装订设备的性能优化设计方法研究
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摘要
产品是一个系统,往往由几个子系统组成,系统性能决定了产品质量,而产品性能取决于产品设计。在产品设计过程中,分性能之间,子系统之间,各参数之间相互耦合,关系之复杂性、网络性、模糊性和动态性凸显。因此,研究产品性能及其系统的协同优化方法与技术具有重要的理论和工程意义。
本文以装订设备为研究对象,研究了性能优化设计理论,规划系统优化设计进程,确定了耦合子系统之间的协同优化机制,通过合理优化使装订设备整机性能显著提高。本文的主要工作和研究结果如下:
1.综述了产品性能优化设计技术的研究现状,通过分析装订设备的发展状况,提出了论文的研究内容,以及装订设备优化设计实现的技术路线。
2.研究了机械产品的全性能优化建模和协同优化方法,并以具体算例完成了协同优化方法在iSIGHT平台上的实现,为系统性能协同优化设计的实现提供依据。完成了装订设备的性能优化建模,在总体性能优化进程规划确立的前提下,探索了多种装订设备的性能优化求解策略。
3.针对打孔刀的工作性能和结构特点,研究单目标和多目标的寻优方法,并以iSIGHT软件作为优化平台,采用正交试验设计、Kriging近似模型及组合优化策略进行多目标优化求解,既保证了切纸性能,也兼顾了安全指标。最后,通过参数化数字模型验证了优化结果符合预期结果。
4.针对装订设备的综合性能设计要求,基于人机工程学原理,提出了手柄作用力的舒适度曲线。并根据子系统间的参数耦合关系,建立了基于工作安全性、操作宜人性、结构紧凑性的系统性能协同优化数学模型,最后通过建立一致性约束,实现了优化求解的自动化。优化结果表明:切纸力减小了27.4%,手柄上的最大作用力降低了50.9%,同时手柄作用力曲线的变化趋势符合优化预期。通过实物打样,经综合测试平台的测试,优化后的理论曲线和实测曲线变化趋势十分吻合,表明协同优化方法可行,手柄作用力的舒适度曲线可信可靠。
5.由于精冲板的结构不规则,精确的优化数学模型较难建立,本文采用iSIGHT软件集成Ansys有限元分析软件进行具体结构的优化分析。优化后,在满足结构强度和刚度的要求下,精冲板的体积(质量)减小了19.8%,并通过对精冲板过孔变形的分析和位移的计算,验证了优化后的精冲板在工作过程中产生的变形量不会影响装订设备的工作性能。最后,基于整机的优化结果,研究了装订设备在一个作业过程中,精冲板在打孔刀的动态载荷作用下,其应力和挠度的变化过程,并做了简要的疲劳分析,为将来的整机的可靠性研究打下基础。
Product is a certain system which is made of several subsystems. The product’s quality is determined by the performance of the system and whether the capability is good or not is determined by the design. In the cause of design, different performance, subsystems and parameters are coupling to form a really complicated network with fuzziness and dynamic as its characters. Therefore, studies on the product’s performance and the effective ways for collaborative optimization of system have some value in theory research and engineer application.
With binding machine as its target, this dissertation is about the design theory for performance optimization, the course for optimization design and the algorithm for collaborative optimization of coupling subsystems is defined which helps to greatly upgrade the entire performance of the binding machine. Main work are as follows:
1. By analysis for the development of binding machine and the study status home and abroad of the optimization technology, the contents of the study and the scheme of the optimal design for binding machine unit is given.
2. By studying on the full performance optimization modeling for the product and the theory of collaborative optimization. A concrete sample is operated successfully on the iSIGHT platform which provide basis for later study on the collaborative optimization way based on the systematic performance. Besides, the collaborative optimization way and various kinds of strategy performance optimization are put forward.
3. According to the unique work performance and structure character of punching blade, a robust design methodology combining design of experiment (DOE),Kriging model technique and the strategy of optimization combination embodied in iSIGHT software are investigated for the design optimization. It guarantees the performance of punching whiling giving attention to the safety.
4. Guided by the design demand for the systematic performance of the binding machine, a curve on comfort is put forward, which is based on the theory of human engineering. The subsystems are designed with the aim of safe work, delightful operation and close structure and the collaborative optimization mathematical model based on systematic performance is built on the study of the coupling relation of parameters in different subsystems. Finally, by setting up coherence restriction, the automation of solution is realized. The result shows that the force for punching reduces by 27.4%, the max acting force of human reduces by 50.9% and meanwhile, the developing trend of the acting force of human with the travel of punching conforms with the expect.
5. Aim at the supporting module, integration with iSIGHT and Ansys is adopted, for the precise mathematical model is hard to establish with the irregular structure. After optimization, the bulk (quality) of ban is reduced by 19.8% whiling conforming to the intension and stiffness demand. And the fact that the opitimized ban’s bending won’t affect the performance of binding machine is verified after the analysis and calculation on the holes displacement. Finally, based on the optimization result of the entire machine, the change cause of stress and flexivity during punching is studied and a brief analysis on fatigue is is rendered which will lay the basis for the reliability study of an entire machine in the future.
本文以装订设备为研究对象,研究了性能优化设计理论,规划系统优化设计进程,确定了耦合子系统之间的协同优化机制,通过合理优化使装订设备整机性能显著提高。本文的主要工作和研究结果如下:
1.综述了产品性能优化设计技术的研究现状,通过分析装订设备的发展状况,提出了论文的研究内容,以及装订设备优化设计实现的技术路线。
2.研究了机械产品的全性能优化建模和协同优化方法,并以具体算例完成了协同优化方法在iSIGHT平台上的实现,为系统性能协同优化设计的实现提供依据。完成了装订设备的性能优化建模,在总体性能优化进程规划确立的前提下,探索了多种装订设备的性能优化求解策略。
3.针对打孔刀的工作性能和结构特点,研究单目标和多目标的寻优方法,并以iSIGHT软件作为优化平台,采用正交试验设计、Kriging近似模型及组合优化策略进行多目标优化求解,既保证了切纸性能,也兼顾了安全指标。最后,通过参数化数字模型验证了优化结果符合预期结果。
4.针对装订设备的综合性能设计要求,基于人机工程学原理,提出了手柄作用力的舒适度曲线。并根据子系统间的参数耦合关系,建立了基于工作安全性、操作宜人性、结构紧凑性的系统性能协同优化数学模型,最后通过建立一致性约束,实现了优化求解的自动化。优化结果表明:切纸力减小了27.4%,手柄上的最大作用力降低了50.9%,同时手柄作用力曲线的变化趋势符合优化预期。通过实物打样,经综合测试平台的测试,优化后的理论曲线和实测曲线变化趋势十分吻合,表明协同优化方法可行,手柄作用力的舒适度曲线可信可靠。
5.由于精冲板的结构不规则,精确的优化数学模型较难建立,本文采用iSIGHT软件集成Ansys有限元分析软件进行具体结构的优化分析。优化后,在满足结构强度和刚度的要求下,精冲板的体积(质量)减小了19.8%,并通过对精冲板过孔变形的分析和位移的计算,验证了优化后的精冲板在工作过程中产生的变形量不会影响装订设备的工作性能。最后,基于整机的优化结果,研究了装订设备在一个作业过程中,精冲板在打孔刀的动态载荷作用下,其应力和挠度的变化过程,并做了简要的疲劳分析,为将来的整机的可靠性研究打下基础。
Product is a certain system which is made of several subsystems. The product’s quality is determined by the performance of the system and whether the capability is good or not is determined by the design. In the cause of design, different performance, subsystems and parameters are coupling to form a really complicated network with fuzziness and dynamic as its characters. Therefore, studies on the product’s performance and the effective ways for collaborative optimization of system have some value in theory research and engineer application.
With binding machine as its target, this dissertation is about the design theory for performance optimization, the course for optimization design and the algorithm for collaborative optimization of coupling subsystems is defined which helps to greatly upgrade the entire performance of the binding machine. Main work are as follows:
1. By analysis for the development of binding machine and the study status home and abroad of the optimization technology, the contents of the study and the scheme of the optimal design for binding machine unit is given.
2. By studying on the full performance optimization modeling for the product and the theory of collaborative optimization. A concrete sample is operated successfully on the iSIGHT platform which provide basis for later study on the collaborative optimization way based on the systematic performance. Besides, the collaborative optimization way and various kinds of strategy performance optimization are put forward.
3. According to the unique work performance and structure character of punching blade, a robust design methodology combining design of experiment (DOE),Kriging model technique and the strategy of optimization combination embodied in iSIGHT software are investigated for the design optimization. It guarantees the performance of punching whiling giving attention to the safety.
4. Guided by the design demand for the systematic performance of the binding machine, a curve on comfort is put forward, which is based on the theory of human engineering. The subsystems are designed with the aim of safe work, delightful operation and close structure and the collaborative optimization mathematical model based on systematic performance is built on the study of the coupling relation of parameters in different subsystems. Finally, by setting up coherence restriction, the automation of solution is realized. The result shows that the force for punching reduces by 27.4%, the max acting force of human reduces by 50.9% and meanwhile, the developing trend of the acting force of human with the travel of punching conforms with the expect.
5. Aim at the supporting module, integration with iSIGHT and Ansys is adopted, for the precise mathematical model is hard to establish with the irregular structure. After optimization, the bulk (quality) of ban is reduced by 19.8% whiling conforming to the intension and stiffness demand. And the fact that the opitimized ban’s bending won’t affect the performance of binding machine is verified after the analysis and calculation on the holes displacement. Finally, based on the optimization result of the entire machine, the change cause of stress and flexivity during punching is studied and a brief analysis on fatigue is is rendered which will lay the basis for the reliability study of an entire machine in the future.
引文
[1]谢友柏.产品性能特征与现代设计[J].中国机械工程, 2000,11(1-2):26-32.
[2]冯培恩,邱清盈,潘双夏等.机械产品的广义优化设计进程研究[J].中国科学(E辑), 1999, 29(4):338-346.
[3]冯培恩,邱清盈,潘双夏等.机械广义优化设计的理论框架[J].中国机械工程, 2000, 11(1-2):126-129.
[4]魏喆,谭建荣,冯毅雄.广义性能驱动的机械产品方案设计方法[J].机械工程学报, 2008, 44(5): 1-10.
[5]闻邦椿.产品全功能与全性能的综合设计[M].北京:机械工业出版社, 2008.
[6]闻邦椿.基于系统工程的产品设计7D规划及1+3+X综合设计法[J].东北大学学报(自然科学版), 2008, 29(9):1217-1223.
[7]戚昌滋主编.优化论方法学—广义优化是现代设计的目标,现代广义设计科学方法学[M].北京:中国建筑工业出版社, 1987:597-689.
[8]濮良贵.机械优化设计[M].兰州:西北工业大学出版社, 1991.
[9] M.Li,S.Azarm. Multi objective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation [J]. Journal of Mechanical Design, 2008, 130(8):081402-1-081402-11.
[10] M.Marseguerra,E.Zio,S.Martorell. Basics of genetic algorithms optimization for RAMS applications [J]. Reliability Engineering and System Safety, 2006, 91:977-991.
[11] S.Martorell,A.Sanchez,S.Carlos. A tolerance interval based approach to address uncertainty for RAMS+C optimization [J]. Reliability Engineering and System Safety, 2007, 92:408-422.
[12]朱学军,王安鹿等.用Pareto遗传算法实现机械/结构系统的多目标优化设计[J].机械科学与技术, 1998, 11,17: 51-53, 56.
[13]费烨,屈福政,王欣.基于演化算法的复杂机械广义优化设计实现策略研究[J].机床与液压, 2002,(5): 22-24.
[14]方卫国,师瑞峰.飞机方案多目标优化的Pareto遗传算法[J].北京航空航天大学学报, 2003, 29(8):668~672.
[15]安伟刚,李为吉,王海峰.某型飞艇外形多目标优化设计及其策略[J].西北工业大学学报, 2007,12.
[16]刘仁云,张义民,刘巧伶.基于多目标优化策略的结构可靠性稳健设计[J].应力力学学报, 2007,2.
[17]邱志平,张宇星.改进遗传算法在飞机总体参数优化中的应用[J].北京航空航天大学学报, 2008, 34(10):1182-1185.
[18] Natalia M, Alexandrov, Lewis R M. Analytical and Computational Aspects of Collaborative Optimization [R]. NASA /T M-2000-210104, Virgjnia: NASA, 2000.
[19] Neu W L, et al. A prototype tool for multidisciplinary design optimization of ships [A]. Proceedings ofNinth Congress of the International Maritime Association of the Mediterranean [C], 2000.
[20] Mosher Todd Jon. Improving Spacecraft Design Using a Multidisciplinary Design Optimization Methodology [D].Boulder: University of Colorado, 2000.
[21] Xiaoyu Gu, John E. Renaud, et al. Decision-Based Collaborative Optimization [J], J. Mech. Des. 2002, 124:1.
[22] Xiaoyu Gu, John E. Renaud, and Charles L. Penninger, Implicit Uncertainty Propagation for Robust Collaborative Optimization [J], J. Mech. Des. 2006, 128:1001.
[23] Brian D. Roth and Ilan M. Kroo. Enhanced Collaborative Optimization: A Decomposition-Based Method for Multidisciplinary Design [A], ASME Conf. Proc. IDETC/CIE2008 (2008). Volume 1: 34th Design Automation Conference, Parts A and B.
[24] Alessandro Giassi, Fouad Bennis, and Jean-Jacques Maisonneuve. Multi-Objective Optimization Using Asynchronous Distributed Applications [J], J. Mech. Des. 2004, 126:767.
[25] Philippe Dépincé, Sébastien Rabeau, and Fouad Bennis. Collaborative Optimization Strategy for Multi-Objective Design [J], ASME Conf. Proc. IDETC/CIE2005, 2005.
[26] Kodiyalam, S. and Sobieszczanski-Sobieski, J. Multidisciplinary design optimization. Some formal methods, framework requirements, and application to vehicle design [J]. International Journal of Vehicle Design (Special Issue), 2001, 25(1/2):3-22.
[27] Kevin F.Hulme. The design of a simulation-based framework for the development of solution approaches in multidisciplinary design optimization :( dissertation of Doctor of Philosophy). University of New York at Buffalo.2000.
[28] M.Alexandra Ahlqvist. Dependency-tracking object-oriented multidisciplinary design optimization (mdo) formulation on a large-scale system :( dissertation of Doctor of Philosophy). University of Central Rorida. 2001.
[29]陈琪锋,戴金海.多目标的分布式协同进化MDO算法[J].国防科技大学学报, 2002, 24(4):12-15.
[30]薛飞,余雄庆.协同优化在机翼气动/结构一体化设计中初步应用[J].航空计算技术, 2004, 34(1): 82-86.
[31]冯培恩,邱清盈,潘双夏,董黎刚,李善春.机械产品的广义优化设计进程研究[J],中国科学, 1999, 29(4): 338-346.
[32]闻邦椿.产品设计的重要性与面向产品总体质量的综合设计法[R],中国科协2003年学术年会主题报告, 2003.
[33]薛彩军,聂宏,姜少飞.机械产品协同优化设计研究综述[J].机械科学与技术, 2005,24(5):620-623.
[34] Sobieszczanski S J, et al. Structural op timization by multilevel decomposition [J]. AIAA Journal, 1985, 23 (11): 1775-1782.
[35] Kusiak, Wang J. Efficient organizing of design activities [J]. International Journal of Production Research, 1993, 31(4): 753-769.
[36] Rogers J L, Bloebaum C L. Ordering design tasks based on coupling strengths [A]. Proceedings of 5th AIAA/NASA/USAF/ISSMO Symposium on multidisciplinary Analysis and Optimization[C], Panama City, 1994.
[37] Michelena N F, Papalambros P Y. A hypergraph framework for optimal model-based decomposition of design problems [J]. Computational Optim zation and Applications, 1997, 8.
[38] Balling R J, Sobieszczanski S J. An algorithm for solving the system-level problem in multileveloptimization [J]. Structural Opt mization, 1995, 9: 168～177.
[39] Sellar R S, et al. Response Surface Based Concurrent Subspace Optimization for Multidisciplinary System Design[R]. AIAA-96-0714, Nevada: AIAA, 1996.
[40] Tappeta R V, Renaud J E. Multi objective collaborative optimization [J]. ASME Journal of Mechanical Design, 1997, 11(3): 403-411.
[41] Alvarez L F. Approximation Model Building for Design Optimization Using the Response Surface Methodology and Genetic Programming [D]. Bradford: University of Bradford, 2000.
[42] Qian Z, Xue C, Pan S. FEA agent for multidisciplinary optimization [J]. Structural and Multidisciplinary Optimization, 2001, 22 (5): 373-383.
[43]苗海龙,赵春辉.书刊装订工艺及设备[M].北京:印刷工业出版社, 2007.
[44]张书.全自动装订机空间圆柱凸轮机构的设计与研究: [硕士学位论文],天津:天津大学, 2007
[45]张付英.纸带打孔机的研制[J].轻工机械, 2000, (1):15-17.
[46]何炬.形形色色打孔机[J].文体用品与科技, 2000, (4):18-19.
[47]庞丽.自动打孔装订机的可靠性设计: [硕士学位论文],天津:天津大学, 2003.
[48]钟毅芳,陈柏鸿,肖人彬,刘继红.复杂机械产品协同寻优相关技术的研究[J].中国机械工程, 2002, 29(4): 20-23.
[49]闻邦椿.产品全功能与全性能的综合设计[M].北京:机械工业出版社, 2007.
[50]姚振强,张雪萍.机械产品对象的系统性设计策略[J].机械工程学报, 2000, 36(6): 22-24, 29.
[51]杜轩,陈柏鸿,彭立焱,钟毅芳等.机械产品的全性能优化建模及求解[J].机械设计与制造工程, 2000, 13(6):515-517.
[52]陈树勋,胡迎春.工程系统设计的两类协同优化讨论[J].现代制造工程, 2004(6): 7-10.
[53]岳珠峰,李立州等.航空发动机涡轮叶片多学科设计优化[M].北京:科学出版社, 2007,105-106.
[54] Douglas C. Montgomery. Design and Analysis of Experiments [M]. Wiley.2004, 347-372.
[55] Vamshi M. Korivi, Su K. Cho, and Amer A. Amer. Port DOE with Parametric Modeling and CFD [A] ASME Conf. Proc. FEDSM2006[C] Miami, Florida, USA Fluids Engineering Division, 2006: 449-455.
[56]邓苗毅,崔聚印等.桥梁结构静力模型修正响应面方法试验设计研究[J].河南科学, 2007, 25(8):606-608.
[57] LeGresley P A, Alonso J J. Airfoil design optimization using reduced order models based on proper orthogonal decomposition [J]. AIAA, 2000:2545.
[58] Kaymaz I. Application of kriging method to structural reliability problems [J]. Structural Safety, 2005, 27 (2):133-151.
[59]余雄庆,丁运亮.多学科设计优化算法及其在飞行器中的应用[J].航空学报, 2000, 21(1):1-6.
[60]何麟书,王书河,张玉珠.飞行器多学科综合设计新算法.航空学报[J]. 2004, 25(5):465-469.
[61] David A. Romero, et al. A Study of Covariance Functions for Multi-Response Metamodeling for Simulation-Based Design and Optimization [A]. ASME Conf. Proc. IDETC/CIE2008[C]. Brooklyn, New York, USA Design Engineering Division and Computers in Engineering Division 2008, 883-893.
[62] Xinwei Shu, et al.Optimum Design and Experimental Study of a Very Low-Specific-Speed Centrifugal Blower Blade [A]. ASME Conf. Proc. GT2009 [C] Orlando, Florida, USA: International Gas Turbine Institute, 2009, 545-553.
[63]陈立周.稳健设计[M].北京:机械工业出版社, 1999.
[64] Simpson TW, Mauery TM, Korte J, etal. Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization[R]. AIAA 98-4755.
[65] Giunta A A, Wojtkiewicz Jr S F, Eldred M S. Overview of Modern Design of Experiments Methods for Computational Simulations[R ]. AIAA 2003-0649.
[66]韩永志,高行山,李立州.基于Kriging模型的涡轮叶片多学科设计优化[J].航空动力学报, 2007, 22(7):1055-1059.
[67]王晓锋,席光.基于Kriging模型的翼型气动性能优化设计[J].航空学报, 2005, 26(5):545-549.
[68]高月华,张崎,王希诚.基于Kriging模型的汽轮机基础动力优化设计[J].计算力学学报, 2008, 25(5): 610-615.
[69] Koba Gyashi Y, Aiyoshi E. Optimization of boiling water reactor loading pattern using an improved genetic algorithm [J] Nuclear Technology , 2003 , 143 (2) : 144-151.
[70] Silva C M, Biscaia E C. Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors [J]. Computers and Chemical Engineering, 2003, 27 (1): 1329 - 1344.
[71] Su C T, Chiang G C L. No conver power economic dispatch by improved genetic algorithm with multiplier updating method [J] Electric Power Component sand Systems , 2004 , 32 (3) :2572273.
[72] Dehuri S, Patnaik S, Ghosh A, Mall R. Application of Elitist Multi-objective Genetic Algorithm for Classification Rule Generation [J]. Applied Soft Computing Journal (S1568-4946) 2008, 8(1):477-487.
[73]江敏,陈一民.遗传算法在多目标优化问题中点应用策略[J].兰州理工大学学报, 2007, 33(3): 92-94.
[74]邱志平,张宇星.改进遗传算法在飞机总体参数优化中的应用[J].北京航空航天大学学报, 2008, 34(10):1182-1185.
[75]谢涛,陈火旺,康立山.多目标优化的演化算法[J].计算机学报, 2003, 26(8): 997-1002.
[76]谢涛.水平轴风力机叶片多目标优化设计及结构动力学分析:[硕士学位论文],广东:汕头大学, 2005.
[77]杨荣山,黄向东等.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), 2009, 7:87.
[78]张鄂.机械与工程优化设计[M] .科学出版社, 2008.
[79]曹景伟,季林红,马祖长等.我国优秀赛艇运动员桨力—时间曲线特征[J].天津体育学院学报, 2007, 22(4):281-283.
[80]吕杰锋,陈建新,徐进波.人机工程学[M].清华大学出版社, 2009.
[81]王继成.产品设计中的人机工程学[M].化学工业出版社, 2004.
[82]魏显君,戈永杰.操纵装置设计的人机工程问题初探[J].科技创新导报, 2008, 23:110.
[2]冯培恩,邱清盈,潘双夏等.机械产品的广义优化设计进程研究[J].中国科学(E辑), 1999, 29(4):338-346.
[3]冯培恩,邱清盈,潘双夏等.机械广义优化设计的理论框架[J].中国机械工程, 2000, 11(1-2):126-129.
[4]魏喆,谭建荣,冯毅雄.广义性能驱动的机械产品方案设计方法[J].机械工程学报, 2008, 44(5): 1-10.
[5]闻邦椿.产品全功能与全性能的综合设计[M].北京:机械工业出版社, 2008.
[6]闻邦椿.基于系统工程的产品设计7D规划及1+3+X综合设计法[J].东北大学学报(自然科学版), 2008, 29(9):1217-1223.
[7]戚昌滋主编.优化论方法学—广义优化是现代设计的目标,现代广义设计科学方法学[M].北京:中国建筑工业出版社, 1987:597-689.
[8]濮良贵.机械优化设计[M].兰州:西北工业大学出版社, 1991.
[9] M.Li,S.Azarm. Multi objective Collaborative Robust Optimization With Interval Uncertainty and Interdisciplinary Uncertainty Propagation [J]. Journal of Mechanical Design, 2008, 130(8):081402-1-081402-11.
[10] M.Marseguerra,E.Zio,S.Martorell. Basics of genetic algorithms optimization for RAMS applications [J]. Reliability Engineering and System Safety, 2006, 91:977-991.
[11] S.Martorell,A.Sanchez,S.Carlos. A tolerance interval based approach to address uncertainty for RAMS+C optimization [J]. Reliability Engineering and System Safety, 2007, 92:408-422.
[12]朱学军,王安鹿等.用Pareto遗传算法实现机械/结构系统的多目标优化设计[J].机械科学与技术, 1998, 11,17: 51-53, 56.
[13]费烨,屈福政,王欣.基于演化算法的复杂机械广义优化设计实现策略研究[J].机床与液压, 2002,(5): 22-24.
[14]方卫国,师瑞峰.飞机方案多目标优化的Pareto遗传算法[J].北京航空航天大学学报, 2003, 29(8):668~672.
[15]安伟刚,李为吉,王海峰.某型飞艇外形多目标优化设计及其策略[J].西北工业大学学报, 2007,12.
[16]刘仁云,张义民,刘巧伶.基于多目标优化策略的结构可靠性稳健设计[J].应力力学学报, 2007,2.
[17]邱志平,张宇星.改进遗传算法在飞机总体参数优化中的应用[J].北京航空航天大学学报, 2008, 34(10):1182-1185.
[18] Natalia M, Alexandrov, Lewis R M. Analytical and Computational Aspects of Collaborative Optimization [R]. NASA /T M-2000-210104, Virgjnia: NASA, 2000.
[19] Neu W L, et al. A prototype tool for multidisciplinary design optimization of ships [A]. Proceedings ofNinth Congress of the International Maritime Association of the Mediterranean [C], 2000.
[20] Mosher Todd Jon. Improving Spacecraft Design Using a Multidisciplinary Design Optimization Methodology [D].Boulder: University of Colorado, 2000.
[21] Xiaoyu Gu, John E. Renaud, et al. Decision-Based Collaborative Optimization [J], J. Mech. Des. 2002, 124:1.
[22] Xiaoyu Gu, John E. Renaud, and Charles L. Penninger, Implicit Uncertainty Propagation for Robust Collaborative Optimization [J], J. Mech. Des. 2006, 128:1001.
[23] Brian D. Roth and Ilan M. Kroo. Enhanced Collaborative Optimization: A Decomposition-Based Method for Multidisciplinary Design [A], ASME Conf. Proc. IDETC/CIE2008 (2008). Volume 1: 34th Design Automation Conference, Parts A and B.
[24] Alessandro Giassi, Fouad Bennis, and Jean-Jacques Maisonneuve. Multi-Objective Optimization Using Asynchronous Distributed Applications [J], J. Mech. Des. 2004, 126:767.
[25] Philippe Dépincé, Sébastien Rabeau, and Fouad Bennis. Collaborative Optimization Strategy for Multi-Objective Design [J], ASME Conf. Proc. IDETC/CIE2005, 2005.
[26] Kodiyalam, S. and Sobieszczanski-Sobieski, J. Multidisciplinary design optimization. Some formal methods, framework requirements, and application to vehicle design [J]. International Journal of Vehicle Design (Special Issue), 2001, 25(1/2):3-22.
[27] Kevin F.Hulme. The design of a simulation-based framework for the development of solution approaches in multidisciplinary design optimization :( dissertation of Doctor of Philosophy). University of New York at Buffalo.2000.
[28] M.Alexandra Ahlqvist. Dependency-tracking object-oriented multidisciplinary design optimization (mdo) formulation on a large-scale system :( dissertation of Doctor of Philosophy). University of Central Rorida. 2001.
[29]陈琪锋,戴金海.多目标的分布式协同进化MDO算法[J].国防科技大学学报, 2002, 24(4):12-15.
[30]薛飞,余雄庆.协同优化在机翼气动/结构一体化设计中初步应用[J].航空计算技术, 2004, 34(1): 82-86.
[31]冯培恩,邱清盈,潘双夏,董黎刚,李善春.机械产品的广义优化设计进程研究[J],中国科学, 1999, 29(4): 338-346.
[32]闻邦椿.产品设计的重要性与面向产品总体质量的综合设计法[R],中国科协2003年学术年会主题报告, 2003.
[33]薛彩军,聂宏,姜少飞.机械产品协同优化设计研究综述[J].机械科学与技术, 2005,24(5):620-623.
[34] Sobieszczanski S J, et al. Structural op timization by multilevel decomposition [J]. AIAA Journal, 1985, 23 (11): 1775-1782.
[35] Kusiak, Wang J. Efficient organizing of design activities [J]. International Journal of Production Research, 1993, 31(4): 753-769.
[36] Rogers J L, Bloebaum C L. Ordering design tasks based on coupling strengths [A]. Proceedings of 5th AIAA/NASA/USAF/ISSMO Symposium on multidisciplinary Analysis and Optimization[C], Panama City, 1994.
[37] Michelena N F, Papalambros P Y. A hypergraph framework for optimal model-based decomposition of design problems [J]. Computational Optim zation and Applications, 1997, 8.
[38] Balling R J, Sobieszczanski S J. An algorithm for solving the system-level problem in multileveloptimization [J]. Structural Opt mization, 1995, 9: 168～177.
[39] Sellar R S, et al. Response Surface Based Concurrent Subspace Optimization for Multidisciplinary System Design[R]. AIAA-96-0714, Nevada: AIAA, 1996.
[40] Tappeta R V, Renaud J E. Multi objective collaborative optimization [J]. ASME Journal of Mechanical Design, 1997, 11(3): 403-411.
[41] Alvarez L F. Approximation Model Building for Design Optimization Using the Response Surface Methodology and Genetic Programming [D]. Bradford: University of Bradford, 2000.
[42] Qian Z, Xue C, Pan S. FEA agent for multidisciplinary optimization [J]. Structural and Multidisciplinary Optimization, 2001, 22 (5): 373-383.
[43]苗海龙,赵春辉.书刊装订工艺及设备[M].北京:印刷工业出版社, 2007.
[44]张书.全自动装订机空间圆柱凸轮机构的设计与研究: [硕士学位论文],天津:天津大学, 2007
[45]张付英.纸带打孔机的研制[J].轻工机械, 2000, (1):15-17.
[46]何炬.形形色色打孔机[J].文体用品与科技, 2000, (4):18-19.
[47]庞丽.自动打孔装订机的可靠性设计: [硕士学位论文],天津:天津大学, 2003.
[48]钟毅芳,陈柏鸿,肖人彬,刘继红.复杂机械产品协同寻优相关技术的研究[J].中国机械工程, 2002, 29(4): 20-23.
[49]闻邦椿.产品全功能与全性能的综合设计[M].北京:机械工业出版社, 2007.
[50]姚振强,张雪萍.机械产品对象的系统性设计策略[J].机械工程学报, 2000, 36(6): 22-24, 29.
[51]杜轩,陈柏鸿,彭立焱,钟毅芳等.机械产品的全性能优化建模及求解[J].机械设计与制造工程, 2000, 13(6):515-517.
[52]陈树勋,胡迎春.工程系统设计的两类协同优化讨论[J].现代制造工程, 2004(6): 7-10.
[53]岳珠峰,李立州等.航空发动机涡轮叶片多学科设计优化[M].北京:科学出版社, 2007,105-106.
[54] Douglas C. Montgomery. Design and Analysis of Experiments [M]. Wiley.2004, 347-372.
[55] Vamshi M. Korivi, Su K. Cho, and Amer A. Amer. Port DOE with Parametric Modeling and CFD [A] ASME Conf. Proc. FEDSM2006[C] Miami, Florida, USA Fluids Engineering Division, 2006: 449-455.
[56]邓苗毅,崔聚印等.桥梁结构静力模型修正响应面方法试验设计研究[J].河南科学, 2007, 25(8):606-608.
[57] LeGresley P A, Alonso J J. Airfoil design optimization using reduced order models based on proper orthogonal decomposition [J]. AIAA, 2000:2545.
[58] Kaymaz I. Application of kriging method to structural reliability problems [J]. Structural Safety, 2005, 27 (2):133-151.
[59]余雄庆,丁运亮.多学科设计优化算法及其在飞行器中的应用[J].航空学报, 2000, 21(1):1-6.
[60]何麟书,王书河,张玉珠.飞行器多学科综合设计新算法.航空学报[J]. 2004, 25(5):465-469.
[61] David A. Romero, et al. A Study of Covariance Functions for Multi-Response Metamodeling for Simulation-Based Design and Optimization [A]. ASME Conf. Proc. IDETC/CIE2008[C]. Brooklyn, New York, USA Design Engineering Division and Computers in Engineering Division 2008, 883-893.
[62] Xinwei Shu, et al.Optimum Design and Experimental Study of a Very Low-Specific-Speed Centrifugal Blower Blade [A]. ASME Conf. Proc. GT2009 [C] Orlando, Florida, USA: International Gas Turbine Institute, 2009, 545-553.
[63]陈立周.稳健设计[M].北京:机械工业出版社, 1999.
[64] Simpson TW, Mauery TM, Korte J, etal. Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization[R]. AIAA 98-4755.
[65] Giunta A A, Wojtkiewicz Jr S F, Eldred M S. Overview of Modern Design of Experiments Methods for Computational Simulations[R ]. AIAA 2003-0649.
[66]韩永志,高行山,李立州.基于Kriging模型的涡轮叶片多学科设计优化[J].航空动力学报, 2007, 22(7):1055-1059.
[67]王晓锋,席光.基于Kriging模型的翼型气动性能优化设计[J].航空学报, 2005, 26(5):545-549.
[68]高月华,张崎,王希诚.基于Kriging模型的汽轮机基础动力优化设计[J].计算力学学报, 2008, 25(5): 610-615.
[69] Koba Gyashi Y, Aiyoshi E. Optimization of boiling water reactor loading pattern using an improved genetic algorithm [J] Nuclear Technology , 2003 , 143 (2) : 144-151.
[70] Silva C M, Biscaia E C. Genetic algorithm development for multi-objective optimization of batch free-radical polymerization reactors [J]. Computers and Chemical Engineering, 2003, 27 (1): 1329 - 1344.
[71] Su C T, Chiang G C L. No conver power economic dispatch by improved genetic algorithm with multiplier updating method [J] Electric Power Component sand Systems , 2004 , 32 (3) :2572273.
[72] Dehuri S, Patnaik S, Ghosh A, Mall R. Application of Elitist Multi-objective Genetic Algorithm for Classification Rule Generation [J]. Applied Soft Computing Journal (S1568-4946) 2008, 8(1):477-487.
[73]江敏,陈一民.遗传算法在多目标优化问题中点应用策略[J].兰州理工大学学报, 2007, 33(3): 92-94.
[74]邱志平,张宇星.改进遗传算法在飞机总体参数优化中的应用[J].北京航空航天大学学报, 2008, 34(10):1182-1185.
[75]谢涛,陈火旺,康立山.多目标优化的演化算法[J].计算机学报, 2003, 26(8): 997-1002.
[76]谢涛.水平轴风力机叶片多目标优化设计及结构动力学分析:[硕士学位论文],广东:汕头大学, 2005.
[77]杨荣山,黄向东等.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), 2009, 7:87.
[78]张鄂.机械与工程优化设计[M] .科学出版社, 2008.
[79]曹景伟,季林红,马祖长等.我国优秀赛艇运动员桨力—时间曲线特征[J].天津体育学院学报, 2007, 22(4):281-283.
[80]吕杰锋,陈建新,徐进波.人机工程学[M].清华大学出版社, 2009.
[81]王继成.产品设计中的人机工程学[M].化学工业出版社, 2004.
[82]魏显君,戈永杰.操纵装置设计的人机工程问题初探[J].科技创新导报, 2008, 23:110.