[1] Celik S, Cakir R. Effect of Friction Stir Welding Parameters on the Mechanical and Microstructure Properties of the Al-Cu Butt Joint. Metals (Basel) 2016;6. https://doi.org/10.3390/met6060133.
[2] Msomi V, Mabuwa S. Analysis of material positioning towards microstructure of the friction stir processed AA1050/AA6082 dissimilar joint. Adv Ind Manuf Eng 2020;1:100002. https://doi.org/https://doi.org/10.1016/j.aime.2020.100002.
[3] Banik A, Saha Roy B, Deb Barma J, Saha SC. An experimental investigation of torque and force generation for varying tool tilt angles and their effects on microstructure and mechanical properties: Friction stir welding of AA 6061-T6. J Manuf Process 2018;31:395–404. https://doi.org/https://doi.org/10.1016/j.jmapro.2017.11.030.
[4] Chen S, Zhang H, Jiang X, Yuan T, Han Y, Li X. Mechanical properties of electric assisted friction stir welded 2219 aluminum alloy. J Manuf Process 2019;44:197–206. https://doi.org/https://doi.org/10.1016/j.jmapro.2019.05.049.
[5] Gangil N, Maheshwari S, Siddiquee AN, Abidi MH, El-Meligy MA, Mohammed JA. Investigation on friction stir welding of hybrid composites fabricated on Al–Zn–Mg–Cu alloy through friction stir processing. J Mater Res Technol 2019;8:3733–40. https://doi.org/https://doi.org/10.1016/j.jmrt.2019.06.033.
[6] Węglowski MS. Friction stir processing – State of the art. Arch Civ Mech Eng 2018;18:114–29. https://doi.org/https://doi.org/10.1016/j.acme.2017.06.002.
[7] Pedapati SR, Paramaguru D, Awang M, Mohebbi H, Korada S V. Effect of process parameters on mechanical properties of AA5052 joints using underwater friction stir welding. J Mech Eng Sci 2020;14:6259–71.
[8] Khourshid AM, El-Kassas AM, Sabry I. Integration between artificial neural network and responses surfaces methodology for modeling of friction stir welding. Int J Adv Eng Res Sci 2015;2:67–73.
[9] Elatharasan G, Kumar VSS. An Experimental Analysis and Optimization of Process Parameter on Friction Stir Welding of AA 6061-T6 Aluminum Alloy using RSM. Procedia Eng 2013;64:1227–34. https://doi.org/https://doi.org/10.1016/j.proeng.2013.09.202.
[10] Kumar A, Khurana MK, Singh G. Modeling and Optimization of Friction Stir Welding Process Parameters for Dissimilar Aluminium Alloys. Mater Today Proc 2018;5:25440–9. https://doi.org/https://doi.org/10.1016/j.matpr.2018.10.349.
[11] Sefene EM, Tsegaw AA. Temperature-based optimization of friction stir welding of AA 6061 using GRA synchronous with Taguchi method. Int J Adv Manuf Technol 2022;119:1479–90. https://doi.org/10.1007/s00170-021-08260-3.
[12] De Weck OL. Multiobjective optimization: History and promise. Invit. Keynote Pap. GL2-2, Third China-Japan-Korea Jt. Symp. Optim. Struct. Mech. Syst. Kanazawa, Japan, vol. 2, 2004, p. 34.
[13] Fradkov AL. Early history of machine learning. IFAC-PapersOnLine 2020;53:1385–90.
[14] Asmare A, Al-Sabur R, Messele E. Experimental Investigation of Friction Stir Welding on 6061-T6 Aluminum Alloy using Taguchi-Based GRA. Metals (Basel) 2020;10:1480. https://doi.org/10.3390/met10111480.
[15] Sadeesh P, Venkatesh Kannan M, Rajkumar V, Avinash P, Arivazhagan N, Devendranath Ramkumar K, et al. Studies on Friction Stir Welding of AA 2024 and AA 6061 Dissimilar Metals. Procedia Eng 2014;75:145–9. https://doi.org/https://doi.org/10.1016/j.proeng.2013.11.031.
[16] Maneiah D, Mishra D, Rao KP, Raju KB. Process parameters optimization of friction stir welding for optimum tensile strength in Al 6061-T6 alloy butt welded joints. Mater Today Proc 2020;27:904–8. https://doi.org/https://doi.org/10.1016/j.matpr.2020.01.215.
[17] Thapliyal S, Mishra A. Machine learning classification-based approach for mechanical properties of friction stir welding of copper. Manuf Lett 2021;29:52–5. https://doi.org/https://doi.org/10.1016/j.mfglet.2021.05.010.
[18] Mishra A. Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints 2021.
[19] Hartl R, Praehofer B, Zaeh MF. Prediction of the surface quality of friction stir welds by the analysis of process data using Artificial Neural Networks. Proc Inst Mech Eng Part L J Mater Des Appl 2020;234:732–51. https://doi.org/10.1177/1464420719899685.
[20] Hartl R, Bachmann A, Habedank JB, Semm T, Zaeh MF. Process Monitoring in Friction Stir Welding Using Convolutional Neural Networks. Metals (Basel) 2021;11. https://doi.org/10.3390/met11040535.
[21] Gomathisankar M, Gangatharan M, Pitchipoo P. A Novel Optimization of Friction Stir Welding Process Parameters on Aluminum Alloy 6061-T6. Mater Today Proc 2018;5:14397–404. https://doi.org/https://doi.org/10.1016/j.matpr.2018.03.025.