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2024 | Vol. 72, nr 5 | art. no. e151043
Tytuł artykułu

Optimized design of truck cab lightweighting based on sensitivity hierarchical comparative analysis method

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Języki publikacji
EN
Abstrakty
EN
The relative sensitivity analysis method is important in the field of vehicle lightweighting. Combined with optimization algorithms, experiment of design (DOE), etc., it can efficiently explore the impact of unit mass of components on performance and search for components with lightweight space. However, this method does not take into account the size level of each component and the order of magnitude differences in sensitivity under different operating conditions. Therefore, this paper proposed a sensitivity hierarchical comparative analysis method, on the basis of which the thicknesses of 10 groups of components were screened out as design variables by considering the lightweighting effect,cab performance, and passive safety. Through the optimal Latin hypercube method, 70 groups of sample points were extracted to carry out the experimental design, the Kriging surrogate model was established and the NSGA-II genetic algorithm was used to obtain the Pareto optimal solution set, and ultimately a weight reduction of 13.13 kg was realized under the premise that the entire performance of the cab improved.
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Rocznik
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art. no. e151043
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
autor
  • Shandong University of Technology, China
autor
autor
  • Shandong University of Technology, China
autor
  • Shandong University of Technology, China
autor
  • Shandong University of Technology, China
autor
  • Rongcheng Compaks New Energy Automobile Co., Ltd., China
Bibliografia
  • [1] M.-H. Jeong, S.-O. Park, and G.-J. Park, “Multi-model optimization for various disciplines using the equivalent static loads method,” Struct. Multidiscip. Optim., vol. 66, no. 3, 2023, doi: 10.1007/s00158-023-03513-z.
  • [2] Z. Ahmad, T. Sultan, M. Zoppi, M. Abid, and G. Jin Park, “Nonlinear response topology optimization using equivalent static loads—case studies,” Eng. Optim., vol. 49, no. 2, pp. 252–268, 2017, doi: 10.1080/0305215X.2016.1187728.
  • [3] A.R. Yildiz and K.N. Solanki, “Multi-objective optimization of vehicle crashworthiness using a new particle swarm based approach,” Int. J. Adv. Manuf. Technol., vol. 59, no. 1, pp. 367–376, 2012, doi: 10.1007/s00170-011-3496-y.
  • [4] Z. Shuai, “Lightweight Optimization Design of Electric Car Body-in-whiteBased on Structural Crash Performance,” M.A. thesis, Jinlin University, China, 2021.
  • [5] I.M. Soboĺ, “Sensitivity estimates for nonlinear mathematical models,” Math. Model. Comput. Exp., vol. 1, p. 407, 1993.
  • [6] A. Saltelli et al., Global sensitivity analysis: the primer. John Wiley & Sons, 2008.
  • [7] A. Saltelli, K. Chan, and E.M. Scott, Sensitivity analysis, Wiley, 2000, pp. 79–80.
  • [8] J. Sobieszczanski-Sobieski, S. Kodiyalam, and R.Y. Yang, “Optimization of car body under constraints of noise, vibration, and harshness (NVH), and crash,” Struct. Multidiscip. Optim., vol. 22, no. 4, pp. 295–306, 2001, doi: 10.1007/s00158-001-0150-6.
  • [9] C.-J. Kim, Y.J. Kang, B.-H. Lee, and H.-J. Ahn, “Sensitivity analysis for reducing critical responses at the axle shaft of a lightweight vehicle,” Int. J. Automot. Technol., vol. 13, no. 3, pp. 451–458, 2012, doi: 10.1007/s12239-012-0042-3.
  • [10] R.M. Ferro and R. Pavanello, “A Simple and Efficient Structural Topology Optimization Implementation Using Open-Source Software for All Steps of the Algorithm: Modeling, Sensitivity Analysis and Optimization,” Comput. Model. Eng. Sci., vol. 136, no. 2, pp. 1371–1397, 2023, doi: 10.32604/cmes.2023.026043.
  • [11] X. Jin, Z. Wang, J. Yang, L. Xu, and G. Yin, “Novel Payload Parameter Sensitivity Analysis on Observation Accuracy of Lightweight Electric Vehicles,” Int. J. Automot. Technol., vol. 24, no. 5, pp. 1313–1324, 2023, doi: 10.1007/s12239-023-0106-6.
  • [12] Y. Wang, “Lightweight Optimization Design of Heavy Duty Commercial Vehicle Cab,” M.Sc. Thesis, Jilin University, China, 2016.
  • [13] J. Büttner et al., “Global sensitivity matrix for vehicle development,” ATZ Worldwide, vol. 123, pp. 26–31, 2021, doi: 10.1007/s38311-020-0630-1.
  • [14] H. Ou, X. Tang, J. Xiao, Y. Wang, and Z. Ma, “Lightweight Body-In-White Design Driven by Optimization Technology,” Automot. Innov., vol. 1, no. 3, pp. 255–262, 2018, doi: 10.1007/s42154-018-0032-x.
  • [15] F. Wang et al., “Lightweight design of a certain mortar base plate based on sensitivity analysis,” J. Braz. Soc. Mech. Sci. Eng., vol. 43, pp. 1–13, 2021.
  • [16] S. Wanlai, “Lightweight Optimization Design and Analysis of BIW for Vehicle Cab,” M.Sc. Thesis, Jilin University, China, 2019.
  • [17] T. Jing, W. Pengxing, and X. Enyong, “Lightweight Design of Commercial Vehicle Cab via Successive Replacement of Response Surface,” Mech. Sci. Tech. Aerosp. Eng., vol. 41, no. 1, pp. 159–164, 2022,doi: 10.13433/j.cnki.1003-8728.20200519.
  • [18] State Administration for Market Regulation, Standardization Administration of the People’s Republic of China. “The protection of the occupants of the cab of commercial vehicles,” China. 2021.
  • [19] J. Tonioli, I. Castro, R. Ripoli, and M.Argentino, “Computational Simulation of the ECE R-29 Safety Test,” SAE Technical Paper 2000-01-3524, 2000, doi: 10.4271/2000-01-3524.
  • [20] D. Wang, C. Xie, Y. Liu, W. Xu, and Q. Chen, “Multi-objective Collaborative Optimization for the Lightweight Design of an Electric Bus Body Frame,” Automot. Innov., vol. 3, no. 3, pp. 250–259, 2020, doi: 10.1007/s42154-020-00105-1.
  • [21] H. Liu, “Lightweight design of commercial vehicle cab based on advanced high strength steel,” M.Sc. Thesis, Shandong University of Technology, China, 2022.
  • [22] J. Zhang et al., “CAE Analysis and improvement Design of the Safety of Commercial Truck in Pendulum impact,” Automob. Technol., vol. 1, pp. 1–5, 2010.
  • [23] Y. Ma, “Lightweight Optimization of Commercial Vehicle Cab Based on Passive Safety,” M.Sc. Thesis, Jinlin University, China, 2014.
  • [24] Shuen Zhao, “Lightweight Optimization Design of Body-in-white Based on NSGA-II Mixed Sensitivity Analysis,” J. Mech. Strength, vol. 41, pp. 887-894, 2019, doi: 10.16579/j.issn.1001.9669.2019.04.020.
  • [25] Q. Wang et al., “Lightweight Design of an Electric Vehicle Body Structural Parts Based on Side Crash Safety,” Automob. Technol., vol. 2, pp. 44–50, 2017.
  • [26] K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182–197, 2002, doi: 10.1109/4235.996017.
  • [27] X. Wang and L. Cao, Genetic Algorithms – Theory. Application and Software Implementation, Xi’an Jiaotong University PressŁŹ Xi’an, China, 2002.
  • [28] S. Verma, M. Pant and V. Snasel, “A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems,” IEEE Access, vol. 9, pp. 57757–57791, 2021, doi: 10.1109/ACCESS.2021.3070634.
  • [29] F. Wei, “Safety Optimization of Commercial Vehicle Cab Frontal Collision Based on Ensemble of Surrogate Models,” J. Wuhan Univ. Technol., vol. 10, pp. 88–97, 2023.
Typ dokumentu
Bibliografia
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