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Cavitation Damage Prediction of Stainless Steels Using an Artificial Neural Network Approach

1
Institute of Failure Analysis and Prevention, School of Materials Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2
The Collaborative Innovation Center for Advanced Aero-Engine, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
3
Shen Yuan Honors College, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
4
School of Material Science and Energy Engineering, Foshan University, Foshan 528000, China
*
Author to whom correspondence should be addressed.
Metals 2019, 9(5), 506; https://doi.org/10.3390/met9050506
Received: 1 April 2019 / Revised: 21 April 2019 / Accepted: 22 April 2019 / Published: 30 April 2019
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Abstract

Cavitation damage has not been well predicted because of its complex relationship of many mechanical and microstructural factors. An artificial neural network approach of the back-propagation network was used to predict cavitation damage of stainless steels, 316L and 420, in terms of the significant influence of cavitation time, roughness, and residual stress on cavitation damage. Mean depth of erosion was used to quantitatively describe cavitation damage of 316L and 420. Prediction accuracy was improved by analyzing the effects of the number and type of input nodes, the number of nodes in the hidden layer, and different activation functions on prediction accuracy. The best performance was in the model with the input nodes of cavitation time and roughness, eleven nodes in the hidden layer, and the activation function of logsig. View Full-Text
Keywords: artificial neural network; cavitation damage; residual stress artificial neural network; cavitation damage; residual stress
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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).
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Gao, G.; Zhang, Z.; Cai, C.; Zhang, J.; Nie, B. Cavitation Damage Prediction of Stainless Steels Using an Artificial Neural Network Approach. Metals 2019, 9, 506.

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