面向切削过程的刀具数字孪生模型
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摘要
刀具磨损状态退化建模和仿真的结果难以真实地反映物理现实,使刀具的选用、更换和修磨决策缺乏可靠依据,严重地影响了刀具精准利用的优化和控制,以及生产系统的动态调控。针对该问题,在数字孪生理念的基础上,提出面向切削过程的刀具数字孪生模型,探讨了其概念、组成、功能和运作流程,详细论述了数字孪生驱动的刀具磨损监测、剩余寿命预测、刀具选用决策和刀具服务,并通过原型系统进行了验证。通过刀具物理对象与虚拟模型的交互与共融,面向切削过程的刀具数字孪生模型可提高刀具全生命周期状态管理的智能性、主动性、预测性,支持面向刀具精准利用的优化、决策和服务。
As the teeth of CNC machine tools,cutting tools are of great importance to machining efficiency,quality,cost and energy consumption.Precise usage of cutting tools is believed to improve economic,environmental and social benefits greatly.However,the problem that the physical cutting tool was difficult to be reacted by modelling and simulation of its degradation process made the cutting tool usage,replacement and sharpening lack of reliable support,which affected optimization and control for precise usage of cutting tools and dynamic adjustment of machining system.Based on the concept of digital twin,a digital twin model for cutting tools in machining process was proposed,and its concept,structure,function and running procedure were investigated in detail.Digital twin-driven cutting tool wear condition monitoring,remaining useful life prediction,cutting tool selection decision-making and cutting service were also addressed deeply.A prototype was developed to illustrate and validate the model.Through interaction and fusion of physical cutting tools and virtual models,the digital twin model for cutting tools in machining process enabled an intelligent,proactive and predictive cutting tool management mode to support optimization,decision-making and service.
As the teeth of CNC machine tools,cutting tools are of great importance to machining efficiency,quality,cost and energy consumption.Precise usage of cutting tools is believed to improve economic,environmental and social benefits greatly.However,the problem that the physical cutting tool was difficult to be reacted by modelling and simulation of its degradation process made the cutting tool usage,replacement and sharpening lack of reliable support,which affected optimization and control for precise usage of cutting tools and dynamic adjustment of machining system.Based on the concept of digital twin,a digital twin model for cutting tools in machining process was proposed,and its concept,structure,function and running procedure were investigated in detail.Digital twin-driven cutting tool wear condition monitoring,remaining useful life prediction,cutting tool selection decision-making and cutting service were also addressed deeply.A prototype was developed to illustrate and validate the model.Through interaction and fusion of physical cutting tools and virtual models,the digital twin model for cutting tools in machining process enabled an intelligent,proactive and predictive cutting tool management mode to support optimization,decision-making and service.
引文
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[17]SUN H,ZHANG X,NIU W.In-process cutting tool remaining useful life evaluation based on operational reliability assessment[J].International Journal of Advanced Manufacturing Technology,2016,86(1/2/3/4):841-851.
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[19]SUN H,CAO D,ZHAO Z,et al.A hybrid approach to cutting tool remaining useful life prediction based on Wiener process[J].IEEE Transactions on Reliability,2018,67(3):1294-1303.
[20]ZHANG G,SUN H.Enabling a cutting tool iPSS based on tool condition monitoring[J].International Journal of Advanced Manufacturing Technology,2018,94(9/10/11/12):3265-3274.
[2]GRIEVES M,VICKERS J.Digital twin:mitigating unpredictable,undesirable emergent behavior in complex system[M]//Trans-disciplinary Perspectives on Complex Systems.Berlin,Germany:Springer-Verlag,2017.
[3]GLAESSGEN E,STARGEL D.The digital twin paradigm for future NASA and U.S.Air Force vehicles[C]//Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures,Structural Dynamics and Materials Conference.Reston,Va.,USA:AIAA,2012:7274-7260.
[4]SCHLUSE M,PRIGGEMEYER M,ATORF L,et al.Experimentable digital twins-streamlining simulation-based systems engineering for Industry 4.0[J].IEEE Transactions on Industrial Informatics,2018,14(4):1722-1731.
[5]WANG X,WANG L.Digital twin-based WEEE recycling,recovery and remanufacturing in the background of Industry 4.0[J].International Journal of Production Research,2018.DOI:10.1080/00207543.2018.1497819.
[6]TAO F,CHENG J,QI Q.IIHub:an industrial Internet-ofThings hub towards smart manufacturing based on cyber physical system[J].IEEE Transactions on Industrial Informatics,2018,14(5):2271-2280.
[7]TAO F,ZHANG M,NEE A.Digital twin driven smart manufacturing[M].Amsterdam,the Netherlands:Elsevier,2019.
[8]TAO Fei,LIU Weiran,LIU Jianhua,et al.Digital twin and its potential application exploration[J].Computer Integrated Manufacturing Systems,2018,24(1):1-16(in Chinese).[陶飞,刘蔚然,刘检华,等.数字孪生及其应用探索[J].计算机集成制造系统,2018,24(1):1-16.]
[9]TAO Fei,LIU Weiran,ZHANG Meng,et al.Five-dimension digital twin model and its ten applications[J].Computer Integrated Manufacturing Systems,2019,25(1):1-18(in Chinese).[陶飞,刘蔚然,张萌,等.数字孪生的五维模型及十大应用领域[J].计算机集成制造系统,2019,25(1):1-18.]
[10]LUO W,HU T,ZHANG C,et al.Digital twin for CNC machine tool:modeling and using strategy[J].Journal of Ambient Intelligence and Humanized Computing,2019,10(3):1129-1140.
[11]ZHANG H,LIU Q,CHEN X.A digital twin-based approach for designing and multi-objective optimization of hollow glass production line[J].IEEE Access,2017,5:26901-26911.
[12]ZHUANG C,LIU J,XIONG H.Digital twin-based smart production management and control framework for the complex product assembly shop-floor[J].The International Journal of Advanced Manufacturing Technology,2018,96(1/2/3/4):1149-1163.
[13]ZHUANG Cunbo,LIU Jianhua,XIONG Hui,et al.Connotation,architecture,and trends of product digital twin[J].Computer Integrated Manufacturing Systems,2017,23(4):753-768(in Chinese).[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.]
[14]TAO F,ZHANG H,LIU W,NEE A.Digital twin in industry:state-of-the-art[J].IEEE Transactions on Industrial Informatics,2019,18(4):2405-2415.DOI:10.1109/TII.2018.2873186.
[15]BOTKINA D,HEDLIND M,OLSSON B,et al.Digital Twin of a Cutting Tool[J].Procedia CIRP,2018,72:215-218.
[16]CAO Dali,SUN Huibin,ZHANG Jiduo,et al.In-process tool condition monitoring based on convolution neural network[J/OL].Computer Integrated Manufacturing Systems,[2018-05-06].http://kns.cnki.net/kcms/detail/11.5946.TP.20180913.1536.020.html(in Chinese).[曹大理,孙惠斌,张纪铎,等.基于卷积神经网络的刀具磨损在线监测[J/OL].[2018-05-06].计算机集成制造系统.http://kns.cnki.net/kcms/detail/11.5946.TP.20180913.1536.020.html.]
[17]SUN H,ZHANG X,NIU W.In-process cutting tool remaining useful life evaluation based on operational reliability assessment[J].International Journal of Advanced Manufacturing Technology,2016,86(1/2/3/4):841-851.
[18]TAO F,ZHANG M,LIU Y,et al.Digital twin driven prognostics and health management for complex equipment.CIRPAnnals-Manufacturing Technology[J],2018,67(1):169-172.
[19]SUN H,CAO D,ZHAO Z,et al.A hybrid approach to cutting tool remaining useful life prediction based on Wiener process[J].IEEE Transactions on Reliability,2018,67(3):1294-1303.
[20]ZHANG G,SUN H.Enabling a cutting tool iPSS based on tool condition monitoring[J].International Journal of Advanced Manufacturing Technology,2018,94(9/10/11/12):3265-3274.