اخبار و اعلانات
آمار
| تعداد نشریات | 31 |
| تعداد شماره‌ها | 699 |
| تعداد مقالات | 6,840 |
| تعداد مشاهده مقاله | 11,125,289 |
| تعداد دریافت فایل اصل مقاله | 7,504,664 |
Statistical testing quality and its Monte Carlo simulation based on fuzzy specification limits | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 2، دوره 19، شماره 3، مرداد و شهریور 2022، صفحه 1-17 اصل مقاله (261.21 K) | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2022.6940 | ||
| نویسندگان | ||
| H. Iranmanesh1؛ A. Parchami* 2؛ B. Sadeghpour Gildeh1 | ||
| 1Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 2Department of Statistics, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran | ||
| چکیده | ||
| This paper presents two approaches for testing quality to make a decision based on the extended process capability indices. Common methods in measuring quality of the manufactured product have widely focused on the precise specification limits, but in this study the lower and upper specification limits are considered as non-precise/fuzzy sets. Based on a general statistical approach using an extended process capability index, the purpose of this study is estimating a critical value to determine whether the process meets the customer requirements. Moreover, a simulation approach to analyze the manufacturing process capability has been suggested for testing quality based on fuzzy specifications by normal data. Meanwhile, this paper discusses how well the Monte Carlo simulation approach can be used for non-normal data. Finally, the real application of the proposed methods is investigated in a real case study. | ||
| کلیدواژه‌ها | ||
| Quality control؛ process capability indices؛ fuzzy specification limits؛ testing hypotheses؛ Monte Carlo simulation | ||
| مراجع | ||
|
[1] Z. Abbasi Ganji, B. Sadeghpour Gildeh, A new fuzzy process capability index for asymmetric tolerance interval, International Journal of Fuzzy System Applications (IJFSA), 6(3) (2017), 74-104.
[2] Z. Abbasi Ganji, B. Sadeghpour Gildeh, Fuzzy process capability indices for simple linear profile, Journal of Applied Statistics, 47(12) (2020), 2136-2158.
[3] M. Abdolshah, R. M. Yusuff, T. S. Hong, M. Y. B. Ismail, A. N. Sadigh, Measuring process capability index Cpmk with fuzzy data and compare it with other fuzzy process capability indices, Expert Systems with Applications, 38(6) (2011), 6452-6457.
[4] M. Arefi, Testing statistical hypotheses under fuzzy data and based on a new signed distance, Iranian Journal of Fuzzy Systems, 15(3) (2018), 153-176.
[5] J. A. Clements, Process capability calculations for non-normal distributions, Quality Progress, 22 (1989), 95-100.
[6] A. C. Cullen, H. C. Frey, C. H. Frey, Probabilistic techniques in exposure assessment, Springer Science and Business Media, New York, 1st edition, 1999.
[7] M. L. Delignette-Muller, C. Dutang, Fitdistrplus: An R package for fitting distributions, Journal of Statistical Software, 64 (2015), 1-34.
[8] D. Grau, Testing capability indices for manufacturing processes with a symmetric tolerance limits and measurement errors, International Journal of Metrology and Quality Engineering, 2(1) (2011), 61-73.
[9] J. M. Juran, Jurans quality control handbook, MacGraw Hill, New York, 3rd edition, 1974.
[10] C. Kahraman, I. Kaya, Fuzzy process capability indices for quality control of irrigation water, Stochastic Environmental Research and Risk Assessment, 23(4) (2009), 451-462.
[11] I. Kaya, C. Kahraman, Fuzzy process capability analyses: An application to teaching processes, Journal of Intelligent and Fuzzy Systems, 19(4,5) (2008), 259-272.
[12] I. Kaya, C. Kahraman, A new perspective on fuzzy process capability indices: Robustness, Expert Systems with Applications, 37(6) (2010), 4593-4600.
[13] I. Kaya, C. Kahraman, Fuzzy robust process capability indices for risk assessment of air pollution, Stochastic Environmental Research and Risk Assessment, 23(4) (2014), 529-541.
[14] M. Kovářík, L. Sarga, Process capability indices for non-normal data, WSEAS Transactions on Business and Economics, 11 (2014), 1-11.
[15] M. T. Moeti, A. Parchami, M. Mashinchi, A note on fuzzy process capability indices, Scientia Iranica, 13(4) (2006), 379-385.
[16] D. C. Montgomery, Introduction to statistical quality control, John Wiley and Sons, New York, 6th edition, 2009.
[17] H. T. Nguyen, C. L. Walker, E. A. Walker, A first course in fuzzy logic, CRC Press, New York, 3rd edition, 2005.
[18] A. Parchami, Fuzzy decision making in testing hypotheses: An introduction to the packages “FPV”, Iranian Journal of Fuzzy Systems, 17(2) (2020), 67-77.
[19] A. Parchami, Fuzzy decision in testing hypotheses by fuzzy data: Two case studies, Iranian Journal of Fuzzy Systems, 17(5) (2020), 127-136.
[20] A. Parchami, H. Iranmanesh, B. Sadeghpour Gildeh, Monte Carlo statistical test for fuzzy quality, Iranian Journal of Fuzzy Systems, 19(1) (2022), 115-124.
[21] A. Parchami, R. Ivani, M. Mashinchi, An application of testing fuzzy hypotheses: A soil study on bioavailability of Cadmium, Scientia Iranica, 18(3) (2011), 470-478.
[22] A. Parchami, M. Mashinchi, Testing the capability of fuzzy processes, Quality Technology and Quantitative Management, 6(2) (2009), 125-136.
[23] A. Parchami, M. Mashinchi, A new generation of process capability indices, Journal of Applied Statistics, 37(1) (2010), 77-89.
[24] A. Parchami, M. Mashinchi, H. R. Maleki, Fuzzy confidence intervals for fuzzy process capability index, Journal of Intelligent and Fuzzy Systems, 17(3) (2006), 287-295.
[25] A. Parchami, M. Mashinchi, V. Partovi Nia, A consistent confidence interval for fuzzy capability index, Applied and Computational Mathematics, 7(1) (2008), 119-125.
[26] A. Parchami, M. Mashinchi, A. Sharayei, An effective approach for measuring the capability of manufacturing processes, Production Planning and Control, 21(3) (2010), 250-257.
[27] A. Parchami, M. Mashinchi, A. R. Yavari, H. R. Maleki, Process capability indices as fuzzy numbers, Austrian Journal of Statistics, 34(4) (2005), 391-402.
[28] A. Parchami, B. Sadeghpour Gildeh, M. Mashinchi, Why fuzzy quality?, International Journal for Quality Research, 10(3) (2016), 457-470.
[29] A. Parchami, B. Sadeghpour Gildeh, M. Nourbakhsh, M. Mashinchi, A new generation of process capability indices based on fuzzy measurements, Journal of Applied Statistics, 41(5) (2014), 1122-1136.
[30] W. L. Pearn, S. Kotz, Application of Clements method for calculating second and third generation process capability indices for non-normal pearsonian populations, Quality Engineering, 7(1) (1994), 139-145.
[31] W. L. Pearn, S. Kotz, Encyclopedia and handbook of process capability indices: A comprehensive exposition of quality control measures, World Scientific, Singapore, 2006.
[32] W. L. Pearn, G. H. Lin, K. S. Chen, Distributional and inferential properties of the process accuracy and process precision indices, Communications in Statistics-Theory and Methods, 27(4) (1998), 985-1000.
[33] B. Sadeghpour Gildeh, Multivariate process capability index based on fuzzy tolerance region, 37th Annual Iranian International Mathematics Conference, (2006), 607-610.
[34] L. Scrucca, qcc: An R package for quality control charting and statistical process control, R News, 4(1) (2004), 11-17.
[35] C. C. Tsai, C. C. Chen, Making decision to evaluate process capability index Cp with fuzzy numbers, The International Journal of Advanced Manufacturing Technology, 30(3) (2006), 334-339.
[36] S. Ugurlu, I. Kaya, A new reliability index based on fuzzy process capability index for travel time in multimodal networks, International Journal of Computational Intelligence Systems, 4(4) (2011), 550-565.
[37] C. Yongting, Fuzzy quality and analysis on fuzzy probability, Fuzzy Sets and Systems, 83 (1996), 283-290. | ||
|
آمار تعداد مشاهده مقاله: 605 تعداد دریافت فایل اصل مقاله: 670 |
||