Improving the Mechanical Property of Dissimilar Al/Mg Zn‐added Ultrasound‐Assisted Friction Stir Lap Welding Joint by Back Propagation Netural Network-Gray Wolf Optimization Algorithm
Improving the Mechanical Property of Dissimilar Al/Mg Zn‐added Ultrasound‐Assisted Friction Stir Lap Welding Joint by Back Propagation Netural Network-Gray Wolf Optimization Algorithm
复制标题
反向传播神经网络-灰狼优化算法提高异种Al/Mg Zn超声辅助搅拌摩擦搭接焊缝力学性能
DOI：
10.1002/adem.201900973
复制
发表时间：
2019
影响因子：
3.6
通讯作者：
Mingfei Chen
中科院分区：
文献类型：
--
作者：
Qi Song;Zhaoxu Ren;Shude Ji;Shiyu Niu;Weiwei Qi;Mingfei Chen
Process parameters of rotating velocity, welding speed, Zn interlayer thickness, and ultrasound power are optimized by the hybrid of back propagation neural network (BPNN) and gray wolf optimization algorithm (GWOA) to obtain a high‐quality Zn‐added ultrasound‐assisted friction stir lap welding joint of 7075‐T6 Al/AZ31B Mg dissimilar alloys. The results state that the prediction accuracy of the trained BPNN model is acceptable. The optimal process parameters combination is obtained by the GWOA which is combined with the trained BPNN. The verification tests are performed under the executable optimal solution, which consists of the rotating velocity of 1054 rpm, the welding speed of 54 mm min−1, the Zn interlayer thickness of 0.05 mm, and the ultrasound power of 1568 W. The tensile shear load of the joint reaches 9.05 kN, and the strength is 11.8% larger than that of the reported optimal joint. The artificial intelligence optimization method of GWOA combined with BPNN can accurately predict and optimize the joint strength, which has great time and economic advantages.
