为获得高质量的7075-T6/AZ31B异种合金Zn中间层-超声辅助FSLW接头，通过RBF-遗传算法对转速、焊接速度、Zn中间层厚度及超声功率四种工艺参数进行了优化. 结果表明，经过训练的RBF神经网络满足预测精度要求；将其与遗传算法相结合，在经多次迭代后可获得最优工艺参数组合. 取可执行最优解转速1 037 r/min、焊接速度82 mm/min、Zn层厚度0.04 mm和超声功率1 440 W进行试验验证，焊接接头拉剪载荷达到8 860 N，与已报道最优接头相比提高11.4%. RBF神经网络与遗传算法相结合的人工智能优化方法可准确预测并优化接头质量，且具有较大的时间及经济优势.
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.
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