DING, Fu-sheng ;LYU, Hong-ming ;CHEN, Jun ;CAO, Hao-ran ;ZHANG, Lan-xiang . Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks. Articles in Press, [S.l.], v. 0, n.0, p. 169-178, march 2025. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/>. Date accessed: 01 jul. 2025. doi:http://dx.doi.org/10.5545/sv-jme.2024.1185.
Ding, F., Lyu, H., Chen, J., Cao, H., & Zhang, L. (0). Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks. Articles in Press, 0(0), 169-178. doi:http://dx.doi.org/10.5545/sv-jme.2024.1185
@article{sv-jmesv-jme.2024.1185,
author = {Fu-sheng Ding and Hong-ming Lyu and Jun Chen and Hao-ran Cao and Lan-xiang Zhang},
title = {Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
keywords = {garbage truck; ejector plate; multi-objective optimization; NSGA-II; Kriging; },
abstract = {This work presents a multi-objective optimization design approach for the ejector plate, a critical component of rear-loading garbage trucks, with the goal of maintaining structural integrity while optimizing lightweight performance. A parametric finite element model of the ejector plate is developed with optimization objectives focused on minimizing mass, maximizing deformation limits, and reducing the maximum von Mises stress. Through sensitivity analysis, seven key variables are identified as constraints. A Box-Behnken design (BBD) is used to systematically design these parameters, and a Kriging surrogate model is created to approximate the objective function, with performance compared to response surface methodology (RSM). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to derive the optimal solution, achieving a lightweight design meeting all structural requirements. The results show that the mass of the ejector plate of the rear-loading waste compactor can be reduced by 6.06 % through structural optimization, while meeting the strength and deformation criteria. This improvement not only enhances waste transportation efficiency, but also lowers production costs and enhances material utilization.},
issn = {0039-2480}, pages = {169-178}, doi = {10.5545/sv-jme.2024.1185},
url = {https://www.sv-jme.eu/sl/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/}
}
Ding, F.,Lyu, H.,Chen, J.,Cao, H.,Zhang, L. 0 March 0. Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks. Articles in Press. [Online] 0:0
%A Ding, Fu-sheng %A Lyu, Hong-ming %A Chen, Jun %A Cao, Hao-ran %A Zhang, Lan-xiang %D 0 %T Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks %B 0 %9 garbage truck; ejector plate; multi-objective optimization; NSGA-II; Kriging; %! Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks %K garbage truck; ejector plate; multi-objective optimization; NSGA-II; Kriging; %X This work presents a multi-objective optimization design approach for the ejector plate, a critical component of rear-loading garbage trucks, with the goal of maintaining structural integrity while optimizing lightweight performance. A parametric finite element model of the ejector plate is developed with optimization objectives focused on minimizing mass, maximizing deformation limits, and reducing the maximum von Mises stress. Through sensitivity analysis, seven key variables are identified as constraints. A Box-Behnken design (BBD) is used to systematically design these parameters, and a Kriging surrogate model is created to approximate the objective function, with performance compared to response surface methodology (RSM). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to derive the optimal solution, achieving a lightweight design meeting all structural requirements. The results show that the mass of the ejector plate of the rear-loading waste compactor can be reduced by 6.06 % through structural optimization, while meeting the strength and deformation criteria. This improvement not only enhances waste transportation efficiency, but also lowers production costs and enhances material utilization. %U https://www.sv-jme.eu/sl/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/ %0 Journal Article %R 10.5545/sv-jme.2024.1185 %& 169 %P 10 %J Articles in Press %V 0 %N 0 %@ 0039-2480 %8 2025-03-19 %7 2025-03-19
Ding, Fu-sheng, Hong-ming Lyu, Jun Chen, Hao-ran Cao, & Lan-xiang Zhang. "Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks." Articles in Press [Online], 0.0 (0): 169-178. Web. 01 Jul. 2025
TY - JOUR AU - Ding, Fu-sheng AU - Lyu, Hong-ming AU - Chen, Jun AU - Cao, Hao-ran AU - Zhang, Lan-xiang PY - 0 TI - Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks JF - Articles in Press DO - 10.5545/sv-jme.2024.1185 KW - garbage truck; ejector plate; multi-objective optimization; NSGA-II; Kriging; N2 - This work presents a multi-objective optimization design approach for the ejector plate, a critical component of rear-loading garbage trucks, with the goal of maintaining structural integrity while optimizing lightweight performance. A parametric finite element model of the ejector plate is developed with optimization objectives focused on minimizing mass, maximizing deformation limits, and reducing the maximum von Mises stress. Through sensitivity analysis, seven key variables are identified as constraints. A Box-Behnken design (BBD) is used to systematically design these parameters, and a Kriging surrogate model is created to approximate the objective function, with performance compared to response surface methodology (RSM). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to derive the optimal solution, achieving a lightweight design meeting all structural requirements. The results show that the mass of the ejector plate of the rear-loading waste compactor can be reduced by 6.06 % through structural optimization, while meeting the strength and deformation criteria. This improvement not only enhances waste transportation efficiency, but also lowers production costs and enhances material utilization. UR - https://www.sv-jme.eu/sl/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/
@article{{sv-jme}{sv-jme.2024.1185},
author = {Ding, F., Lyu, H., Chen, J., Cao, H., Zhang, L.},
title = {Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
doi = {10.5545/sv-jme.2024.1185},
url = {https://www.sv-jme.eu/sl/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/}
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TY - JOUR AU - Ding, Fu-sheng AU - Lyu, Hong-ming AU - Chen, Jun AU - Cao, Hao-ran AU - Zhang, Lan-xiang PY - 2025/03/19 TI - Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks JF - Articles in Press; Vol 0, No 0 (0): Articles in Press DO - 10.5545/sv-jme.2024.1185 KW - garbage truck, ejector plate, multi-objective optimization, NSGA-II, Kriging, N2 - This work presents a multi-objective optimization design approach for the ejector plate, a critical component of rear-loading garbage trucks, with the goal of maintaining structural integrity while optimizing lightweight performance. A parametric finite element model of the ejector plate is developed with optimization objectives focused on minimizing mass, maximizing deformation limits, and reducing the maximum von Mises stress. Through sensitivity analysis, seven key variables are identified as constraints. A Box-Behnken design (BBD) is used to systematically design these parameters, and a Kriging surrogate model is created to approximate the objective function, with performance compared to response surface methodology (RSM). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to derive the optimal solution, achieving a lightweight design meeting all structural requirements. The results show that the mass of the ejector plate of the rear-loading waste compactor can be reduced by 6.06 % through structural optimization, while meeting the strength and deformation criteria. This improvement not only enhances waste transportation efficiency, but also lowers production costs and enhances material utilization. UR - https://www.sv-jme.eu/sl/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/
Ding, Fu-sheng, Lyu, Hong-ming, Chen, Jun, Cao, Hao-ran, AND Zhang, Lan-xiang. "Multi-Objective Optimization Design of the Ejector Plate for Rear-Loader Garbage Trucks" Articles in Press [Online], Volume 0 Number 0 (19 March 2025)
Articles in Press
© The Authors 2025. CC BY 4.0 Int.
This work presents a multi-objective optimization design approach for the ejector plate, a critical component of rear-loading garbage trucks, with the goal of maintaining structural integrity while optimizing lightweight performance. A parametric finite element model of the ejector plate is developed with optimization objectives focused on minimizing mass, maximizing deformation limits, and reducing the maximum von Mises stress. Through sensitivity analysis, seven key variables are identified as constraints. A Box-Behnken design (BBD) is used to systematically design these parameters, and a Kriging surrogate model is created to approximate the objective function, with performance compared to response surface methodology (RSM). The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied to derive the optimal solution, achieving a lightweight design meeting all structural requirements. The results show that the mass of the ejector plate of the rear-loading waste compactor can be reduced by 6.06 % through structural optimization, while meeting the strength and deformation criteria. This improvement not only enhances waste transportation efficiency, but also lowers production costs and enhances material utilization.