基于数字孪生的零件智能制造车间调度云平台
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摘要
零件制造车间中工作要素路径的未知性、时间的不确定性,以及生产要素信息的孤立性,严重影响了车间调度的有效运行。基于数字孪生技术提出一种解决零件智能制造车间调度问题的新方法——调度云平台,构建了调度云平台的框架模型以及调度工作流程;搭建了调度云平台的全生命周期监控系统,监控产品的实时运行状态;基于全生命周期监控系统实时监控的数据,利用大数据分析技术对车间生产过程中多源动态扰动进行预测和诊断,在此基础上提前由调度云平台对动态扰动制定相应扰动策略;为阐述如何将所提模型落地,以某企业零件智能制造车间为例,对调度云平台模型进行应用验证,同时指出了基于数字孪生的零件智能制造车间未来的工作方向。
The unknownness of the working factor path,the uncertainty of time and the isolation of production factor information in the parts manufacturing workshop seriously affect the effective operation of the shop scheduling.Based on the digital twinning technology,a new method to solve the problem of intelligent manufacturing workshop scheduling was proposed.The framework model and scheduling workflow of the scheduling cloud platform was constructed.Then the whole lifecycle monitoring system of the scheduling cloud platform was built for monitoring the real-time running status of the product.Based on the real-time monitoring data of the whole life cycle monitoring system,the big data analysis technology was used to predict and diagnose the multi-source dynamic disturbance in the workshop production process,on the basis of which the scheduling cloud platform dynamically determined the dynamic disturbance Perturbation strategy.The intelligent manufacturing workshop of an enterprise was taken as an example to verify the application of the dispatching cloud platform model,and the future working direction of the intelligent manufacturing workshop based on digital twins was also pointed out at the same time.
The unknownness of the working factor path,the uncertainty of time and the isolation of production factor information in the parts manufacturing workshop seriously affect the effective operation of the shop scheduling.Based on the digital twinning technology,a new method to solve the problem of intelligent manufacturing workshop scheduling was proposed.The framework model and scheduling workflow of the scheduling cloud platform was constructed.Then the whole lifecycle monitoring system of the scheduling cloud platform was built for monitoring the real-time running status of the product.Based on the real-time monitoring data of the whole life cycle monitoring system,the big data analysis technology was used to predict and diagnose the multi-source dynamic disturbance in the workshop production process,on the basis of which the scheduling cloud platform dynamically determined the dynamic disturbance Perturbation strategy.The intelligent manufacturing workshop of an enterprise was taken as an example to verify the application of the dispatching cloud platform model,and the future working direction of the intelligent manufacturing workshop based on digital twins was also pointed out at the same time.
引文
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[19]ZHANG Yuyan,LI Xinyu,GAO Liang,et al.A new subset based deep feature learning method for intelligent fault diagnosis of bearing[J].Expert Systems with Applications,2018,110:125-142.
[20]DING Kai,JIANG Pingyu,SU Shilong.RFID-enabled social manufacturing system for inter-enterprise monitoring and dispatching of integrated production and transportation tasks[J].Robotics and Computer-Integrated Manufacturing,2018,49:120-133.
[21]WEN Long,LI Xinyu,GAO Liang,et al.A new convolutional neural network-based data-driven fault diagnosis method[J].IEEE Transactions on Industrial Electronics,2018,65(7):5990-5998.
[22]TAO Fei,ZHANG Meng,CHENG Jiangfeng,et al.Digital twin workshop:a new paradigm for future workshop[J].Computer Integrated Manufacturing Systems,2017,23(1):1-9(in Chinese).[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.]
[23]YI Shuping,LIU Mi,WEN Peihan.Overview of cloud manufacturing service based on lifecycle theory[J].Computer Integrated Manufacturing Systems,2016,22(4):871-883(in Chinese).[易树平,刘觅,温沛涵.基于全生命周期的云制造服务研究综述[J].计算机集成制造系统,2016,22(4):871-883.]
[24]ZAKARIA Z,PETROVIC S.Genetic algorithms for matchup rescheduling of the flexible manufacturing systems[J].Computers&Industrial Engineering,2012,62(2):670-686.
[25]WANG J Q,FAN G Q,YAN F Y,et al.Research on initiative scheduling mode for a physical internet based manufacturing system[J].International Journal of Advanced Manufacturing Technology,2016,84(1/2/3/4):47-58.
[2]TANG Qiuhua,CHEN Li,WANG Xuelan.Manufacturing workshop reconfiguration based on improved genetic algorithm[J].Journal of Wuhan University of Scienceand Technology,2011,37(3):161-166(in Chinese).[唐秋华,陈立,王雪兰.基于改进遗传算法的车间布局重构[J].武汉科技大学报,2011,37(3):161-166.]
[3]APPRISO.Digital twin:manufacturing excellence through virtual factory replication[EB/OL].(2014-05-06)[2018-02-13].http://www.apriso.com.
[4]TUEGEL E J,INGRAFFEA A R,EASON T G,et al.Reengineering aircraft structural life prediction using a digital twin[J].International Journal of Aerospace Engineering,2011,21.DOI:10.1155/2011/154798.
[5]SMARSLOK B,CULLER A,MAHADEVAN S.Error quantification and confidence assessment of aerothermal model predictions for hypersonic aircraft[C]//Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics,and Materials Conference,Reston,Va.,USA:AIAA,2012.DOI:10.2514/6.2012-1817.
[6]BIELEFELDT B,HOCHHALTER J,HARTL D.Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach[C]//Proceedings of the ASME Conference on Smart Materials,A-daptive Structures and Intelligent Systems.New York,N.Y.,USA:ASME,2015.DOI:10.1115/SMASIS2015-8975.
[7]GRIEVES M,VICKERS J.Digital twin:mitigating unpredictable,undesirable emergent behavior in complex systems[M]//Transdisciplinary Perspectives on Complex Systems.Berlin,Germany:Springer-Verlag,2017.
[8]HOCHHALTER J,LESER W P,NEWMAN J A,et al.Coupling damage sensing particles to the digital twin concept[EB/OL].[2018-03-25].https://ntrs.nasa.gov/search.jspR=20140006408.
[9]Tuegel E(2012)The airframe digital twin:some challenges to realization[C]//Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics and Materials Conference.Reston,Va.,USA:AIAA,2012:AIAA2012-1812.
[10]FOURGEAU E,GOMEZ E,ADLI H,et al.System engineering workbench for multi-views systems methodology with3DEXPERIENCE platform:The aircraft radar use case[M]//Complex Systems Design&Management Asia.Berlin,Germany:Spinger-Verlag,2016.
[11]SIEMENS.The digital twin[EB/OL].(2015-11-17)[2018-03-05].http://www.siemens.com/customer-magazine/en/home/induster/digitalization-in-machine-building/the-digitaltwin.html.
[12]DEB K,PRATAP A,AGARWAL S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE transactions on evolutionary computation,2002,6(2):182-197.
[13]OUELHADJ D,PETROVIC S.A survey of dynamic scheduling in manufacturing systems[J].Journal of Scheduling,2009,12(4):417-431.
[14]IMEN D,KINZA N M,ASSIA L.A new genetic algorithm for flexible job-shop scheduling problems[J].Journal of Mechanical Science and Technology,2015,29(3):1273-1281.
[15]WANG Junliang,ZHANG Jie QIN Wei,et al.Robust scheduling on flexible job shop with uncertain processing time[J].Computer Integrated Manufacturing Systems,2015,26(5):627-632(in Chinese).[汪俊亮,张洁,秦威,等.加工时间不确定的柔性作业车间鲁棒调度方法[J].中国机械工程,2015,26(5):627-632.]
[16]ADDONA D M,TETI R.Genetic algorithm-based optimization of cutting parameters in turning processes[J].Procedia CIRP,2013,7:323-328.
[17]ZHANG G,SHAO X,LI P,et al.An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J].Computers&Industrial Engineering,2009,56(4):1309-1318.
[18]LIU Aijun,YANG Yu,XING Qingsong,et al.Dynamic scheduling on multi-objective flexible job shop[J].Computer Integrated Manufacturing Systems,2011,17(12):2629-2637(in Chinese).[刘爱军,杨育,邢青松,等.柔性作业车间多目标动态调度[J].计算机集成制造系统,2011,17(12):2629-2637.]
[19]ZHANG Yuyan,LI Xinyu,GAO Liang,et al.A new subset based deep feature learning method for intelligent fault diagnosis of bearing[J].Expert Systems with Applications,2018,110:125-142.
[20]DING Kai,JIANG Pingyu,SU Shilong.RFID-enabled social manufacturing system for inter-enterprise monitoring and dispatching of integrated production and transportation tasks[J].Robotics and Computer-Integrated Manufacturing,2018,49:120-133.
[21]WEN Long,LI Xinyu,GAO Liang,et al.A new convolutional neural network-based data-driven fault diagnosis method[J].IEEE Transactions on Industrial Electronics,2018,65(7):5990-5998.
[22]TAO Fei,ZHANG Meng,CHENG Jiangfeng,et al.Digital twin workshop:a new paradigm for future workshop[J].Computer Integrated Manufacturing Systems,2017,23(1):1-9(in Chinese).[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.]
[23]YI Shuping,LIU Mi,WEN Peihan.Overview of cloud manufacturing service based on lifecycle theory[J].Computer Integrated Manufacturing Systems,2016,22(4):871-883(in Chinese).[易树平,刘觅,温沛涵.基于全生命周期的云制造服务研究综述[J].计算机集成制造系统,2016,22(4):871-883.]
[24]ZAKARIA Z,PETROVIC S.Genetic algorithms for matchup rescheduling of the flexible manufacturing systems[J].Computers&Industrial Engineering,2012,62(2):670-686.
[25]WANG J Q,FAN G Q,YAN F Y,et al.Research on initiative scheduling mode for a physical internet based manufacturing system[J].International Journal of Advanced Manufacturing Technology,2016,84(1/2/3/4):47-58.