A structural optimization design of turntable in a 63 m aerial fire truck based on finite element analysis and intelligent algorithms

With the increasing demand for high-rise rescue equipment, lightweight design of large aerial fire trucks is important for reducing structural redundancy while maintaining safety. This study aims to improve the lightweight design of the turntable in a 63 m hybrid-boom aerial fire truck by using a finite element analysis (FEA) and intelligent-optimization procedure validated by real-vehicle testing. A finite element model of the DG63 eight-section boom-turntable system was established, and static and modal analyses were performed under three representative working conditions: maximum working height, maximum working outreach, and 49 m operation. A real-vehicle stress test using a wireless dynamic strain testing system was then conducted to validate the finite element model. The turntable was selected as the optimization target because of its critical role in structural safety and the mass reduction potential indicated by the preceding FEA and real-vehicle test results. Sensitivity analysis was used to identify six plate-thickness design variables. Least squares regression, support vector regression, and particle swarm optimization-back propagation (PSO-BP) neural network models were compared for response prediction, and an improved genetic algorithm (GA) was then used for constrained mass minimization. After optimization, the turntable mass was reduced from 1.447 t to 0.995 t, corresponding to a 31.2% reduction. The maximum stress and deformation of the optimized turntable were 163.4 MPa and 2.292 mm, respectively, which were below the allowable stress of 172.5 MPa and deformation limit of 60 mm. The predicted mass was 0.986 t, with an absolute error of 0.009 t and a relative error of 0.9%. These results demonstrate that the proposed method can reduce the mass of the turntable while satisfying strength and stiffness requirements, and can provide a reference for lightweight design of key load-bearing structures in large aerial fire trucks.

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Publication Details

Journal
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Published
2026-09-05
DOI
https://doi.org/10.1177/09544070261485138
Primary Topic
Mechanical Engineering and Vibrations Research
Type
article
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article

A structural optimization design of turntable in a 63 m aerial fire truck based on finite element analysis and intelligent algorithms

Chuanyin Tang, Guojun Li, Meirong Wei, Yefeng Long et al.
Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Mechanical Engineering and Vibrations Research
article

A structural optimization design of turntable in a 63 m aerial fire truck based on finite element analysis and intelligent algorithms

Chuanyin Tang, Guojun Li, Meirong Wei, Yefeng Long, Miaolin Zhang, Fangzhen Du, Yun Qu
article en

Abstract

With the increasing demand for high-rise rescue equipment, lightweight design of large aerial fire trucks is important for reducing structural redundancy while maintaining safety. This study aims to improve the lightweight design of the turntable in a 63 m hybrid-boom aerial fire truck by using a finite element analysis (FEA) and intelligent-optimization procedure validated by real-vehicle testing. A finite element model of the DG63 eight-section boom-turntable system was established, and static and modal analyses were performed under three representative working conditions: maximum working height, maximum working outreach, and 49 m operation. A real-vehicle stress test using a wireless dynamic strain testing system was then conducted to validate the finite element model. The turntable was selected as the optimization target because of its critical role in structural safety and the mass reduction potential indicated by the preceding FEA and real-vehicle test results. Sensitivity analysis was used to identify six plate-thickness design variables. Least squares regression, support vector regression, and particle swarm optimization-back propagation (PSO-BP) neural network models were compared for response prediction, and an improved genetic algorithm (GA) was then used for constrained mass minimization. After optimization, the turntable mass was reduced from 1.447 t to 0.995 t, corresponding to a 31.2% reduction. The maximum stress and deformation of the optimized turntable were 163.4 MPa and 2.292 mm, respectively, which were below the allowable stress of 172.5 MPa and deformation limit of 60 mm. The predicted mass was 0.986 t, with an absolute error of 0.009 t and a relative error of 0.9%. These results demonstrate that the proposed method can reduce the mass of the turntable while satisfying strength and stiffness requirements, and can provide a reference for lightweight design of key load-bearing structures in large aerial fire trucks.

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
Shenyang Fire Research Institute (CN), System Equipment (China) (CN), Northeastern University (CN)
Openalex Percentile: Top 19%
Mechanical Engineering and Vibrations Research
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