Abstract
The initiation and propagation of short crack is a complex process of nonlinear dynamics. The material field has been concerning of the law of short crack and put forward a number of models and predictive equations. But most of them have to meet the problems which are unclear physical parameters meaning, narrow range of applications etc. That is the reason why there is not yet widely recognized quantitative model of crack evolution so far. The introduction of genetic algorithm-BP neural networks could solve those problems by avoiding building the explicit model equation and directly extracting the potential rules from the data. In this paper, the short crack for low cycle is studied under complex stress at high temperature. The material adopted in the experiment is Q245R steel. The initiation, propagation and coalescence of short crack are observed. The comparisons between experiment results and neural network simulation results show that genetic algorithm-BP neural networks simulation method can predict the law of short crack with higher prediction accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yu, M.: Study on Initiation and Propagation of Short Crack for Low Cycle Fatigue at High Temperature and Complex Stress States. J. Dalian University of Technology 4 (2006)
Hong, Y.S., Fang, B.: Theory and meso-scopic process of initiation and propagation of short fatigue crack. J. Advances in Mechanics 23(4), 468–486 (1993)
Paris, P.C., Erdogan, F.: A critical analysis of crack propagation laws. J. Trans. ASME, Ser. 85, 528–534 (1963)
Miller, K.J., Van Elst, H.C., Bakker, A.: Proceedings Fracture Control of Engineering Structures ECF6. Amsterdam, 149–167(1986)
Hobson, P.D., Brown, M.W., de los Rios, E.R., Miller, K.J.: The Behavior of Short Fatigue Cracks, pp. 441–459. EGP Publication 1, Mechanical Engineering Publications, London (1986)
Obrtlik, K., Polak, J., Hajek, M.: Short fatigue crack behavior in 316L stainless steel. J. Int. Fatigue 19, 471–475 (1997)
Yin, G.Z., Dai, G.F., Wan, L.: Characteristics of bifurcation, chaos and self-organization of evolution of micro-cracks in rock. Chinese Journal of Rock Mechanics and Engineering 21(5), 635–639 (2002)
Sun, D.H., Sun, X.F., Liu, X.B.: Analysis on the parameter domains of short fatigue cracks system. Journal of Xiamen University (Natural Science) 39(2), 175–179 (2002)
Ni, K.: Advances in stochastic theory of fatigue damage accumulation. Advances in Mechanics 29(1), 45–46 (1999)
Li, P.Y., Feng, Y.L., Zhang, W.M.: System of obstacle avoidance used in deep-seabed vehicle based on BP neural network and genetic algorithm. Journal of central South University (Science and Technology) 41(4), 1374–1378 (2010)
Wang, L., Wang, Z., Yu, M.: Experimental study and numerical simulation on coalescence and interference of short cracks for low cycle fatigue at high temperature. Journal of Mechanical Strength 138(04), 642–646 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Z., Zhao, Z., Wang, L., Wang, K. (2011). Study on the Law of Short Fatigue Crack Using Genetic Algorithm-BP Neural Networks. In: Liu, D., Zhang, H., Polycarpou, M., Alippi, C., He, H. (eds) Advances in Neural Networks – ISNN 2011. ISNN 2011. Lecture Notes in Computer Science, vol 6677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21111-9_66
Download citation
DOI: https://doi.org/10.1007/978-3-642-21111-9_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21110-2
Online ISBN: 978-3-642-21111-9
eBook Packages: Computer ScienceComputer Science (R0)