Title: Temperature prediction of FSW medium thickness 2219 aluminium alloy based on intelligent algorithm

Authors: Xiaohong Lu; Banghua Yang; Xiangyue Meng; Shixuan Sun; Yihan Luan; Steven Y. Liang

Addresses: State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' Aerospace Science and Technology First Institute, Capital Aerospace Machinery Company, 100071 Beijing, China ' State Key Laboratory of High-Performance Precision Manufacturing, Dalian University of Technology, 116024 Dalian, China ' The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA

Abstract: The thickness of launch vehicle fuel tank reaches 18 mm, and is welded using friction stir welding (FSW) technology. The increase in thickness will directly affect the temperature distribution of welded joint, thereby affecting the welding quality. Temperature measurement experiments of FSW 18 mm thick 2219 aluminium alloy were conducted. Based on experimental data, a prediction model of peak temperature in Nugget Zone based on BP and improved GA-BP neural network was built. The results showed that the GA-BP neural network had higher prediction accuracy. Subsequently, a temperature prediction model of peak temperature both on advancing and retreating sides was established based on GA-BP and PSO-BP neural network. The results indicated that PSO-BP model showed better performance to realise dual-objective prediction. The temperature prediction model achieves accurate prediction of the temperature of FSW 2219 aluminium alloy thick plate, providing reference for the control of welding temperature of the fuel tank. [Submitted 22 August 2021; Accepted 13 November 2023]

Keywords: friction stir welding; FSW; temperature; BP neural network; genetic algorithm; particle swarm algorithm.

DOI: 10.1504/IJMR.2024.136585

International Journal of Manufacturing Research, 2024 Vol.19 No.1, pp.98 - 118

Accepted: 13 Nov 2023
Published online: 07 Feb 2024 *

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