Digital twin modeling method for CNC machine tool

CNC machine tool (CNCMT) is the mother machine of industry, which plays an important role in the coming smart manufacturing. The intelligence of CNCMT has a big significance, which will enables its self-sensing, self-prediction and self-maintenance without user concerns. In order to realize the intelligence of CNCMT, a Digital Twin (DT) modeling method for CNCMT is researched, including a multi-domain unified modeling method, a mapping method and an autonomous strategy. This paper provides a demonstration of DT modeling method for CNCMT.

[1]  Stephen T. Newman,et al.  Making CNC machine tools more open, interoperable and intelligent - a review of the technologies , 2006, Comput. Ind..

[2]  Tullio Tolio,et al.  Simulation of Complex Manufacturing Systems via HLA-Based Infrastructure , 2011, 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation.

[3]  Fei Tao,et al.  Big Data in product lifecycle management , 2015, The International Journal of Advanced Manufacturing Technology.

[4]  Jay Lee,et al.  A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems , 2015 .

[5]  Sohyung Cho,et al.  Tool breakage detection using support vector machine learning in a milling process , 2005 .

[6]  Peter Beater Modeling and Digital Simulation of Hydraulic Systems in Design and Engineering Education using Modelica and HyLib , 2000 .

[7]  Hilding Elmqvist,et al.  Modelica — A unified object-oriented language for physical systems modeling , 1997 .

[8]  Fei Tao,et al.  Data and knowledge mining with big data towards smart production , 2017, J. Ind. Inf. Integr..

[9]  Jian Sheng Zhang,et al.  Research of CNC Fault Diagnosis Based on RBF Neural Network , 2012 .

[10]  Michael Möhring,et al.  Industry 4.0 - Potentials for Creating Smart Products: Empirical Research Results , 2015, BIS.

[11]  Hilding Elmqvist,et al.  Modelica—An International Effort to Design an Object-Oriented Modeling Language , 1998 .

[12]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[13]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[14]  Fei Tao,et al.  Internet of Things in product life-cycle energy management , 2016, J. Ind. Inf. Integr..

[15]  G. Gary Wang,et al.  Definition and Review of Virtual Prototyping , 2002, J. Comput. Inf. Sci. Eng..