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The influence of friction stir welding (FSW) in automotive applications is significantly high in recent days as it can boast beneficial factors such as less distortion, minimized residual stresses and enhanced mechanical properties. Since there is no emission of harmful gases, it is regarded as a green technology, which has an energy efficient clean environmental solid-state welding process. In this research work, the FSW technique is employed to weld the AA8011–AZ31B alloy. In addition, the L16 orthogonal array is employed to conduct the experiments. The influences of parameters on the factors such as microstructure, hardness and tensile strength are determined. Microstructure images have shown tunnel formation at low rotational speed and vortex occurrence at high rotational speed. To attain high quality welding, the process parameters are optimized by using a hybrid method called an artificial neural network based genetic algorithm (ANN-GA). The confirmation tests are carried out under optimal welding conditions. The results obtained are highly reliable, which exhibits the optimal features of the hybrid method.
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Tom
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art. no. e140098
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Bibliogr. 38 poz., rys., tab.
Twórcy
autor
- Department of Mechanical Engineering, OASYS Institute of Technology, Trichy, Tamilnadu, India
autor
- Department of Mechanical Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamilnadu, India
autor
- Department of Mechanical Engineering, K. Ramakrishnan College of Engineering, Trichy, Tamilnadu, India
Bibliografia
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- [4] T. Sun, M.J. Roy, D. Strong, P.J. Withers, and P.B. Prangnell, “Comparison of residual stress distributions in conventional and stationary shoulder high-strength aluminum alloy friction stir welds”, J. Mater. Process. Technol., vol. 242, pp. 92–100, 2017.
- [5] H. Lina, Y. Wua, and S. Liu, “Impact of initial temper of base metal on microstructure and mechanical properties of friction stir welded AA 7055 alloy”, Mater. Charact., vol. 146, pp. 159–168, 2018, doi: 10.1016/j.jmapro.2018.04.017.
- [6] H.J. Aval and A. Loureiro, “Effect of reverse dual rotation process on properties of friction stir welding of AA7075 to AISI304”, Trans. Nonferrous Met. Soc. China, vol. 29, pp. 964–975, 2019, doi: 10.1016/S1003-6326(19)65005-3.
- [7] N.Z. Khan, A.N. Siddiquee, Z.A. Khan, and A.K. Mukhopadhyay, “Mechanical and microstructural behavior of friction stir welded similar and dissimilar sheets of AA2219 and AA7475 aluminum alloys”, J. Alloys Compd., vol. 695, pp. 2902–2908, 2017, doi: 1 0.1016/j.jallcom.2016.11.389.
- [8] S. Jannet, P.K. Mathews, and R. Raja, “Comparative investigation of friction stir welding and fusionwelding of 6061 T6 – 5083 O aluminum alloy based on mechanical properties and microstructure”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 62, no. 4, pp. 791–795, 2014.
- [9] M.K. Kulekci, U. Esme, F. Kahraman, and S. Ocalir, “Advanced hybrid welding and manufacturing technologies”, Mater. Test., vol. 58, no. 4, pp. 362–370, 2016, doi: 10.3139/120.110858.
- [10] B. Çevik, Y. Özçatalbaş, and B. Gülenç, “Effect of tool material on microstructure and mechanical properties in friction stir welding”, Mater. Test., vol. 58, no. 1, pp. 36–42, 2016, doi: 10.3139/120.110816.
- [11] I. Kucukrendec, “The investigation of suitable welding parameters in poly propylene sheets joined with friction stir welding”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 1, pp. 133–140, 2019.
- [12] I. Kucukrendec, “Mechanical and microstructural properties of EN AW-6060 aluminum alloy joints produced by friction stir welding”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 63, no. 2, pp. 475–478, 2015.
- [13] G. Kumar, R. Kumar, and R. Kumar, “Optimization of process parameters of friction stir welded AA5082-AA7075 butt joints using resonance fatigue properties”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 1, pp. 99–108, 2020.
- [14] W. Guo, G. You, G. Yuan, and X. Zhang, “Microstructure and mechanical properties of dissimilar inertia friction welding of 7A04 aluminum alloy to AZ31 magnesium alloy”, J. Alloys Compd., 2017, vol. 695, pp. 3267–3277, doi: 10.1016/j.jallcom.2016.11.218.
- [15] Z. Liu, S. Ji, and X. Meng, “Improving Joint Formation and Tensile Properties of Dissimilar Friction Stir Welding of Aluminum and Magnesium Alloys by Solving the Pin Adhesion Problem”, J. Mater. Eng. Perform., vol. 27, no. 3, pp. 1404–1413, 2018, doi: 10.1007/s11665-018-3216-y.
- [16] M. Ravichandran, M. Thirunavukkarasu, S. Sathish, and V. Anandakrishnan, “Optimization of welding parameters to attain maximum strength in friction stir welded AA7075 joints”, Mater. Test., vol. 58,no. 3, pp. 206–210, 2016, doi: 10.3139/120.110838.
- [17] N. Sabarirajan and A.N. Sait, “Optimization and thermal analysis of friction stir welding of AA 6061-AA 8011 joints”, Mater. Test., vol. 62, no. 3, pp. 317–328, 2020, doi: 10.3139/120.111473.
- [18] S. Deshwal, A. Kumar, and D. Chhabra, “Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement”, CIRP J. Manuf. Sci. Technol., vol. 31, no. 4, pp. 189–199, 2020, doi: 10.1016/j.cirpj.2020.05.009.
- [19] S.C. Cagan, M. Aci, B.B. Buldum, and C. Aci, “Artificial neural networks in mechanical surface enhancement technique for the prediction of surface roughness and microhardness of magnesium alloy”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 4, pp. 729–739, 2019.
- [20] H.D. Naghibi, M. Shakeri, and M. Hosseinzadeh, “Neural Network and Genetic Algorithm Based Modeling and Optimization of Tensile Properties in FSW of AA 5052 to AISI 304 Dissimilar Joints”, Trans. Indian Inst. Met., vol. 69, pp. 891–900, 2016, doi: 10.1007/s12666-015-0572-2.
- [21] Y. Rong et al., “Parameters optimization of laser brazing in crimping butt using Taguchi and BPNN-GA”, Opt. Lasers Eng., vol. 67, pp. 94–104, 2015, doi: 10.1016/j.optlaseng.2014.10.009.
- [22] K.K. Babu et al., “Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modelling with genetic algorithm”, The Int. J. Adv. Manuf. Technol., vol. 94, pp. 3117–3129, 2018, doi: 10.1007/s00170-017-0897-6.
- [23] A.S.F. Britto, R.E. Raja, and M.C. Mabel, “Prediction and optimization of mechanical strength of diffusion bonds using integrated ANN-GA approach with process variables and metallographic characteristics”, J. Manuf. Processes, vol. 32, pp. 828–838, 2018, doi: 10.1016/j.jmapro.2018.04.015.
- [24] H. Wang, Z. Zhang, and L. Liu, “Prediction and fitting of weld morphology of Al alloy-CFRP welding-rivet hybrid bonding joint based on GA-BP neural network”, J. Manuf. Processes, vol. 63, pp. 109–120, 2021, doi: 10.1016/j.jmapro.2020.04.010.
- [25] S. Chen, H. Zhang, X. Jiang, T. Yuan, Y. Han, and X. Li, “Mechanical properties of electric assisted friction stir welded 2219 aluminum alloy,” J. Manuf. Processes, vol. 44, pp. 197–206, 2019, doi: 10.1016/j.jmapro.2019.05.049.
- [26] A. Kumar and L.S. Raju, “Influence of tool pin profiles on friction stir welding of copper”, Mater. Manuf. Processes, vol. 27, no. 12, pp. 414–1418, 2012, doi: 10.1080/10426914.2012.689455.
- [27] B. Cieniawska, K. Pento ́s, and D. Łuczycka, “Neural modeling and optimization of the coverage of the sprayed surface”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 68, no. 3, pp. 601–608, 2020.
- [28] D. Rajeev, D. Dinakaran, and S.C.E. Singh, “Artificial neural network based tool wear estimation on dry hard turning processes of AISI4140 steel using coated carbide tool”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, no. 4, pp. 553–559, 2017.
- [29] Y. Li, B. Yub, B. Wang, T.H. Lee, and M. Banu, “Online quality inspection of ultrasonic composite welding by combining artificial intelligence technologies with welding process signatures”, Mater. Des., vol. 194, pp. 108912, 2020.
- [30] P.Q. Baban and I.N. Rahimabadi, “Input-output pairing criterion applied in the genetic algorithm for unstable linear systems”, Bull. Pol. Acad. Sci. Tech. Sci., vol. 64, no. 4, pp. 873–876, 2016.
- [31] A. Abdollahzadeh, A. Shokuhfar, J.M. Cabrera, A.P. Zhilyaev, and H. Omidvar, “In-situ nanocomposite in friction stir welding of 6061-T6 Aluminum alloy to AZ31 magnesium alloy”, J. Mater. Process. Technol., vol. 263, pp. 296–307, 2019, doi: 10.1016/j.jmatprotec.2018.08.025.
- [32] A.H. Baghdadi, Z. Sajuri, N.F.M. Selamat, M.Z. Omar, Y. Miyashita, and A.H. Kokabi, “Effect of intermetallic compounds on the fracture behavior of dissimilar friction stir welding joints of Mg and Al alloys,” Int. J. Miner. Metall. Mater., vol. 26, no. 10, pp. 1285–1298, 2019.
- [33] L. Zhanga and X. Wanga, “Microstructure Evolution and Properties of Friction Stir Welding Joint for 6082-T6 Aluminum Alloy”, Mater. Res., vol. 21, no. 6, pp. e20180285, 2018.
- [34] C.L. Yang, C.S. Wu, and X.Q. Lv, “Numerical analysis of mass transfer and material mixing in friction stir welding of aluminum/magnesium alloy”, J. Manuf. Processes, vol. 32, 380–394, 2018.
- [35] V.P. Singh, S.K. Patel, A. Ranjan, and B. Kuriachen, “Recent research progress in solid state friction-stir welding of aluminium–magnesium alloys: a critical review”, J. Mater. Res. Technol., vol. 9, no. 3, pp. 6217–6256, 2020.
- [36] W. Hu, Z. Ma, S. Ji, Q. Song, M. Chen, W. Jiang, “Improving the mechanical property of dissimilar Al/Mg hybrid friction stir welding joint by PIO-ANN”, J. Mater. Sci. Technol., vol. 53, pp. 41–52, 2020.
- [37] Z. Liang, G. Qin, L. Wang, X. Meng, and F. Li, “Microstructural characterization and mechanical properties of dissimilar friction welding of 1060 aluminum to AZ31B magnesium alloy”, Mater. Sci. Eng., A, vol. 645, pp. 170–180, 2015.
- [38] J. Verma, R.V. Taiwade, C. Reddy, and R.K. Khatirkar, “Effect of Friction Stir Welding Process Parameters on Mg-AZ31B/Al-AA6061 Joints,” Mater. Manuf. Processes, vol. 33, no. 3, pp. 1–26, 2017.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-92496898-361c-45f9-bd98-aa7267132e02