DOI QR코드

DOI QR Code

A Study on Quality Improvement by Evaluation and Application of GUM-based Measurement Uncertainty

GUM 기반 측정불확도의 평가 및 적용에 의한 품질개선

  • Insoo Choi (Department of Industrial and Management Engineering, Hanyang University) ;
  • Sun Hur (Department of Industrial and Management Engineering, Hanyang University)
  • 최인수 (한양대학교 산업경영공학과) ;
  • 허선 (한양대학교 산업경영공학과)
  • Received : 2023.08.06
  • Accepted : 2023.09.05
  • Published : 2023.09.30

Abstract

Purpose: Measurement results obtained under non-ideal measurement environment conditions may contain uncertain factors. As a result, the reliability of measurement results may be deteriorated. In this study, we tried to find ways to improve quality by evaluating and applying measurement uncertainty based on GUM. Methods: In the flatness measurement of semiconductor parts, uncertainty factors that could occur under actual environmental conditions of workers were derived, and measurement uncertainties were calculated, and methods for minimizing the main factors affecting the measurement results were analyzed. Results: Depending on the part and the coordinate measuring machine, it was shown that the effect of dispersion caused by repeated measurements as type A uncertainty and the effect of the calibration results of equipment as type B uncertainty have the main influence. Conclusion: Depending on the uncertainty factors of type A and type B and the influence of the total expanded uncertainty, the central value and confidence interval of the initial measurement results showed fluctuations. It is considered that analysis and measures for the main uncertainty factors are needed as quality improvement in the industrial field.

Keywords

References

  1. C. Hubsch and K. Luders. 2020. Benefits and Limitations of Measurement Uncertainty Evaluation in Industrial Laboratories. Accreditation and Quality Assurance 25(6):547-554. 
  2. David, F. 2014. Good Practice Guide No. 41 CMM Measurement Strategies. NPL 49-73. 
  3. Erik, B., Daniel, R., and Chad, S. 2018. Artificial Intelligence and the Modern Productivity Paradox. National Bureau of Economic Research 23-51. 
  4. H. Huang. 2022. Practitioner's Perspective on the GUM Revision, Part I: Two Key Problems and Solutions. The International Journal of Metrology 26-37. 
  5. Hamed, T. 2020. Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research. International Journal of Academic Research in Management 5(3):28-36. 
  6. ISO/IEC GUIDE 98-3:2008 Uncertainty of Measurement - Part 3: Guide to the Expression of Uncertainty in Meas urement (GUM:1995):4-25. 
  7. Jailton, C. D. and Paulo, R. G. Couto. 2018. Methods for Evaluation of Measurement Uncertainty. Metrology 9-26. 
  8. Kim, M., Kim, J. M., Yang, S. Ho., and Sun, T.B. 2017. A Study on Measurement Uncertainty of Insensitive Munitions Tests. Journal of Korea Society for Quality Management 45(3):553-546. 
  9. KS B ISO14253-2 Geometrical Product Specifications (GPS) - Inspection by Measurement of Workpieces and Measuring Equipment - Part 2: Guide to the Estimation of Uncertainty in GPS Measurement, in Calibration of Measuring Equipment and in Product Verification:15-22. 
  10. KS Q ISO/IEC 17025. KOLAS-G-005. Conformity Assessment - General Requirements for the Competence of Testing and Calibration Laboratories. 2021. 
  11. Lee, S. H. and Lim, K. 2016. Some Relationships between Measurement Capability Indices of Type 1 Study, Gage R&R Study, and ISO 22514-7. Journal of Korea Society for Quality Management 41(4):77-94.  https://doi.org/10.7469/JKSQM.2016.44.1.077
  12. Lee, S. H. and Lim, K. 2019. A Statistical Program for Measurement Process Capability Analysis Based on KS Q ISO 22514-7 Using R. Journal of Korea Society for Quality Management 47(4):713-723. 
  13. M. Mendoza and Velasco. S. 2018. Advantages and Disadvantages of Measurement Uncertainty Evaluation in Chemical Analysis. Measurement 116:474-481. 
  14. P. Aghion, B. Jones, and C. Jones. 2018. Artificial Intelligence and Economic Growth. National Bureau of Economic Research of Economic Research:237-282. 
  15. R. Pendrill. 2014. Using Measurement Uncertainty in Decision-making and Conformity Assessment. Metrologia 51:S206-S218.  https://doi.org/10.1088/0026-1394/51/4/S206
  16. R. Shashidhar and N. Choudhary. 2019. Thickness dependent studies of hetero-junction solar cell synthesized on quartz substrate by spray pyrolysis technique. Indian Journal of Pure & Applied Physics 58:36-43. 
  17. Robert H. S., Martin P., E. Morse, Wolfgang K., Maurizio G., F. Hartig, G. Goch, Ben H., A. Forbes, and WT Estler. 2016. Advances in Large-Scale Metrology - Review and Future Trends. Manufacturing Technology 65(2):643-665.  https://doi.org/10.1016/j.cirp.2016.05.002
  18. W. Gao, H. Haitjema, F.Z. Fang, R.K. Leach, C.F. Cheung, E. Savio, and J.M. Linares. 2019, On-machine and In-process Surface Metrology for Precision Manufacturing. Manufacturing Technology 68(2):843-866. https://doi.org/10.1016/j.cirp.2019.05.005