A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation
School of Reliability and Systems Engineering, Beihang University, Haidian District, Beijing 100191, China
Author to whom correspondence should be addressed.
Academic Editor: Daolun Chen
Received: 27 March 2017 / Revised: 5 May 2017 / Accepted: 12 May 2017 / Published: 18 May 2017
The relationships between the fatigue crack growth rate
and stress intensity factor range
are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model (
approach). The results show that the predictions of MLAs are superior to those of
approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
Scifeed alert for new publications
Never miss any articles
matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
Define your Scifeed now
Share & Cite This Article
MDPI and ACS Style
Wang, H.; Zhang, W.; Sun, F.; Zhang, W. A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation. Materials 2017, 10, 543.
Wang H, Zhang W, Sun F, Zhang W. A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation. Materials. 2017; 10(5):543.
Wang, Hongxun; Zhang, Weifang; Sun, Fuqiang; Zhang, Wei. 2017. "A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation." Materials 10, no. 5: 543.
Show more citation formats
Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
[Return to top]
For more information on the journal statistics, click here
Multiple requests from the same IP address are counted as one view.