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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
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Academic Editor: Daolun Chen
Materials 2017, 10(5), 543; https://doi.org/10.3390/ma10050543
Received: 27 March 2017 / Revised: 5 May 2017 / Accepted: 12 May 2017 / Published: 18 May 2017
The relationships between the fatigue crack growth rate ( d a / d N ) and stress intensity factor range ( Δ K ) 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 ( K * approach). The results show that the predictions of MLAs are superior to those of K * 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. View Full-Text
Keywords: fatigue crack growth; machine learning algorithms; fatigue life prediction; extreme learning machine (ELM); stress ratio fatigue crack growth; machine learning algorithms; fatigue life prediction; extreme learning machine (ELM); stress ratio
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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.

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