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A Statistical Method for Designing and analyzing tolerances of Unidentified Distributions

Author Affiliations

  • 1Department of Management, Firoozkooh Branch, Islamic Azad University, Firoozkooh, IRAN

Res. J. Recent Sci., Volume 2, Issue (11), Pages 55-64, November,2 (2013)

Abstract

The mechanical tolerances are set to restrict too large dimensional and geometrical variation in a product. Tolerances have to be set in such a manner that functionality, manufacturability, costs and interchangeability are optimized and balanced between each other. The tolerances and available tolerance design techniques are represented in this text. Statistical tolerance design is emphasized because statistical behavior describes the nature of the manufacturing processes more realistically than worst-case methods. To this end, the Generalized Lambda Distribution (GLD) has been used for design of tolerance. This distribution is highly flexible and based on the available data, can identify and present the related probability distribution function and their statistics. After recognizing the underlying probability distribution function, the results can be employed for the design of tolerance.

References

  1. Syrjala T., Tolerance Design and Coordinate Measurement in Product Development, Helsinki university of technology, Department of Mechanical Engineering, Thesis submitted in partial fulfillment of the requirements for the degree of M.S. in Engineering, (2004)
  2. Chandra M. Jeya, Quality control, CRC Press LLC, 5-22 2001) 3.Yourstone S. and Zimmer W., Non-normality and the design of control charts for average, Decision sciences, 23), 1099-1113 (1992)
  3. Kittlitz R.G., Transforming the exponential for SPC applications, J. of quality technology, (31), 301-308 (1999)
  4. Peam W.L., Kotz S. and Johnson N.L., Distribution and inferential properties of process capability indices, J. of quality technology, 24), 216-231 (1992)
  5. Thakur N.S., Yadav K. and Pathak S., On Mean Estimation with Imputation in Two- Phase Sampling Design, Res. J. of Mathematical and Statistical Sci., ), 1-9 (2013)
  6. Rekha R.C. and Vikas S., Retailer’s profit maximization Model for Weibull deteriorating items with Permissible Delay on Payments and Shortages, Res. J. of Mathematical and Statistical Sci., ), 16-20 (2013)
  7. Roman U.C., Porey P.D., Patel P.L. and Vivekanandan N., Assessing Adequacy of Probability Distributional Model for Estimation of Design Storm, ISCA J. of Engineering Sci., 1(1), 19-25 (2012)
  8. Fournier B., Rupin N., Bigerelle M., Najjar D., Iost A., Application of the generalized lambda distribution in a statistical process control methodology, J. of Process control, (16), 1087-1098 (2006)
  9. Gunter B., The use and Abuse of C chart 1-4. Quality progress, part 1: 22), 72-73, part 2: 22) 108-109, part 3: 22), 79-80, and part 4: 22), 86-87, (1989 a-d)
  10. Tukey J.W., The future of data analysis, annals J. of mathematical statistics,33), 1-67 (1962)
  11. Joiner, B. L., Rosenblatt, J. R., Some properties of the range in samples from Tukey’s symmetric lambda distribution, J. of the American statistical association, (66), 394 (1971)
  12. Ganeshan R., Are more supplier better? generating the Gau and Ganeshan procedure, J. of Oper. Res. Soc., (52), 122-123 (2001)
  13. Ozturk A. and Dale R.F., A study of fitting the generalized lambda distribution to solar radiation data, J. Appl. Meteorol., (21), 995-1004 (1982)
  14. Delaney H.D. and Vargha A., The effect on non-normality on student’s two-sample t-test the annual meeting of the American educational research association, New Orlean, (2000)
  15. Gawand H., Mundada R.S. and Swaminathan P., Design Patterns to Implement Safety and Fault Tolerance, Int. J. of Computer Applications (18, (2011)
  16. Gawand H., Mundada R.S. and Swaminathan P., Design Patterns to Implement Safety and Fault Tolerance, Int. J. of Computer Applications (18)2, (2011)
  17. Dengiz B., The generalized lambda distribution in simulation of m/m/1 queue systems, J. Fac. Engng. Arch. Gazi univ., (), 161-171 (1988)
  18. Zaven A., Karian and Edvard J., Dudewiz, Fitting statistical distributions The generalized lambda distribution and generalized bootstrap methods, CRC press, (2000)
  19. Chowdhury D., and Arbabian M.A., Design of Robust CMOS Circuits for Soft Error Tolerance, Department of EECS, Univ. of California, Berkeley, CA 94720, (2011)
  20. Shweta Ms., Meshram S., and Ujwala Ms., A. Belorkar, Design Approach For Fault Tolerance in FPGA Architecture, International J. of VLSI design & Communication Systems (VLSICS), ), (2011)
  21. Kuang W., Xiao E., Ibarra C.M. and Zhao P., Design Asynchronous Circuits for Soft Error Tolerance, University of Texas - Pan American, Edinburg, (2007)
  22. Aljazar A-N. L., Generalized Lambda Distribution and Estimation Parameters, The Islamic University of Gaza, M.S. Theses, (2005)
  23. Tarsitano A., Fitting The Generalized Lambda Distribution to Income data, COMPSTAT’2004 Symposium, Physica-Verlag, (2004)
  24. Yi X., and Jerome, Y., Continuous Setting and Gaussian Generalized Lambda Distribution Model for Synthetic CDO Pricing, Hong Kong University of Science and Technology, (2008)
  25. Bigerelle M., Najjar D., Fournier B., Rupin N., Iost A., Application of lambda distribution and bootstrap analysis to the prediction of fatigue lifetime and confidence intervals, Int. J. Fatigue, (28), 223-236(2006)
  26. Acar A., Rais-Rohani M. and Eamon C.D., Reliability Estimation using Dimension Reduction and Extended Generalized Lambda Distribution, 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Schaumburg, IL, (2008)
  27. Karvanen J., and Nuutinen A., Characterizing the generalized lambda distribution by L-moments, Math. ST, (2007)
  28. Karian Z.A., and Dudewicz E.J., Fitting statistical distributions: the generalized lambda distribution and generalized bootstrap method, CRC press, (2000)
  29. Tarsitano A., Estimation of the generalized lambda distribution parameters for grouped data, J. ofCommunication in statistics theory and methods, (34), 1689-1709 (2005)
  30. Ramberg J., and Schmeiser B., An approximate method for generating asymmetric random variables, communications of the ACM, ) 78-82 (1974)