Experimental Modeling and Multi-Response Optimization in Friction Stir Welding Process Parameters of AA2024-T3 Using Response Surface Methodology and Desirability Approach
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40799-023-00691-9.pdf
Reference46 articles.
1. Boulahem K, Ben Salem S, Bessrour J (2018) Prediction model of ultimate tensile strength and investigation on microstructural characterization of friction stir welded AA2024-T3. Int J Adv Manuf Technol 95:1473–1486. https://doi.org/10.1007/s00170-017-1215-z
2. Boukraa M, Chekifi T, Lebaal N (2022) Friction stir welding of aluminum using a multi-objective optimization approach based on both Taguchi method and grey relational analysis. Exp Tech. https://doi.org/10.1007/s40799-022-00573-6
3. Boukraa M, Chekifi T, Lebaal N, Aissani M (2022) Robust optimization of both dissolution time and heat affected zone over the friction stir welding process using SQP technique. Exp Tech 46:677–689. https://doi.org/10.1007/s40799-021-00515-8
4. Nagarajan BM, Manoharan M (2022) Influence of cooling conditions on tensile lap shear strength and microstructure of friction stir welded aluminum alloy 5052–H32 and polycarbonate light weight hybrid joint. J Manuf Process 82:390–402. https://doi.org/10.1016/j.jmapro.2022.08.008
5. Dinesh Kumar R, IlharUl Hassan MS, Muthukumaran S, Venkateswaran T, Sivakumar D (2019) Single and multi-response optimization and validation of mechanical properties in dissimilar friction stir welded AA2219-T87 and AA7075-T73 alloys using T-GRA. Exp Tech 43:245–259. https://doi.org/10.1007/s40799-019-00305-3
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