Improving the Mechanical Property of Dissimilar Al/Mg Zn‐Added Ultrasound‐Assisted Friction Stir Lap Welding Joint by Back Propagation Neural Network–Gray Wolf Optimization Algorithm. Issue 12 (16th October 2019)

• Record Type: Journal Article
• Authors: Song, Qi; Ren, Zhaoxu; Ji, Shude; Niu, Shiyu; Qi, Weiwei; Chen, Mingfei
• Is Part Of: Advanced engineering materials. Volume 21:Issue 12(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-10-16
• Subjects: Al/Mg dissimilar alloys -- back propagation neural network -- friction stir lap welding -- gray wolf optimization algorithm -- process parameters optimization; Materials -- Periodicals; 620.11
• DOI: 10.1002/adem.201900973
• Languages: English
• ISSNs: 1438-1656

Abstract

Abstract : 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. Abstract : The process parameters for the Zn‐added ultrasound‐assisted friction stir lap welding of 7075‐T6 Al/AZ31B Mg alloys are optimized by establishing a hybrid artificial intelligence optimization model of back propagation neural network and gray wolf optimization algorithm. The quality of the joint corresponding to the executable optimal solution is evidently improved compared to the reported optimal joint.