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Application of Pearson Parametric Distribution Model in Fatigue Life Reliability Evaluation

Authors: E. A. Azrulhisham, Y. M. Asri, A. W. Dzuraidah, A. H. Hairul Fahmi

Abstract:

The aim of this paper is to introduce a parametric distribution model in fatigue life reliability analysis dealing with variation in material properties. Service loads in terms of responsetime history signal of Belgian pave were replicated on a multi-axial spindle coupled road simulator and stress-life method was used to estimate the fatigue life of automotive stub axle. A PSN curve was obtained by monotonic tension test and two-parameter Weibull distribution function was used to acquire the mean life of the component. A Pearson system was developed to evaluate the fatigue life reliability by considering stress range intercept and slope of the PSN curve as random variables. Considering normal distribution of fatigue strength, it is found that the fatigue life of the stub axle to have the highest reliability between 10000 – 15000 cycles. Taking into account the variation of material properties associated with the size effect, machining and manufacturing conditions, the method described in this study can be effectively applied in determination of probability of failure of mass-produced parts.

Keywords: Stub axle, Fatigue life reliability, Stress-life, PSN curve, Weibull distribution, Pearson system

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082555

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References:


[1] R.I. Stephens, A. Fatemi, R.R. Stephens and H.O. Fuchs, Metal Fatigue in Engineering, 2nd Edition, Wiley Interscience, New York, 2001.
[2] A guide for fatigue testing and statistical analysis of fatigue data. American Society for Testing and Materials, Philadelphia, ASTM STP No. 91-A; 1963
[3] J.D. Booker, M. Raines and K.G. Swift, Designing Capable and Reliable Products, Butterworth-Heinemann, Oxford, 2001.
[4] P.W. Hovey, A.P. Berens and D.A. Skinn, "Risk analysis for aging aircraft", Flight Dynamic Directorate, vol. 1, Wright Laboratory, Ohio, October 1991.
[5] R.E. Melchers, Structural Reliability Analysis and Prediction, 2nd Edition, John Wiley & Sons Ltd, Chichester, 1999
[6] J. Schijve, "Statistical distribution functions and fatigue of structures", International Journal of Fatigue, vol. 7, no. 9, pp. 1031-1039, 2005.
[7] K.J. Jun, T.W. Park, S.H. Lee, S.P. Jung and J.W. Yoon, "Prediction of fatigue life and estimation of its reliability on the parts of an air suspension system", International Journal of Automotive Technology, vol. 9, no. 6, pp. 741-747, 2008.
[8] J.A. Bannantine, J.J. Comer and J.L. Handrock, Fundamentals of Metal Fatigue Analysis, Prentice Hall, New Jersey, 1989.
[9] B. Sudret, Z. Guede, P. Hornet, J. Stephan and M. Lemaire, "Probabilistic assessment of fatigue life including statistical uncertainties in the SN curve", in Transactions of the 17th International Conference on Structural Mechanics in Reactor Technology, Prague, Czech Republic, August 2003.
[10] G. Genet, A Statistical Approach to Multi-Input Equivalent Fatigue Loads for the Durability of Automotive Structures, Chalmers University of Technology and Goteborg University, Goteborg, Sweden, 2006.
[11] G.J. Hahn and S.S. Shapiro, Statistical Models in Engineering, John Wiley and Sons, New York, 1967.