Skip to main content
Log in

P–S–N Curve Fitting Method Based on Sample Aggregation Principle

  • Technical Article---Peer-Reviewed
  • Published:
Journal of Failure Analysis and Prevention Aims and scope Submit manuscript

Abstract

Fatigue life tests play a central role in the design of mechanical systems, as the structural reliability estimation depends heavily on the fatigue strength of material, which need to be determined by experiments. The classical statistical analysis, however, can lead to results of limited usefulness when the number of specimens on test is small. Instead, this approach can potentially give more accurate estimates by applying the overall sample at each stress level. For the case of lognormal distribution, equivalent conversion of fatigue life is presented first. Then, P–S–N curve parameters are obtained based on sample aggregation principle. Furthermore, simulation test and experimental test are conducted to validate the proposed method, respectively, demonstrating that this method can sensibly outperform conventional ones. Finally, determination of minimum number of specimens in fatigue testing is presented, which can obtain P–S–N curves with a certain accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. O.H. Basquin, The exponential law of endurance tests. ASTM 10, 625–630 (1910)

    Google Scholar 

  2. ISO B S, Metallic materials—fatigue testing—statistical planning and analysis of data (2003)

  3. Standard A. E739, Standard practice for statistical analysis of linear or linearized stress–life (S–N) and strain–life (ϵ–N) fatigue data (2015)

  4. L. D’Angelo, M. Rocha, A. Nussbaumer, et al., S-N-P fatigue curves using maximum likelihood, in ECCS European Convention for Constructional Steelwork (2014)

  5. N. Stojković et al., Algorithm for determination of s-n curves of the structural elements subjected to cyclic loading. Facta Univ. Ser. Archit. Civil Eng. 16(1), 81–91 (2018)

    Article  Google Scholar 

  6. S.A. Faghidian, A. Jozie, M.J. Sheykhloo et al., A novel method for analysis of fatigue life measurements based on modified Shepard method. Int. J. Fatigue 68(68), 144–149 (2014)

    Article  Google Scholar 

  7. X.W. Liu, D.G. Lu, P.C.J. Hoogenboom, Hierarchical Bayesian fatigue data analysis. Int. J. Fatigue 100, 418–428 (2017)

    Article  Google Scholar 

  8. M. Guida, F. Penta, A Bayesian analysis of fatigue data. Struct. Saf. 32(1), 64–76 (2010)

    Article  Google Scholar 

  9. T.T. Pleune, O.K. Chopra, Using artificial neural networks to predict the fatigue life of carbon and low-alloy steels. Nucl. Eng. Des. 197(1–2), 1–12 (2000)

    Article  Google Scholar 

  10. T. Bučar, M. Nagode, M. Fajdiga, An improved neural computing method for describing the scatter of S-N, curves. Int. J. Fatigue 29(12), 2125–2137 (2007)

    Article  Google Scholar 

  11. Y. Al-Assaf, H.E. Kadi, Fatigue life prediction of composite materials using polynomial classifiers and recurrent neural networks. Compos. Struct. 77(4), 561–569 (2007)

    Article  Google Scholar 

  12. T. Bučar, M. Nagode, M. Fajdiga, A neural network approach to describing the scatter of S-N curves. Int. J. Fatigue 28(4), 311–323 (2006)

    Article  Google Scholar 

  13. H. Fu, Linear variance regression analysis. Acta Aeronaut. Astronaut. Sin. 15(3), 295–302 (1994)

    Google Scholar 

  14. H. Fu, C. Liu, Small sample test method for S-N and P-S-N curves. J. Mech. Strength 28(4), 552–555 (2006)

    Google Scholar 

  15. L. Xie, J. Liu, N. Wu et al., Backwards statistical inference method for P–S–N curve fitting with small-sample experiment data. Int. J. Fatigue 63(63), 62–67 (2014)

    Article  Google Scholar 

  16. P.C. Gope, Determination of sample size for estimation of fatigue life by using Weibull or log-normal distribution. Int. J. Fatigue 21(8), 745–752 (1999)

    Article  Google Scholar 

  17. P.C. Gope, Determination of minimum number of specimens in SN testing. Trans. Am. Soc. Mech. Eng. J. Eng. Mater. Technol. 124(4), 421–427 (2002)

    Article  Google Scholar 

  18. H. Fu, A confidence lower limit of population percentile. J. Beijing Univ. Aeronaut. Astronaut. 3, 1–8 (1990)

    Google Scholar 

  19. L. Xie, Principle of sample polymerization and method of p-s-n curve fitting. J. Mech. Eng. 49(15), 96–104 (2013)

    Article  Google Scholar 

  20. P.H. Wirsching, Statistical summaries of fatigue data for design purposes. New York. NASA Contractor Report 3697 (1983)

  21. P.H. Wirsching, Y.T. Wu, Probabilistic and statistical methods of fatigue analysis and design, in Pressure Vessel and Piping Technology 1985. A Decade of Progress 1985, ed. by C. (Raj) Sundararajan (American Society of Mechanical Engineers, New York, 1985), pp. 793–819

    Google Scholar 

  22. S. Zhu, X. Fang, M. Wu, et al., Mechanical Engineering Material Performance Data Sheet (China Machine Press, Beijing, 1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiufeng Tan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tan, X. P–S–N Curve Fitting Method Based on Sample Aggregation Principle. J Fail. Anal. and Preven. 19, 270–278 (2019). https://doi.org/10.1007/s11668-019-00586-1

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-019-00586-1

Keywords

Navigation