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Development of Probabilistic Fatigue Life Distribution Functions with Lower and Upper Bounds
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 28, 2017 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
A probabilistic distribution function roughly consists of two parts: the middle part and the tails. The fatigue life distribution at a stress/load level is often described with two-parameter lognormal or Weibull distribution functions, which are more suitable for modeling the mean (middle) behaviors. The domains of the conventional probabilistic distribution functions are often unbounded, either infinite small (0 for the two-parameter Weibull) or infinite large or both. For most materials in low- and medium-cycle fatigue regimes, the domains of fatigue lives are usually bounded, and the inclusion of the bounds in a probabilistic model is often critical in some applications, such as product validation and life management. In this paper, four- and five-parameter Weibull distribution functions for the probabilistic distributions with bounds are developed. Finally, the applications of these new models in fatigue data analysis and damage assessment are provided and discussed.
CitationWei, Z., Nayaki, R., Mandapati, R., and Hamilton, J., "Development of Probabilistic Fatigue Life Distribution Functions with Lower and Upper Bounds," SAE Technical Paper 2017-01-0354, 2017, https://doi.org/10.4271/2017-01-0354.
Data Sets - Support Documents
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- Lee, Y.L., Pan, J., Hathaway, R., and Barkey, M., 2005, Fatigue Testing and Analysis: Theory and Practice, Elsevier Butterworth-Heinemann, Boston, USA.
- Yang, G., Life Cycle Reliability Engineering, John Wiley & Sons, Inc., Hoboken, New Jersey, 2007.
- Meeker, W.Q., Escobar, L.A., Statistical Methods for Reliability Data, Wiley, New York, 1998.
- Harlow, D.G., Statistical characterization of bi-modal behavior, Acta Materialia, 2011, 59, 5048-5053.
- Jha, S.K., Larsen, J.M.M Rosenberger, A.H., Hartman, G.A., Dual fatigue failure modes in Ti-6Al-2Sn-4Zr-6Mo and consequences on probabilistic life prediction, Scripta Materialia, 2003, 48, 1637-1642.
- Jha, S.K., Larsen, J.M.M Rosenberger, Towards a physics-based description of fatigue variability behavior in probabilistic life-prediction, Engineering Fracture Mechanics, 2009, 76, 681-694.
- Cashman, G.T., A statistical methodology for the preparation of a competing modes fatigue design curve, Journal of Engineering Materials and Technology, 2007, 129, 159-168.
- Weibull, W., A statistical distribution function of wide applicability, Journal of Applied Mechanics, 1951,18, 293-297.
- Manson, S.S., Halford, G.R., Fatigue and Durability of Structural Materials, American Society for Metals International, Metals Park, OH, 2006.
- Wei, Z., Luo, L., Start, M., and Gao, L., "Uncertainty Characterization and Quantification in Product Validation and Reliability Demonstration," SAE Technical Paper 2016-01-0270, 2016, doi:10.4271/2016-01-0270.
- Wei, Z., Zhu, G., Gao, L., and Luo, L., "Failure Mode Effects and Fatigue Data Analyses of Welded Vehicle Exhaust Components and Its Applications in Product Validation," SAE Int. J. Mater. Manf. 9(3):594-604, 2016, doi:10.4271/2016-01-0374.
- O’Connor, P.D.T., Kleyner, A., Practical Reliability Engineering, 5th edition, Wiley, 2012.
- Kies, J.A., The strength of glass, Naval Research Lab, Report No.5093, Washington D.C. 1958.
- Smith, F., Hoeppner, D.W., Use of the four parameter Weibull function for fitting fatigue and compliance calibration data, Engineering Fracture Mechanics, 1990, 36, 173-178.
- Phani, K.K., A new modified Weibull distribution function, J. Am. Ceram. Soc., 1987, 70,182-184.
- Neter, J., Wasserman, W., Kutner, M.H., Applied Linear Statistical Models, Richards D. Irwin, Inc., Homewood, IL, 1990.
- Dodson, B., The Weibull Analysis Handbook, Second Edition, ASQ Quality Press, Milwaukee, Wisconsin, 2006.
- Wei, Z., Luo, L., Ellinghaus, K., Pieszkalla, M., Harlow, D.G., Nikbin, K., Statistical and probabilistic analysis of thermal-fatigue test data generated using V-shape specimen testing method, Proceedings of the ASME 2013 Pressure Vessels & Piping Division Conference, PVP2013-97628, July 14-18, 2013, Paris, France.
- Ryan, B., Joiner, B., Cryer, J., Minitab Handbook : Updated for Release 16, Sixth Edition, Boston, MA, USA, 2012.
- Shen, C.L., The statistical analysis of fatigue data, Ph.D. dissertations, The University of Arizona, Tucson, 1994.
- Wei, Z., Yang, F., Konson, D., Nikbin, K., A design approach based on historical test data and Bayesian statistics, PVP2013-97627, Proceedings of the ASME 2013 Pressure Vessels & Piping Division Conference, July 14-18, 2013, Paris, France.
- Wei, Z., "Sample Size Reduction Based on Historical Design Information and Bayesian Statistics," SAE Int. J. Mater. Manf. 7(1):96-107, 2014, doi:10.4271/2013-01-2440.