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Artificial Neural Network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters
Abstract
The objective of this paper is to provide the fatigue life of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters. At first, the fatigue life of the riveted joints in AA2024 aluminum alloy plates is obtained by experimental tests. Then, an artificial neural network is applied to estimate the fatigue life of riveted lap joints based on the number of lateral and longitudinal holes, punch pressure, gap between the edge of hole and rivet, rivet shank diameter, and rivet shank length. Also, meta heuristic optimization algorithm is applied to calculate the riveting process parameters. Finally, sensitivity analysis is used to obtain the influence of parameters affecting the riveting process on the fatigue life.
Citations
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6
CrossRef
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0
Web of Science
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9
Scopus
Authors (8)
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Reza Masoudi Nejad
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Nima Sina
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Wenchen Ma
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Wei Song
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Shun-Peng Zhu
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Ricardo Branco
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Aboozar Gholami
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Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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INTERNATIONAL JOURNAL OF FATIGUE
no. 178,
ISSN: 0142-1123 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Masoudi Nejad R., Sina N., Ma W., Song W., Zhu S., Branco R., Macek W., Gholami A.: Artificial Neural Network based fatigue life assessment of riveted joints in AA2024 aluminum alloy plates and optimization of riveted joints parameters// INTERNATIONAL JOURNAL OF FATIGUE -Vol. 178, (2024), s.107997-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.ijfatigue.2023.107997
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
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