Qiang Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Qiang Chen has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Dequan Wang
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Qingchun Wang
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Xiangyue Yuan
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Yilu Zhao
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication - Zhongjia Chen
School of Technology, Beijing Forestry University, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
Multi-Level Matching Optimization Design of Thin-Walled Beam Cross-Section for Tri-Axle Unmanned Forestry Vehicle Frame
With the advancement of forestry modernization, the research and development of forestry vehicles provide solid technical support for the efficiency and sustainability of forest operations. This study aims to reduce the mass of the forest-use tri-axle unmanned vehicle frame through structural optimization design, improve its static and dynamic characteristics, and enhance vehicle mobility and environmental adaptability while maintaining or enhancing its structural strength and stability. Initially, the finite element model of the vehicle frame was established using the finite element software Hypermesh (2022), and its static and dynamic characteristics were analyzed using OptiStruct (2022) software. The accuracy of the finite element calculations was verified through experiments. Subsequently, a sensitivity analysis method was employed to screen the design variables of the thin-walled beam structure of the forest-use tri-axle unmanned vehicle. Response surface models were created using least squares regression (LSR) and radial basis function network (RBF). Considering indicators such as frame mass, modal frequency, and maximum bending and torsional stresses, the multi-objective genetic algorithm (MOGA) was applied to achieve a multi-objective lightweight design of the vehicle frame. This comprehensive optimization method is rarely reported in forestry vehicle design. By employing the proposed optimization approach, a weight reduction of 10.1 kg (a 7.44% reduction) was achieved for the vehicle frame without compromising its original static and dynamic performance. This significant lightweighting result demonstrates considerable practical application potential in the field of forestry vehicle lightweight design. It responds to the demand for efficient and environmentally friendly forestry machinery under forestry modernization and holds important implications for reducing energy consumption and operational costs.
Open AccessMulti-Level Matching Optimization Design of Thin-Walled Beam Cross-Section for Tri-Axle Unmanned Forestry Vehicle Frame
With the advancement of forestry modernization, the research and development of forestry vehicles provide solid technical support for the efficiency and sustainability of forest operations. This study aims to reduce the mass of the forest-use tri-axle unmanned vehicle frame through structural optimization design, improve its static and dynamic characteristics, and enhance vehicle mobility and environmental adaptability while maintaining or enhancing its structural strength and stability. Initially, the finite element model of the vehicle frame was established using the finite element software Hypermesh (2022), and its static and dynamic characteristics were analyzed using OptiStruct (2022) software. The accuracy of the finite element calculations was verified through experiments. Subsequently, a sensitivity analysis method was employed to screen the design variables of the thin-walled beam structure of the forest-use tri-axle unmanned vehicle. Response surface models were created using least squares regression (LSR) and radial basis function network (RBF). Considering indicators such as frame mass, modal frequency, and maximum bending and torsional stresses, the multi-objective genetic algorithm (MOGA) was applied to achieve a multi-objective lightweight design of the vehicle frame. This comprehensive optimization method is rarely reported in forestry vehicle design. By employing the proposed optimization approach, a weight reduction of 10.1 kg (a 7.44% reduction) was achieved for the vehicle frame without compromising its original static and dynamic performance. This significant lightweighting result demonstrates considerable practical application potential in the field of forestry vehicle lightweight design. It responds to the demand for efficient and environmentally friendly forestry machinery under forestry modernization and holds important implications for reducing energy consumption and operational costs.