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A recurrent neural network for modeling crack growth of aluminium alloy
Journal article   Peer reviewed

A recurrent neural network for modeling crack growth of aluminium alloy

L. Zhi, Y. Zhu, H. Wang, Z. Xu and Z. Man
Neural Computing and Applications, Vol.27(1), pp.197-203
2016
DOI: https://doi.org/10.1007/s00521-014-1712-z
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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.

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14 Times Cited - Web of Science

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Collaboration types
Domestic collaboration
International collaboration
Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.61 Artificial Intelligence & Machine Learning
4.61.493 Neural-Fuzzy Integration
Web Of Science research areas
Computer Science, Artificial Intelligence
ESI research areas
Engineering
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