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Fuzzy Measurement System Analysis Approach: A Case Study

Year 2022, Volume 14, Issue 1, 176 - 185, 31.01.2022
https://doi.org/10.29137/umagd.986483

Abstract

In quality control, gathering relevant and timely data is essential to monitor and determine process variation. Since process data are obtained through measuring instruments that contain uncertainties, an ideal measurement system that has a statistical characteristic of zero error does not exist. Measurement System Analysis (MSA), one of the requirements of ISO/TS 16949, is an experimental and mathematical method of determining the variation arising from measurement systems rather than from a process or product. MSA is used to minimize the risk of wrong decisions regarding process control. Recently, the fuzzy approach has been utilized to cope with the vagueness of the obtained data in MSA studies. This paper analyzes the use of Fuzzy MSA in a company that manufactures automotive parts.

References

  • Bokov, V.B. (2011). Pneumatic gauge steady-state modelling by theoretical and empirical methods, Journal of Measurement, Volume 44, Issue 2, 303-311.
  • Darestani, S.A., Ghane, N., Ismail, Y. and Tadi, A.M. (2019). Developing Fuzzy Tool Capability Measurement System Analysis, Journal of Optimization in Industrial Engineering, Vol.14, 79-92.
  • Faraz, A. and Bameni Moghadam, M. (2007). Fuzzy Control Chart: A Better Alternative for Shewhart Average Chart. Quality and Quantity, 41, 375–385.
  • Ford, General Motors and Chrysler, (1995), Measurement System Analysis; Reference Manual, 126p.
  • Ford, (2002), Automotive Industry Action Group (AIAG), Measurement Systems Analysis Reference Manual. 3rd ed., Chrysler, Ford, General Motors Supplier Quality Requirements Task Force.
  • Golbay, M. and Kahraman, C.(2006), An Alternative Approach to Fuzzy Control Charts: Direct Fuzzy Approach. Information sciences, 77, 6, 1463-1480.
  • Göndör, V., & Koczor, Z. (2010), Improvement of the Measurement System Analysis Using Experimental Design. Obuda University E-Bulletin, 1(1), 35–42.
  • Hajipour, V., Kazemi, A. and Mausavi, S.M. (2013), A fuzzy expert system to increase accuracy and precision in measurement system analysis, Journal of Measurement, Volume 46, 8, 2270-2780.
  • Hong, D.H. (2004), A Note on Cpk Index Estimation Using Fuzzy Numbers, European Journal of Operational Research, 158, 529–532.
  • Kazemi, A., Haleh, H., Hajipour, V. and Rahmati, S.H.A. (2010), Developing a Method for Increasing Accuracy and Precision in Measurement System Analysis: A Fuzzy Approach, Journal of Industrial Engineering 6, 25-32.
  • Kahraman, C. and Kaya, I., (2008) Depreciation and income tax considerations under fuzziness. Fuzzy Engineering Economics with the application, 233, 159-171.
  • Koprivica, S.M. and Filipovic, J. (2018) Application of Traditional and Fuzzy Quality Function Deployment in the Product Development Process, Engineering Management Journal, Vol. 30, Pages 98-107.
  • Lee, H.T. (2001), Cpk Index Estimation Using Fuzzy Numbers. European Journal of Operational Research, 129, 683-688. Montgomery, D.C. (2009), Statistical Quality Control: A Modern Introduction, sixth ed., Wiley, New York.
  • Montgomery, D.C. and Runger, G.C. (1993), Gauge Capability and Designed Experiments. Part I: Basic methods. Quality Engineering, 6, 115-135.
  • Nasiri, M. and Darestani, S.A. (2016), International Journal of Productivity and Quality Management, 2016 Vol.18 No.4, pp.474 – 498.
  • Parchami, A. and Mashinchi, M. (2007), Fuzzy Estimation for Process Capability Indices. Information Sciences, 177, 1452–1462.
  • Parchami, A., Mashinchi, M., Yavari, A.R. and Maleki, H.R. (2005), Process Capability Indices as Fuzzy Numbers. Austrian Journal of Statistics, 34, 4, 391–402.
  • Smith, R.R., McCrary, S.W. and Callahan, R.N. (2007), Gauge Repeatability and Reproducibility Studies and Measurement System Analysis: A Multi-Method Exploration of the State of Practice. Journal of Quality Technology, 23, 1, 1-11.
  • Wang, Y., Zhang, D., Zhang, S., Wang, H. and Cong, H. (2018), VLSI Circuit Measurement System Analysis Based on Random Fuzzy Variables, 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2373-6844.
  • Yeh, T.M. and Sun, J. (2013). Using the Monte Carlo Simulation Methods in Gauge Repeatability and Reproducibility of Measurement System Analysis, Journal of Applied Research and Technology, 11(5). https://doi.org/10.1016/S1665-6423(13)71585-2.
  • Yeh, T.-M., Pai, F.Y. and Huang, C.-W. (2015) Using Fuzzy Theory in %GR&R and NDC of Measurement System Analysis, Engineering, 7, 161-176.
  • Zadeh, L.A. (1965). Fuzzy sets, Journal of Information and Control, Volume 8, Issue 3, 338-353.

Year 2022, Volume 14, Issue 1, 176 - 185, 31.01.2022
https://doi.org/10.29137/umagd.986483

Abstract

References

  • Bokov, V.B. (2011). Pneumatic gauge steady-state modelling by theoretical and empirical methods, Journal of Measurement, Volume 44, Issue 2, 303-311.
  • Darestani, S.A., Ghane, N., Ismail, Y. and Tadi, A.M. (2019). Developing Fuzzy Tool Capability Measurement System Analysis, Journal of Optimization in Industrial Engineering, Vol.14, 79-92.
  • Faraz, A. and Bameni Moghadam, M. (2007). Fuzzy Control Chart: A Better Alternative for Shewhart Average Chart. Quality and Quantity, 41, 375–385.
  • Ford, General Motors and Chrysler, (1995), Measurement System Analysis; Reference Manual, 126p.
  • Ford, (2002), Automotive Industry Action Group (AIAG), Measurement Systems Analysis Reference Manual. 3rd ed., Chrysler, Ford, General Motors Supplier Quality Requirements Task Force.
  • Golbay, M. and Kahraman, C.(2006), An Alternative Approach to Fuzzy Control Charts: Direct Fuzzy Approach. Information sciences, 77, 6, 1463-1480.
  • Göndör, V., & Koczor, Z. (2010), Improvement of the Measurement System Analysis Using Experimental Design. Obuda University E-Bulletin, 1(1), 35–42.
  • Hajipour, V., Kazemi, A. and Mausavi, S.M. (2013), A fuzzy expert system to increase accuracy and precision in measurement system analysis, Journal of Measurement, Volume 46, 8, 2270-2780.
  • Hong, D.H. (2004), A Note on Cpk Index Estimation Using Fuzzy Numbers, European Journal of Operational Research, 158, 529–532.
  • Kazemi, A., Haleh, H., Hajipour, V. and Rahmati, S.H.A. (2010), Developing a Method for Increasing Accuracy and Precision in Measurement System Analysis: A Fuzzy Approach, Journal of Industrial Engineering 6, 25-32.
  • Kahraman, C. and Kaya, I., (2008) Depreciation and income tax considerations under fuzziness. Fuzzy Engineering Economics with the application, 233, 159-171.
  • Koprivica, S.M. and Filipovic, J. (2018) Application of Traditional and Fuzzy Quality Function Deployment in the Product Development Process, Engineering Management Journal, Vol. 30, Pages 98-107.
  • Lee, H.T. (2001), Cpk Index Estimation Using Fuzzy Numbers. European Journal of Operational Research, 129, 683-688. Montgomery, D.C. (2009), Statistical Quality Control: A Modern Introduction, sixth ed., Wiley, New York.
  • Montgomery, D.C. and Runger, G.C. (1993), Gauge Capability and Designed Experiments. Part I: Basic methods. Quality Engineering, 6, 115-135.
  • Nasiri, M. and Darestani, S.A. (2016), International Journal of Productivity and Quality Management, 2016 Vol.18 No.4, pp.474 – 498.
  • Parchami, A. and Mashinchi, M. (2007), Fuzzy Estimation for Process Capability Indices. Information Sciences, 177, 1452–1462.
  • Parchami, A., Mashinchi, M., Yavari, A.R. and Maleki, H.R. (2005), Process Capability Indices as Fuzzy Numbers. Austrian Journal of Statistics, 34, 4, 391–402.
  • Smith, R.R., McCrary, S.W. and Callahan, R.N. (2007), Gauge Repeatability and Reproducibility Studies and Measurement System Analysis: A Multi-Method Exploration of the State of Practice. Journal of Quality Technology, 23, 1, 1-11.
  • Wang, Y., Zhang, D., Zhang, S., Wang, H. and Cong, H. (2018), VLSI Circuit Measurement System Analysis Based on Random Fuzzy Variables, 2018 Eighth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), 2373-6844.
  • Yeh, T.M. and Sun, J. (2013). Using the Monte Carlo Simulation Methods in Gauge Repeatability and Reproducibility of Measurement System Analysis, Journal of Applied Research and Technology, 11(5). https://doi.org/10.1016/S1665-6423(13)71585-2.
  • Yeh, T.-M., Pai, F.Y. and Huang, C.-W. (2015) Using Fuzzy Theory in %GR&R and NDC of Measurement System Analysis, Engineering, 7, 161-176.
  • Zadeh, L.A. (1965). Fuzzy sets, Journal of Information and Control, Volume 8, Issue 3, 338-353.

Details

Primary Language English
Subjects Industrial Engineering
Journal Section Articles
Authors

Eda BEYLİHAN (Primary Author)
Ondokuz Mayıs Üniversitesi
0000-0002-6163-1637
Türkiye


Sermin ELEVLİ
ONDOKUZ MAYIS ÜNİVERSİTESİ
0000-0002-7712-5536
Türkiye

Publication Date January 31, 2022
Published in Issue Year 2022, Volume 14, Issue 1

Cite

APA Beylihan, E. & Elevli, S. (2022). Fuzzy Measurement System Analysis Approach: A Case Study . International Journal of Engineering Research and Development , 14 (1) , 176-185 . DOI: 10.29137/umagd.986483

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