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کد مقاله : 537248 بازدید : 112 صفحه: 55 - 68

نوع مقاله: پژوهشی

کنترل فرآیند شکل دهی حرارتی قطعات تزیینی خوردرو با استفاده از رویکرد پروفایل

محورهای موضوعی : مدیریت صنعتی

Karim Atashgar 1 , Omid Asghari Zargarabadi 2

1 - Assistant Professor of industrial engineering, Malek Ashtar University, Tehran, Iran
2 - M.A Student in Industrial Engineering, Malek Ashtar University, Tehran, Iran

تاریخ دریافت : 1396/10/16 تاریخ پذیرش : 1396/10/16 تاریخ انتشار : 1393/06/01

کلید واژه: Profile, پروفایل, ترموفرمینگ, فاز یک کنترل آماری, Thermoforming, Automotive Decoration, Phase One statistical control,

چکیده مقاله :

در بسیاری از فرآیندهای تولیدی و خدماتی می توان بجای پایش یک مشخصه کیفی یا یک بردار مشخصات کیفی، با استفاده از رویکرد رگرسیونی، پارامترهای رابط بین متعیرهای مستقل و متغیر پاسخ را پایش کرد. در ادبیات کنترل فرآیند آماری به رابطه رگرسیونی بین متغیرهای مستقل و متغیر پاسخ، پروفایل گفته می شود. گزارش های علمی منتشر شده توسط محققین نشان می دهد که پایش پرفایل ها در فرآیندهایی که بتوان رابطه بین متغیرهای مستقل و پاسخ را بدرستی بدست آورد، موجب اثربخشی بیشتر کنترل آماری آن فرآیند می شود. رویکرد پروفایلی به مهندسین کیفیت کمک می کند که بتوانند رفتار عملکردی مشخصه های کیفی یک فرآیند را، به صورت یک تابع بررسی نمایند. در این مقاله با استفاده از رویکرد پروفایلی، فرآیند ترموفرمینگ (Thermoforming یا شکل دهی حرارتی) قطعه روگلگیری خودرو سمند، برای فاز 1 کنترل آماری، مورد توجه قرار گرفته است. این تحقیق نشان می دهد که با استفاده از پارامترهای رابطه رگرسیونی بدست آمده در فاز یک کنترل آماری، بصورت اثربخشی می توان فرآیند مورد نظر را در فاز دو کنترل آماری، پایش نمود.

چکیده انگلیسی:

In many manufacturing/ service cases, a process is monitored effectively by a functional relationship between an independent variable(s) and a response variable rather than measurements on a single quality specification or a quality specification vector. This regression relationship is referred to a profile in the statistical process control literature. Scientific reports address profile monitoring leads to a more effective quality management in the case that the relationship function is adopted correctly. Profile approach helps an industrial process management to analyze the behavior of quality specifications by a curve. In this paper, the management of thermoforming process for a cover part of the fender of SAMAND automotive is investigated. The model proposed for phase-I of this research address the capability of monitoring the process for phase-II of statistical control. 

منابع و مأخذ:


  1. aghaei A, Maserrat N, 2008. Monitoring of the disease: new user profiles Case Study: Lung Disease international journal of industrial engineering and production management 22, 54-63
    2.
  2. Atashgar k, 2012. Advanced statistical quality control, chapter 6, Research & Educational Institute of defense industry, Tehran, Iran.
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  3. Moemeni M, Ghayoumi A.F, 2011. Statistical analysis with SPSS fourteen edition, chapter six, Momeni Press
    4.
  4. Mahmoud MA, Woodall WH, F, 2004. Phase I analysis of linear profiles with calibration applications. Technometrics 46,380-391.
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  5. Rauwendaal C, 2013. Polymer Extrusion, 5th Edition, Hansen Publishers, Munich.
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  6. Rauwendaal C, 2000 Statistical Process Control in Injection Molding and Extrusion Hanser Publishers,Munich.
    7.
  7. Kang L. & Albin SL, 2000.On-Line Monitoring When the Process Yields a Linear Profile, Journal of Quality Technology 32, 418-426
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  8.  Neter J, Wasserman W, and Kutner MH, 1990.Applied Linear Statistical Models,Third Edition, Richard D. Irwin, Inc, Boston, MA.
    9.
  9. Mestek O, Pavlik J, and Suchanek, M. 1994, Multivariate Control Charts: Control Charts for Calibration Curves, Fresenius Journal of Analytical Chemistry 350, 344-351
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  10. Young TM, Winistorfer PM., and Wang S, 1999.Multivariate Control Charts of MDF and OSB Vertical Density Profile Attributes. Forest Products Journal 49, 79-86.
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  11.  Walker E and Wright S. 2002, Comparing Curves Using Additive Models, Journal of Quality Technology 34, 118-129
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  12. Jin J. and Shi, J. 1999, Feature-Preserving Data Compression of Stamping Tonnage Information Using Wavelets. Technometrics 41, 327-339.
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  13. Jin J. and Shi J. 2001, Automatic Feature Extraction of Waveform Signals for In-Process Diagnostic Performance Improvement. Journal of Intelligent Manufacturing 12, 257-268.
    14.
  14. Colosimo BM, Pacella M, and Semeraro Q, 2008. Statistical process control for geometric specifications: On the monitoring of roundness profiles. Journal of Quality Technology 40, 1-18
    15.
  15. Atashgar K, Amir A, Keramatee Nejad M, 2015. Monitoring Allan variance nonlinear profile using artificial neural network approach Quality Engineering and Technology 5,162-177
    16.
  16. Vaghefi A, Tajbakhsh SD, Noorossana R, 2009. Phase II monitoring of nonlinear profiles. Communications in Statistics-Theory and Methods 38, 1834-1851.
    17.
  17. Noorossana R, Eyvazian M, Amiri A, Mahmoud MA, 2010. Statistical monitoring of multivariate multiple linear regression profiles in Phase I with calibration application. Quality and Reliability Engineering International 26(3), 291–303.
    18.
  18. Amiri A, Changliang Z and Mohammad H, 2014.Monitoring Correlated Profile and Multivariate Quality Characteristics Quality and Reliability Engineering International 30, 133-142.
    19.
  19. Zou C, Ning X, Tsung F, 2012. LASSO-based multivariate linear profile monitoring.Annals of Operations Research 192.3–19.
    20.
  20. Yeh AB, Huwang L, Li YM, 2009. Profile monitoring for a binary response. IIE Transactions 41.931–941.
    21.
  21. Kazemzadeh RB, Noorossana R, Amiri A, 2008. Phase-I monitoring of polynomial profiles. Communications in Statistics-Theory and Methods 37, 1671-1686.
    22.
  22. Amiri A, Jensen WA, & Kazemzadeh RB, 2010. A case study on monitoring polynomial profiles in the automotive industry. Quality and Reliability Engineering International 26, 509-520
    23.
  23. Qiu P, Zou C, Wang Z,2010.Nonparametric Profile Monitoring by Mixed Effects Modeling. Technometrics 52,265–277.
    24.
  24. Moguerza JM, Muoz A, Psarakis S, 2007. Monitoring nonlinear profiles using support vectormachines. Lecture Notes in Computer Science 4789, 574–583.
    25.
  25.  Ding Y, Zeng L, Zhou S, 2006.Phase I analysis for monitoring nonlinear profiles in manufacturing processes. Journal of Quality Technology, 38,199–216.
    26.
_||_

  1. Saghaei A, Maserrat N, 2008. Monitoring of the disease: new user profiles Case Study: Lung Disease international journal of industrial engineering and production management 22, 54-63
    2.
  2. Atashgar k, 2012. Advanced statistical quality control, chapter 6, Research & Educational Institute of defense industry, Tehran, Iran.
    3.
  3. Moemeni M, Ghayoumi A.F, 2011. Statistical analysis with SPSS fourteen edition, chapter six, Momeni Press
    4.
  4. Mahmoud MA, Woodall WH, F, 2004. Phase I analysis of linear profiles with calibration applications. Technometrics 46,380-391.
    5.
  5. Rauwendaal C, 2013. Polymer Extrusion, 5th Edition, Hansen Publishers, Munich.
    6.
  6. Rauwendaal C, 2000 Statistical Process Control in Injection Molding and Extrusion Hanser Publishers,Munich.
    7.
  7. Kang L. & Albin SL, 2000.On-Line Monitoring When the Process Yields a Linear Profile, Journal of Quality Technology 32, 418-426
    8.
  8.  Neter J, Wasserman W, and Kutner MH, 1990.Applied Linear Statistical Models,Third Edition, Richard D. Irwin, Inc, Boston, MA.
    9.
  9. Mestek O, Pavlik J, and Suchanek, M. 1994, Multivariate Control Charts: Control Charts for Calibration Curves, Fresenius Journal of Analytical Chemistry 350, 344-351
    10.
  10. Young TM, Winistorfer PM., and Wang S, 1999.Multivariate Control Charts of MDF and OSB Vertical Density Profile Attributes. Forest Products Journal 49, 79-86.
    11.
  11.  Walker E and Wright S. 2002, Comparing Curves Using Additive Models, Journal of Quality Technology 34, 118-129
    12.
  12. Jin J. and Shi, J. 1999, Feature-Preserving Data Compression of Stamping Tonnage Information Using Wavelets. Technometrics 41, 327-339.
    13.
  13. Jin J. and Shi J. 2001, Automatic Feature Extraction of Waveform Signals for In-Process Diagnostic Performance Improvement. Journal of Intelligent Manufacturing 12, 257-268.
    14.
  14. Colosimo BM, Pacella M, and Semeraro Q, 2008. Statistical process control for geometric specifications: On the monitoring of roundness profiles. Journal of Quality Technology 40, 1-18
    15.
  15. Atashgar K, Amir A, Keramatee Nejad M, 2015. Monitoring Allan variance nonlinear profile using artificial neural network approach Quality Engineering and Technology 5,162-177
    16.
  16. Vaghefi A, Tajbakhsh SD, Noorossana R, 2009. Phase II monitoring of nonlinear profiles. Communications in Statistics-Theory and Methods 38, 1834-1851.
    17.
  17. Noorossana R, Eyvazian M, Amiri A, Mahmoud MA, 2010. Statistical monitoring of multivariate multiple linear regression profiles in Phase I with calibration application. Quality and Reliability Engineering International 26(3), 291–303.
    18.
  18. Amiri A, Changliang Z and Mohammad H, 2014.Monitoring Correlated Profile and Multivariate Quality Characteristics Quality and Reliability Engineering International 30, 133-142.
    19.
  19. Zou C, Ning X, Tsung F, 2012. LASSO-based multivariate linear profile monitoring.Annals of Operations Research 192.3–19.
    20.
  20. Yeh AB, Huwang L, Li YM, 2009. Profile monitoring for a binary response. IIE Transactions 41.931–941.
    21.
  21. Kazemzadeh RB, Noorossana R, Amiri A, 2008. Phase-I monitoring of polynomial profiles. Communications in Statistics-Theory and Methods 37, 1671-1686.
    22.
  22. Amiri A, Jensen WA, & Kazemzadeh RB, 2010. A case study on monitoring polynomial profiles in the automotive industry. Quality and Reliability Engineering International 26, 509-520
    23.
  23. Qiu P, Zou C, Wang Z,2010.Nonparametric Profile Monitoring by Mixed Effects Modeling. Technometrics 52,265–277.
    24.
  24. Moguerza JM, Muoz A, Psarakis S, 2007. Monitoring nonlinear profiles using support vectormachines. Lecture Notes in Computer Science 4789, 574–583.
    25.
  25.  Ding Y, Zeng L, Zhou S, 2006.Phase I analysis for monitoring nonlinear profiles in manufacturing processes. Journal of Quality Technology, 38,199–216.
    26.

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