Improving joint quality of hybrid friction stir welded Al/Mg dissimilar alloys by RBFNN-GWO system

被引：31

作者：

Song, Qi ^{[1
,2
]}

Wang, Hairui ^{[1
]}

Ji, Shude ^{[1
,2
]}

Ma, Zhongwei ^{[1
]}

Jiang, Wenhui ^{[2
,3
]}

Chen, Mingfei ^{[2
]}

机构：

[1] Shenyang Aerosp Univ, Coll Aerosp Engn, Shenyang 110136, Peoples R China

[2] Engn Res Ctr Ind Multirotor UAV Liaoning Prov, Shenyang 110136, Peoples R China

[3] SLZY Shenyang High Tech Co Ltd, Shenyang 1101172, Peoples R China

来源：

JOURNAL OF MANUFACTURING PROCESSES | 2020年 / 59卷

基金：

中国国家自然科学基金;

关键词：

Friction stir welding; Stationary shoulder; Ultrasonic; Grey wolf optimization; Radial basis function neural network; GREY WOLF OPTIMIZER; ALUMINUM-ALLOY; WELDING PROCESS; REGRESSION-ANALYSIS; AL-ALLOY; MG; MAGNESIUM; MICROSTRUCTURE; METAL;

D O I：

10.1016/j.jmapro.2020.10.037

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Welding of Al/Mg dissimilar alloys faces many challenges. In this study, ultrasonic-stationary shoulder assisted friction stir welding (U-SSFSW) was employed to join the dissimilar alloys of AZ31B Mg and 6061-T6 Al. Radial basis function neural network (RBFNN) was used to model the relationships between the inputs of welding speed, rotating speed and ultrasonic power and the output of ultimate tensile strength (UTS) of U-SSFSW joint. After that, grey wolf optimization (GWO) algorithm was used to explore the maximum UTS and the corresponding optimal process parameters. The maximum UTS reached 158 MPa under the RBFNN-GWO system optimized process parameters. The microstructure and fracture behavior were analyzed to clarify the superiorities of the optimal process parameters and the enhancement mechanism of U-SSFSW technique under the optimized parameters.

引用

页码：750 / 759

页数：10

共 61 条

[1] Process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials
Ai, Yuewei
Shao, Xinyu
Jiang, Ping
Li, Peigen
Liu, Yang
Yue, Chen
[J]. APPLIED PHYSICS A-MATERIALS SCIENCE & PROCESSING, 2015, 121 (02): : 555 - 569
[2] Optimization of friction stir welding process using NSGA-II and DEMO
Alkayem, Nizar Faisal
Parida, Biswajit
Pal, Sukhomay
[J]. NEURAL COMPUTING & APPLICATIONS, 2019, 31 (Suppl 2) : 947 - 956
[3] Artificial neural network modeling studies to predict the friction welding process parameters of Incoloy 800H joints
Anand, K.
Barik, Birendra Kumar
Tamilmannan, K.
Sathiya, P.
[J]. ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2015, 18 (03): : 394 - 407
[4] Control of welding residual stress for dissimilar laser welded materials
Anawa, E. M.
Olabi, A. G.
[J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2008, 204 (1-3) : 22 - 33
[5] Optimization of different welding processes using statistical and numerical approaches - A reference guide
Benyounis, K. Y.
Olabi, A. G.
[J]. ADVANCES IN ENGINEERING SOFTWARE, 2008, 39 (06) : 483 - 496
[6] Modeling of TIG welding process using conventional regression analysis and neural network-based approaches
Dutta, Parikshit
Pratihar, Dilip Kumar
[J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2007, 184 (1-3) : 56 - 68
[7] Formation of Liquid and Intermetallics in Al-to-Mg Friction Stir Welding
Firouzdor, Vahid
Kou, Sindo
[J]. METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLURGY AND MATERIALS SCIENCE, 2010, 41A (12): : 3238 - 3251
[8] Friction stir welding process of dissimilar metals of 6061-T6 aluminum alloy to AZ31B magnesium alloy
Fu, Banglong
Qin, Guoliang
Li, Fei
Meng, Xiangmeng
Zhang, Jianzhong
Wu, Chuansong
[J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2015, 218 : 38 - 47
[9] Fu Yong Fu Yong, 2018, Chinese Journal of Zoonoses, V34, P1001
[10] A novel Random Walk Grey Wolf Optimizer
Gupta, Shubham
Deep, Kusum
[J]. SWARM AND EVOLUTIONARY COMPUTATION, 2019, 44 : 101 - 112

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