Parameters optimization for friction stir lap welding of Al/Mg dissimilar alloys based on RBF-GA

The hybrid of RBF neural network with genetic algorithm (GA) was employed to optimize process parameters of rotating velocity, welding speed, Zn interlayer thickness and ultrasound power, thus obtaining a dissimilar 7075-T6 Al/AZ31B Mg Zn-added ultrasound assisted friction stir lap welding joint with a high quality. The results stated that the prediction accuracy of the trained RBF neural network was accepted. GA was combined with RBF neural network, and the optimal combination of welding process parameters was obtained after many iterations. The verification test was performed under the executable optimal solution which consisted of the rotating velocity of 1 037 r/min, the welding speed of 82 mm/min, the Zn interlayer thickness of 0.04 mm and the ultrasound power of 1 440 W. The tensile shear load of the joint was reached 8 860 N, which was 11.4% larger than that of the reported optimal joint. The artificial intelligence optimization method of RBF neural network with GA can accurately predict and optimize the joint quality, which has great time and economic advantages.