FUSHENG, Ding ;HONGMING, Lyu ;JUN, Chen ;HAORAN, Cao ;LANXIANG, Zhang . Multi-objective optimization design of the ejection panel for rear-loader garbage trucks. Articles in Press, [S.l.], v. 0, n.0, p. , march 2025. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/>. Date accessed: 20 may. 2025. doi:http://dx.doi.org/.
Fusheng, D., Hongming, L., Jun, C., Haoran, C., & Lanxiang, Z. (0). Multi-objective optimization design of the ejection panel for rear-loader garbage trucks. Articles in Press, 0(0), . doi:http://dx.doi.org/
@article{.,
author = {Ding Fusheng and Lyu Hongming and Chen Jun and Cao Haoran and Zhang Lanxiang},
title = {Multi-objective optimization design of the ejection panel for rear-loader garbage trucks},
journal = {Articles in Press},
volume = {0},
number = {0},
year = {0},
keywords = {Rear-loader garbage truck, Ejection panel , Multi-Objective optimization, Lightweight design, Non-dominated sorting genetic algorithm II, Kriging surrogate model; },
abstract = {With the improvement of people’s quality of life, the increased generation of household waste has made efficient waste disposal a primary concern for municipal management and equipment manufacturers. This paper focuses on the multi-objective optimization design of the core component of rear loader garbage trucks-the ejection panel . Firstly, an ejection panel model is developed, and relevant design variables are identified. The optimization objectives include minimizing the mass, maximum deformation, and maximum von Mises stress of the ejection panel . A sensitivity analysis of fourteen parameters is performed, resulting in the selection of the seven most influential parameters as constraints. Then, the Box-Behnken Design (BBD) methodology is applied to design experiments for these key parameters. Using the experimental data, a surrogate model for the objective function is constructed through Kriging interpolation. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to achieve a successful lightweight design of the ejection panel structure while meeting all the design requirements. The optimization results show a significant mass reduction of 6.06%, which improves waste transportation efficiency, reduces production cost and improves material utilization.},
issn = {0039-2480}, pages = {}, doi = {},
url = {https://www.sv-jme.eu/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/}
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Fusheng, D.,Hongming, L.,Jun, C.,Haoran, C.,Lanxiang, Z. 0 March 0. Multi-objective optimization design of the ejection panel for rear-loader garbage trucks. Articles in Press. [Online] 0:0
%A Fusheng, Ding %A Hongming, Lyu %A Jun, Chen %A Haoran, Cao %A Lanxiang, Zhang %D 0 %T Multi-objective optimization design of the ejection panel for rear-loader garbage trucks %B 0 %9 Rear-loader garbage truck, Ejection panel , Multi-Objective optimization, Lightweight design, Non-dominated sorting genetic algorithm II, Kriging surrogate model; %! Multi-objective optimization design of the ejection panel for rear-loader garbage trucks %K Rear-loader garbage truck, Ejection panel , Multi-Objective optimization, Lightweight design, Non-dominated sorting genetic algorithm II, Kriging surrogate model; %X With the improvement of people’s quality of life, the increased generation of household waste has made efficient waste disposal a primary concern for municipal management and equipment manufacturers. This paper focuses on the multi-objective optimization design of the core component of rear loader garbage trucks-the ejection panel . Firstly, an ejection panel model is developed, and relevant design variables are identified. The optimization objectives include minimizing the mass, maximum deformation, and maximum von Mises stress of the ejection panel . A sensitivity analysis of fourteen parameters is performed, resulting in the selection of the seven most influential parameters as constraints. Then, the Box-Behnken Design (BBD) methodology is applied to design experiments for these key parameters. Using the experimental data, a surrogate model for the objective function is constructed through Kriging interpolation. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to achieve a successful lightweight design of the ejection panel structure while meeting all the design requirements. The optimization results show a significant mass reduction of 6.06%, which improves waste transportation efficiency, reduces production cost and improves material utilization. %U https://www.sv-jme.eu/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/ %0 Journal Article %R %& %P 1 %J Articles in Press %V 0 %N 0 %@ 0039-2480 %8 2025-03-19 %7 2025-03-19
Fusheng, Ding, Lyu Hongming, Chen Jun, Cao Haoran, & Zhang Lanxiang. "Multi-objective optimization design of the ejection panel for rear-loader garbage trucks." Articles in Press [Online], 0.0 (0): . Web. 20 May. 2025
TY - JOUR AU - Fusheng, Ding AU - Hongming, Lyu AU - Jun, Chen AU - Haoran, Cao AU - Lanxiang, Zhang PY - 0 TI - Multi-objective optimization design of the ejection panel for rear-loader garbage trucks JF - Articles in Press DO - KW - Rear-loader garbage truck, Ejection panel , Multi-Objective optimization, Lightweight design, Non-dominated sorting genetic algorithm II, Kriging surrogate model; N2 - With the improvement of people’s quality of life, the increased generation of household waste has made efficient waste disposal a primary concern for municipal management and equipment manufacturers. This paper focuses on the multi-objective optimization design of the core component of rear loader garbage trucks-the ejection panel . Firstly, an ejection panel model is developed, and relevant design variables are identified. The optimization objectives include minimizing the mass, maximum deformation, and maximum von Mises stress of the ejection panel . A sensitivity analysis of fourteen parameters is performed, resulting in the selection of the seven most influential parameters as constraints. Then, the Box-Behnken Design (BBD) methodology is applied to design experiments for these key parameters. Using the experimental data, a surrogate model for the objective function is constructed through Kriging interpolation. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to achieve a successful lightweight design of the ejection panel structure while meeting all the design requirements. The optimization results show a significant mass reduction of 6.06%, which improves waste transportation efficiency, reduces production cost and improves material utilization. UR - https://www.sv-jme.eu/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/
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TY - JOUR AU - Fusheng, Ding AU - Hongming, Lyu AU - Jun, Chen AU - Haoran, Cao AU - Lanxiang, Zhang PY - 2025/03/19 TI - Multi-objective optimization design of the ejection panel for rear-loader garbage trucks JF - Articles in Press; Vol 0, No 0 (0): Articles in Press DO - KW - Rear-loader garbage truck, Ejection panel , Multi-Objective optimization, Lightweight design, Non-dominated sorting genetic algorithm II, Kriging surrogate model, N2 - With the improvement of people’s quality of life, the increased generation of household waste has made efficient waste disposal a primary concern for municipal management and equipment manufacturers. This paper focuses on the multi-objective optimization design of the core component of rear loader garbage trucks-the ejection panel . Firstly, an ejection panel model is developed, and relevant design variables are identified. The optimization objectives include minimizing the mass, maximum deformation, and maximum von Mises stress of the ejection panel . A sensitivity analysis of fourteen parameters is performed, resulting in the selection of the seven most influential parameters as constraints. Then, the Box-Behnken Design (BBD) methodology is applied to design experiments for these key parameters. Using the experimental data, a surrogate model for the objective function is constructed through Kriging interpolation. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to achieve a successful lightweight design of the ejection panel structure while meeting all the design requirements. The optimization results show a significant mass reduction of 6.06%, which improves waste transportation efficiency, reduces production cost and improves material utilization. UR - https://www.sv-jme.eu/article/multi-objective-optimization-design-of-the-ejection-panel-for-rear-loader-garbage-trucks/
Fusheng, Ding, Hongming, Lyu, Jun, Chen, Haoran, Cao, AND Lanxiang, Zhang. "Multi-objective optimization design of the ejection panel for rear-loader garbage trucks" Articles in Press [Online], Volume 0 Number 0 (19 March 2025)
Articles in Press
With the improvement of people’s quality of life, the increased generation of household waste has made efficient waste disposal a primary concern for municipal management and equipment manufacturers. This paper focuses on the multi-objective optimization design of the core component of rear loader garbage trucks-the ejection panel . Firstly, an ejection panel model is developed, and relevant design variables are identified. The optimization objectives include minimizing the mass, maximum deformation, and maximum von Mises stress of the ejection panel . A sensitivity analysis of fourteen parameters is performed, resulting in the selection of the seven most influential parameters as constraints. Then, the Box-Behnken Design (BBD) methodology is applied to design experiments for these key parameters. Using the experimental data, a surrogate model for the objective function is constructed through Kriging interpolation. Finally, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to achieve a successful lightweight design of the ejection panel structure while meeting all the design requirements. The optimization results show a significant mass reduction of 6.06%, which improves waste transportation efficiency, reduces production cost and improves material utilization.