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Validating a destructive measurement system using Gauge R&R — a case study

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The research study aims to evaluate the precision of the measurement system using Gauge R&R. An experimental research design adopting a positivist empirical approach with deductive strategy was followed to assess the effectiveness of Crossed Gauge R&R technique for validating a measurement system using destructive testing. Crossed Gauge R&R technique in Minitab was found to be highly effective in quantifying different components of measurement variation relative to process variation. Clue generation from the Crossed Gauge R&R study combined with manufacturing and measurement process know-how helped in identifying and eliminating the root causes for measurement variation. Overall Crossed Gauge R&R proved successful in validating the burst strength test equipment. However, it should be noted that manufacturing and test equipment played an equally important part in developing and executing the gauge R&R study and accurately analysing the results. So, Crossed Gauge R&R should be used as an aid rather than the solution for measurement system validation.
Rocznik
Strony
34--42
Opis fizyczny
Bibliogr. 38 poz., rys., tab.
Twórcy
  • O.P. Jindal Global University, India
  • O.P. Jindal Global University, India
  • O.P. Jindal Global University, India
Bibliografia
  • Automotive Industry Action Group (AIAG). (2002). Measurement System Analysis, Reference Manual, 3rd ed.
  • Barbosa, G.F., Peres, G.F., & Hermosilla, J.L.G. (2014). R&R (repeatability and reproducibility) gage study applied on gaps’ measurements of aircraft assemblies made by a laser technology device. Production Engineering - Research and Development, 8(4), 477-489. doi: 10.1007/s11740-014-0553-z
  • Bhakhri, R., & Belokar, R.M. (2017). Quality Improvement Using GR&R: A Case Study. International Research Journal of Engineering and Technology, 4(6), 3018-3023.
  • Box, G.E.P., Hunter, W.G., & Hunter, S.J. (1978). Statistics for Experimenters. New York, United States: Wiley.
  • Breyfogle, F. W., & Meadows, B. (2001). Bottom-Line Successwith Six Sigma. Milwaukee, United States: Quality Progress, ASQ.
  • Breyfogle, F.W., Cupello, J.M., & Meadows, B. (2001a). Managing Six Sigma: A Practical Guide to Understanding, Assessing, and Implementing the Strategy That Yields Bottom-Line Success. New York, United States: Wiley.
  • Burdick, R.K., Borror, C.M., & Montgomery, D.C. (2003). A review of measurement systems capability analysis. Journal of Quality Technology, 35(4), 342-354.
  • Burdick, R.K., Park, Y.J., & Montgomery, D.C. (2005). Confidence intervals for misclassification rates in a gauge R&R study. Journal of Quality Technology, 37(4), 294-303.
  • Diering, M., Hamrol, A., & Kujawińska, A. (2015). Measurement System Analysis Combined with Shewhart’s Approach. Key Engineering Materials, 637, 7-11.
  • Dolezal, K.K., Burdick, R.K., & Birch, N.J. (1998). Analysis of a two-factor R&R study with fixed operators. Journal of Quality Technology, 30(2), 163-170.
  • Engel, J., & De Vries, B. (1997). Evaluating a well-known criterion for measurement precision. Journal of Quality Technology, 29(4), 469-476.
  • Gorman, D., & Bower, K.M. (2002). Measurement System Analysis And Destructive Testing, Six Sigma Forum Magazine. American Society for Quality, 1(4), 16-19.
  • Han, Y., & He, Z. (2007). An Applied Study of Destructive Measurement System Analysis. Second IEEE Conference on Industrial Electronics and Applications.
  • Hoffa, D.W., & Laux, C.M. (2007). Gauge R&R: An Effective Methodology for Determining the Adequacy of a New Measurement System for Micron-level Metrology. Journal of Industrial Technology, 23(4).
  • Ishikawa, K. (1982). Guide to quality control. White Plains, United States: Quality Resources.
  • Juran, J.M. (1990). Quality control handbook (4th ed.). New York, United States: McGraw Hill.
  • Juran, J.M., & Godfrey, A.B. (1999). Juran;s Quality Control Handbook, 5th ed. New York, United States: McGraw- Hill.
  • Liangxing, S., Wei, C., & Liang, F.L. (2014). An Approach for Simple Linear Profile Gauge R&R Studies. Discrete Dynamics in Nature and Society, 2014, 816980.
  • Mast, D.J., & Trip, R. (2005). Gage R&R studies for destructive measurements. Journal of Quality Technology, 37(1), 40-49.
  • Measurement Systems Analysis (MSA) Work Group. (2010).
  • Messina, W.S. (1987). Statistical Quality Control for Manufacturing Managers, New York, United States: Wiley.
  • Miller, I., & Freund, J. (1965). Probability and Statistics for Engineers. New Jersey, United States: Prentice-Hall, Englewood Cliffs.
  • Minitab. (2002). Minitab Statistical Software. Release 13. Pennsylvania, United States: State College.
  • Montgomery, D.C. (2001). Design and Analysis of Experiments, fifth edition. New York, United States: John Wiley & Sons.
  • Montgomery, D.C. (2013). Introduction to Statistical Quality Control, seventh edition. New York, United States: John Wiley & Sons.
  • Pan, J.H. (2004). Determination of the optimal allocation of parameters for gauge repeatability and reproducibility study. International Journal of Quality and Reliability Management, 21(6), 672-682.
  • Pan, J.H. (2006). Evaluating the gauge repeatability and reproducibility for different industries. Quality and Quantity, 40(4), 499-518.
  • Pande, P., Neuman, R., & Cavanagh, R. (2002). The Six Sigma Way: Team Field Book. New York, United States: McGraw-Hill.
  • Persijn, M., & Nuland, Y.V. (1996). Relation between measurement system capability and process capability. Quality Engineering, 9(1), 95-97.
  • Peruchi, R.S., Balestrassi, P.P., Paiva, A.P., Ferreira, J.R., & Carmelossi, M.D. (2013). A new multivariate gage R&R method for correlated characteristics. International Journal of Production Economics, 144(1), 301-315.
  • Phillips, A.R., Jeffries, R., Schneider, J., & Frankoski, S.P. (1997). Using Repeatability and Reproducibility Studies to Evaluate a Destructive Test Method. Journal of Quality Engineering, 10(2), 283-290.
  • Smith, R.R., McCrary, S.W., & Callahan, R.N. (2007). Gauge repeatability and reproducibility studies and measurement system analysis: A multimethod exploration of the state of practice. Journal of Quality Technology, 23(1), 1-11.
  • Sujova, A., Marcinekova, K., & Simanova, Ľ. (2019). Influence of Modern Process Performance Indicators on Corporate Performance — the Empirical Study. Engineering Management in Production and Services, 11(2), 119-129. doi: 10.2478/emj-2019-0015
  • Tsai, P. (1989). Variable gauge repeatability and reproducibility study using the analysis of variance method. Quality Engineering, 1(1), 107-115.
  • Vardeman, S.B., & Job, J.M. (1999). Statistical quality assurance methods for engineers. New York, United States: John Wiley & Sons, Inc.
  • Wesff, E. (2012). Chinese OEM reduces returns with improved product testing. The Global Voice of Quality, 4(2), 1-6.
  • Wheeler, D.J. (1990). Evaluating the Measurement Process when Testing is Destructive. TAPPI Polymers and Laminations Conference. Boston, United States: TAPPI Press.
  • Zimon, D. (2017). The Influence of Quality Management Systems for Improvement of Logistics Supply in Poland. Oeconomia Copernicana, 8(4), 643-655.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-499e95ee-5f71-4819-aa6c-338d356db8d9
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