Robust Multi-Objective Optimization of Mechanical Properties of Friction Stir Welding Using Neural Network and Modified-NSGA-II
الموضوعات :
Mostafa Akbari
1
(Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran)
Hossein Rahimi Asiabaraki
2
(Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran)
الکلمات المفتاحية: Optimization, Neural network, Friction Stir Welding, robust,
ملخص المقالة :
In this paper, the optimal parameters of the FSW welding process to improve the joint's mechanical properties are obtained using robust multi-objective optimization. First, the properties of the weld zone, such as the chemical composition of the weld, are investigated using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). The hardness and tensile properties of the weld were investigated to evaluate the mechanical properties of the joint. The results show at the AA7075 side, the highest hardness is observed in the TMAZ, and the hardness is reduced in the SZ. Tensile testing revealed that the joint's mechanical characteristics were superior to those of the basic metals. In order to obtain the relationship between the process input parameters and the mechanical properties of the obtained joint, an artificial neural network model (ANN) was used. The relationship obtained by ANN was then used to obtain the optimal values of process parameters considering uncertainties in a robust optimization algorithm. In this way, using such an obtained feed-forward neural network and the Monte Carlo simulation, a multi-objective genetic algorithm is used for the robust Pareto optimization of the friction stir welding parameters having probabilistic uncertainties in parameters. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was used to get the best optimum solution. The robust optimal process parameters were determined by robust multivariate optimization to be 1467 rpm rotational speed and 11 mm/min traverse velocity.
[1] Robitaille, B., et al., Mechanical properties of 2024-T3 AlClad aluminum FSW lap joints and impact of surface preparation. International Journal of Fatigue, 2021. 143: p. 105979.
[2] Sen, M., S. shankar, and S. Chattopadhyaya, Investigations into FSW joints of dissimilar aluminum alloys. Materials Today: Proceedings, 2020. 27: p. 2455-2462.
[3] Dhanesh Babu, S.D., et al., Development of Thermo Mechanical Model for Prediction of Temperature Diffusion in Different FSW Tool Pin Geometries During Joining of AZ80A Mg Alloys. Journal of Inorganic and Organometallic Polymers and Materials, 2021. 31(7): p. 3196-3212.
[4] Eivani, A.R., et al., A novel approach to determine residual stress field during FSW of AZ91 Mg alloy using combined smoothed particle hydrodynamics/neuro-fuzzy computations and ultrasonic testing. Journal of Magnesium and Alloys, 2021. 9(4): p. 1304-1328.
[5] Xu, N., et al., Microstructure and tensile properties of rapid-cooling friction-stir-welded AZ31B Mg alloy along thickness direction. Transactions of Nonferrous Metals Society of China, 2020. 30(12): p. 3254-3262.
[6] Akbari, M. and P. Asadi, Dissimilar friction stir lap welding of aluminum to brass: Modeling of material mixing using coupled Eulerian–Lagrangian method with experimental verifications. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2020. 234(8): p. 1117-1128.
[7] Akbari, M., P. Asadi, and R.A. Behnagh, Modeling of material flow in dissimilar friction stir lap welding of aluminum and brass using coupled Eulerian and Lagrangian method. The International Journal of Advanced Manufacturing Technology, 2021. 113(3): p. 721-734.
[8] Zhang, J., et al., Improving performance of friction stir welded AZ31/AM60 dissimilar joint by adjusting texture distribution and microstructure. Materials Science and Engineering: A, 2020. 778: p. 139088.
[9] Yuvaraj, K.P., et al., Optimization of FSW tool parameters for joining dissimilar AA7075-T651 and AA6061 aluminium alloys using Taguchi Technique. Materials Today: Proceedings, 2021. 45: p. 919-925.
[10] Han, M.-S., et al., Optimum condition by mechanical characteristic evaluation in friction stir welding for 5083-O Al alloy. Transactions of Nonferrous Metals Society of China, 2009. 19, Supplement 1(0): p. s17-s22.
[11] Amancio-Filho, S.T., et al., Preliminary study on the microstructure and mechanical properties of dissimilar friction stir welds in aircraft aluminium alloys 2024-T351 and 6056-T4. Journal of Materials Processing Technology, 2008. 206(1–3): p. 132-142.
[12] Ghiasvand, A., et al., Effects of tool offset, pin offset, and alloys position on maximum temperature in dissimilar FSW of AA6061 and AA5086. International Journal of Mechanical and Materials Engineering, 2020. 15.
[13] Verma, S. and V. Kumar, Optimization of friction stir welding parameters of dissimilar aluminium alloys 6061 and 5083 by using response surface methodology. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2021: p. 09544062211005804.
[14] Khakhali, A., et al., Reliability-based robust multi-objective crashworthiness optimisation of S-shaped box beams with parametric uncertainties. International Journal of Crashworthiness, 2010. 15(4): p. 443-456.
[15] Standard Test Methods for Tension Testing of Metallic Materials, in ASTM E8 / E8M-21. 2021, ASTM International: West Conshohocken.
[16] Akbari, M., et al., The effect of in-process cooling conditions on temperature, force, wear resistance, microstructural, and mechanical properties of friction stir processed A356. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2016. 232(5): p. 429-437.
[17] Shojaeefard, M.H., et al., Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm. Materials & Design, 2013. 44(0): p. 190-198.
[18] Akbari, M., et al., Investigation of the effect of friction stir processing parameters on temperature and forces of Al–Si aluminum alloys. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 2015. 232(3): p. 213-229.
[19] Akbari, M., et al., Wear Performance of A356 Matrix Composites Reinforced with Different Types of Reinforcing Particles. Journal of Materials Engineering and Performance, 2017. 26(9): p. 4297-4310.
[20] Khakhali, A., et al., Robust Design of S-Shaped Box Beams Subjected to Compressive Load. Mathematical Problems in Engineering, 2010. 2010.
[21] Etghani, M.M., et al., A hybrid method of modified NSGA-II and TOPSIS to optimize performance and emissions of a diesel engine using biodiesel. Applied Thermal Engineering, 2013. 59(1–2): p. 309-315.
[22] Amanifard, N., et al., Modelling and Pareto optimization of heat transfer and flow coefficients in microchannels using GMDH type neural networks and genetic algorithms. Energy Conversion and Management, 2008. 49(2): p. 311-325.
[23] Jamali, A., et al., Multi-objective evolutionary optimization of polynomial neural networks for modelling and prediction of explosive cutting process. Engineering Applications of Artificial Intelligence, 2009. 22(4–5): p. 676-687.
