A Comparison Study of Machine Learning Based Algorithms for Fatigue Crack Growth Calculation

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...

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Bibliographic Details
Published in:Materials Vol. 10; no. 5; p. 543
Main Authors: Wang, Hongxun, Zhang, Weifang, Sun, Fuqiang, Zhang, Wei
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 18.05.2017
MDPI
Subjects:
Algorithms
Alloys
Approximation
Artificial intelligence
Back propagation
Back propagation networks
Crack propagation
Extrapolation
Fatigue failure
Fracture mechanics
Genetic algorithms
Global optimization
Growth models
Growth rate
Machine learning
Mathematical analysis
Modelling
Neural networks
Nonlinearity
Radial basis function
Stress intensity factors
Stress ratio
Test procedures
stress ratio
fatigue life prediction
fatigue crack growth
machine learning algorithms
extreme learning machine (ELM)
ISSN:1996-1944, 1996-1944
Online Access:Get full text
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Summary: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.
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ISSN:1996-1944
1996-1944
DOI:10.3390/ma10050543