产品装配质量设计、预测与控制理论、方法及其应用
|
摘要
装配是产品制造全生命周期中需耗费大量时间和精力的关键环节,也是产品获得整体性能的最后环节,因此直接关系到产品质量。随着全球一体化市场的形成,大多数零部件可以通过外购获得,企业的生产模式正发生改变,装配阶段在企业的整个制造过程中所占的比重越来越大,装配质量对产品质量的影响也越来越大,因此根据装配质量调整装配工艺,对控制产品实际装配质量并提高最终产品性能具有重要意义。本文在对装配质量设计、预测与评价相关技术研究现状进行总结和分析的基础上,针对装配精度分析、工艺过程分析、装配性能预测和配合精度控制等方面进行了深入的研究,根据研究成果开发了装配质量预测与控制软件系统,并在相关科研项目中得到了成功的应用。
论文的主要内容如下：
第一章概述了装配规划技术的发展历程与研究现状,探讨了装配质量设计、预测与控制研究的关键技术,分析了面向装配工艺优化的装配质量设计、预测与控制的研究意义,并介绍了本文的研究内容和组织结构。
第二章提出了基于装配序列偏差传递模型的装配精度设计方法。利用尺寸变动度建立装配精度的评价准则,基于装配精度评价准则在构建的偏差元与变动关系矩阵基础上自动建立偏差传递模型,并创建了有向关联图的模型表达。将偏差传递过程分解为零件内与零件间的偏差传递,通过对各类公差约束下偏差的解析实现零件内偏差传递计算,通过细化分解各类装配形式的定位实现零件间的偏差传递计算,根据偏差传递过程求解累积偏差确定尺寸变动度实现对装配精度分析,进而完成装配精度相关设计。设计人员可据此选择装配精度最高的装配序列,同时可对零件设计公差优化进行指导。
第三章提出了基于协同虚拟装配并发控制的工艺过程分析方法。根据用户的操作意愿,对并发装配行为进行分类,并以此建立柔性并发控制框架。对于主动并发装配行为,根据装配模型的关节是否死锁分别采取基于操作分歧度的并发控制和基于装配单元自适应运动的并发控制。对于被动并发装配行为,采用操作权限动态分配的并发控制,并辅以可视化协商的并发控制完成方案协商优化过程。提出的并发控制方法在允许并发行为发生的同时,有效协调了操作,提高了协同装配的效率,扩展了协同装配的应用范围,更好的辅助装配质量的预测与控制,实现对装配工艺过程的分析。
第四章提出了基于不完备样本多准则修正的装配性能预测方法。对特征参数进行基于测量数据拓扑结构分析的加权计算求解确定估计值,基于数据分布构建了面向不确定度的小样本信息熵计算方法确定置信区间处理粗大误差,提高测量数据的可靠性,而后采用灰熵关联分析对特征参数进行筛选,缩减预测模型的规模,并对装配前后发生变化的特征参数通过有限元方法修正,利用理论模型计算和广义回归神经网络的误差预测合成装配性能预测结果。提出的方法可以处理不完备样本条件下的性能预测问题,且具有较好的预测效果。
第五章提出了基于配合精度波动控制的装配分组定向优化方法。利用修正的信噪比进行装配件敏感度分析,完成分组选配模型的基准确定,根据各零件制造公差的超限分析结果,基于中心定位、双向划分的自适应分组构建方法生成初始分组方案,通过分组稳定性分析进行定向进化,生成优化的分组方案。该方法表明可以在宽公差带下保证装配后的产品具有较高的配合精度,而且充分利用了现有的零件,且适用于多零件的分组装配。
第六章介绍了面向精密机电产品的装配质量预测与控制原型系统的设计与开发,论述了系统的体系架构,然后对各模块功能进行详细阐述,并结合伺服阀、人造宝石加工机床等产品的应用对本文方法与系统功能进行验证。
第七章对本文进行了总结,归纳了主要的研究成果和创新点,并对今后工作进行了展望。
Assembly is the key link which consumes much time and energy in the product manufacturing lifecycle, and it is also the final link to obtain the integral performance. Therefore, assembly is directly related to the quality of products. With the forming of global market, most parts can be obtained through outsourcing, so the enterprise production mode is changed. Assembly phase takes more and more proportion in the whole manufacturing process, and the influence of assembly quality on the quality of the products is also growing. Therefore, adjustment of assembly process according to the prediction results of assembly quality is highly significant to control the actual assembly quality and improve the product performance. Deep researches on Assembly precision establishment, assembly process analysis, assembly performance prediction and assembly precision control are carried out based on summary and analysis of the prediction and control technologies of assembly quality. According to the research results, software system of assembly quality prediction and control is developed, which is successful applied in the relevant scientific research projects.
The main contents of this dissertation are as follows:
Chapter1summerizes the development history and research status of the assembly process planning technology. The key technologies of assembly quality design, prediction and control are explored. The research significance of assembly quality design, prediction and control oriented to assembly process optimization is analyzed. Then the research contents and organization structure are introduced.
Chapter2proposes the design method of assembly precision based on deviation propagation model of assembly sequence. According to the degree of dimensional variation, evaluation criteria of assembly precision are established. Deviation propagation model of assembly sequence is automatically created based on deviation cell and variable relation matrixes, while the model expression is decribed in the directed graph. Deviation propagation process is divided into propagation in the part and propagation among the parts. Accumulative deviation in the part is calculated based on analysis of deviation under tolerance restriction, and accumulative deviation among the parts is calculated based on analysis of assembly location. Prediction of assembly precision is realized by solving the degree of dimensional variation on basis of the deviation propagation process. The designer is able to obtain the assembly sequence with best assembly quality and improve the tolerance of part.
Chapter3proposes collaborative concurrency control methed based on flexible conflict resolution of assembly behavior. According to the user's intention, the concurrent assembly behavior is classified, and a flexible framework of concurrency control is established. For active concurrent assembly behavior, concurrency control method bassed on the degree of manipulation ramification is adopted oriented to deadlock joints, and concurrency control method bassed on adaptive motion of assembly unit is adopted oriented to free joints. For passive concurrent assembly behavior, negotiation optimization process of assembly program is completed using concurrency control based on dynamic allocation of operating authority and visual consultation. The proposed method dealing with concurrent acts effectively aids assembly quality prediction and control, which improves the efficiency of collaborative assembly and extends the application range of collaborative assembly.
Chapter4proposes assembly performance prediction method based on multi-criteria modification of incomplete samples. Estimated value is solved by weighted calculation according to topological structure analysis of measurement data of chararteristic parameters, and confidence interval is determined by small sample information entropy calculation oriented to uncertainty of data distribution. Gross error is rejected according to the procedure metioned above, which improves the reliability of measurement data. The characteristic parameters are screened of by gray entropy correlation analysis, and the sacle of prediction model is reduced. The characteristic parameter which changes after assembly is modified by FEA method. Predicative value of assembly performance is composed of ideal value calculated by ideal model and performance error predicated by generalized regression neural network. The proposed method is suit for dealing with performance prediction of incomplete samples, which has better prediction effect.
Chapter5proposes selective assembly method of oriented optimization based on assembly precision wave control. The grouping model of matching is pretreated according to sensitivity analysis of assembly parts on basis of improved signal noise ratio. The initial grouping scheme is generated using adaptive grouping based on central location and bi-directional division after transfinite analysis of manufacturing tolerance. The optimization grouping scheme is generated according to directed evolution based on grouping stability analysis. The proposed method ensures that the product has high fitting precision with wide manufacturing tolerance, which makes full use of the existing parts, and it is applicable to multi-part selective assembly.
Chapter6develops the prototype system for assembly quality prediction and control oriented to precision mechanical and electrical products with research results. System architecture and function modules are described. The applications of servo valve and artificial-gem processing machine show the validity and feasibility of the new theory and method proposed in the dissertation.
Chapter7summarizes the key research contents and achievements, and givens conclusions along with recommendations for future research.
论文的主要内容如下：
第一章概述了装配规划技术的发展历程与研究现状,探讨了装配质量设计、预测与控制研究的关键技术,分析了面向装配工艺优化的装配质量设计、预测与控制的研究意义,并介绍了本文的研究内容和组织结构。
第二章提出了基于装配序列偏差传递模型的装配精度设计方法。利用尺寸变动度建立装配精度的评价准则,基于装配精度评价准则在构建的偏差元与变动关系矩阵基础上自动建立偏差传递模型,并创建了有向关联图的模型表达。将偏差传递过程分解为零件内与零件间的偏差传递,通过对各类公差约束下偏差的解析实现零件内偏差传递计算,通过细化分解各类装配形式的定位实现零件间的偏差传递计算,根据偏差传递过程求解累积偏差确定尺寸变动度实现对装配精度分析,进而完成装配精度相关设计。设计人员可据此选择装配精度最高的装配序列,同时可对零件设计公差优化进行指导。
第三章提出了基于协同虚拟装配并发控制的工艺过程分析方法。根据用户的操作意愿,对并发装配行为进行分类,并以此建立柔性并发控制框架。对于主动并发装配行为,根据装配模型的关节是否死锁分别采取基于操作分歧度的并发控制和基于装配单元自适应运动的并发控制。对于被动并发装配行为,采用操作权限动态分配的并发控制,并辅以可视化协商的并发控制完成方案协商优化过程。提出的并发控制方法在允许并发行为发生的同时,有效协调了操作,提高了协同装配的效率,扩展了协同装配的应用范围,更好的辅助装配质量的预测与控制,实现对装配工艺过程的分析。
第四章提出了基于不完备样本多准则修正的装配性能预测方法。对特征参数进行基于测量数据拓扑结构分析的加权计算求解确定估计值,基于数据分布构建了面向不确定度的小样本信息熵计算方法确定置信区间处理粗大误差,提高测量数据的可靠性,而后采用灰熵关联分析对特征参数进行筛选,缩减预测模型的规模,并对装配前后发生变化的特征参数通过有限元方法修正,利用理论模型计算和广义回归神经网络的误差预测合成装配性能预测结果。提出的方法可以处理不完备样本条件下的性能预测问题,且具有较好的预测效果。
第五章提出了基于配合精度波动控制的装配分组定向优化方法。利用修正的信噪比进行装配件敏感度分析,完成分组选配模型的基准确定,根据各零件制造公差的超限分析结果,基于中心定位、双向划分的自适应分组构建方法生成初始分组方案,通过分组稳定性分析进行定向进化,生成优化的分组方案。该方法表明可以在宽公差带下保证装配后的产品具有较高的配合精度,而且充分利用了现有的零件,且适用于多零件的分组装配。
第六章介绍了面向精密机电产品的装配质量预测与控制原型系统的设计与开发,论述了系统的体系架构,然后对各模块功能进行详细阐述,并结合伺服阀、人造宝石加工机床等产品的应用对本文方法与系统功能进行验证。
第七章对本文进行了总结,归纳了主要的研究成果和创新点,并对今后工作进行了展望。
Assembly is the key link which consumes much time and energy in the product manufacturing lifecycle, and it is also the final link to obtain the integral performance. Therefore, assembly is directly related to the quality of products. With the forming of global market, most parts can be obtained through outsourcing, so the enterprise production mode is changed. Assembly phase takes more and more proportion in the whole manufacturing process, and the influence of assembly quality on the quality of the products is also growing. Therefore, adjustment of assembly process according to the prediction results of assembly quality is highly significant to control the actual assembly quality and improve the product performance. Deep researches on Assembly precision establishment, assembly process analysis, assembly performance prediction and assembly precision control are carried out based on summary and analysis of the prediction and control technologies of assembly quality. According to the research results, software system of assembly quality prediction and control is developed, which is successful applied in the relevant scientific research projects.
The main contents of this dissertation are as follows:
Chapter1summerizes the development history and research status of the assembly process planning technology. The key technologies of assembly quality design, prediction and control are explored. The research significance of assembly quality design, prediction and control oriented to assembly process optimization is analyzed. Then the research contents and organization structure are introduced.
Chapter2proposes the design method of assembly precision based on deviation propagation model of assembly sequence. According to the degree of dimensional variation, evaluation criteria of assembly precision are established. Deviation propagation model of assembly sequence is automatically created based on deviation cell and variable relation matrixes, while the model expression is decribed in the directed graph. Deviation propagation process is divided into propagation in the part and propagation among the parts. Accumulative deviation in the part is calculated based on analysis of deviation under tolerance restriction, and accumulative deviation among the parts is calculated based on analysis of assembly location. Prediction of assembly precision is realized by solving the degree of dimensional variation on basis of the deviation propagation process. The designer is able to obtain the assembly sequence with best assembly quality and improve the tolerance of part.
Chapter3proposes collaborative concurrency control methed based on flexible conflict resolution of assembly behavior. According to the user's intention, the concurrent assembly behavior is classified, and a flexible framework of concurrency control is established. For active concurrent assembly behavior, concurrency control method bassed on the degree of manipulation ramification is adopted oriented to deadlock joints, and concurrency control method bassed on adaptive motion of assembly unit is adopted oriented to free joints. For passive concurrent assembly behavior, negotiation optimization process of assembly program is completed using concurrency control based on dynamic allocation of operating authority and visual consultation. The proposed method dealing with concurrent acts effectively aids assembly quality prediction and control, which improves the efficiency of collaborative assembly and extends the application range of collaborative assembly.
Chapter4proposes assembly performance prediction method based on multi-criteria modification of incomplete samples. Estimated value is solved by weighted calculation according to topological structure analysis of measurement data of chararteristic parameters, and confidence interval is determined by small sample information entropy calculation oriented to uncertainty of data distribution. Gross error is rejected according to the procedure metioned above, which improves the reliability of measurement data. The characteristic parameters are screened of by gray entropy correlation analysis, and the sacle of prediction model is reduced. The characteristic parameter which changes after assembly is modified by FEA method. Predicative value of assembly performance is composed of ideal value calculated by ideal model and performance error predicated by generalized regression neural network. The proposed method is suit for dealing with performance prediction of incomplete samples, which has better prediction effect.
Chapter5proposes selective assembly method of oriented optimization based on assembly precision wave control. The grouping model of matching is pretreated according to sensitivity analysis of assembly parts on basis of improved signal noise ratio. The initial grouping scheme is generated using adaptive grouping based on central location and bi-directional division after transfinite analysis of manufacturing tolerance. The optimization grouping scheme is generated according to directed evolution based on grouping stability analysis. The proposed method ensures that the product has high fitting precision with wide manufacturing tolerance, which makes full use of the existing parts, and it is applicable to multi-part selective assembly.
Chapter6develops the prototype system for assembly quality prediction and control oriented to precision mechanical and electrical products with research results. System architecture and function modules are described. The applications of servo valve and artificial-gem processing machine show the validity and feasibility of the new theory and method proposed in the dissertation.
Chapter7summarizes the key research contents and achievements, and givens conclusions along with recommendations for future research.
引文
[Anderl 1996] Anderl R, Mendgen R. Modeling with constraints:theoretical foundation and application[J]. Computer Aided Design,1996,28(3):155-168.
[Anselm 2006] Anselmetti B. Generation of functional tolerancing based on positioning feature[J]. Computer Aided Design,2006,38:902-919.
[Asante 2009] Asante J N. A small displacement torsor model for tolerance analysis in a workpiece-fixture assembly [J]. Proceedings of the Institution of Mechanical Engineers, Part B:Journal of Engineering Manufacture,2009,223(8): 1005-1020.
[Asha 2008] Asha A, Kannan S M, Jayabalan V. Optimization of clearance variation in selective assembly for components with multiple qulity characteristics[J]. International Journal of Advanced Manufacturing Technology,2008,38: 1026-1044.
[Baldwi 1991] Baldwin D F, Abell T E, De Fazio T L, et al. An integrated computer aid for generating and evaluating assembly sequences for mechanical products[J]. IEEE Transactions on Robotics and Automation,1991,7(1):78-94.
[Berzak 1992] Berzak N. Seleetive robotic assembly[J]. International Conforence of Design Theory Methodolog[J]. ASME,1992,48:71-75.
[Bidarr 2001a] Bidarra R, Van D B E, Bronsvoort W F. Collaborative Modeling with Features[C]. Proeeedings of 2001 ASME Design Engineering Technical Conferences. Pittsburgh, Peunsylvania, USA, DETC2001/CIE-21286.
[Bidarr 2001b] Bidarra R, Van d B E, Bronsvoort W F. Interactive facilities for collaborative feature modeling on the web[C]. Proceedings of the Tenth Portuguese Conference on Computer Graphics. Lisbon, Portugal Oversea Publishing House.2001:43-52.
[Bidarr 2002] Bidarra R, Kranendonk N, Noort A, et al. A collaborative framework for integrated part and assembly modeling[C]. Proceedings of the 7th ACM Symposium on Solid Modeling and Applications. New York, N. Y., USA: ACM Press,2002:389-400.
[Bjorke 1989] Bjorke O. Computer-Aided Tolerancing[M].2nd edition, New York, ASME Press,1989.
[Bonnev 1995] Bonneville F, Perrand C, Henrioud J M. A genetic algorithm to generate and evaluate assembly plans[C]. Proceedings of the IEEE Symposium on Emerging Technology and Factory Automation, New Jersey, USA, 1995:231-239.
[Boothr 1994] Boothroyd G. Product design for manufacturing and assembly[J]. Computer Aided Design,1994,26(7):505-520.
[Bourja 1986] Bourjault A, Lhote A. Modeling an assembly process[C]. IEEE International Conference on Automation of Manufacturing Industry,1986,20(2):183-198.
[Boyer 1985] Boyer D E, Nazemets J W. Introducing statistical selective assembly-a means of producing high precision assemblies from low precision components[C]. Annual International Industrial Engineering Conference, Institute of Industrial Engineers,1985:562-570.
[Cameli 2001] Camelio J, Hu S J. Modeling variation propagation of multi-station assembly systems with compliant parts[C]. Proceeding of the Design Engineering Technical Conference, Pittsburgh, PA,2001.
[Chan 1999] Chan K C, Linn R J. A grouping method for seleetive assembly of parts of dissimilar distribution[J]. Quality Engineering,1999,11 (2):221-234.
[Chen 2001] Chen L, Song Z J, Liavas B. Exploration of a multi-user collaborative assembly environment on the internet a case study[C]. Proceedings of the ASME Design Technieal Conferences and Computers and Information Engineering Conference. Pittsburgh, Pennsylvania, DETC/CIE-21291, 2001:9-12.
[Chen 2003] Chen L, Song Z J, Feng L. Internet-enabled real-time collaborative assembly modeling via an e-Assembly system:status and promise[J]. Computer-Aided Design,2003,36:835-847.
[Chryss 2000] Chryssolouris G, Mavrikios D, Fragos D, et al. A virtual Reality-based Experimentation Environment for the vertification of human-related factors in assembly processes[J]. Robotics and Computer Integrated Manufacturing, 2000,16:267-276.
[Cooper 1998] Cooper S, Taleb-Bendiab A. Concensus:multi-party negotiation support for conflict resolution in concurrent engineering design[J]. Journal of intelligent manufacturing,1998,9(2):155-159.
[Coulla 1998] Coullard C R, Gamble A B, Jones P C. Matching Problems in Selective Assembly Operations[J]. Annuals of Operations Research,1998(76):95-107.
[Craig 2005] Craig J J. Introduction to Robotics:Mechanics and Control, Third Edition[M]. Pearson Education, Inc.2005.
[Dantan 2005] Dantan J Y, Mathieu L, Ballua, et al. Tolerance synthesis:quantifier notion and virtual boundary[J]. Computer Aided Design,2005,37:231-240.
[Dini 1992] Dini G, Santochi M. Automated sequencing and subassembly detection in assembly planning[J]. Annals of the CIRP,1992,41(1):1-4.
[Djurdj 2003] Djurdjanovic D, Ni J. Dimensional error of fixtures, locating and measurement datum features in the stream of variation modeling in machining[J]. Transactions of ASME, Journal of Manufacturing Science and Engineering,2003,125:716-730.
[Ellis 1989] Ellis C A, Gibbs S J. Concurrency Control in Groupware Systems[J]. Proceedings of 1989 ACM SIGMOD International Conference on the Management of Data. Portland, Oregon, USA,1989:387-423.
[Ellis 1991] Ellis C A, Gibbs S J, Rein G L.-Groupware some issues and experiences[J].Communications of the ACM,1991,34(1):38-58.
[Feigen 1999] Feigenbaum A V, The new quality for the twenty-first century[J]. The TQM Magazine,1999,11(6):376-383.
[Franci 2004] Francisco J T, Chris K, Vincenzo L, et al. A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1°resolution from satellite passive microwave and infrared data[J]. Journal of Applied Meteorology,2004,43(4):576-594.
[Fu 2010]Fu Z M, Mo J H, Chen L, et al. Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal[J]. Materials and design,2010(31):267-277.
[Gao 1998]Gao J, Chase K W, Magleby S P. Generalized 3-D tolerance analysis of mechanical assemblies with small kinematic adjustments[J]. HE Transactions, 1998,30(4):367-377.
[Grandl 2005] Grandl R. Virtual process week in the experimental vehicle build at BMW AG[J]. Robotics and Computer Integrated Manufacturing,2005,17(2):65-71.
[Greenb 1994] Greenberg S, Marwood D. Real-time groupware as a distributed system: Concurrency control and its effect on the interface[J]. Proceedings of ACM Conference on Computer Supported Cooperative Work, Chapel Hill, USA, 1994:207-217.
[Hakeem 2008] Hakeem M A, Kamil M, Arman I. Prediction of temperature profiles using artificial neural networks in a vertical thermosiphon reboiler[J]. Applied Thermal Engineering.2008,28:1572-1579.
[He 1992]He J R, Lin G C. Computerized trace method for establishing equations for eimensions and tolerances in design and manufacture. International Journal of Advancd Technology,1992(7):210-217.
[Hird 1988] Hird G, et al. Possibilities for Integrated Design and Assembly planning[J]. Developments in Assembly Automation,1988:155-166.
[Holt 2004] Holt P O B, Ritchie J M, Day P N. Immersive virtual reality in cable and pipe routing:Design metaphors and cognitive ergonmoics[J]. Journal of Computing and Information Science in Engineering,2004,4:161-170.
[Homem 1990] Homem D M, Sanderson A C. Evaluation and selection of assembly plans[C]. Proceeding of the IEEE International Conference on Robotics and Automation, 1990:1588-1593.
[Homem 1990] Homem D M, Sanderson A C. Evaluation and selection of assembly plans [C]. Proceeding of the IEEE International Conference on Robotics and Automation, 1990:1588-1593.
[Homen 1991] Homen M L S, Sanderson A C. A correct and complete algorithm for the generation of mechanical assembly sequences[J]. IEEE Transactions on Robotics and Automation,1991,7(2):228-240.
[Hu 1997]Hu S J. Stream of variation theory for automotive body assembly[J]. Annals of the CIRP,1997,46(1):1-6.
[Huang 1999] Huang G Q, Mak K L. Design for manufacture and assembly on the Internet[J]. Computers in Industry,1999,38:17-30.
[Iyama 1995] Iyama T M, Mizuno, Goto S, et al. A race-matching method for ball bearing manufacture[J]. JSME International Journal Series C:Dynamies, Control, Robotics, Design and Manufacturing,1995,1 (38):167-170.
[James 1994] James H, Huang O H T. Automated path planning for integrated assembly Design[J]. Computer Aided Design,1994,26(9):658-666.
[Jayara 1997] Jayaram S, Connacher H I, Lyons K W. Virtual assembly using virtual reality techniques[J]. Computer Aided Design,1997,29(8):575-584.
[Jayara 1999] Jayaram S, Jayaram U, Wang Y, et al. VADE:a virtual assembly design environment[J]. IEEE Computer Graphics and Applications, 1999,19(6):44-50.
[Jin 1999]Jin J, Shi J J. State space modeling of sheet metal assembly for dimensional control[J]. Journal of Manufacturing Science and Engineering,1999, 121(4):756-762.
[Jing 2009] Jing S X, He F Z, Han S H, et al. A Method for Topological Entity Correspondence in a Replicated Collaborative CAD System[J]. Computers in Industry,2009,60:467-475.
[Jones 1998] Jones R E, Wilson R H, Calton T L. On Constraints in Assembly Planning[J]. IEEE Transactions on Robotics and Automation,1998,14(6):849-863.
[Kan 2001]Kan H Y, Duffy V G, Chuan J S. An internet virtual reality collaborative environment for effective product design[J]. Computer in Industry, 2001,45(2):197-213.
[Kannan 2001] Kannan S M, Jayabalan V. A new grouping method to minimize surplus parts in selective assembly for complex assemblies[J]. International Journal of Production Research,2001,39(9):1851-1863.
[Kannan 2003] Kannan S M, Jayabalan V, Jeevanantham K. Genetic algorithm for minimizing assembly variation in seleetive assembly[J]. International Journal of production Researeh,2003,41(14):3301-3313.
[Kannan 2008] Kannan S M, Jeevanantham A K, Jayabalan V. Modeling and analysis of selective assembly using Taguchi's loss function[J]. International Journal of Production Research,2008,46(15):4309-4330.
[Kannan 2009] Kannan S M, Sivasubramanian R, Jayabalan V. Particle swarm optimization for minimizing assembly variation in selective assembly[J]. International Journal of Advanced Manufacturing Technology,2009,42:793-803.
[Kern 2003] Kern D C. Forecasting manufacturing variation using historical process capability data:Applications for random assembly, selective assembly and serial processing[D]. Massachusetts Institute of Technology, USA,2003.
[Khatib 1986] Khatib O. Real-time Obstacle avoidance for manipulators and mobile robots[J]. International Journal on Robotics Research,1986,5(1):90-98.
[Kulkar 1985] Kulkarni S V, Garg T K. Optimal allocation of tolerances in engineering designs governed by systems of simultaneous tolerance equations using seleetive assembly[J]. Journal of Industrial Engineering,1985:160-168.
[Kumar 2007a] Kumar M S, Kannan S M. Optimum manufacturing tolerance to selective assembly technique for different assembly specifications by using genetic algorithm[J]. International Journal of Advanced Manufacturing Technology, 2007,32:591-598.
[Kumar 2007b] Kumar M S, Kannan S M, Jayabalan V. A new algorithm for minimizing surplus parts in selective assembly by using genetic algorithm[J]. International Journal of Production Research,2007,45(20):4793-4822.
[Kurata 2007] Kurata E, Mori H. Short-term load forecasting using informative vector machine[J]. IEEE Transactions on Power and Energy,2007,127(4):566-572.
[Laperr 1999] Laperriere L, Lafond P. Tolerance analysis and synthesis using virtual joints[C]. Proceedings of 6th CIRP seminar on computer aided tolerancing, Enschede, Netherland,1999:452-458.
[Lara 2003] Lara M A, Nof S Y. Computer-supported conflict resolution for collaborative facility designers[J]. International Journal Production Research,2003, 41(2):207-233.
[Lawles 1999a] Lawless J F, Machay R J, Robinson J A. Analysis of variation transmission in manufacturing processes-part I[J]. Journal of Quality Technology,1999, 31(2):131-142.
[Lawles 1999b] Lawless J F, Machay R J, Robinson J A. Analysis of variation transmission in manufacturing processes-part II[J]. Journal of Quality Technology,1999, 31(2):142-154.
[Lee 2007]Lee W K, Hyun C H, Lee H, et al. Model reference adaptive synchronization of T-S fuzzy discrete chaotic systems using output tracking control[J]. Chaos, Solitons and Fractals.2007,34:1590-1598.
[Li 1992]Li R K, Hwang C L. A framework for automatic DFA system develop[J]. Computers & Industrial Engineering,1992,22(4):403-413.
[Li 2003]Li J R, Khoo L P, Tor S B. Desktop virtual reality for maintenance training: An object oriented prototype system(V-REALISM)[J]. Computer in Industry, 2003,52:109-125.
[Li 2008]Li M, Gao S M, Fuh J Y H, et al. Replicated Concurrency Control for Collaborative Feature Modelling:A Fine Granular Approach[J]. Computers in Industry,2008,59:873-881.
[Linbar 2004] Lingbarger J M, Kessler G D. Concurrency Control Mechanisms for Closely Coupled Collaboration in MultiThreaded Pear-to-Peer Virtual Environments[J]. Teleoperators & Virtual Environments,2004,13(3):296-314.
[Lozano 1987] Lozano-perez T. A simple motion planning algorithm for general robot manipulators[J]. IEEE Journal of Robotics and Automation, 1987,3(3):224-238.
[Lu 2006a]Lu, C, Fuh J YH, Wong YS, et al. Design modification in a collaborative assembly design environment[J]. Computing and Information Science in Engineering,2006,26(2):200-208..
[Lu 2006b]Lu C, Fuh J Y H, Wong Y S. Evaluation of Product Assemblability in Different Assembly Sequences Using a tolerancing approach[J]. International Journal of Production Research,2006,44(23):5037-5063.
[Mantri 1999] Mantripragada R, Whitney D E. Modeling and controlling variation propagation in mechanical assemblies using state transition models[J]. IEEE Transactions on Robotics and Automation,1999,15(1):124-140.
[Marsh 2006] Marsh J, Glencross M, Pettifer S, et al. A network architecture supporting consistent rich behavior in collaborative interactive applications[J]. IEEE Transaction on Visualization and Computer Graphics,2006,12(3):405-406.
[Matsuu 2007] Matsuura S, Shinozaki N. Optimal binning strategies under squared error loss in selective assembly with measurement error[J]. Communications in Statistics-Theory and Methods,2007,36(16):2863-2876
[Matsuu 2010] Matsuura S, Shinozaki N. Optimal binning strategies under squared error loss in selective assembly with a tolerance constraint[J]. Communications in Statistics-Theory and Methods,2010,39(4):592-605.
[Matsuu 2011a] Matsuura S, Shinozaki N. Optimal process design in selective assembly when components with smaller variance are manufactured at three shifted means[J]. International Journal of Production Research,2011,49(3):869-882.
[Matsuu 2011b] Matsuura S. Optimal partitioning of probability distributions under general convex loss functions in selective assembly[J]. Communications in Statistics-Theory and Methods,2011,40:1545-1560.
[Mccull 1943] Mcculloch W S, Pitts W H. A logical calculus of the ideas immanent in nervous activity [J]. Bulletin of Mathematical Biophysics.1943,5:115-133.
[Mease 2004] Mease D, Nair V N, Sudjianto A. Selective assembly in manufacturing: Statistical issues and optimal binning strategies[J]. Technometrics,2004, 46(2):165-175.
[Metin 2008] Metin D, Ihsna Y. Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management[J]. Information Sciences,2008, 178(6):1717-1733.
[Minho 1997] Minho C, David C G. Modeling the assembly of compliant, non-ideal parts[J]. Computer-aided Design,1997,29(10):701-708.
[Mize 2000] Mize C D, Ziegert J C. Neural network thermal error compensation of a machining center[J]. Precision Engineering,2000,24(4):338-346.
[Moczul 1998] Moczulsky, et al. Method-specific knowledge compilation:towards practical design support system[J]. Artificial Intelligence in Design.1998:427-444.
[Moreir 2011] Moreira L O, Sousa F R C, Machado J C. A distributed concurrency control mechanism for XML data[J]. Journal of Computer and System Sciences,2011, 77:1009 1022.
[Nidama 2001] Nidamarthi S, Allen R H, Sriram R D. Observations from supplementing the traditional design process via internet-based collaborative tool[J]. International Journal Computer Integrated Manufacture,2001,14(1):95-107.
[Noh 2005]Noh S D, Park Y J, Kong S H, et al. Concurrent and collaborative process planning for automotive general assembly[J], International Journal of Advanced Manufacturing Technology,2005,26(5):572-584.
[Oliver 1994] Oliver J, Huang H T. Automated path planning of integrated assembly design[J]. Computer Aided Design,1994,26(9):57-63.
[Ozger 2007] Ozger M, Sen Z. Prediction of wave parameters by using fuzzy logic approach[J]. Ocean Engineering,2007,34:460-469.
[Pan 1995]Pan D. Concurrent engineering and it's application intransforming management mechanism[C]. Proceedings of 18th International Conference on Computer & Industrial Engineening,1995,28(12):117-120.
[Peng 2000] Peng Q, Hall F R. Lister P M. Application and evalution of VR based CAPP system[J]. Journal of Materials Processing Technology, 2000,107(11):153-159.
[Polat 1993] Polat F, Shekhar S. Distributed conflict resolution among cooperative expert system[J]. Expert System,1993,10(4):227-236.
[Pugh 1992] Pugh G A. Selective assembly with components of dissimilar variance[J]. Computer Industrial Engineering,1992,23(1):487-491.
[Qiu 2012]Qiu C, Liu Z Y, Tan J R, et al. Multi-scale based unified modeling and simulation of part surface topography [J]. International of journal of innovative computing, information and control,2012,8(3A):1789-1798.
[Rajago 1998] Rajagopalan S, Cutkosky M R. Tolerance represention for mechanism assemblies in layered manufacturing[C]. Proceedings of DETC98:13-18.
[Ravira 2009] Raviram P, Umarani S D, Wahidabanu R S D. Application of Radial Basis Function for Concurrency Control in CAD with KBMS[C]. International Conference on Intelligent and Multi-Agent Systems. Chennai, INDIA,2009.
[Requie 1986] Requieha A A G, Chan S C. Representation of geometric feartures, tolerances and attributes in solid modelers based on constructive geometry[J]. Robotics and Automation,1986,2(3):156-166.
[Robinso 2007] Robinson G, Ritchie J M, Day P N, et al. System design and user evaluation of Co-Star:An immersive stereoscopic system for cable harness design[J]. Computer Aided Design,2007,39:245-257.
[Rojo 2004] Rojo-Alvarez J L, Martinez-Ramon M, Prado-Cumplido M D, et al. Support vector method for robust ARMA system identification[J]. IEEE Transactions on Signal Processing,2004,52(1):155-164.
[Salat 2004] Salat R, Osowski S. Accurate fault location in the power transmission line using Support Vector Machine approach[J]. IEEE Transactions on Power Systems,2004,19(2):979-986.
[Shan 1995] Shan Y S, Koram Y. Obstacle accommodation motion planning[J]. IEEE Transaction on Robotics and Automation,1995,11 (1):17-27.
[Sharma 2009] Sharma S, Biswal B B, Dash P et al. Optimized robotic assembly sequence using ACO[C].5th International Conference on MEMS NANO, and Smart Systems, ICMENS 2009:20-24.
[Shyams 2001] Shyamsundar N, Gadh R. Internet- based collaborative product design with assembly features and virtual design spaces[J]. Computer-Aided Design, 2001,33(9):637-651.
[Shyams 2002] Shyamsundar N, Gadh R.Collaborative virtual prototyping of Product Assemblies over the Internet. Computer-Aided Design,2002,34(10):755-768.
[Su 2009]Su Q, Lai S J, Liu J. Geometric computation based assembly sequencing and evaluating in terms of assembly angle, direction, reorientation, and stability [J]. Computer-Aided Design,2009,41:479-489.
[Suyken 1999] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.
[Swarmi 1996] Swarminathan A, Barker K S. An experience-based assembly sequence planner for mechanical assemblies[J]. IEEE Transaction on Robotics and Automation,1996,12(2):252-267.
[Tax 2004]Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004,54:45-66.
[Tonsho 1992] Tonshoff H K, Menzel E, Park H S. A knowledge-based system for automated assembly planning[J]. Annals of the CIRP,1992,41(1):19-24.
[Turner 1993] Turner J. A feasibility space approach for automated tolerancing[J]. ASME Journal of Engineering for Industry,1993,115:341-346.
[Vapnik 2000] Vanpik V N. The nature of statistical learning theory[M].2nd edition, New York, Springer-Verlag,2000.
[Velasq 2008] Velasquez J D, Lara M A, Nof S Y. Systematic resolution of conflict sitations in collaborative facility design[J]. International Journal Production Economics, 2008,116:139-153.
[Wang 2004] Wang Q H, Li J R. A desktop VR prototype for industrial training applications[J]. Virtual Reality,2004(7):187-197.
[Wang 2005] WANG J.F., LIU J.H., ZHONG Y.F. A novel ant colony algorithm forassembly sequence planning[J]. The International Journal of AdvancedManufacturing Technology,2005,25(11):1137-1143.
[Wang 2006] Wang J, Sun S Y. Predictive control based on support vector machine model[C]. Proceddings of the 6th World Congress on Intelligent Control and Automation, Dalian, China,2006:1683-1687.
[Wang 2007] Wang W. An adaptive predictor for dynamic system forecasting[J]. Mechanical System and Signal Processing.2007,21:809-823.
[Whitne 1993] Whitney D E, Gilbert O L.(1993) Representation of geometric variations using matrix transforms for statistical tolerance analysis in assemblies[C]. IEEE International Conference on Robotics and Automation,1993:314-321.
[Wu 2012]Wu D L, Zhen X J, Fan X M, et al. A virtual environment for complex products collaborative assembly operation simulation[J]. Journal of Intelligent Manufacturing,2012,23(3):821-833.
[Ye 1999]Ye N, Banerjee P, Banerjee A, et al. A comparative study of assembly planning in traditional and virtual environments[C]. IEEE Transaction on System, Man and Cybemetics-Part C:Applications and Reviews, 1999,29(4):546-555.
[Yilmaz 2007] Yilmaz M, Ertunc H M. The prediction of mechanical behavior for steel wires and cord materials using neural networks[J]. Materials and Design, 2007(28):599-608.
[Zha 1998]Zha X F, Lim Y E, Fok S C. Integrated knowledge-based assembly sequence planning[J]. International Journal of Advanced Manufacturing Technology, 1998,14(l):50-64.
[Zhang 1995] Zhang Y, Fang X D. A new algorithm of minimizing the surplus parts in selective assembly[J]. Computer and Industrial Engineering, 1995,28(2):341-350.
[Zhang 2002] Zhang L J, Tang M, Tong R F. A feature-based collaborative CAD system[C]. Proceedings of the 7th International Conference on Computer Supported Cooperative Work in Design. Riode Janeiro, Brazil. IEEE, Institute of Electrical&Electronics Engineers, Inc.2002:193-197.
[Zhong 2002] Zhong W. Huang Y, Hu S J. Variation propagation modeling for machining system with different configurations, IMECE, ASME, New Orleans,2002.
[Zhou 2003] Zhou S, Huang Q, Shi J J. State space modeling of dimensional variation propagation in multistage machining process using differential motion vector[J]. IEEE Transactions on Robotics and Automation,2003, 19(2):296-309.
[陈亮 2006] 陈亮,金国栋,罗志伟.产品多学科协同设计的协商模型[J].机械工程学报,2006,42(8)：175-181.
[陈龙 2010] 陈龙,黄晨,江浩斌等.基于智能体——神经网络的轮胎侧偏特性[J].机械工程学报,2010,48(2)：153-158.
[戴光明 2004] 戴光明.避障路径规划的算法研究[D].武汉,湖北：华中科技大学,2004.
[董兴辉 2006] 董兴辉,于建国,吴克和等.协同预装配若干技术研究[J].计算机集成制造系统.2006,12(7)：1042-1046.
[杜世昌 2011] 杜世昌,王猛,奚立峰.多工序加工系统产品尺寸误差传递建模[J].机械工程学报,2011,47(16)：143-149.
[高贯斌 2010] 高贯斌,王文,林铿等.基于RBF神经网络的关节转角误差补偿[J].机械工程学报,2010,46(12)：20-24.
[顾廷权 1998] 顾廷权,高国安,徐向阳.装配工艺规划中装配序列生成与评价方法研究[J].计算机集成制造系统-CIMS,1998,2(1):25-39.
[何博侠 2008] 何博侠,张志胜,戴敏等.机械装配过程的偏差传递建模理论[J].机械工程学报,2008,44(12)：63-68.
[胡洁 2001] 胡洁.基于变动几何约束网络的形位公差设计理论与方法的研究[D].杭州,浙江：浙江大学,2001.
[胡洁 2002] 胡洁,吴昭同.变动几何约束网络的运动学模型及其应用研究[J].计算机辅助设计与图形学学报,2002,14(6)：594-597.
[贾振元 2009] 贾振元,马建伟,王福吉等.多零件几何要素影响下的装配产品特性预测方法[J].机械工程学报,2009,45(7)：168-173.
[贾振元 2010] 贾振元,马建伟,刘巍等.多几何要素影响下液压阀件特性的混合神经网络预测模型[J].机械工程学报,2010,46(2)：126-131.
[李云峰 2007] 李云峰.电液伺服阀中弹簧管刚度精密测量技术的研究[D].哈尔滨,黑龙江：哈尔滨工业大学,2007.
[郦仕云 2006] 郦仕云,宁汝新,王恒.面向装配的虚拟协同设计环境研究与实现[J].机械工程学报.2006,42(S1)：129-134.
[廖念钊 2007] 廖念钊,古莹菴,莫雨松等.互换性与技术测量[M].中国计量出版社,2007.
[林小夏 2010] 林小夏,张树有,陈婧等.产品仿真分析中曲面不均匀分布载荷施加方法[J].机械工程学报,2010,46(1)：123-127.
[林小夏 2011] 林小夏.产品设计、仿真与数控加工异构信息集成技术及应用研究[D].杭州,浙江：浙江大学,2011.
[刘检华 2006] 刘检华,宁汝新,阎艳.集成化虚拟装配工艺规划系统研究[J].中国机械工程,2006,17(23)：2486-2491.
[刘明周 2004] 刘明周,郭嘉,李旗号.机械产品精密配合中的选配方法研究[J].机械工程学报.2004,40(6)：165-168.
[刘明周 2006] 刘明周,张凤琴,吴俊峰等.基于田口质量观的机械产品选配方法[J].机械工程学报.2006,42(10)：127-131.
[刘明周 2007] 刘明周,陈诚和,葛茂根等.机械产品选配问题的二分图非形式化描述[J].农业机械学报.2007,38(11)：133-136.
[刘伟东 2012] 刘伟东,宁汝新,刘检华等.机械装配偏差源及其偏差传递机理分析[J].机械工程学报,2012,48(1)：156-168.
[刘伟伟 2006] 刘伟伟,孟祥旭,徐延宁.一种支持实时协同虚拟装配的体系架构[J].系统仿真学报,2006,18(10)：2805-2809.
[刘巽尔 2006] 刘巽尔.偏差、公差和误差[J],机械工业标准化与质量,2006.10：15-17.
[刘玉生 2005] 刘玉生,高曙明.三维CAD系统中变动孔特征通用生成方法[J].机械工程学报,2005,41(7)：112-118.
[陆宁 2010] 陆宁.基于群智能与算法融合的电力负荷组合预测[D].武汉,湖北：华中科技大学,2010.
[茅健 2007] 茅健.基于数学定义的公差建模与误差评定技术的研究[D].博士论文,浙江大学,2007.
[孟秀丽 2005] 孟秀丽,易红,倪中华等.基于多目标决策的协同设计冲突消解方法研究[J].计算机集成制造系统,2005,11(5)：625-629.
[南风强 2010] 南风强,汪惠芬,郝翠霞等.网络协同数字化预装配系统关键技术[J].计算机集成制造系统.2010,16(4)：724-730.
[邵景文2002a] 邵景文,张振家,冯国泰等.计算机装配工艺系统研究[J].航天制造技术,2002,2(1)：52-55.
[邵景文2002b] 邵景文,张振家,马玉林等.并行组合模拟退火算法在计算机辅助选择系统的应用[J].机械设计,2002,11(1)：39-41.
[孙风栋 2007] 孙风栋.Oracle数据库基础[M].北京：电子工业出版社,2007.
[谭建荣 2005] 谭建荣.CAD方法与技术[M].北京：科学出版社,2005.
[唐静远 2009] 唐静远.模拟电路故障诊断的特征提取及支持向量机集成方法研究[D].成都,四川：电子科技大学,2009.
[田兆青2007] 田兆青,来新民,林忠钦.多工位薄板装配偏差流传递的状态空间模型[J].机械工程学报,2007,43(2)：202-209.
[王凡 2010] 王凡.基于支持向量机的交通流预测方法研究[D].大连,辽宁：大连理工 大学,2010.
[王福吉 2011] 王福吉,贾振元,阳江源等.基于动态模糊神经网络的机床时变定位误差补偿[J].机械工程学报,2011,47(13)：175-179.
[王峻峰 2004] 王峻峰.分布环境下的协同装配序列规划[D].武汉,湖北：华中科技大学,2004.
[王中宇 2000] 王中宇,夏新涛,朱坚民.测量不确定度的非统计理论[M].北京：国防工业出版社,2000.
[夏平均 2007] 夏平均.基于虚拟现实的卫星装配工艺设计方法及其应用[D].哈尔滨,黑龙江：哈尔滨工业大学,2007.
[肖田元 2004] 肖田元.虚拟制造[M].北京：清华大学出版社,2004.
[谢天保 2007] 谢天保.协商理论在敏捷制造系统中的应用研究[D].西安,陕西：西安理工大学,2007.
[徐国平 2008] 徐国平.基于支持向量机的动调陀螺仪寿命预测方法研究[D].上海：上海交通大学,2008.
[杨剑锋 2007] 杨剑锋.蚁群算法及其应用研究[D].杭州,浙江：浙江大学,2007.
[杨将新 2010] 杨将新,徐旭松,曹衍龙等.基于装配定位约束的功能公差规范设计[J].机械工程学报,2010,46(2)：1-8.
[余春艳 2004] 余春艳,侯宏仑,吴明晖等.协同设计中以实体为中心的并发操作控制机制[J].计算机辅助设计与图形学学报.2004,16(9)：1289-1294.
[张钹 1991] 张钹,张铃.求解机械装配规划的新方法[J].计算机学报,1991,14(8)：561-569.
[张晗雨 2009] 张晗雨.WPF全视角分析[M].北京：机械工业出版社,2009.
[张开富 2006] 张开富.飞机部件装配误差累积分析与容差优化方法研究[D].西安,陕西：西北工业大学,2006.
[张庆英 2001] 张庆英.一种满足分组装配配合要求的新方法[J].武汉理工大学学报.2001,25(2)：158-161.
[张学敏 2009] 张学敏.MATLAB基础及应用[M].北京：中国电力出版社,2009.
[赵家黎 2006] 赵家黎,郭伟,牛占文等.基于误差流理论的多工位装配过程质量稳定性[J].机械工程学报,2006,42(11)：88-93.
[Anselm 2006] Anselmetti B. Generation of functional tolerancing based on positioning feature[J]. Computer Aided Design,2006,38:902-919.
[Asante 2009] Asante J N. A small displacement torsor model for tolerance analysis in a workpiece-fixture assembly [J]. Proceedings of the Institution of Mechanical Engineers, Part B:Journal of Engineering Manufacture,2009,223(8): 1005-1020.
[Asha 2008] Asha A, Kannan S M, Jayabalan V. Optimization of clearance variation in selective assembly for components with multiple qulity characteristics[J]. International Journal of Advanced Manufacturing Technology,2008,38: 1026-1044.
[Baldwi 1991] Baldwin D F, Abell T E, De Fazio T L, et al. An integrated computer aid for generating and evaluating assembly sequences for mechanical products[J]. IEEE Transactions on Robotics and Automation,1991,7(1):78-94.
[Berzak 1992] Berzak N. Seleetive robotic assembly[J]. International Conforence of Design Theory Methodolog[J]. ASME,1992,48:71-75.
[Bidarr 2001a] Bidarra R, Van D B E, Bronsvoort W F. Collaborative Modeling with Features[C]. Proeeedings of 2001 ASME Design Engineering Technical Conferences. Pittsburgh, Peunsylvania, USA, DETC2001/CIE-21286.
[Bidarr 2001b] Bidarra R, Van d B E, Bronsvoort W F. Interactive facilities for collaborative feature modeling on the web[C]. Proceedings of the Tenth Portuguese Conference on Computer Graphics. Lisbon, Portugal Oversea Publishing House.2001:43-52.
[Bidarr 2002] Bidarra R, Kranendonk N, Noort A, et al. A collaborative framework for integrated part and assembly modeling[C]. Proceedings of the 7th ACM Symposium on Solid Modeling and Applications. New York, N. Y., USA: ACM Press,2002:389-400.
[Bjorke 1989] Bjorke O. Computer-Aided Tolerancing[M].2nd edition, New York, ASME Press,1989.
[Bonnev 1995] Bonneville F, Perrand C, Henrioud J M. A genetic algorithm to generate and evaluate assembly plans[C]. Proceedings of the IEEE Symposium on Emerging Technology and Factory Automation, New Jersey, USA, 1995:231-239.
[Boothr 1994] Boothroyd G. Product design for manufacturing and assembly[J]. Computer Aided Design,1994,26(7):505-520.
[Bourja 1986] Bourjault A, Lhote A. Modeling an assembly process[C]. IEEE International Conference on Automation of Manufacturing Industry,1986,20(2):183-198.
[Boyer 1985] Boyer D E, Nazemets J W. Introducing statistical selective assembly-a means of producing high precision assemblies from low precision components[C]. Annual International Industrial Engineering Conference, Institute of Industrial Engineers,1985:562-570.
[Cameli 2001] Camelio J, Hu S J. Modeling variation propagation of multi-station assembly systems with compliant parts[C]. Proceeding of the Design Engineering Technical Conference, Pittsburgh, PA,2001.
[Chan 1999] Chan K C, Linn R J. A grouping method for seleetive assembly of parts of dissimilar distribution[J]. Quality Engineering,1999,11 (2):221-234.
[Chen 2001] Chen L, Song Z J, Liavas B. Exploration of a multi-user collaborative assembly environment on the internet a case study[C]. Proceedings of the ASME Design Technieal Conferences and Computers and Information Engineering Conference. Pittsburgh, Pennsylvania, DETC/CIE-21291, 2001:9-12.
[Chen 2003] Chen L, Song Z J, Feng L. Internet-enabled real-time collaborative assembly modeling via an e-Assembly system:status and promise[J]. Computer-Aided Design,2003,36:835-847.
[Chryss 2000] Chryssolouris G, Mavrikios D, Fragos D, et al. A virtual Reality-based Experimentation Environment for the vertification of human-related factors in assembly processes[J]. Robotics and Computer Integrated Manufacturing, 2000,16:267-276.
[Cooper 1998] Cooper S, Taleb-Bendiab A. Concensus:multi-party negotiation support for conflict resolution in concurrent engineering design[J]. Journal of intelligent manufacturing,1998,9(2):155-159.
[Coulla 1998] Coullard C R, Gamble A B, Jones P C. Matching Problems in Selective Assembly Operations[J]. Annuals of Operations Research,1998(76):95-107.
[Craig 2005] Craig J J. Introduction to Robotics:Mechanics and Control, Third Edition[M]. Pearson Education, Inc.2005.
[Dantan 2005] Dantan J Y, Mathieu L, Ballua, et al. Tolerance synthesis:quantifier notion and virtual boundary[J]. Computer Aided Design,2005,37:231-240.
[Dini 1992] Dini G, Santochi M. Automated sequencing and subassembly detection in assembly planning[J]. Annals of the CIRP,1992,41(1):1-4.
[Djurdj 2003] Djurdjanovic D, Ni J. Dimensional error of fixtures, locating and measurement datum features in the stream of variation modeling in machining[J]. Transactions of ASME, Journal of Manufacturing Science and Engineering,2003,125:716-730.
[Ellis 1989] Ellis C A, Gibbs S J. Concurrency Control in Groupware Systems[J]. Proceedings of 1989 ACM SIGMOD International Conference on the Management of Data. Portland, Oregon, USA,1989:387-423.
[Ellis 1991] Ellis C A, Gibbs S J, Rein G L.-Groupware some issues and experiences[J].Communications of the ACM,1991,34(1):38-58.
[Feigen 1999] Feigenbaum A V, The new quality for the twenty-first century[J]. The TQM Magazine,1999,11(6):376-383.
[Franci 2004] Francisco J T, Chris K, Vincenzo L, et al. A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1°resolution from satellite passive microwave and infrared data[J]. Journal of Applied Meteorology,2004,43(4):576-594.
[Fu 2010]Fu Z M, Mo J H, Chen L, et al. Using genetic algorithm-back propagation neural network prediction and finite-element model simulation to optimize the process of multiple-step incremental air-bending forming of sheet metal[J]. Materials and design,2010(31):267-277.
[Gao 1998]Gao J, Chase K W, Magleby S P. Generalized 3-D tolerance analysis of mechanical assemblies with small kinematic adjustments[J]. HE Transactions, 1998,30(4):367-377.
[Grandl 2005] Grandl R. Virtual process week in the experimental vehicle build at BMW AG[J]. Robotics and Computer Integrated Manufacturing,2005,17(2):65-71.
[Greenb 1994] Greenberg S, Marwood D. Real-time groupware as a distributed system: Concurrency control and its effect on the interface[J]. Proceedings of ACM Conference on Computer Supported Cooperative Work, Chapel Hill, USA, 1994:207-217.
[Hakeem 2008] Hakeem M A, Kamil M, Arman I. Prediction of temperature profiles using artificial neural networks in a vertical thermosiphon reboiler[J]. Applied Thermal Engineering.2008,28:1572-1579.
[He 1992]He J R, Lin G C. Computerized trace method for establishing equations for eimensions and tolerances in design and manufacture. International Journal of Advancd Technology,1992(7):210-217.
[Hird 1988] Hird G, et al. Possibilities for Integrated Design and Assembly planning[J]. Developments in Assembly Automation,1988:155-166.
[Holt 2004] Holt P O B, Ritchie J M, Day P N. Immersive virtual reality in cable and pipe routing:Design metaphors and cognitive ergonmoics[J]. Journal of Computing and Information Science in Engineering,2004,4:161-170.
[Homem 1990] Homem D M, Sanderson A C. Evaluation and selection of assembly plans[C]. Proceeding of the IEEE International Conference on Robotics and Automation, 1990:1588-1593.
[Homem 1990] Homem D M, Sanderson A C. Evaluation and selection of assembly plans [C]. Proceeding of the IEEE International Conference on Robotics and Automation, 1990:1588-1593.
[Homen 1991] Homen M L S, Sanderson A C. A correct and complete algorithm for the generation of mechanical assembly sequences[J]. IEEE Transactions on Robotics and Automation,1991,7(2):228-240.
[Hu 1997]Hu S J. Stream of variation theory for automotive body assembly[J]. Annals of the CIRP,1997,46(1):1-6.
[Huang 1999] Huang G Q, Mak K L. Design for manufacture and assembly on the Internet[J]. Computers in Industry,1999,38:17-30.
[Iyama 1995] Iyama T M, Mizuno, Goto S, et al. A race-matching method for ball bearing manufacture[J]. JSME International Journal Series C:Dynamies, Control, Robotics, Design and Manufacturing,1995,1 (38):167-170.
[James 1994] James H, Huang O H T. Automated path planning for integrated assembly Design[J]. Computer Aided Design,1994,26(9):658-666.
[Jayara 1997] Jayaram S, Connacher H I, Lyons K W. Virtual assembly using virtual reality techniques[J]. Computer Aided Design,1997,29(8):575-584.
[Jayara 1999] Jayaram S, Jayaram U, Wang Y, et al. VADE:a virtual assembly design environment[J]. IEEE Computer Graphics and Applications, 1999,19(6):44-50.
[Jin 1999]Jin J, Shi J J. State space modeling of sheet metal assembly for dimensional control[J]. Journal of Manufacturing Science and Engineering,1999, 121(4):756-762.
[Jing 2009] Jing S X, He F Z, Han S H, et al. A Method for Topological Entity Correspondence in a Replicated Collaborative CAD System[J]. Computers in Industry,2009,60:467-475.
[Jones 1998] Jones R E, Wilson R H, Calton T L. On Constraints in Assembly Planning[J]. IEEE Transactions on Robotics and Automation,1998,14(6):849-863.
[Kan 2001]Kan H Y, Duffy V G, Chuan J S. An internet virtual reality collaborative environment for effective product design[J]. Computer in Industry, 2001,45(2):197-213.
[Kannan 2001] Kannan S M, Jayabalan V. A new grouping method to minimize surplus parts in selective assembly for complex assemblies[J]. International Journal of Production Research,2001,39(9):1851-1863.
[Kannan 2003] Kannan S M, Jayabalan V, Jeevanantham K. Genetic algorithm for minimizing assembly variation in seleetive assembly[J]. International Journal of production Researeh,2003,41(14):3301-3313.
[Kannan 2008] Kannan S M, Jeevanantham A K, Jayabalan V. Modeling and analysis of selective assembly using Taguchi's loss function[J]. International Journal of Production Research,2008,46(15):4309-4330.
[Kannan 2009] Kannan S M, Sivasubramanian R, Jayabalan V. Particle swarm optimization for minimizing assembly variation in selective assembly[J]. International Journal of Advanced Manufacturing Technology,2009,42:793-803.
[Kern 2003] Kern D C. Forecasting manufacturing variation using historical process capability data:Applications for random assembly, selective assembly and serial processing[D]. Massachusetts Institute of Technology, USA,2003.
[Khatib 1986] Khatib O. Real-time Obstacle avoidance for manipulators and mobile robots[J]. International Journal on Robotics Research,1986,5(1):90-98.
[Kulkar 1985] Kulkarni S V, Garg T K. Optimal allocation of tolerances in engineering designs governed by systems of simultaneous tolerance equations using seleetive assembly[J]. Journal of Industrial Engineering,1985:160-168.
[Kumar 2007a] Kumar M S, Kannan S M. Optimum manufacturing tolerance to selective assembly technique for different assembly specifications by using genetic algorithm[J]. International Journal of Advanced Manufacturing Technology, 2007,32:591-598.
[Kumar 2007b] Kumar M S, Kannan S M, Jayabalan V. A new algorithm for minimizing surplus parts in selective assembly by using genetic algorithm[J]. International Journal of Production Research,2007,45(20):4793-4822.
[Kurata 2007] Kurata E, Mori H. Short-term load forecasting using informative vector machine[J]. IEEE Transactions on Power and Energy,2007,127(4):566-572.
[Laperr 1999] Laperriere L, Lafond P. Tolerance analysis and synthesis using virtual joints[C]. Proceedings of 6th CIRP seminar on computer aided tolerancing, Enschede, Netherland,1999:452-458.
[Lara 2003] Lara M A, Nof S Y. Computer-supported conflict resolution for collaborative facility designers[J]. International Journal Production Research,2003, 41(2):207-233.
[Lawles 1999a] Lawless J F, Machay R J, Robinson J A. Analysis of variation transmission in manufacturing processes-part I[J]. Journal of Quality Technology,1999, 31(2):131-142.
[Lawles 1999b] Lawless J F, Machay R J, Robinson J A. Analysis of variation transmission in manufacturing processes-part II[J]. Journal of Quality Technology,1999, 31(2):142-154.
[Lee 2007]Lee W K, Hyun C H, Lee H, et al. Model reference adaptive synchronization of T-S fuzzy discrete chaotic systems using output tracking control[J]. Chaos, Solitons and Fractals.2007,34:1590-1598.
[Li 1992]Li R K, Hwang C L. A framework for automatic DFA system develop[J]. Computers & Industrial Engineering,1992,22(4):403-413.
[Li 2003]Li J R, Khoo L P, Tor S B. Desktop virtual reality for maintenance training: An object oriented prototype system(V-REALISM)[J]. Computer in Industry, 2003,52:109-125.
[Li 2008]Li M, Gao S M, Fuh J Y H, et al. Replicated Concurrency Control for Collaborative Feature Modelling:A Fine Granular Approach[J]. Computers in Industry,2008,59:873-881.
[Linbar 2004] Lingbarger J M, Kessler G D. Concurrency Control Mechanisms for Closely Coupled Collaboration in MultiThreaded Pear-to-Peer Virtual Environments[J]. Teleoperators & Virtual Environments,2004,13(3):296-314.
[Lozano 1987] Lozano-perez T. A simple motion planning algorithm for general robot manipulators[J]. IEEE Journal of Robotics and Automation, 1987,3(3):224-238.
[Lu 2006a]Lu, C, Fuh J YH, Wong YS, et al. Design modification in a collaborative assembly design environment[J]. Computing and Information Science in Engineering,2006,26(2):200-208..
[Lu 2006b]Lu C, Fuh J Y H, Wong Y S. Evaluation of Product Assemblability in Different Assembly Sequences Using a tolerancing approach[J]. International Journal of Production Research,2006,44(23):5037-5063.
[Mantri 1999] Mantripragada R, Whitney D E. Modeling and controlling variation propagation in mechanical assemblies using state transition models[J]. IEEE Transactions on Robotics and Automation,1999,15(1):124-140.
[Marsh 2006] Marsh J, Glencross M, Pettifer S, et al. A network architecture supporting consistent rich behavior in collaborative interactive applications[J]. IEEE Transaction on Visualization and Computer Graphics,2006,12(3):405-406.
[Matsuu 2007] Matsuura S, Shinozaki N. Optimal binning strategies under squared error loss in selective assembly with measurement error[J]. Communications in Statistics-Theory and Methods,2007,36(16):2863-2876
[Matsuu 2010] Matsuura S, Shinozaki N. Optimal binning strategies under squared error loss in selective assembly with a tolerance constraint[J]. Communications in Statistics-Theory and Methods,2010,39(4):592-605.
[Matsuu 2011a] Matsuura S, Shinozaki N. Optimal process design in selective assembly when components with smaller variance are manufactured at three shifted means[J]. International Journal of Production Research,2011,49(3):869-882.
[Matsuu 2011b] Matsuura S. Optimal partitioning of probability distributions under general convex loss functions in selective assembly[J]. Communications in Statistics-Theory and Methods,2011,40:1545-1560.
[Mccull 1943] Mcculloch W S, Pitts W H. A logical calculus of the ideas immanent in nervous activity [J]. Bulletin of Mathematical Biophysics.1943,5:115-133.
[Mease 2004] Mease D, Nair V N, Sudjianto A. Selective assembly in manufacturing: Statistical issues and optimal binning strategies[J]. Technometrics,2004, 46(2):165-175.
[Metin 2008] Metin D, Ihsna Y. Developing a fuzzy analytic hierarchy process (AHP) model for behavior-based safety management[J]. Information Sciences,2008, 178(6):1717-1733.
[Minho 1997] Minho C, David C G. Modeling the assembly of compliant, non-ideal parts[J]. Computer-aided Design,1997,29(10):701-708.
[Mize 2000] Mize C D, Ziegert J C. Neural network thermal error compensation of a machining center[J]. Precision Engineering,2000,24(4):338-346.
[Moczul 1998] Moczulsky, et al. Method-specific knowledge compilation:towards practical design support system[J]. Artificial Intelligence in Design.1998:427-444.
[Moreir 2011] Moreira L O, Sousa F R C, Machado J C. A distributed concurrency control mechanism for XML data[J]. Journal of Computer and System Sciences,2011, 77:1009 1022.
[Nidama 2001] Nidamarthi S, Allen R H, Sriram R D. Observations from supplementing the traditional design process via internet-based collaborative tool[J]. International Journal Computer Integrated Manufacture,2001,14(1):95-107.
[Noh 2005]Noh S D, Park Y J, Kong S H, et al. Concurrent and collaborative process planning for automotive general assembly[J], International Journal of Advanced Manufacturing Technology,2005,26(5):572-584.
[Oliver 1994] Oliver J, Huang H T. Automated path planning of integrated assembly design[J]. Computer Aided Design,1994,26(9):57-63.
[Ozger 2007] Ozger M, Sen Z. Prediction of wave parameters by using fuzzy logic approach[J]. Ocean Engineering,2007,34:460-469.
[Pan 1995]Pan D. Concurrent engineering and it's application intransforming management mechanism[C]. Proceedings of 18th International Conference on Computer & Industrial Engineening,1995,28(12):117-120.
[Peng 2000] Peng Q, Hall F R. Lister P M. Application and evalution of VR based CAPP system[J]. Journal of Materials Processing Technology, 2000,107(11):153-159.
[Polat 1993] Polat F, Shekhar S. Distributed conflict resolution among cooperative expert system[J]. Expert System,1993,10(4):227-236.
[Pugh 1992] Pugh G A. Selective assembly with components of dissimilar variance[J]. Computer Industrial Engineering,1992,23(1):487-491.
[Qiu 2012]Qiu C, Liu Z Y, Tan J R, et al. Multi-scale based unified modeling and simulation of part surface topography [J]. International of journal of innovative computing, information and control,2012,8(3A):1789-1798.
[Rajago 1998] Rajagopalan S, Cutkosky M R. Tolerance represention for mechanism assemblies in layered manufacturing[C]. Proceedings of DETC98:13-18.
[Ravira 2009] Raviram P, Umarani S D, Wahidabanu R S D. Application of Radial Basis Function for Concurrency Control in CAD with KBMS[C]. International Conference on Intelligent and Multi-Agent Systems. Chennai, INDIA,2009.
[Requie 1986] Requieha A A G, Chan S C. Representation of geometric feartures, tolerances and attributes in solid modelers based on constructive geometry[J]. Robotics and Automation,1986,2(3):156-166.
[Robinso 2007] Robinson G, Ritchie J M, Day P N, et al. System design and user evaluation of Co-Star:An immersive stereoscopic system for cable harness design[J]. Computer Aided Design,2007,39:245-257.
[Rojo 2004] Rojo-Alvarez J L, Martinez-Ramon M, Prado-Cumplido M D, et al. Support vector method for robust ARMA system identification[J]. IEEE Transactions on Signal Processing,2004,52(1):155-164.
[Salat 2004] Salat R, Osowski S. Accurate fault location in the power transmission line using Support Vector Machine approach[J]. IEEE Transactions on Power Systems,2004,19(2):979-986.
[Shan 1995] Shan Y S, Koram Y. Obstacle accommodation motion planning[J]. IEEE Transaction on Robotics and Automation,1995,11 (1):17-27.
[Sharma 2009] Sharma S, Biswal B B, Dash P et al. Optimized robotic assembly sequence using ACO[C].5th International Conference on MEMS NANO, and Smart Systems, ICMENS 2009:20-24.
[Shyams 2001] Shyamsundar N, Gadh R. Internet- based collaborative product design with assembly features and virtual design spaces[J]. Computer-Aided Design, 2001,33(9):637-651.
[Shyams 2002] Shyamsundar N, Gadh R.Collaborative virtual prototyping of Product Assemblies over the Internet. Computer-Aided Design,2002,34(10):755-768.
[Su 2009]Su Q, Lai S J, Liu J. Geometric computation based assembly sequencing and evaluating in terms of assembly angle, direction, reorientation, and stability [J]. Computer-Aided Design,2009,41:479-489.
[Suyken 1999] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Processing Letters,1999,9(3):293-300.
[Swarmi 1996] Swarminathan A, Barker K S. An experience-based assembly sequence planner for mechanical assemblies[J]. IEEE Transaction on Robotics and Automation,1996,12(2):252-267.
[Tax 2004]Tax D M J, Duin R P W. Support vector data description[J]. Machine Learning, 2004,54:45-66.
[Tonsho 1992] Tonshoff H K, Menzel E, Park H S. A knowledge-based system for automated assembly planning[J]. Annals of the CIRP,1992,41(1):19-24.
[Turner 1993] Turner J. A feasibility space approach for automated tolerancing[J]. ASME Journal of Engineering for Industry,1993,115:341-346.
[Vapnik 2000] Vanpik V N. The nature of statistical learning theory[M].2nd edition, New York, Springer-Verlag,2000.
[Velasq 2008] Velasquez J D, Lara M A, Nof S Y. Systematic resolution of conflict sitations in collaborative facility design[J]. International Journal Production Economics, 2008,116:139-153.
[Wang 2004] Wang Q H, Li J R. A desktop VR prototype for industrial training applications[J]. Virtual Reality,2004(7):187-197.
[Wang 2005] WANG J.F., LIU J.H., ZHONG Y.F. A novel ant colony algorithm forassembly sequence planning[J]. The International Journal of AdvancedManufacturing Technology,2005,25(11):1137-1143.
[Wang 2006] Wang J, Sun S Y. Predictive control based on support vector machine model[C]. Proceddings of the 6th World Congress on Intelligent Control and Automation, Dalian, China,2006:1683-1687.
[Wang 2007] Wang W. An adaptive predictor for dynamic system forecasting[J]. Mechanical System and Signal Processing.2007,21:809-823.
[Whitne 1993] Whitney D E, Gilbert O L.(1993) Representation of geometric variations using matrix transforms for statistical tolerance analysis in assemblies[C]. IEEE International Conference on Robotics and Automation,1993:314-321.
[Wu 2012]Wu D L, Zhen X J, Fan X M, et al. A virtual environment for complex products collaborative assembly operation simulation[J]. Journal of Intelligent Manufacturing,2012,23(3):821-833.
[Ye 1999]Ye N, Banerjee P, Banerjee A, et al. A comparative study of assembly planning in traditional and virtual environments[C]. IEEE Transaction on System, Man and Cybemetics-Part C:Applications and Reviews, 1999,29(4):546-555.
[Yilmaz 2007] Yilmaz M, Ertunc H M. The prediction of mechanical behavior for steel wires and cord materials using neural networks[J]. Materials and Design, 2007(28):599-608.
[Zha 1998]Zha X F, Lim Y E, Fok S C. Integrated knowledge-based assembly sequence planning[J]. International Journal of Advanced Manufacturing Technology, 1998,14(l):50-64.
[Zhang 1995] Zhang Y, Fang X D. A new algorithm of minimizing the surplus parts in selective assembly[J]. Computer and Industrial Engineering, 1995,28(2):341-350.
[Zhang 2002] Zhang L J, Tang M, Tong R F. A feature-based collaborative CAD system[C]. Proceedings of the 7th International Conference on Computer Supported Cooperative Work in Design. Riode Janeiro, Brazil. IEEE, Institute of Electrical&Electronics Engineers, Inc.2002:193-197.
[Zhong 2002] Zhong W. Huang Y, Hu S J. Variation propagation modeling for machining system with different configurations, IMECE, ASME, New Orleans,2002.
[Zhou 2003] Zhou S, Huang Q, Shi J J. State space modeling of dimensional variation propagation in multistage machining process using differential motion vector[J]. IEEE Transactions on Robotics and Automation,2003, 19(2):296-309.
[陈亮 2006] 陈亮,金国栋,罗志伟.产品多学科协同设计的协商模型[J].机械工程学报,2006,42(8)：175-181.
[陈龙 2010] 陈龙,黄晨,江浩斌等.基于智能体——神经网络的轮胎侧偏特性[J].机械工程学报,2010,48(2)：153-158.
[戴光明 2004] 戴光明.避障路径规划的算法研究[D].武汉,湖北：华中科技大学,2004.
[董兴辉 2006] 董兴辉,于建国,吴克和等.协同预装配若干技术研究[J].计算机集成制造系统.2006,12(7)：1042-1046.
[杜世昌 2011] 杜世昌,王猛,奚立峰.多工序加工系统产品尺寸误差传递建模[J].机械工程学报,2011,47(16)：143-149.
[高贯斌 2010] 高贯斌,王文,林铿等.基于RBF神经网络的关节转角误差补偿[J].机械工程学报,2010,46(12)：20-24.
[顾廷权 1998] 顾廷权,高国安,徐向阳.装配工艺规划中装配序列生成与评价方法研究[J].计算机集成制造系统-CIMS,1998,2(1):25-39.
[何博侠 2008] 何博侠,张志胜,戴敏等.机械装配过程的偏差传递建模理论[J].机械工程学报,2008,44(12)：63-68.
[胡洁 2001] 胡洁.基于变动几何约束网络的形位公差设计理论与方法的研究[D].杭州,浙江：浙江大学,2001.
[胡洁 2002] 胡洁,吴昭同.变动几何约束网络的运动学模型及其应用研究[J].计算机辅助设计与图形学学报,2002,14(6)：594-597.
[贾振元 2009] 贾振元,马建伟,王福吉等.多零件几何要素影响下的装配产品特性预测方法[J].机械工程学报,2009,45(7)：168-173.
[贾振元 2010] 贾振元,马建伟,刘巍等.多几何要素影响下液压阀件特性的混合神经网络预测模型[J].机械工程学报,2010,46(2)：126-131.
[李云峰 2007] 李云峰.电液伺服阀中弹簧管刚度精密测量技术的研究[D].哈尔滨,黑龙江：哈尔滨工业大学,2007.
[郦仕云 2006] 郦仕云,宁汝新,王恒.面向装配的虚拟协同设计环境研究与实现[J].机械工程学报.2006,42(S1)：129-134.
[廖念钊 2007] 廖念钊,古莹菴,莫雨松等.互换性与技术测量[M].中国计量出版社,2007.
[林小夏 2010] 林小夏,张树有,陈婧等.产品仿真分析中曲面不均匀分布载荷施加方法[J].机械工程学报,2010,46(1)：123-127.
[林小夏 2011] 林小夏.产品设计、仿真与数控加工异构信息集成技术及应用研究[D].杭州,浙江：浙江大学,2011.
[刘检华 2006] 刘检华,宁汝新,阎艳.集成化虚拟装配工艺规划系统研究[J].中国机械工程,2006,17(23)：2486-2491.
[刘明周 2004] 刘明周,郭嘉,李旗号.机械产品精密配合中的选配方法研究[J].机械工程学报.2004,40(6)：165-168.
[刘明周 2006] 刘明周,张凤琴,吴俊峰等.基于田口质量观的机械产品选配方法[J].机械工程学报.2006,42(10)：127-131.
[刘明周 2007] 刘明周,陈诚和,葛茂根等.机械产品选配问题的二分图非形式化描述[J].农业机械学报.2007,38(11)：133-136.
[刘伟东 2012] 刘伟东,宁汝新,刘检华等.机械装配偏差源及其偏差传递机理分析[J].机械工程学报,2012,48(1)：156-168.
[刘伟伟 2006] 刘伟伟,孟祥旭,徐延宁.一种支持实时协同虚拟装配的体系架构[J].系统仿真学报,2006,18(10)：2805-2809.
[刘巽尔 2006] 刘巽尔.偏差、公差和误差[J],机械工业标准化与质量,2006.10：15-17.
[刘玉生 2005] 刘玉生,高曙明.三维CAD系统中变动孔特征通用生成方法[J].机械工程学报,2005,41(7)：112-118.
[陆宁 2010] 陆宁.基于群智能与算法融合的电力负荷组合预测[D].武汉,湖北：华中科技大学,2010.
[茅健 2007] 茅健.基于数学定义的公差建模与误差评定技术的研究[D].博士论文,浙江大学,2007.
[孟秀丽 2005] 孟秀丽,易红,倪中华等.基于多目标决策的协同设计冲突消解方法研究[J].计算机集成制造系统,2005,11(5)：625-629.
[南风强 2010] 南风强,汪惠芬,郝翠霞等.网络协同数字化预装配系统关键技术[J].计算机集成制造系统.2010,16(4)：724-730.
[邵景文2002a] 邵景文,张振家,冯国泰等.计算机装配工艺系统研究[J].航天制造技术,2002,2(1)：52-55.
[邵景文2002b] 邵景文,张振家,马玉林等.并行组合模拟退火算法在计算机辅助选择系统的应用[J].机械设计,2002,11(1)：39-41.
[孙风栋 2007] 孙风栋.Oracle数据库基础[M].北京：电子工业出版社,2007.
[谭建荣 2005] 谭建荣.CAD方法与技术[M].北京：科学出版社,2005.
[唐静远 2009] 唐静远.模拟电路故障诊断的特征提取及支持向量机集成方法研究[D].成都,四川：电子科技大学,2009.
[田兆青2007] 田兆青,来新民,林忠钦.多工位薄板装配偏差流传递的状态空间模型[J].机械工程学报,2007,43(2)：202-209.
[王凡 2010] 王凡.基于支持向量机的交通流预测方法研究[D].大连,辽宁：大连理工 大学,2010.
[王福吉 2011] 王福吉,贾振元,阳江源等.基于动态模糊神经网络的机床时变定位误差补偿[J].机械工程学报,2011,47(13)：175-179.
[王峻峰 2004] 王峻峰.分布环境下的协同装配序列规划[D].武汉,湖北：华中科技大学,2004.
[王中宇 2000] 王中宇,夏新涛,朱坚民.测量不确定度的非统计理论[M].北京：国防工业出版社,2000.
[夏平均 2007] 夏平均.基于虚拟现实的卫星装配工艺设计方法及其应用[D].哈尔滨,黑龙江：哈尔滨工业大学,2007.
[肖田元 2004] 肖田元.虚拟制造[M].北京：清华大学出版社,2004.
[谢天保 2007] 谢天保.协商理论在敏捷制造系统中的应用研究[D].西安,陕西：西安理工大学,2007.
[徐国平 2008] 徐国平.基于支持向量机的动调陀螺仪寿命预测方法研究[D].上海：上海交通大学,2008.
[杨剑锋 2007] 杨剑锋.蚁群算法及其应用研究[D].杭州,浙江：浙江大学,2007.
[杨将新 2010] 杨将新,徐旭松,曹衍龙等.基于装配定位约束的功能公差规范设计[J].机械工程学报,2010,46(2)：1-8.
[余春艳 2004] 余春艳,侯宏仑,吴明晖等.协同设计中以实体为中心的并发操作控制机制[J].计算机辅助设计与图形学学报.2004,16(9)：1289-1294.
[张钹 1991] 张钹,张铃.求解机械装配规划的新方法[J].计算机学报,1991,14(8)：561-569.
[张晗雨 2009] 张晗雨.WPF全视角分析[M].北京：机械工业出版社,2009.
[张开富 2006] 张开富.飞机部件装配误差累积分析与容差优化方法研究[D].西安,陕西：西北工业大学,2006.
[张庆英 2001] 张庆英.一种满足分组装配配合要求的新方法[J].武汉理工大学学报.2001,25(2)：158-161.
[张学敏 2009] 张学敏.MATLAB基础及应用[M].北京：中国电力出版社,2009.
[赵家黎 2006] 赵家黎,郭伟,牛占文等.基于误差流理论的多工位装配过程质量稳定性[J].机械工程学报,2006,42(11)：88-93.