کاربرد کیفیت فازی ذوزنقه ای در صنعت خودروسازی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 ﮔﺮوه آﻣﺎر، داﻧﺸﮑﺪه ﻋﻠﻮم رﯾﺎﺿﯽ، داﻧﺸﮕﺎه ﻓﺮدوﺳﯽ ﻣﺸﻬﺪ، ﻣﺸﻬﺪ، اﯾﺮان

2 ﮔﺮوه آﻣﺎر، داﻧﺸﮑﺪه رﯾﺎﺿﯽ و راﯾﺎﻧﻪ، داﻧﺸﮕﺎه ﺷﻬﯿﺪ ﺑﺎﻫﻨﺮ ﮐﺮﻣﺎن، ﮐﺮﻣﺎن، اﯾﺮان

10.22034/jfsa.2022.310751.1095

چکیده

آزﻣﻮن ﻓﺮﺿﯿﻪ آﻣﺎری ﯾﮏ روش ﻣﻮﺛﺮ ﺑﺮای ﺗﺼﻤﯿﻢﮔﯿﺮی در ﻣﻮرد ﮐﺎراﯾﯽ ﯾﮏ ﻓﺮاﯾﻨﺪ ﺗﻮﻟﯿﺪی ﻣﯽﺑﺎﺷﺪ. ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﮐﯿﻔﯿﺖ ﻓﺎزی ﺑﻪ ﺟﺎی ﺣﺪود ﻣﺸﺨﺼﺎت ﻓﻨﯽ دﻗﯿﻖ، ﻣﯽﺗﻮاﻧﯿﻢ ﺗﺼﻤﯿﻤﺎت ﻣﻄﻤﺌﻦﺗﺮی ﺑﺮای ﺑﺮرﺳﯽ ﺗﻮاﻧﺎﯾﯽ ﮐﺎراﯾﯽ ﻓﺮاﯾﻨﺪﻫﺎی ﺗﻮﻟﯿﺪی ﺑﮕﯿﺮﯾﻢ. در اﯾﻦ ﻣﻘﺎﻟﻪ ﯾﮏ ﻣﻄﺎﻟﻌﻪ ﮐﺎرﺑﺮدی ﺑﺮ اﺳﺎس ﮐﯿﻔﯿﺖ ﻓﺎزی ﺑﺎ اﺳﺘﻔﺎده از ﺷﺎﺧﺺ ﯾﺎﻧﮕﺘﯿﻨﮓ اراﺋﻪ ﺷﺪه اﺳﺖ. روﯾﮑﺮد ﭘﯿﺸﻨﻬﺎدی ﺑﻪ ﮐﺎر ﺑﺮدهﺷﺪه در اﯾﻦ ﻣﻄﺎﻟﻌﮥ ﮐﺎرﺑﺮدی، ﯾﮏ ﺗﮑﻨﯿﮏ ﺑﺮای آزﻣﻮدن ﺗﻮاﻧﺎﯾﯽ ﯾﮏ ﻓﺮاﯾﻨﺪ ﻧﺮﻣﺎل در ﺗﻮﻟﯿﺪ ﻣﺤﺼﻮﻻت در ﺣﺪود ﻣﺸﺨﺼﺎت ﻓﺎزی از ﭘﯿﺶﺗﻌﯿﯿﻦﺷﺪه ﻣﯽﺑﺎﺷﺪ. ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﭘﯿﭽﯿﺪﮔﯽ ﻓﺮﻣﻮلﻫﺎی ﺷﺎﺧﺺﻫﺎی ﮐﺎراﯾﯽ ﺣﺘﯽ ﺗﺤﺖ ﺷﺮاﯾﻂ ﻧﺮﻣﺎل ﺑﻮدن دادهﻫﺎ، ﻣﻤﮑﻦ اﺳﺖ ﺑﺎ ﭼﺎﻟﺶ ﻋﺪم ﺗﻮاﻧﺎﯾﯽ ﭘﯿﺪا ﮐﺮدن ﺗﻮزﯾﻊ آﻣﺎری ﺑﺮآوردﮔﺮ ﮐﺎراﯾﯽ ﻓﺮاﯾﻨﺪ روﺑﺮو ﺷﻮﯾﻢ. ﻫﻤﭽﻨﯿﻦ اﯾﻦ ﭼﺎﻟﺶ ﻧﯿﺰ ﺑﺮای آزﻣﻮن ﮐﺎراﯾﯽ ﻓﺮاﯾﻨﺪ ﺑﺮ اﺳﺎس ﮐﯿﻔﯿﺖ ﻓﺎزی دﯾﺪه ﻣﯽﺷﻮد.

کلیدواژه‌ها



 ﭘﺮﭼﻤﯽ، ع. و ﻣﺎﺷﯿﻦﭼﯽ، م.(۱۳۸۶) ﮐﯿﻔﯿﺖ ﻓﺎزی و ﻧﺴﻞ ﺟﺪﯾﺪی از ﺷﺎﺧﺺﻫﺎی[1]
ﮐﺎرآﯾﯽ. اﻧﺪﯾﺸﻪ آﻣﺎری، ﺷﻤﺎره اول ﺳﺎل دوازدﻫﻢ، ﺻﺺ. ٨۶ ﺗﺎ ۶٧.

[2]    T. C. Chang , K. S. Chen, C. M. Yu, (2016). Process quality assessment model of hand tools: A case study on the handle of ratchet torque wrench. International Journal of Reliability Quality and Safety Engineering, 23(05), 1650017.
[3]    S. M. Chen, T. M. Hung, (2021). What can fuzziness do for capability analysis based on fuzzy data. Scientia Iranica, 28(2), 1049–1064.
[4]    K. S. Chen, T. H. Huang, (2021). A Fuzzy Evaluation Model Aimed at Smaller- the-Better-Type Quality Characteristics. Mathematics, 9(19), 2513.
[5]    K. S. Chen, T. C. Chang, (2020). A fuzzy approach to determine process qual- ity for one-sided specification with imprecise data. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 234(9), 1198–1206.
[6]    A. C. Cullen, H. C. Frey, C. H. Frey, (1999). Probabilistic techniques in exposure assessment. Springer Science & Business Media, New York, 1st edition.
[7]    Z. Lin, H. Ayed, B. Bouallegue, H. Tomaskova, S. Jafarzadeh Ghoushchi, G. Haseli, (2021). An integrated mathematical attitude utilizing fully fuzzy bwm and fuzzy waspas for risk evaluation in a SOFC. Mathematics, 9(18), 2328.
[8]    A. Parchami, (2020). Fuzzy decision in testing hypotheses by fuzzy data: Two case studies. Iranian Journal of Fuzzy Systems, 17(5), 127–136.
[9]    A. Parchami, H. Iranmanesh, B. Sadeghpour Gildeh, (2021). Statistical testing quality and its Monte Carlo simulation based on fuzzy specification limits, Iranian Journal of Fuzzy Systems.  [10]        A. Parchami, M. Mashinchi (2010). A new generation of process capability in- dices, Journal of Applied Statistics, 37 (1), 77–89.
[11]        A. Parchami, S. Ç. Onar, B. Öztayşi, C. Kahraman, (2017). Process capability analysis using interval type-2 fuzzy sets. International Journal of Computational Intelligence Systems, 10(1), 721–733.
[12]        A. Parchami, B. Sadeghpour Gildeh, M. Mashinchi (2016). Why Fuzzy Quality?. International Journal for Quality Research, 10 (3), 457–470.
[13]        B. Sadeghpour Gildeh, (2003). Comparison of Cp, Cpk and Cp-tilde process capability indices in the case of measurement error occurrence. IFSA World Congress, Istanbul, Turkey, 563–567.
[14]        C. Yongting (1996). Fuzzy quality and analysis on fuzzy probability. Fuzzy Sets and Systems, 83, 283–290.
[15]        L.A. Zadeh, (1968). Probability measures of fuzzy events. Journal of Mathemat- ical Analysis and Applications, 23 (2), 421–427.
[16]        L. A. Zadeh, (1975). The concept of a linguistic variable and its application to approximate reasoning-III. Information sciences, 9(1), 43–80