Skip to main content
SpringerLink
Log in

Fatigue Damage Assessment of Magnesium Alloy Materials Based on Artificial Neural Network

  • Conference paper
  • First Online:
Application of Intelligent Systems in Multi-modal Information Analytics (MMIA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1384))

  • 956 Accesses

Abstract

Artificial Neural Network (ANN) is a highly natural, interconnected nonlinear dynamic network system based on the natural mechanism that mimics the structural characteristics of human brain neurons. It can handle knowledge, thought, learning and memory. In this article, we will use AZ31B and ZK60 extruded magnesium alloy to perform uniaxial on various paths. And multi-axis fatigue test. Combined with the fatigue test data of AZ61A magnesium alloy in the literature, the BP neural network technology is used to predict the behavior of magnesium alloy under various displacement paths. The fatigue life is compared with the results predicted by the traditional multiaxial fatigue model based on the critical level method. The experimental results here show that the pore pressure and fatigue analysis of cast magnesium alloys can provide a more reasonable basis for mechanical design. The results show that the artificial neural network has more advantages in predicting the life of magnesium alloys. It has a relatively accurate effect on the identification of other units, and can successfully determine the damage degree of the damaged unit.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adavi, M., Salehi, M., Roudbari, M.: Artificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes. Med. J. Islamic Repub. Iran 30(1), 312 (2016)

    Google Scholar 

  2. Pandey, A., Mishra, A.: Application of artificial neural networks in yield prediction of potato crop. Russ. Agric. Sci. 43(3), 266–272 (2017)

    Article  Google Scholar 

  3. Bonelli, M.G., Ferrini, M., Manni, A.: Artificial neural networks to evaluate organic and inorganic contamination in agricultural soils. Chemosphere 186(2), 124–131 (2017)

    Article  Google Scholar 

  4. Saied, A., Overill, R.E., Radzik, T.: Detection of known and unknown DDoS attacks using Artificial Neural Networks. Neurocomputing 172(JAN.8), 385–393 (2016)

    Article  Google Scholar 

  5. Monteiro, R.V.A., Guimaraes, G.C., Moura, F.A.M., et al.: Estimating photovoltaic power generation: performance analysis of artificial neural networks, Support Vector Machine and Kalman filter. Electr. Power Syst. Res. 143(Feb.), 643–656 (2017)

    Article  Google Scholar 

  6. Carvalho, D., Garcia-Martinez, N.A., Lado, J.L., et al.: Real-space mapping of topological invariants using artificial neural networks. Phys. Rev. B 97(11), 115453.1–115453.8 (2018)

    Google Scholar 

  7. Emamgholizadeh, S., Shahsavani, S., Eslami, M.A.: Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K). Chin. Geogr. Sci. 27(05), 747–759 (2017)

    Article  Google Scholar 

  8. Carleo, G., Troyer, M.: Solving the quantum many-body problem with artificial neural networks. Science 355(6325), 602–606 (2016)

    Article  MathSciNet  Google Scholar 

  9. Alanis, A.Y.: Electricity prices forecasting using artificial neural networks. IEEE Latin Am. Trans. 16(1), 105–111 (2018)

    Article  Google Scholar 

  10. Daniel, J., Abudhahir, A., Paulin, J.J.: Magnetic Flux Leakage (MFL) based defect characterization of steam generator tubes using artificial neural networks. J. Magn. 22(1), 34–42 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission, China (Grant No. KJQN202000839) and The Dr. Scientific Research Funds of CTBU (Grant No. 1956042).

Author information

Authors and Affiliations

  1. College of Mechanical Engineering, Chongqing University, Chongqing, 400044, China

    Yanyan Jia

  2. Chongqing Ceprei Industrial Technology Research Institute, Chongqing, 401332, China

    Yanyan Jia & Chuan Yang

  3. Chongqing Key Laboratory of Manufacturing Equipment Mechanism Design and Control, Chongqing Technology and Business University, Chongqing, 400067, China

    Yanyan Jia

Authors
  1. Yanyan Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chuan Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chuan Yang .

Editor information

Editors and Affiliations

  1. Decision and Information Sciences, Oakland University, Rochester, MI, USA

    Vijayan Sugumaran

  2. Shanghai University, Shanghai, China

    Zheng Xu

  3. Department of Informatics, University of Leicester, Leicester, UK

    Huiyu Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jia, Y., Yang, C. (2021). Fatigue Damage Assessment of Magnesium Alloy Materials Based on Artificial Neural Network. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2021. Advances in Intelligent Systems and Computing, vol 1384. Springer, Cham. https://doi.org/10.1007/978-3-030-74811-1_82

Download citation

Publish with us

Policies and ethics

Search

Navigation