Optimization of friction stir welding process parameters using multi-criteria decision making approach
Author:
Affiliation:
1. Mechatronics Engineering , Firat Universitesi , Elazig 23119 , Türkiye
2. Mechatronics , Firat Universitesi , Elazig , Türkiye
Abstract
Publisher
Walter de Gruyter GmbH
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science
Link
https://www.degruyter.com/document/doi/10.1515/mt-2022-0432/pdf
Reference21 articles.
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2. B. Parida and S. Pal, “Fuzzy assisted grey Taguchi approach for optimisation of multiple weld quality properties in friction stir welding process,” Sci. Technol. Weld. Join., vol. 20, no. 1, pp. 35–41, 2015, https://doi.org/10.1179/1362171814Y.0000000251.
3. P. K. Sahu, K. Kumari, S. Pal, and S. K. Pal, “Hybrid fuzzy-grey-Taguchi based multi weld quality optimization of Al/Cu dissimilar friction stir welded joints,” Adv. Manuf., vol. 4, pp. 237–247, 2016, https://doi.org/10.1007/s40436-016-0151-8.
4. K. K. Babu, K. Panneerselvam, P. Sathiya, et al.., “Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm,” Int. J. Adv. Manuf. Technol., vol. 94, pp. 3117–3129, 2018, https://doi.org/10.1007/s00170-017-0897-6.
5. F. Sarsılmaz and G. Kavuran, “Prediction of the optimal FSW process parameters for joints using machine learning techniques,” Mater. Test., vol. 63, no. 12, pp. 1104–1111, 2021, https://doi.org/10.1515/mt-2021-0058.
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