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客车悬架橡胶衬套对整车性能影响研究与多目标优化
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摘要
以改善高速客车乘坐舒适性为目的,论文针对轻型客车悬架系统橡胶衬套的优化匹配问题开展研究工作。研究内容包括:本课题研究现状分析;橡胶衬套静态、动态性能试验分析;高精度橡胶衬套数学模型建立;车身有限元模态分析与试验验证;整车刚弹耦合仿真模型建立;橡胶衬套刚度特性对整车性能的影响规律研究;客车车内噪声的声固耦合有限元分析;橡胶衬套各向刚度灵敏度计算;悬架橡胶衬套参数多目标匹配优化;整车及悬架改进效果的试验验证。本文在精确建立橡胶衬套数学模型及整车动力学仿真模型的基础上,进行了悬架橡胶衬套对汽车平顺性、操纵稳定性及NVH性能影响的研究,并提出了针对以上三种性能的橡胶衬套性能参数多目标优化方法,使得对汽车多个相互影响、甚至矛盾的性能指标进行协同优化成为可能,进而建立了一套针对悬架系统橡胶衬套的匹配设计方法和流程。本文的研究成果对现有车型悬架系统的性能提升具有重要意义,对于汽车其它子系统的匹配优化也具有一定的指导作用。
Recent years in the automotive technology development, with the increasing ofautomobile speed, the ride comfort, handling stability and NVH (Noise, Vibration&Harshness) characteristics of high speed vehicle has attracted more and more attention.Rubber bushing components have been widely used in automotive suspension system and itsmain purpose is to use the superior noise and vibration reduction function of rubber elasticelement. At the same time, the nonlinear characteristics of the rubber itself will affect theelastic kinematics of suspension system, which affects the handling stability of vehicle.Rubber bushings used in the suspension system is conducive to attenuate the vibration andshock of the road excitation on vehicle body, especially the high frequency excitation fromthe road, and be able to improve vehicle NVH performance significantly. However, therubber bushings also cause changes in the suspension system stiffness, and make thesuspension showing a certain nonlinear properties, such as system response lag. Thenonlinear properties cause the negative effects of the automobile steering characteristics,which reduces the handling stability of vehicle. The problems above cause the contradictionsof vehicle ride comfort, handling stability and NVH characteristics in suspension matchprocess. Furthermore, the increasing speed of car makes this contradiction more significantand the impact of factors that not reflected in low speed becomes more prominent in highspeed. In view of the complexity of rubber bushing mathematical modeling andmulti-objective optimization algorithm, the optimal matching of suspension bushings,considering the vehicle ride comfort, handling stability and NVH performances, becamedifficult problem in the development process of the suspension system.
     A domestic light bus had been studied in this paper and with the method of combiningfinite element and multi-body dynamics, the influence of vehicle ride comfort, handing stability and in-car noise affected by rubber bushing stiffness parameters were analyzed inthis paper. A multi-objective optimization of rubber bushing parameters was carried out byconsidered the performance indexes of the above three. The multi-objective optimizationproposed in this paper established an effective matching optimization design process forrubber bushing of suspension systems. This paper focuses on the influence of vehicleperformance affected by rubber bushing stiffness and the matching optimization study ofrubber bushing, and its main contents include the following five parts:
     In the first part of this paper, the purpose of this research project was proposed, and thesignificance of rubber bushing matching optimization for suspension systems developmentwas also illustrated. Then, the research status of the rubber bushings and multi-objectiveoptimization method was introduced and summarized, which described the rubber bushingstatic and dynamic characteristics of the calculation method and genetic algorithmmulti-objective optimization development overview. Finally, the main content andsignificance of this study have been proposed.
     In the second part, for the purpose of solving the difficult problem of rubber bushingmathematical modeling in suspension research areas, the paper carried out the research work.The rubber bushing mathematical models in the traditional vehicle dynamics studies weresimplified into elastic elements with nonlinear characteristics, which caused the lowprecision of suspension dynamics. In response to these problems, on the basis of existinglinear friction rubber bushing model, elastic lag rubber bushing model and elastic-plasticsuperposition rubber bushing model, a high-precision rubber bushing model based onsuperimposed hyperelastic unit, fractional derivative unit and friction unit is established inthis paper. The test modeling process has also been introduced in detail. The experimentalresults showed that the high-precision rubber bushing model could correctly describe thenonlinear dynamic characteristics of rubber bushing.
     In the third part, a rigid-elastic coupling vehicle model was established by usingSIMPACK and NASTRAN software. By using the user-defined force interface in SIMPACK,the rubber bushing model established by the programming language was imported to rigid-elastic coupling vehicle model. In this paper, the finite element method was used togenerate the flexible body model of bus frame and body. The real vehicle test data were usedto validate the rigid-elastic coupling simulation model. The results showed that the vehiclesimulation model could be used to simulate the linear and nonlinear dynamic characteristicsof suspension systems. The rigid-elastic coupling model established in this paper can providea good simulation model for multi-objective optimization of rubber bushing, and laid thefoundation for future research.
     In the fourth part, the influence of vehicle ride comfort, handing stability and in-carnoise affected by rubber bushing stiffness parameters were analyzed. In order to study thecab noise caused by road excitation, the dynamic load spectrum of suspension rubberbushings was calculated by the simulation of rigid-elastic coupling vehicle model. Theacoustic-structure coupling method was used to calculate the sound pressure value of driver'sright ear, which was used to evaluate vehicle structure-borne noise caused by road excitation.From the perspective of sensitivity analysis, the relationship between rubber bushingstiffness and sensitivity value of suspension characteristic was also studied. The sensitivityanalysis results can be used to determine the optimization objectives, thus avoiding theblindness of the optimization and exponentially reducing workload of optimization.
     In the fifth part, vehicle dynamics model and MATLAB co-simulation method was usedto carry out the multi-objective optimization of rubber bushing parameters. In theoptimization process, the RMS value of vibration acceleration of driver's seat track, the rollangle of vehicle body and the sound pressure level of driver's right ear were treated asobjective functions, and the rubber bushing stiffness parameters were selected asoptimization objectives. NSGA-II genetic algorithm was used to carry out themulti-objective optimization of vehicle ride comfort, handling stability and NVHperformance. According to the results of optimization, new rubber bushings were producedfor the vehicle test validation. The verification results showed that, the re-match suspensionrubber bushings could improve the ride comfort and handing stability of vehicle, and reducethe interior noise in the body at high speed.
     The paper uses simulation analysis and experimental methods to research the optimalmatching of the suspension system of rubber bushing, and elaborates on the matching designof bus suspension rubber bushings to form a more complete technical processes and researchideas. The research results of this article are significance for improving the performance ofexisting models’ suspension systems, and it also has a guidance function for matchingoptimization of the other vehicle subsystems.
引文
[1] Blundell M.V. Influence of rubber bush compliance on vehicle suspensionmovement[J]. Materials&Design, v19, n1-2, p29-37, Feb1998.
    [2]杨树凯,宋传学,安晓娟,蔡章林.用虚拟样机方法分析悬架衬套弹性对整车转向特性的影响[J].吉林大学学报(工学版),2007,37(5):994-999.
    [3] Deb K. Multi-objective optimization using evolutionary algorithms[J]. England:John Wiley&Sons, p13-46,2001.
    [4]杨荣山,黄向东,袁仲荣,赵克刚.多目标优化方法在悬架几何设计上的应用[J].华南理工大学学报(自然科学版),2009,37(7):85-89.
    [5] Donald M. Baskin, David B. Reed and Thomas N. Seel, A Case Study in StructuralOptimization of an Automotive Body-In-White Design[C]. SAE:2008-01-0880.
    [6]王涛.汽车悬架参数的多目标多标准决策优化[J].农业机械学报,2009,40(4):27-32.
    [7] Chernykh V.V. and Matusov I.B. Modeling and optimization of characteristics ofthe conventional suspension of passenger car wheels[J]. Journal of MachineryManufacture and Reliability, v41, n1, p20-25, February2012.
    [8] Kang D.O., Heo S.J., Kim M.S., Choi W.C. and Kim I.H. Robust designoptimization of suspension system by using target cascading method[J].International Journal of Automotive Technology, v13, n1, p109-122, January2012.
    [9]潘孝勇,柴国钟,上官文斌,徐驰.小振幅谐波位移激励下橡胶减振元件动态特性计算方法的研究[J].振动与冲击,2009,28(2):151-154.
    [10] Boyles Stephen D. Bush-based sensitivity analysis for approximating subnetworkdiversion[J]. Transportation Research Part B: Methodological, v46, n1, p139-155,January2012.
    [11] Goryacheva I.G. and Mezrin A.M. Simulation of combined wearing of the shaftand bush in a heavily loaded sliding bearing[J]. Journal of Friction and Wear, v32,n1, p1-7, February2011.
    [12] Zdunek A.B., Bercvier M. Numerical evaluation of finite element methods forrubber parts[C]. SAE:860817.
    [13] Sasso M., Palmieria G., Chiappinia G., et al. Characterization of hyperelasticrubber-like materials by biaxial and uniaxial stretching tests based on opticalmethods[J]. Polymer Testing,2008,27(8):995-1004.
    [14] Miller K. and Turner D.M. Novel composition of cupro-nickel granules and rubberto prevent marine fouling[J]. Plastics&Rubber Inst, p6.1-6.14,1985.
    [15] Shangguan W B, Lu Z H, Shi J J. Finite element analysis of static elasticcharacteristics of the rubber isolators in automotive dynamic systems[C]. SAE:2003-01-0240.
    [16] Uhlig K. and Hehn W. Rubber band suspension for a nuclear demagnetizationcryostat[J]. Cryogenics, v28, n9, p612-614, Sep1988.
    [17] Gent A. N. A new constitutive relation for rubber[J]. Rubber Chemistry AndTechnology,1996,69(1):59-61.
    [18] Boyce M. C. and Arruda E. M. Constitutive models of rubber elasticity: areview[J]. Rubber chemistry and technology,2000,73(3):504-52.
    [19]王娜,面向汽车耐久性分析的底盘橡胶衬套建模研究[D].吉林大学硕士学位论文,2011.
    [20] Narraway Rex. Suspension bushes under test[J]. Design Engineering (London), p23, Jun1997.
    [21] Tupholme G.E. An analogy between radially-loaded rubber bush mountings andaxially-loaded bonded rubber blocks[J]. Materials and Design, v32, n10, p5038-5042, December2011.
    [22] Olsson A.K. Finite element procedures in modeling the dynamic properties ofrubber[D]. Ph.D Thesis Lund University, Sweden,2007.
    [23] Turner D.M. Triboelastic model for the mechanical behaviour of rubber[J].Plastics and Rubber Processing and Applications, v9, n4, p197-201,1988.
    [24] Bagley R.L. and Torvik P.J. On the fractional calculus model of viscoelasticbehavior[J]. Journal of Rheology, v30, n1, p133-155, Feb1986.
    [25]于增亮,张立军,罗鹰.一种新的橡胶衬套半经验动力学模型[J].汽车技术,2010,(8):6-11.
    [26]潘孝勇.橡胶隔振器动态特性计算与建模方法的研究[D].浙江工业大学博士学位论文,2009.
    [27]苏志勇.轴对称橡胶衬套高精度模型的建立及应用[D].吉林大学博士学位论文,2007.
    [28]范培蕾.多目标优化方法及其在高超声速试飞器系统中的应用研究[D].国防科学技术大学博士学位论文,2009.
    [29] Zitzler E. Evolutionary Algorithms for Multi-objective Optimization: Methods andApplications[D]. Swiss Federal Institute of Technology, Switzerland,1999.
    [30] Zitzler E, Thiele L. Multi-objective Optimization Using Evolutionary Algorithms-A Comparative Study[J]. In Parallel Problem Solving from Nature V, Amsterdam,Springer,1998:292-301.
    [31] Eckart Z., Kalyanmoy D. Comparison of multi-objective evolutionary algorithms:empirical results[J]. evolutionary computation,2000,8(2):173-195.
    [32] Schaffer J.D. Multiple objective optimization with vector evaluated geneticalgorithms[C]. Genetic Algorithm and their Application: Proceedings of the FirstInternational Conference on Genetic Algorithms,1985:93-100.
    [33] Kongar E., Gupta S.M. Disassembly-to-order System Using Linear PhysicalProgramming[C]. IEEE International Symposium on Electronics and theEnvironment, IEEE Computer Society,2002:312-317.
    [34] Messac A., Ismail-Yahaya A.. Multi-objective Robust Design Using PhysicalProgramming[J]. Structural and Multidisciplinary Optimization,2002,23(5):357-371.
    [35] Messac A., Sundararaj G.J. Physical Programming's Ability to Generate aWell-distributed Set of Pareto Points[C]. AIAA/ASME/ASCE/AHS Structures,Structural Dynamics&Materials Conference,2000,3:1742-1754.
    [36] Messac A.. Physical Programming: Effective Optimization for ComputationalDesgin[J]. AIAA Journal,1996,34(1):149~158.
    [37] Tappeta R.V., Renaud J.E., Messac A.. Interactive Physical Programming: TradeoffAnalysis and Decision Making in Multicriteria Optimization[J]. AIAA Journal,2000,38(5):917-926.
    [38]田志刚,黄洪钟,姚新胜.模糊物理规划及其在结构设计中的应用[J].中国机械工程,2002,24:2131-2133.
    [39]黄洪钟,田志刚,关立文.基于神经网络的交互式物理规划及其在机械设计中的应用研究[J].机械工程学报,2002,38(4):51-57.
    [40] Tian Z G,Huang H Z,Guan L W.Fuzzy Physical Programming and Its Applicationin Optimization of Through Passenger Train Plan[J]. Proc.of the3rd InternationalConference on Traffic and Transportation Studies, NewYork,2002:498-503.
    [41]黄洪钟,田志刚.基于广义模糊随机强度的模糊可靠性计算理论[J].机械工程学报,2002,38(8):50-53.
    [42] Fonseca C.M. and Fleming P.J. Genetic Algorithms for Multi-objectiveOptimization: Formulation, Discusstion and Generation[J]. Proc. of the5thInternational Conference on Genetic Algorithms,1993,416-423.
    [43] Srinivas N. and Deb K. Multi-objective Optimization Using Non-dominatedSorting in Genetic Algorithms[J]. Evolutionary Computation,1994,2(3):221-248.
    [44] Horn J. and Nafpliotis N. Multi-objective Optimization Using the Niched ParetoGenetic Algorithm[D].1993.
    [45] Horn J., Nafpliotis N. and Goldberg D.E. A Niched Pareto Genetic Algorithm forMulti-objective Optimization[J]. Proc. of1st IEEE Conference on EvolutionaryComputation,1994,1:82-87.
    [46] Zitzler E. and Thiele L. Multi-objective Evolutionary Algorithm: A ComparativeCase Study and the Strength Pareto Approach[J]. IEEE Transactions onEvolutionary Computation,1999,3:257-271.
    [47] Zitzler E. SPEA2: Improving the Strength Pareto Evolutionary Algorithm forMultiobjective Optimization[J]. European2001-Evolutionary Methods for Design,Optimization and Control with Applications to Industrial Problems,2001:95-100.
    [48] Knowles J.D. and Corne D.W. Approximating the Nondominated Front Using thePareto Archived Evolutionary Strategy[J]. Evolutionary Computation,2000,8:149-172.
    [49] Corne D.W. The Pareto Envelope-based Selection Algorithm for Multi-objectiveOptimization[J]. In Lecture Notes in Computer Science.Eds.Proc.Parallel ProblemSolving for Nature-PPSN IV,2000,1917:839-848.
    [50] Corne D.W. PESA-2: Region-based Selection in Evolutionary Multi-objectiveOptimization[J]. Proc. of the Genetic and Evolutionary Computation Conference,2001:283-290.
    [51] Erickson M., Mayer A. and Horn J. The Niched Pareto Genetic Algorithms2Applied to the Design of Groundwater Remediation System[J]. Proc. of theGenetic and Evolutionary Computation Conference, Morgan Kaufmann Publishers,2001:681-695.
    [52] Coello C.A. and Pulido G.T. A Micro-genetic Algorithm for Multi-objectiveOptimization[J]. Proc. of the Genetic and Evolutionary Computation Conf., SanFrancisco: Morgan Kaufmann Publishers,2001:274-282.
    [53] Deb K., Pratap A. and Agarwal S.A. Fast and Elitist Multi-objective GeneticAlgorithm: NSGA2[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182~197.
    [54] Laumanns M., Thiele L. and Deb K. Combining Convergence and Diversity inEvolutinoary Multi-objective Optimization[J]. Evolutionary Computation, theMassachusetts Institute of Technology,2002,10(3):1-21.
    [55] Deb K. and Saxena D.K. On Finding Pareto-optimal Solutions throughDimensionality Reduction for Certain Large-dimensional Multi-objectiveOptimization Problems[C]. Technical Report, India Institute of TechnologyKanpur,2005.
    [56] Saxena D.K. and Deb K. Non-linear Dimensionality Reduction Procedure forCertain Large-dimensional Multi-objective Optimization Problems: EmployingCorrentropy and a Novel Variance Unfolding[C]. Proc. of the4th InternationalConference on Evolutionary Multi-criterion Optimization,2007:772-787.
    [57] Coello C.A., Pulido G.T. and Lechuga M.S. Handling Multiple Objectives withParticle Swarm Optimization[J]. IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
    [58] Beume Nicola, Naujoks Boris and Emmerich Michael. SMS-EMOA:Multi-objective Selection based on Dominated Hypervolume[J]. European Journalof Operational Research,2007,181:1653-1669.
    [59]郑金华.多目标进化算法及其应用[M].北京:科学出版社,2007.
    [60]李智勇,陈友文.一种融入小生境技术的遗传禁忌算法[J].湖南大学学报(自然科学版),2010,37(4):81-84.
    [61]雷德明.多目标智能优化算法及应用[M].北京:科学出版社,2009.
    [62]袁东辉,刘大有,申世群.基于蚁群—遗传算法的改进多目标数据关联方法[J].通信学报,2011,32(6):17-23.
    [63]王瑞琪,张承慧,李珂.基于改进混沌优化的多目标遗传算法[J].控制与决策,2011,26(9):1391-1397.
    [64]朱学军,陈彤,薛量,李峻.多个体参与交叉的Pareto多目标遗传算法[J].电子学报,2001,29(1):106-109.
    [65]高晋.基于虚拟样机技术的悬架K&C特性及其对整车影响的研究[D].吉林大学博士学位论文,2010.
    [66]杨树凯.橡胶衬套对悬架弹性运动与整车转向特性影响的研究[D].吉林大学博士学位论文,2008.
    [67]杨荣山.轿车底盘平台开发中多目标优化方法的研究及应用[D].华南理工大学博士学位论文,2009.
    [68] Cai Jianfen J. and Salovey Ronald. Model filled rubber. I: Effect of particlemorphology on suspension rheology[J]. Polymer Engineering and Science, v39, n9, p1696-1709,1999.
    [69] Petek N.K. and Kicher T.P. Empirical model for the design of rubber shearbushings[J]. Rubber Chemistry and Technology, v60, n2, p298-309, May-Jun1987.
    [70] Walker C.G. Development of the rolling rubber bush primary suspension[J].Railway Engineer, n2, p32-34,1984.
    [71] Cai Jianfen J., Salovey Ronald. Model filled rubber. II. Particle compositiondependence of suspension rheology[J]. Journal of Polymer Science, Part B:Polymer Physics, v37, n8, p815-824, April15,1999.
    [72] Berg Mats. Non-linear rubber spring model for rail vehicle dynamics analysis[J].Vehicle System Dynamics, v30, n3-4, p197-212, Sep1998.
    [73]银花.基于分数导数粘弹性理论的车辆_路面作用研究[D].南京林业大学博士学位论文,2010.
    [74]陈前,朱德慰.关于复合结构振动分析中粘弹性材料本构方程的形式[J].应用力学学报,1987,4(1):39-51.
    [75] Bagley R.L. and Torvik P.J. Fractional calculus-a different approach to the analysisof viscoelastically damped structures[J].AIAAJ,1983,21(5):741-748.
    [76]潘孝勇,上官文斌,柴国钟,徐驰.基于超弹性、分数导数和摩擦模型的碳黑填充橡胶隔振器动态建模[J].振动与冲击,2007,26(10):6-10.
    [77]陈树越,潘宏侠.线性振动系统加权递推最小二乘法时域参数识别[J].振动与冲击,1995,14(4):52-56.
    [78]于增亮,张立军,余卓平.橡胶衬套力学特性半经验参数化模型[J].机械工程学报,2010,46(14):115-123.
    [79] Maalej A.Y., Guenther D.A. and Ellis J.R. Experimental development of tyre forceand moment models[J]. International Journal of Vehicle Design, v10, n1, p34-50,1989.
    [80] Bakker Egbert, Nyborg Lars and Pacejka Hans B. Tyre modeling for use in vehicledynamics studies[J]. SAE Technical Paper Series,1987.
    [81]瞿宏敏,程军.汽车动力学模拟中的轮胎模型述评[J].汽车技术,1996,(7):1-8.
    [82]何大刚.大客车车身结构强度及刚度分析[J].机械研究与应用,2001,14:4-6
    [83]王海霞,汤文成. CJ6121GCHK型客车车身骨架有限元建模及结果分析方法研究[J].汽车工程,2001(23):33-36.
    [84]刘伟.基于虚拟样机技术的重型车辆动力学建模与仿真分析[D].青岛大学硕士学位论文,2009.
    [85]聂彦鑫,李孟良,过学迅,杨波.基于谐波叠加法的路面不平度重构[J].汽车科技,2009,(4):55-57.
    [86]张永林.用谐波叠加法重构随机道路不平顺高程的时域模型[J].农业工程学报,2003,19(6):32-35.
    [87]陈无畏,李欣冉,陈晓新,王磊.车辆悬架中高频振动传递分析与橡胶衬套刚度优化[J].农业机械学报,2011,42(10):25-29.
    [88]陈志勇,史文库,沈志宏,郭福祥,方德广.轻型客车车身车架整体结构有限元模态分析[J].振动与冲击,2010,(29)10:244-246
    [89]何琳.声学理论与工程应用[M].北京:科学出版社,2006
    [90]陈志勇.轻型车驾驶室液压悬置性能匹配研究[D].吉林大学博士学位论文,2011.
    [91]张伟,刘献栋,单颖春,何田.基于声传递向量法的路面激励引起车内噪声的仿真研究[J].振动工程学报,2010,(6):625-629.
    [92]吴利广.基于整车操稳性能的衬套刚度特性及结构参数优化[D].吉林大学硕士学位论文,2011.
    [93] Merrell G.B. Sensitivity analysis of maximum doses for a below-ground vaultlow-level radioactive waste disposal facility[J]. Proceedings of the Symposium onWaste Management, v2, p727,1989.
    [94] Chavant C. and Cohouet J. Sensitivity analysis of electromagnetic fieldsdepending on flaw shape in NDT[J]. Proceedings of the World Conference onNon-Destructive Testing, v1, p259,1992.
    [95]邓江华,刘献栋,李兴虎,单颖春.车身阻尼层结构的声灵敏度分析及优化[J].噪声与振动控制,2009,(1):54-57.
    [96]刘桂萍.基于微型遗传算法的多目标优化方法及应用研究[D].湖南大学博士学位论文,2007.
    [97]刘伟,史文库,桂龙明,方德广,郭福祥.基于平顺性与操纵稳定性的悬架系统多目标优化[J].吉林大学学报(工学版),2011,41(5):1199-1204.
    [98]杜恒,魏建华.基于遗传算法的连通式油气悬架平顺性与道路友好性参数优化[J].振动与冲击,2011,30(8):133-138.
    [99]程方晓.基于自适应保持多样性遗传算法的汽车动力传动系多目标优化[D].吉林大学博士学位论文,2011.
    [100]张晓缋,戴冠中,徐乃平.一种新的优化搜索算法──遗传算法[J].控制理论与应用,1995,12(3):265-272.
    [101]周双喜,杨彬.实现无功优化的新算法──遗传算法[J].电力系统自动化,1995,19(11):19-23.
    [102]赵新昱,陈文伟,牛晓丽.遗传算法和遗传规划对比研究[J].系统工程与电子技术,2000,22(12):84-87.
    [103] Deb K., Pratap A. and Agarwal S. A fast and elitist multiobjective geneticalgorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
    [104] Srinivas N., Deb Kalyanmoy. Multiobjective Optimization Using NondominatedSorting in Genetic Algorithms[J]. Evolutionary Computation,1994,2(3):221-248.
    [105]李伟平,王世东,周兵,张利轩,马义超.基于响应面法和NSGA-Ⅱ算法的麦弗逊悬架优化[J].湖南大学学报(自然科学版),2011,38(6):27-32.

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