[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A recurrent neural network for modeling crack growth of aluminium alloy

  • Extreme Learning Machine and Applications
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A new recurrent neural model for crack growth process of aluminium alloy is developed in this work. It is shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The output feedback loops in the neural model play the role of capturing the fine changes of crack growth dynamics. The Extreme Learning Machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable-amplitude loading can be accurately modeled. The simulation results with the averaged experimental data of the 2024-T351 aluminium alloy show that the excellent modeling and prediction performance of the recurrent neural model can be achieved for fatigue crack growth of aluminium alloys.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Patankar R, Ray A, Lakhtakia A (1998) A state-space model of fatigue crack growth. Int J Fract 90:235–249

    Article  Google Scholar 

  2. Patankar R, Ray A (2000) State-space modeling of fatigue crack growth in ductile alloys. Eng Fract Mech 66:129–151

    Article  Google Scholar 

  3. Ray A, Patankar R (2001) Fatigue crack growth under variable-amplitude loading: Part I—model formulation in state-space setting. Appl Math Model 25:979–994

    Article  MATH  Google Scholar 

  4. Ray A, Patankar R (2001) Fatigue crack growth under variable-amplitude loading: Part II—code development and model validation. Appl Math Model 25:995–1013

    Article  MATH  Google Scholar 

  5. Dowling NE (2013) Mechanical behavior of materials. Pearson Education Limited, Edinburgh

    Google Scholar 

  6. Dabayeh A, Topper T (1995) Changes in crack-opening stress after underload and overload in 2024-T351 aluminium alloy. Int J Fatigue 4:261–269

    Article  Google Scholar 

  7. Huang G-B, Chen L, Siew C (2006) Universal approximation using incremental constructive feedforaward neural networks with random hidden notes. IEEE Trans Neural Netw 17(4):879–892

    Article  Google Scholar 

  8. Huang G-B, Zhu Q, Siew CK (2006) Extreme learning machine: theory and application. Neurocomputing 70:489–501

    Article  Google Scholar 

  9. Huang G-B, Zhou H, Ding X, Zhang R (2011) Extreme learning machine for regression and multi-class classification. IEEE Trans Syst Man Cybern Part B 42(2):513–529

  10. Haykin S (2009) Neural networks and learning machines, 3rd edn. Pearson Prentice Hall, New Jersey

    Google Scholar 

  11. Man Z, Lee K, Wang DH, Cao Z, Miao C (2011) A new robust training algorithm for a class of single hidden layer neural networks. Neurocomputing 74:2491–2501

    Article  Google Scholar 

  12. Man Z, Wang W, Khoo S, Yin J (2012) Optimal sinusoidal modelling of gear mesh vibration signals for gear diagnosis and prognosis. Mech Syst Signal Process 33:256–275

    Article  Google Scholar 

  13. Man Z, Lee K, Wang DH, Cao Z, Khoo S (2012) A robust single-hidden layer feedforward network based pattern classifier. IEEE Trans Neural Netw 23(12):1974–1986

    Article  Google Scholar 

  14. Porter TR (1972) Method of analysis and prediction for variable amplitude fatigue crack growth. Eng Fract Mech 4:717–735

    Article  Google Scholar 

Download references

Acknowledgments

The first author would like to acknowledge the support from the 2013 Lishui High talents’ cultivation Project (2013RC08) and Zhejiang Provincial Natural Science Foundation of China (LQ13F020005).

Author information

Authors and Affiliations

  1. School of Engineering, Lishui University, Zhejiang, 323000, China

    Linxian Zhi

  2. Office for Research, Lishui University, Zhejiang, 323000, China

    Yuyang Zhu

  3. Lishui CA Steer-by-Wire Technological Co. Ltd, Zhejiang, 323000, China

    Hai Wang & Zhengming Xu

  4. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, VIC, 3122, Australia

    Hai Wang & Zhihong Man

Authors
  1. Linxian Zhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuyang Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hai Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhengming Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhihong Man
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hai Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhi, L., Zhu, Y., Wang, H. et al. A recurrent neural network for modeling crack growth of aluminium alloy. Neural Comput & Applic 27, 197–203 (2016). https://doi.org/10.1007/s00521-014-1712-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1712-z

Keywords

Search

Navigation