基于RBF-GA的铝/镁异材FSLW工艺参数优化  

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

作　　者：胡为[1] 常新新 姬书得[1] 李峰 宋崎[1] 牛士玉 HU Wei;CHANG Xinxin;JI Shude;LI Feng;SONG Qi;NIU Shiyu(Shenyang Aerospace University,Shenyang,110136,China;AVIC Xian Aircraft Industry(Group)Co.,Ltd.Aircraft Maintenance Center,Xi'an,710089,China)

机构地区：[1]沈阳航空航天大学,沈阳110136 [2]航空工业西安飞机工业(集团)有限责任公司飞机维修中心,西安710089

出　　处：《焊接学报》2020年第6期54-59,84,I0004,共8页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(51874201)。

摘　　要：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1037 r/min、焊接速度82 mm/min、Zn层厚度0.04 mm和超声功率1440 W进行试验验证,焊接接头拉剪载荷达到8860 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 1037 r/min,the welding speed of 82 mm/min,the Zn interlayer thickness of 0.04 mm and the ultrasound power of 1440 W.The tensile shear load of the joint was reached 8860 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.

关 键 词：RBF神经网络 遗传算法 铝/镁异材 搅拌摩擦搭接焊 工艺参数优化 

分 类 号：TG453[金属学及工艺—焊接]