Using the Monte Carlo simulation methods in gauge repeatability and reproducibility of measurement system analysis
Open accessAbstract
Measurements are required to maintain the consistent quality of all finished and semi-finished products in a productionline. Many firms in the automobile and general precision industries apply the TS 16949:2009 Technical Specificationsand Measurement System Analysis (MSA) manual to establish measurement systems. This work is undertaken toevaluate gauge repeatability and reproducibility (GR&R) to verify the measuring ability and quality of the measurementframe, as well as to continuously improve and maintain the verification process. Nevertheless, the implementation ofGR&R requires considerable time and manpower, and is likely to affect production adversely. In addition, the evaluationvalue for GR&R is always different owing to the sum of man-made and machine-made variations. Using a Monte Carlosimulation and the prediction of the repeatability and reproducibility of the measurement system analysis, this study aimsto determine the distribution of %GR&R and the related number of distinct categories (ndc). This study uses two casestudies of an automobile parts manufacturer and the combination of a Monte Carlo simulation, statistical bases, and theprediction of the repeatability and reproducibility of the measurement system analysis to determine the probability densityfunction, the distribution of %GR&R, and the related number of distinct categories (ndc). The method used in this studycould evaluate effectively the possible range of the GR&R of the measurement capability, in order to establish aprediction model for the evaluation of the measurement capacity of a measurement system.
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Computer-generated random numbers evenly distributed in [0, 1] can simulate possible reliability, tolerance, and confidence interval through the input of a simulated probability distribution function.
“Yeh and Sun [12] mention that computer-generated random numbers evenly distributed in [0, 1] can simulate the possible reliability, tolerance, and CI through the input of a simulated probability distribution function, Figure 2.”
From Application of Monte Carlo Simulation to Study the Probability of Confidence Level under the PFMEA’s Action Priority (2022) · cites this paper as [12]
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Cited by
Sorted by citations
Sun, J.-J., Yeh, T.-M., & Pai, F.-Y. (2022) · Mathematics · doi:10.3390/math10152596 · Cited by 10 · see citing works
Self-citation
- Authors
- Yeh, T.-M., & Sun, J.-J.
- Affiliations
- Dayeh University (TW)
- ORCID
- Yeh, Tsu-Ming
- Venue
- Journal of Applied Research and Technology
- Published
- 2013-10
- Volume
- 11
- Issue
- 5
- Pages
- 780-796
- ISSN
- 1665-6423 (ISSN-L)
- OpenAlex
- W2114862436
- Cited by
- 39
- References
- 30
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 5 · 2021: 6 · 2020: 4 · 2019: 3 · 2018: 3 · 2017: 3 · 2016: 1 · 2015: 3
- FWCI
- 1.47
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Journal of Applied Research and Technology (publishedVersion, cc-by-nc-sa)
- Redalyc (Universidad Autónoma del Estado de México) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Advanced Statistical Process Monitoring
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Scientific Measurement and Uncertainty Evaluation
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
- Keywords
- gauge repeatability and reproducibility, measurement system analysis, Monte Carlo simulation, measurement capability, automotive parts manufacturing, probability density function, prediction model
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