中国战略性新兴产业研究与发展:智慧工业
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参考文献

[1]顾燕.智能传感器发展现状探究[J].无线互联科技,2017(21):12-13.

[2]罗林,胥玉萍,宋春华.传感器的最新应用与发展[J].信息通信,2016(3),176-177.

[3]高伟.浅谈智能压力传感器[J].科技领域,2014(2):413.

[4]张映锋,郭振刚,钱成,等.基于过程感知的底层制造资源智能化建模及其自适应协同优化方法研究[J].机械工程学报,2018,54(16):1-10.

[5]CHHIM P, CHINNAM R B, SADAWI N. Product design and manufacturing process based ontology for manufacturing knowledge reuse [J]. Journal of Intelligent Manufacturing, 2019, 30(2):905-916.

[6]刘航,杜江,白瑀.基于多维度本体的制造业领域知识语义建模研究[J].制造技术与机床,2019(9):140-146.

[7]BOCK C E, ZHA X F, SUH H, et al. Ontological product modeling for collaborative design [J]. Advanced Engineering Informatics, 2010, 24(4):510-524.

[8]SADIK A R, URBAN B. An ontology-based approach to enable knowledge representation and reasoning in worker-cobot agile manufacturing [J]. Future Internet, 2017, 9(4):90.

[9]OGUZ O S, RAMPELTSHAMMER W, PAILLAN S, et al. An ontology for hu-man-human interactions and learning interaction behavior policies [J]. ACM Transactions on Human-Robot Interaction, 2019, 8(3):1-26.

[10]仇永涛.离散智能车间扰动预测与高效运行管控方法研究[D].无锡:江南大学,2020.

[11]付敬奇.执行器及其应用[M].北京:机械工业出版社,2009.

[12]张学凯.防错技术在电子产品生产线的应用研究[D].上海:上海交通大学,2008.

[13]陶飞,程颖,程江峰,等.数字孪生车间信息物理融合理论与技术[J].计算机集成制造系统,2017,23(8):1603-1611.

[14]LIU M, MA J, LIN L, et al. Intelligent assembly system for mechanical products and key technology based on internet of things [J]. J Intell Manuf, 2017, 28(2):271-299.

[15]GIUSTO D, MORABITO G, IERA A, et al. The Internet of things [M]. New York: Springer, 2010.

[16]张俊.基于RFID和电子看板的装配生产监控系统的研究[D].杭州:浙江大学,2007.

[17]孙志楠.汽车装配线多层次、多信息融合的3D虚拟监控关键技术[D].南京:南京航空航天大学,2016.

[18]刘明周,马靖,赵志彪,等.物联网环境下的机械产品管控一体智能装配系统建模[J].计算机集成制造系统,2015,21(3):669-679.

[19]刘明周,王强,凌琳.基于实时信息驱动的生产车间运行驾驶舱研究及实现[J].计算机集成制造系统,2015,21(8):2052-2062.

[20]HOZDI C E, KOZJEK D, BUTALA P. A cyber-physical approach to the man-agement and control of manufacturing systems [J]. Strojniski Vestnik, 2020, 66(1).

[21]冯明涛.基于深度学习的机器人视觉三维感知与识别方法研究[D].长沙:湖南大学,2019.

[22]HORNUNG A, WURM K M, BENNEWITZ M, et al. OctoMap: an efficient probabilistic 3D mapping framework based on octrees [J]. Autonomous Robots, 2013, 34: 189-206.

[23]GUO Y, BENNAMOUN M, SOHEL F, et al. A comprehensive performance evaluation of 3D local feature descriptors [J]. International Journal of Computer Vision, 2016, 116(1):66-89.

[24]张昊若.面向机器人抓取的弱纹理物体六自由度位姿估计方法研究[D].上海:上海交通大学,2019.

[25]FU M, ZHOU W. DeepHMap++: combined projection grouping and correspon-dence learning for full of pose estimation [J]. Sensors, 2019, 19(5):1032-1050.

[26]ZHUANG C, WANG Z, ZHAO H, et al. Semantic part segmentation method based 3D object pose estimation with RGB-D images for bin-picking [J]. Robotics and Computer-Integrated Manufacturing, 2021, 68: 102086.

[27]MNIH V, K AVUKCUOGLLI K, SILVER D, et al. Human-level control through deep reinforcement learning [J]. Nature, 2019, 518(7540):529-533.

[28]LEVEN P, HUTCHINSON S. A framework for real-time path Planning in changing environments [J]. The International Journal of Robotics Re-search, 2001, 21(12):999-1030.

[29]GUTTA P R, CHINTHALA V S, MANCHO JU R V, et al. A review on facility layout design of an automated guided vehicle in flexible manufactur-ing system [J]. Materials Today: Proceedings, 2018, 5(2):3981-3986.

[30]BOZER Y A, SRINIVASAN M M Tandem configurations for automated guid-ed vehicle systems and the analysis of single vehicle loops [J]. IIE Transactions, 1988, 23(1):72-82.

[31]龚劬.图论与网络最优化算法[M].重庆:重庆大学出版社,2009.

[32]GASKINS R J, TANCHOCO J M A. Flow path design for automated guided vehicle systems [J]. International Journal of Production Research, 1987, 25(5):667-676.

[33]KASPI M, TANCHOCO J M A Optimal flow path design of unidirectional AGV systems [J]. International Journal of Production Research, 1990, 28(6):1023-1030.

[34]KASPI M, KESSELMAN U, TANCHOCO J M A. Optimal solution for the flow path design problem of a balanced unidirectional AGV system [J]. Inter-national Journal of Production Research, 2002, 40(2):389-401.

[35]KOOPMANS J C, BECKMANN M. Assignment problems and the location of economic activities [J]. Econometrica, 1957, 25(1):53-76.

[36]CHIANG W C, CHI C. Intelligent local search strategies for solving fa-cility layout problems with the quadratic assignment problem formu-lation [J]. European Journal of Operational Research, 1998, 106(2-3):457-488.

[37]SCHOLZ D, PETRICK A, DOMSCHKE W. STaTS:A slicing tree and tabu search based heuristic for the unequal area facility layout problem [J]. Europe-an Journal of Operational Research, 2009, 197(1):166-178.

[38]马庆吉.基于改进灰狼算法的柔性作业车间调度方法研究[D].武汉:华中科技大学,2019.

[39]雷鸣.基于进化计算的多目标柔性作业车间调度问题研究[D].兰州:兰州交通大学,2020.

[40]李黎.混合量子粒子群算法在柔性作业车间调度中的研究与应用[D].大连:大连交通大学,2019.

[41]KOUISS K, PIERREVAL H, MEBARKI N. Using multi-agent architecture in FMS for dynamic scheduling [J]. Journal of Intelligent Manufacturing, 1997, 8(1):41-47

[42]WANG J Q, FAN G Q, YAN F Y, et al. Research on initiative schedul-ing mode for a physical internet-based manufacturing system [J]. International Journal of Advanced Manufacturing Technology, 2016, 84 (1-4):47-58.

[43]ZHANG Y F, QIAN C, LYU Jingxiang, et al. Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor [J]. IEEE Transactions on Industrial Informatics, 2017, 13(2):737-747.

[44]ZHANG Y F, WANG J, LIU Y. Game theory based real-time multi-objec-tive flexible job shop scheduling considering environmental impact [J]. Journal of Cleaner Production, 2017, 167(11):665-679.

[45]陶飞,张萌,程江峰,等.数字孪生车间:一种未来车间运行新模式[J].计算机集成制造系统,2017,23(1):1-9.

[46]TA0 F, CHENG J F, QI Q L, et a1.Digital twin driven product design, manufacturing and service with big data [J]. The Interactional Journal of Advanced Manufacturing Technology, 2018, 94: 3563-3576.

[47]FOURGEAU E, GOMEZ E, ADLI H, et a1.System engineering workbench for multi-views systems methodology with 3DEXPERIENCE Platform the aircraft radar use case [M]//Complex Systems Design & Management Asia. Berlin: Springer International Publishing, 2016.

[48]GRIEVES M, VICKERS J. Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems [M]//Transdisciplinary Perspectives on Complex Systems. Berlin:Springer International Publishing, 2017.

[49]TUEGEL E, INGRAFFEA A, EASON T, et a1.Reengineering aircraft structural life prediction using a digital twin [J]. International Jour-nal of Aerospace Engineering, 2011. DDI: 10.1155/2011/154798.

[50]庄存波,刘检华,熊辉,等.产品数字孪生体的内涵、体系结构及其发展趋势[J].计算机集成制造系统,2017,23(4):753-768.

[51]于勇,范胜廷,彭关伟,等.数字孪生模型在产品构型管理中应用探讨[J].航空制造技术,2017,526(7):41-45.

[52]ZHANG H, LIU Q, CHEN X, et a1.A digitaltwin-based approach for designing and multi—objective optimization of hollow glass production 1ine [J]. IEEE Access, 2017(5):26901-26911.

[53]WANG J Q, FAN G Q, YAN F Y, et a1.Research on initiative scheduling mode for a physical internet-based manufacturing system [J]. International Journal of Advanced Manufacturing Technology, 2016, 84(1):47-58.

[54]QU T, PAN Y H, LYU X, et al. IoT-based real-time production logistics synchronization mechanism and method toward customer order dynam-ics [J]. Transactions of the Institute of Measurement and Contral, 2017, 39(4):429-445.

[55]屈挺,张凯,罗浩,等.物联网驱动的“生产-物流”动态联动机制、系统及案例[J].机械工程学报,2015,51(20):36-44.

[56]KOREN Y.Reconfigurable manufacturing systems [J]. Journal of Manufac-turing Systems,1999,29(4):130-141.

[57]MEHRABI M G, KANNATEY-ASIBU E. Mapping theory: a new approach to design of multi-sensor monitoring of reconfigurable machining systems (RMS) [J]. Journal of Manufacturing Systems, 2001, 20(5):297-304.

[58]DING Y, SHI J J, CEGLAREK D. Diagnosability analysis of multi-station manufacturing processes [J]. Journal of Dynamic Systems Measurement and Control-Transactions of the ASME, 2002, 124(1):1-13.

[59]SCHOLZ-REITER B, LAPPE D, GRUNDSTEIN S. Capacity adjustment based on reconfigurable machine tools-harmonising throughput time in job-shop manufacturing [J]. CIRP Annals,2015,64(1):403-406.

[60]RENNA P. Decision-making method of reconfigurable manufacturing systems'reconfiguration by a Gale-Shapley model [J]. Journal of Manufacturing Systems,2017,45:149-158.

[61]罗振壁,朱耀祥.现代制造系统[M].北京:机械工业出版社,2000.

[62]王国庆,胡新平,刘欣,等.伺服舱铸造单元在首都航天机械公司的实践[J].航天制造技术,2006(4): 1-3.

[63]王国庆,胡新平,刘欣,等.机械加工单元的实用工艺布局方法与工艺优化[J]. 航天制造技术,2006(2):1-5.

[64]李京生.面向多品种变批量生产的重构调度方法[D].北京:北京理工大学,2014.

[65]徐雨.离散型生产线的制造单元重构技术研究[D].贵阳:贵州大学,2019.

[66]XIA T, XI L, PAN E, et al. Reconfiguration-oriented opportunistic maintenance policy for reconfigurable manufacturing systems [J]. Re-liability Engineering & System Safety, 2017, 166: 87-98.

[67]李晔,王宇晗,胡俊.小型可重组数控机床的设计[J].制造技术与机床,2002,1(5):25-27.

[68]张根宝,王化培.可重构机床及其关键技术[J].制造技术与机床,2002,1(5):22-24.

[69]蔡宗琰,严新民.可重构制造系统重构算法的实例研究[J].计算机辅助设计与图形学报,2003,15(2):162-166.

[70]白俊杰,龚毅光,王宁生,等.面向订单制造的可重构制造系统中虚拟制造单元构建技术[J].计算机集成制造系统,2009,15(2):313-320.

[71]郑华林. 面向大规模定制的生产管理模式及其产品族建模技术研究[D]. 重庆:重庆大学.

[72]范兴柱. 基于知识的可重构FAS系统及仿真软件的研究与开发[D].南京:南京航空航天大学.

[73]龚东军,陈淑玲,王文江,等. 论智能制造的发展与智能工厂的实践[J].机械制造,2019,57(2):4.

[74]佚名.《工业云应用发展白皮书(2016)》正式发布[J]. 电信工程技术与标准化,2017,30(1):1.

[75]何珍.面向智能物料输送系统的感知互联互通技术研究与实现[D].南京:南京航空航天大学.

[76]孙晓梅.基于物联网的军械仓库信息管理可视化技术研究[D].南京:南京理工大学.

[77]孟祥庆.光纤测温自动报警技术在信息机房的应用[J].2021(2018-10):82-83.

[78]陈琢.混沌在小信号测量与系统参数估计中的应用[D].杭州:浙江大学,2003.

[79]崔淑琴.智能压力传感器的研究与设计[D].哈尔滨:哈尔滨理工大学.

[80]王茜,董学仁,马玉贞,等.智能信息处理技术在石墨纤维热电偶中的应用[J].仪器仪表用户,2004,11(2):2.

[81]宫芃成. 浅析智能传感器及其应用发展[J]. 通讯世界,2019,26(1):2.

[82]任智,王青明,郭晓金. 无线传感器网络中基于最小跳数路由的节点休眠算法[J]. 计算机应用,2011,31(1):5.

[83]胡国强,李茵,蔚继承. 基于6LoWPAN和CoAP的农业环境信息传感系统的设计与实现[J]. 现代电子技术,2016,39(23):5.

[84]刘锋.基于ZigBee的人体生理参数采集和传输系统的设计与实现[D].武汉:华中科技大学,2012.

[85]陈辉皇.多源信息系统中的决策规则挖掘研究[D].漳州:闽南师范大学.

[86]郭志伟.通用智能人性化排课问题的研究[D].西安:西北大学.

[87]钱亚东.支持协同设计的知识管理系统研究与开发[D].杭州:浙江大学,2006.

[88]江涛.自动化技术在机械设计制造中的应用[J]. 现代制造技术与装备,2016(9):168-168.

[89]刘强.基于RRT算法的机械臂运动规划技术研究[D].桂林:桂林电子科技大学.

[90]张莉萍,高英敏,于锁清. Pro/E和RP技术在卡板设计中的应用[J]. 机械工程师,2004(8):2.

[91]庄存波,刘检华,熊辉. 分布式自主协同制造——一种智能车间运行新模式[J]. 计算机集成制造系统,2019,25(8):10.

[92]陶飞,刘蔚然,刘检华,et al. 数字孪生及其应用探索[J]. 计算机集成制造系统,2018,24(1):18.

[93]Fei, Tao, Jiangfeng, et al. Digital twin-driven product design, manu-facturing and service with big data [J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(9-12):3563-3576.