Reliability-Based Robust Multi-Objective Optimization of Friction Stir Welding Lap Joint AA1100 Plates

Document Type: Research Paper

Authors

1 Automotive Simulation and Optimal Design Research Laboratory, School of Automotive Engineering, Tehran, Iran

2 University of Science and Technology, Tehran, Iran

10.22034/jsm.2019.1877986.1493

Abstract

The current paper presents a robust optimum design of friction stir welding (FSW) lap joint AA1100 aluminum alloy sheets using Monte Carlo simulation, NSGA-II and neural network. First, to find the relation between the inputs and outputs a perceptron neural network model was obtained. In this way, results of thirty friction stir welding tests are used for training and testing the neural network. Using such obtained neural network model, for the reliability robust design of the FSW, a multi-objective genetic algorithm is employed. In this way, the statistical moments of the forces, temperature, strength, elongation, micro-hardness of welded zone, grain size and welded zone thickness are considered as the conflicting objectives. The optimization process was followed by multi criteria decision making process, NIP and TOPSIS, to propose optimum points for each of the pin profiles. It is represented that some beneficial design principles are involved in FSW which were discovered by the proposed optimization process.

Keywords

[1] Thomas W.M., Nicholas E.D., Needham J.C., Murch M.G., Temple-Smith P., Dawes C.J.,1995, Friction Welding, In Google Patents.
[2] Dawes C., Thomas W., 1995, Friction stir joining of aluminium alloys, TWI Bulletin 6(1): 1.
[3] Vijayan S., Raju R., Rao S.R.K., 2010, Multiobjective optimization of friction stir welding process parameters on aluminum alloy AA 5083 using taguchi-based grey relation analysis, Materials and Manufacturing Processes 25(11): 1206-1212.
[4] Suresha C., Rajaprakash B., Upadhya S., 2011, A study of the effect of tool pin profiles on tensile strength of welded joints produced using friction stir welding process, Materials and Manufacturing Processes 26(9): 1111-1116.
[5] Cox C.D., Gibson B.T., Strauss A.M., Cook G.E., 2012, Effect of pin length and rotation rate on the tensile strength of a friction stir spot-welded al alloy: a contribution to automated production, Materials and Manufacturing Processes 27(4): 472-478.
[6] Ganesh P., Kumar V.S., 2015, Superplastic forming of friction stir welded AA6061-T6 alloy sheet with various tool rotation speed, Materials and Manufacturing Processes 30(9): 1080-1089.
[7] Montazerolghaem H., Badrossamay M., Tehrani A.F., Rad S.Z., Esfahani M.S., 2015, Dual-rotation speed friction stir welding: Experimentation and modeling, Materials and Manufacturing Processes 30(9): 1109-1114.
[8] Shojaeefard M.H., Behnagh R.A., Akbari M., Givi M.K.B., Farhani F., 2013, Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm, Materials & Design 44: 190-198.
[9] Buffa G., Fratini L., Micari F., 2012, Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys, Journal of Manufacturing Processes 14(3): 289-296.
[10] Okuyucu H., Kurt A., Arcaklioglu E., 2007, Artificial neural network application to the friction stir welding of aluminum plates, Materials & Design 28(1): 78-84.
[11] Shojaeefard M.H., Akbari M., Tahani M., Farhani F., 2013, Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass, Advances in Materials Science and Engineering 2013: 574914.
[12] Asadi P., Besharati Givi M. K., Rastgoo A., Akbari M., Zakeri V., Rasouli S., 2012, Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks, International Journal of Advanced Manufacturing Technology 63:1095-1107.
[13] Collette Y., Siarry P., 2003, Multiobjective Optimization: Principles and Case Studies, Decision Engineering Series, Springer, Berlin.
[14] Srinivas N., Deb K., 1994, Multiobjective optimization using nondominated sorting in genetic algorithms, Evolutionary Computation 2(3): 221-248.
[15] Nariman-Zadeh N., Darvizeh A., Jamali A., 2006, Pareto optimization of energy absorption of square aluminum columns using multi-objective genetic algorithms, Journal of Engineering Manufacture, Proceedings of the Institution of Mechanical Engineers, Part B 220(2): 213-224.
[16] Atashkari K., Nariman-Zadeh N., Go¨lcu M., Khalkhali A., Jamali A., 2007, Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms, Journal of Energy Conversion and Management 48: 1029-1041.
[17] Amanifard N., Nariman-Zadeh N., Borji M., Khalkhali A., Habibdoust A., 2008, Modelling and Pareto optimization of heat transfer and flow coefficients in micro channels using GMDH type neural networks and genetic algorithms, Journal of Energy Conversion and Management 49: 311-325.
[18] Khalkhali A., Safikhani H., 2012, Applying evolutionary optimization on the airfoil design, Journal of Computational and Applied Research in Mechanical Engineering 2(1): 51-62.
[19] Shojaeefard M.H., Khalkhali A., Faghihian H., Dahmardeh M., 2018, Optimal platform design using non-dominated sorting genetic algorithm II and technique for order of preference by similarity to ideal solution; application to automotive suspension system, Engineering Optimization 50(3): 471-482.
[20] Khalkhali A., Khakshournia S., Nariman-zadeh N., 2014, A hybrid method of FEM, modified NSGAII and TOPSIS for structural optimization of sandwich panels with corrugated core, Journal of Sandwich Structures & Materials 16(4): 398-417.
[21] Khalkhali A., 2015, Best compromising crashworthiness design of automotive S-rail using TOPSIS and modified NSGAII, Journal of Central South University 22(1):121-133.
[22] Jamali A., Hajiloo A., Nariman-zadeh N., 2010, Reliability based robust Pareto design of linear state feedback controllers using a multi-objective uniform-diversity genetic algorithm (MUGA), Expert Systems With Applications 37: 401-413.
[23] LÖnn D., Öman M., Nilsson L., Simonsson K., Finite element based robustness study of a truck cab subjected to impact loading, International Journal of Crashworthiness 14(2): 111-124.
[24] Ditlevsen O., Madsen O.H.,1996, Structural Reliability Methods, John Wiley and Sons, New York.
[25] Papadrakakis M., Lagaros N.D., Plevris V., 2004, Structural optimization considering the probabilistic system response, International Journal of Theoretical and Applied Mechanics 31(3-4): 361-393.
[26] Khakhali A., Nariman-zadeh N., Darvizeh A., Masoumi A., Notghi B., 2010, Reliability-based robust multi-objective crashworthiness optimisation of S-shaped box beams with parametric uncertainties, International Journal of Crashworthiness 15(4): 443-456.
[27] Khakhali A., Darvizeh A., Masoumi A., Nariman-zadeh N., Shiri A., 2010, Robust design of s-shaped box beams subjected to compressive load, Mathematical Problems in Engineering 2010: 627501.
[28] Fonseca C.M., Fleming P.J., 1996, Nonlinear system identification with multiobjective genetic algorithms, Proceedings of the 13th World Congress, International Federation of Automatic Control, Pergamon Press, San Francisco.
[29] Iba H., Kuita T., deGaris H., Sator T., 1993, System identification using structured genetic algorithms, Proceedings of 5th International Conference on Genetic Algorithms, Urbana.
[30] Khalkhali A., Ebrahimi-Nejad S., Malek N.G., 2018, Comprehensive optimization of friction stir weld parameters of lap joint AA1100 plates using artificial neural networks and modified NSGA-II, Materials Research Express 5(6): 066508.