Data Analysis and Intelligent Optimization of Robotic Friction Stir Welding Parameters for 6061-T6 Aluminum Alloy

ZHAO Yunqiang; GUAN Yuankai; LIU Zhe; DENG Jun; LIN Zhicheng; Oleg Ganushchak; Yevhenii Illyashenko

doi:10.3969/j.issn.1674-6457.2026.07.012

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (7) : 132-142. DOI: 10.3969/j.issn.1674-6457.2026.07.012

Advanced Joining Technology

Data Analysis and Intelligent Optimization of Robotic Friction Stir Welding Parameters for 6061-T6 Aluminum Alloy

ZHAO Yunqiang^1,2, GUAN Yuankai¹, LIU Zhe^2,*, DENG Jun², LIN Zhicheng², Oleg Ganushchak³, Yevhenii Illyashenko³

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Abstract

The work aims to propose an efficient and precise data-driven approach to achieve rapid prediction and optimization of the robotic friction stir welding (FSW) process for 6061-T6 aluminum alloy. Firstly, by combining numerical simulation with artificial intelligence technology, a three-dimensional transient heat source model and a sequentially coupled thermo-mechanical model were developed to accurately simulate the temperature and residual stress fields under various welding parameters. Subsequently, a back-propagation neural network (BPNN) model was established and trained, with the rotational rate, welding speed and plunge force as input parameters, and the simulated peak temperature, maximum longitudinal residual stress, and ultimate tensile strength as output parameters. The BPNN model demonstrated remarkable predictive accuracy with the corresponding determination coefficients (R²) of 0.9348, 0.840 7, and 0.726 2 for peak temperature, residual stress, and tensile strength. The maximum prediction errors remained below 5%. Furthermore, rotational rate exerted the most significant effect on peak temperature, while residual stress distribution was predominantly controlled by welding speed. These results validate that the proposed hybrid strategy effectively offers the efficient prediction and optimization of both robotic FSW parameters and welding quality. Compared with traditional single methods regarding experimental or numerical simulation, this approach significantly reduces computational costs without sacrificing accuracy. The BPNN model is more efficient than conventional finite element simulation by several orders of magnitude. This provides a reliable theoretical basis and practical engineering tools for the intelligent design and optimization of complex welding processes.

Key words

robotic friction stir welding / aluminum alloy / neural network / numerical simulation / process optimization

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ZHAO Yunqiang, GUAN Yuankai, LIU Zhe, DENG Jun, LIN Zhicheng, Oleg Ganushchak, Yevhenii Illyashenko. Data Analysis and Intelligent Optimization of Robotic Friction Stir Welding Parameters for 6061-T6 Aluminum Alloy[J]. Journal of Netshape Forming Engineering. 2026, 18(7): 132-142 https://doi.org/10.3969/j.issn.1674-6457.2026.07.012

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Funding

National Key Research and Development Program of China (2023YFE0201500); Key Talent Plan Project of Guangdong Province (2023TQ07C702); GDAS’ Project of Science and Technology Development (2024GDASQNRC-0103); Young S&T Talent Training Program of Guangdong Provincial Association for S&T, China (SKXRC2025080); Guangdong Basic and Applied Basic Research Foundation (2025A1515011033)