journal article·2006-09

Methods for estimating Weibull parameters for brittle materials

Dongfang Wu, Jiancheng Zhou, Yongdan Li

Journal of Materials Science· Volume 41· Issue 17
Publisher page

Abstract

A Monte Carlo simulation is used to obtain the statistical properties of the Weibull parameters estimated by the linear regression, weighted linear regression, maximum likelihood and moments methods, respectively. Results reveal that the estimated Weibull modulus is always biased, which has a much lower accuracy than the scale parameter. The mean square error is adopted as a criterion for the comparison of the estimation methods. It is shown that both the probability estimators and the weight factors have great effects on the estimation precision of the Weibull modulus. The weighted linear regression with a weight factor of W i=3.3P i −27.5[1−(1−P i)0.025] and a probability estimator of P i=(i−0.3)/(n+0.4) gives the most accurate estimation for sample sizes of 9–52. The maximum likelihood method recommended for any sample size by previous authors, comes first only for sample sizes larger than or equal to 53; furthermore, it is less conservative than the regression methods, and hence results in a lower safety in reliability predictions.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Fatigue and fracture mechanics · Probabilistic and Robust Engineering Design · Material Properties and Failure Mechanisms

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (28)

Show source-provided references
1. ASTM standard C1239-00, “Standard Practice for Reporting Uniaxial Strength Data and Estimating Weibull Distribution Parameters for Advanced Ceramics”, ASTM International
2. Trustrum K, Jayatilaka A De S (1983) J Mater Sci 18:276510.1007/BF00547593 ↗
3. Madjoubi MA, Bousbaa C, Hamidouche M, Bouaouadja N (1999) J Eur Ceram Soc 19:295710.1016/S0955-2219(99)00087-4 ↗
4. Wu DF, Li YD, Shi YH, Fang ZP, Wu DH, Chang L (2002) Chem Eng Sci 57:349510.1016/S0009-2509(02)00252-X ↗
5. Wu DF, Li YD (2002) Stud Surf Sci Catal 143:10110.1016/S0167-2991(00)80647-3 ↗
6. Weibull W (1951) J Appl Mech 18:29310.1115/1.4010337 ↗
7. Khalili A, Kromp K (1991) J Mater Sci 26:674110.1007/BF02402669 ↗
8. Bergman B (1984) J Mater Sci Lett 3:68910.1007/BF00719924 ↗
9. Sullivan JD, Lauzon PH (1986) ibid 5:1245
10. Trustrum K, Jayatilaka A De S (1979) J Mater Sci 14:108010.1007/BF00561290 ↗
11. Bergman B (1986) J Mater Sci Lett 5:61110.1007/BF01731525 ↗
12. Faucher B, Tyson WR (1988) ibid 7:1199
13. Langlois R (1991) ibid 10:1049
14. Wu DF, Jiang H (2003) ibid 22:1745
15. Papargyris AD (1998) J Eur Ceram Soc 18:45110.1016/S0955-2219(97)00165-9 ↗
16. Song LY, Wu DF, Li YD (2003) J Mater Sci Lett 22:165110.1023/B:JMSL.0000004640.71595.f3 ↗
17. Wu DF, Li YD, Zhang JP, Chang L, Wu DH, Fang ZP, Shi YH (2001) Chem Eng Sci 56:703510.1016/S0009-2509(01)00340-2 ↗
18. Wu DF, Zhou JC, Li YD (2006) J Eur Ceram Soc 26:109910.1016/j.jeurceramsoc.2005.01.044 ↗
19. Thoman DR, Bain LJ, Antle CE (1970) Technometrics 12:36310.1080/00401706.1970.10488674 ↗
20. Kapur KC, Lamberson LR (1977) Reliability in engineering design. Wiley, New York
21. McCool JT (1970) IEEE Trans Reliability 19:210.1109/TR.1970.5216370 ↗
22. Fernandez-Saez J, Chao J, Duran J, Amo JM (1993) J Mater Sci Lett 12:149310.1007/BF00277075 ↗
23. Barbero E, Fernandez -Saez J, Navarro C (1999) ibid 18:1441
24. Cox DR (1987) Applied statistics, principles and examples. Chapman Hall, London
25. Davies IJ (2001) J Mater Sci Lett 20:99710.1023/A:1010983602277 ↗
26. Idem (2004) J Mater Sci 39:144110.1023/B:JMSC.0000013913.84004.cd ↗
27. Peterlik H (1995) ibid 30:1972
28. Wu DF, Lu GZ, Jiang H, Li YD (2004) J Am Ceram Soc 87:179910.1111/j.1551-2916.2004.01799.x ↗