inspection using machine vision.\u201d Image and Vision Computing, 2010, 28: pp. 890-901.\r\n[8]\tThomas A. D. H., Rodd M. G., Holt J. D., \u201cNeill C.J. Real-time industrial visual inspection: a review.\u201d Real-Time Imaging, 1995, 1: pp. 139-158.\r\n[9]\tPfeifer T., Wiegers L., \u201cReliable tool wear monitoring by optimized image and illumination control in machine vision.\u201d Measurement, 2000, 28: pp. 209-218.\r\n[10]\tGolnabi H., Asadpourr A., \u201cDesign and application of industrial machine vision systems.\u201d Robotics and Computer-Integrated Manufacturing, 2007, 23: pp. 630-637.\r\n[11]\tLahajnar F., Bernard R., Pernus F., Kovacic S., \u201cMachine vision system for inspecting electric plates.\u201d Computers in Industry, 2002, 47: pp. 113-122.\r\n[12]\tSeulin R., Bonnot N., Merienne F., Gorria P., \u201cSimulation process for the design and optimization of a machine vision system for specular surface inspection.\u201d Proceeding SPIE 4576 Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II 129, 2002 doi:10.1117\/12.455250.\r\n[13]\tSahoo A. K., Tiwari M. K., Mileham A. R., \u201cSix sigma based approach to optimize radial forging operation variables.\u201d Journal of materials processing technology, 2008, 202: pp. 125-126.\r\n[14]\tYeh D. Y., Cheng C. H., Chi M. L., \u201cA modified two-tuple FLC model for evaluating the performance of SCM: By the Six Sigma DMAIC process.\u201d Applied Soft Computing, 2007, 7: pp. 1027-1034.\r\n[15]\tRohini R., Mallikarjun. J., \u201cSix Sigma: Improving the Quality of Operation Theatre.\u201d Procedia-Social and Behavioral Sciences, 2011, 25: pp.273-280.\r\n[16]\tTaguchi G., Sayed M. E., Hsaing C., \u201cQuality Engineering and Quality Systems.\u201d McGraw-Hill, NY, 1989.\r\n[17]\tMaghsoodloo S., Ozdemir G., Jordan V., Huang C. H., \u201cStrengths and Limitations of Taguchi\u2019s Contributions to Quality, Manufacturing, and Process Engineering.\u201d Journal of Manufacturing Systems, 2004, 23(2): pp. 73-126.\r\n[18]\tZhang J. Z., Chen J. C., Kirby E. D., \u201cSurface roughness optimization in an end-milling operation using the Taguchi design method.\u201d Journal of Materials Processing Technology, 2007, 184: pp. 233-239.\r\n[19]\tAntony, J., Knowles, G., Roberts, P., \u201cGauge capability analysis: classical versus ANOVA.\u201d Quality Assurance: Good Practice, Regulation and Law, 1998, 6: pp. 173-181.\r\n[20]\tCheng S. W., Ye K. Q., \u201cGeometric Isomorphism and Minimum Aberration for Factorial Designs with Quantitative Factors.\u201d The Annals of Statistics, 2004, 32 (5): pp. 2168-2185.\r\n[21]\tMoshat S., Datta S., Bandyopadhyay A., Pal P. K., \u201cOptimization of CNC end milling process parameters using PCA-based Taguchi method.\u201d International Journal of Engineering, Science and Technology, 2010, 2(1): pp. 92-102.\r\n[22]\tLentner M. BishoP T., \u201cExperimental design and analysis (2nd edition)\u201d, Blacksburg, VA 24063: Valley Book Company, 1993, ISBN 0-9616255-2-X.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 126, 2017"}