Determining the Behaviour of Fatigue Strain Histories of Vehicle Coil Springs by Using Statistical Inferences

Abstract:

The evaluation of fatigue behavior of real components under service loading is important in reliability analysis. The present paper investigates the characteristics of two strain signals spectrum by using statistical inferences. The data used in this study are obtained from strain gauges installed on coil spring component of car suspension system driven over two different road surfaces. The coil springs are made of SAE 5160 carbon steel materials. The strain signals are explored to produce the summary statistics (i.e. root-mean-square, kurtosis, skewness etc.) and the rainflow cycle counting is performed to obtain total number of cycles and damage per cycle. Further, distribution fitting is applied to the cycle-counted strain ranges data. The results show that both signals fit well to a mixed Weibull distribution with three subpopulations.

• Periodical: Applied Mechanics and Materials (Volume 786)
• Pages: 409-414
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.786.409
• Online since: August 2015
• Authors: Mashitah Mahmud*, Shahrum Abdullah, Ahmad Kamal Ariffin, Z.M. Nopiah, M.F.M. Yunoh, Nor Izan Izura Mansor
• Keywords: Mixed Weibull, Probabilistic, Rainflow, Reliability, Strain

References

[1] R. Tovo, A damage-based evaluation of probability density distribution for rain-flow ranges from random processes, International Journal of Fatigue, vol. 22, p.425–429, (2000).

DOI: 10.1016/s0142-1123(00)00006-2

[2] Y. Liu and S. Mahadevan, Stochastic fatigue damage modeling under variable amplitude loading, International Journal of Fatigue, vol. 29, pp.1149-1161, (2007).

DOI: 10.1016/j.ijfatigue.2006.09.009

[3] H. Shen, J. Lin, and E. Mu, Probabilistic model on stochastic fatigue damage, International Journal of Fatigue, vol. 22, p.569–572, (2000).

DOI: 10.1016/s0142-1123(00)00030-x

[4] B. Echard, N. Gayton, and A. Bignonnet, A reliability analysis method for fatigue design, International Journal of Fatigue, vol. 59, p.292–300, (2014).

DOI: 10.1016/j.ijfatigue.2013.08.004

[5] S. Abdullah, J. A. Giacomin, and J. R. Yates, A mission synthesis algorithm for fatigue damage analysis, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 218, pp.243-258, (2004).

DOI: 10.1243/095440704322955786

[6] W. Li, T. Sakai, Q. Li, and P. Wang, Statistical analysis of fatigue crack growth behavior for grade B cast steel, Materials & Design, vol. 32, pp.1262-1272, (2011).

DOI: 10.1016/j.matdes.2010.09.036

[7] D. -S. S. J. -K. Kim, A statistical approach for predicting the crack retardation due to a single tensile overload, International Journal of Fatigue, vol. 25, pp.335-342, (2003).

DOI: 10.1016/s0142-1123(02)00145-7

[8] W. F. Wu and C. C. Ni, Statistical aspects of some fatigue crack growth data, Engineering Fracture Mechanics, vol. 74, pp.2952-2963, (2007).

DOI: 10.1016/j.engfracmech.2006.08.019

[9] E. Castillo and A. Fernández-Canteli, A unified statistical methodology for modeling fatigue damage, (2009).

DOI: 10.1007/978-1-4020-9182-7_7

[10] M. Nagode and M. Fajdiga, A general multi-modal probability density function suitable for the rainflow ranges of stationary random processes, International Journal of Fatigue, vol. 20, pp.211-223, (1998).

DOI: 10.1016/s0142-1123(97)00106-0

[11] R. I. Stephens, A. Fatemi, R. R. Stephens, and H. O. Fuchs, Metal Fatigue in Engineering, 2 ed. Canada: John Wiley & Sons, Inc, (2001).

[12] N. E. Dowling, Mechanical Behavior of Materials, 4th ed.: Pearson Education Limited, (2013).

[13] D. Benasciutti, Fatigue analysis of random loadings, Doctor of Philosophy, Department of Engineering, University of Ferrara, (2004).