Robust Multi-Objective Optimization of Mechanical Properties of Friction Stir Welding Using Neural Network and Modified-NSGA-II

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.