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Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters
Abstract
In this paper, the fracture behavior and fatigue crack growth rate of the 2024-T351 aluminum alloy has been investigated. At first, the 2024-T351 aluminum alloys have been welded using friction stir welding procedure and the fracture toughness and fatigue crack growth rate of the CT specimens have been studied experimentally based on ASTM standards. After that, in order to predict fatigue crack growth rate and fracture toughness, artificial neural network is used. To obtain the best neuron number in the hidden layer of the artificial neural network, different neuron numbers are tested and the best network based on the performance is selected. Then the fitting method is applied and the fitted surfaces that illustrate the behavior of welding are shown and the results of artificial neural network and fitting method are compared. Also, multi-objective optimization algorithm is used to obtain the best welding parameters and finally sensitivity analysis is applied to measure the effect of rotational and traverse speeds on the fracture toughness and fatigue crack growth rate.
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Authors (6)
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Reza Masoudi Nejad
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Nima Sina
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Danial Ghahremani Moghadam
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Ricardo Branco
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Filippo Berto
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Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ijfatigue.2022.106840
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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INTERNATIONAL JOURNAL OF FATIGUE
no. 160,
ISSN: 0142-1123 - Language:
- English
- Publication year:
- 2022
- Bibliographic description:
- Masoudi Nejad R., Sina N., Ghahremani Moghadam D., Branco R., Macek W., Berto F.: Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters// INTERNATIONAL JOURNAL OF FATIGUE -Vol. 160, (2022), s.106840-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ijfatigue.2022.106840
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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