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  • ارائه مدلی جهت کنترل فرآیندآماری به منظور بهینه‌سازی راندمان و کیفیت در صنایع تولیدی

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کد مقاله : IMJ-2306-1854 (R1) بازدید : 251 صفحه: 1 - 15

10.30495/imj.2023.1988808.1854

نوع مقاله: پژوهشی

ارائه مدلی جهت کنترل فرآیندآماری به منظور بهینه‌سازی راندمان و کیفیت در صنایع تولیدی

محورهای موضوعی : مدیریت صنعتی

abbas morovvati 1 , Seyed Jalaledin Hosseini Ghoncheh 2 , Hasan Haleh 3

1 - MA Student, Department of Industrial Management, Faculty of Management and Economics, Islamic Azad University, Science and Research Unit, Tehran, Iran
2 - Department of Mathematics, Islamic Azad University, Takestan branch, Takestan, Iran
3 - Department of Industrial Management, Golpayegan Technical and Engineering Faculty, Isfahan University of Technology, Golpayegan, Iran

تاریخ دریافت : 1402/03/24 تاریخ پذیرش : 1402/06/13 تاریخ انتشار : 1402/07/01

کلید واژه: خوشه بندی, ﮐﻨﺘﺮل ﻓﺮآﯾﻨﺪآماری, صنایع تولیدی, راندمان, کیفیت,

چکیده مقاله :

در این پژوهش یک مدل کنترل فرآیند آماری ترکیبی برای شناسایی عوامل تأثیرگذار بر راندمان و کیفیت در صنایع تولیدی و قطعه ساز ارائه شده و سپس تحت کنترل قراردادن و بهینه سازی این فرآیندها مد نظر قرار می گیرد. صنایع تولیدی و قطعه ساز به عنوان بدنه اصلی صنایع کشور جهت مطالعه موردی و پیاده سازی در نظر گرفته شده است. جهت کشف عوامل اثرگذار بر راندمان از تکنیک‌های خوشه بندی استفاده می شود. و سپس با استفاده از الگوریتم‌های درخت تصمیم به پیش بینی راندمان و کیفیت در این صنایع پرداخته می‌شود و در مرحله پایانی جهت رسم نمودارهای کنترل، از نمودارهای کنترل پراکندگی و میانگین متغیرها استفاده می گردد. جدول مقایسه‌ای پارامترها توسط خروجی نرم افزار کلمنتاین تهیه شده و در بخش شبکه عصبی از نرم افزار رپیدماینر استفاده می‌شود. نتایج حاصل از شناسایی عوامل اثرگذار و پیش بینی از نظر فنی به مقادیر هدف نزدیک بوده و نمودارهای کنترل با حدود کنترل فنی مشخصه‌ها همخوانی داشته و جهت بهینه سازی مقدار هدف که راندمان و کیفیت است مفید می باشد.

چکیده انگلیسی:

In this research, a combined statistical process control model is presented to identify factors affecting efficiency and quality in manufacturing and component manufacturing industries, and then controlling and optimizing these processes is considered. Manufacturing and component industries are considered as the main body of the country's industries for case study and implementation. Clustering techniques are used to discover factors affecting efficiency. And then using decision tree algorithms to predict efficiency and quality in these industries, and in the final stage, control charts of dispersion and average variables are used to draw control charts. The comparison table of the parameters is prepared by the output of the Clementine software, and RapidMiner software is used in the neural network section. The results obtained from the identification of influencing and forecasting factors are close to the target values from a technical point of view, and the control charts are consistent with the technical control limits of the characteristics and are useful for optimizing the target value, which is efficiency and quality.

منابع و مأخذ:


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  2. Alt, F.B. (1985). Multivariate Quality Control. Encyclopedia of the Statistical Sciences, 6:110-122
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  13. Mehrafrooz, Z. & Noorossana, R. (2011). An integrated model based on statistical process control and maintenance. Computers & Industrial Engineering, 61(4), 1245-1255.
    14.
  14. Mirsepassi, Naser. & Motaghi, Mohammad Hossein. Selajgeh. Sanjar. (2009). Examining the factors related to the slowness of the implementation of the comprehensive quality management system in the public sector of Iran. Journal of Management Research, 81, Summer. (In Persian)
    15.
  15. Montgomery, Douglas. (2007). Statistical Quality Control. Translated by Nur al-Sana, Rasool. Tehran: University of Science and Technology Publications, 8th (In Persian)
    16.
  16. Murphy, B.J. (1987). Selecting out of control variables with hotellings multivariate quality control procedure. The Statistician, 36, 571-583.
    17.
  17. Roland, Caulcutt. (1996). Assembly Automation. Statistical process control (SPC), 16(4), 10–14.
    18.
  18. Ryan, T.P. (1989). Statistical Methods for Quality Improvement. John Wiley & sons Inc.
    19.
  19. Srivastava, M.S. & Khatri, C.G. (1979). An introduction to Multivariate statistics. North Holland Inc.
    20.
  20. Tsacle, E. G., and N. A. Aly. (1996). "An expert system model for implementing statistical process control in the health care industry." Computers & industrial engineering1-2, 450-477.
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_||_

  1. Alexander, S.M. (1987). The application of expert system to manufacturing process control. Computers ind.Engng, 12:307-312.
    2.
  2. Alt, F.B. (1985). Multivariate Quality Control. Encyclopedia of the Statistical Sciences, 6:110-122
    3.
  3. Anderson, T.W., John, Wiley & Sons, Hnc. (1984). An Introduction to multivariate statistical analysis. Wiley series in probability and statistics, 3rd
    4.
  4. Bag, Monark, Susanta Kumar Gauri, and Shankar Chakraborty. "An expert system for control chart pattern recognition." The International Journal of Advanced Manufacturing Technology62 (2012): 291-301.
    5.
  5. Doganaksoy, N., Faltin, F.W. & Tucker, W.T. (1991), Identification of out of control quality characteristics in multivariate manufacturing environment. Communication in statistics, Theory and methods, 20(9):2275-2290.
    6.
  6. Fatemi Qomi, Mohammad Taqi. (2001). Statistical Quality Control. Tehran. University of Technology Amir Kabir. (In Persian)
    7.
  7. S. Bunney. & B.G. Dale. (1997). The implementation of quality management tools and techniques: A study. The TQM Magazine, 9(3), 183–189.
    8.
  8. Hosni, Y.A. & Elshennawy, A.K. (1988). Knowledge-based Quality Control System. Computers Ind.Engng, 15,331-337.
    9.
  9. Ismaili, M. (2014). Data Mining Concepts and Technique. Tehran. Niaze danesh. (in persian)
    10.
  10. Jackson, J.E. (1980). Principal Components & Factor Analysis: Part I- Principal components. Journal of Quality Technology, 12(4), 201-213.
    11.
  11. Johnson, R.A & Wichern, D.W. (1992). Applied Multivariate Statistical Analysis. Prentice Hall, 3rd

  12. Masud, A. S., & Thenappan, M. S. (1993). A knowledge-based advisory system for statistical quality control. The International Journal of Production Research, 31(8), 1891-1900.
    13.
  13. Mehrafrooz, Z. & Noorossana, R. (2011). An integrated model based on statistical process control and maintenance. Computers & Industrial Engineering, 61(4), 1245-1255.
    14.
  14. Mirsepassi, Naser. & Motaghi, Mohammad Hossein. Selajgeh. Sanjar. (2009). Examining the factors related to the slowness of the implementation of the comprehensive quality management system in the public sector of Iran. Journal of Management Research, 81, Summer. (In Persian)
    15.
  15. Montgomery, Douglas. (2007). Statistical Quality Control. Translated by Nur al-Sana, Rasool. Tehran: University of Science and Technology Publications, 8th (In Persian)
    16.
  16. Murphy, B.J. (1987). Selecting out of control variables with hotellings multivariate quality control procedure. The Statistician, 36, 571-583.
    17.
  17. Roland, Caulcutt. (1996). Assembly Automation. Statistical process control (SPC), 16(4), 10–14.
    18.
  18. Ryan, T.P. (1989). Statistical Methods for Quality Improvement. John Wiley & sons Inc.
    19.
  19. Srivastava, M.S. & Khatri, C.G. (1979). An introduction to Multivariate statistics. North Holland Inc.
    20.
  20. Tsacle, E. G., and N. A. Aly. (1996). "An expert system model for implementing statistical process control in the health care industry." Computers & industrial engineering1-2, 450-477.
    21.
  21. Willborn Walter, W.O. (1990). Expert System In Support Of Quality Management. Annual Quality Congress Transactions, 44,758-763.
    22.

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