Digital twin model for cutting tools in machining process

被引：0

作者：

Sun H. ^{[1
]}

Pan J. ^{[1
]}

Zhang J. ^{[1
]}

Mo R. ^{[1
]}

机构：

[1] Key Lab of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi'an

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2019年 / 25卷 / 06期

关键词：

Condition monitoring; Cutting tool selection decision-making; Cutting tool service; Cutting tools; Digital twin; Machining process; Remaining useful life prediction;

D O I：

10.13196/j.cims.2019.06.015

中图分类号：

学科分类号：

摘要：

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. © 2019, Editorial Department of CIMS. All right reserved.

引用

页码：1474 / 1480

页数：6

共 20 条

[1] Grives M., Digital twin: manufacturing excellence through virtual factory replication, (2014)
[2] Grieves M., Vickers J., Digital twin: mitigating unpredictable, undesirable emergent behavior in complex system, Trans-disciplinary Perspectives on Complex Systems, (2017)
[3] Glaessgen E., Stargel D., The digital twin paradigm for future NASA and U. S. Air Force vehicles, Proceedings of the 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, pp. 7260-7274, (2012)
[4] Schluse M., Priggemeyer M., Atorf L., Et al., Experimentable digital twins-streamlining simulation-based systems engineering for Industry 4.0, IEEE Transactions on Industrial Informatics, 14, 4, pp. 1722-1731, (2018)
[5] Wang X., Wang L., Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0, International Journal of Production Research
[6] Tao F., Cheng J., Qi Q., IIHub: an industrial Internet-of-Things hub towards smart manufacturing based on cyber physical system, IEEE Transactions on Industrial Informatics, 14, 5, pp. 2271-2280, (2018)
[7] Tao F., Zhang M., Nee A., Digital Twin Driven Smart Manufacturing, (2019)
[8] Tao F., Liu W., Liu J., Et al., Digital twin and its potential application exploration, Computer Integrated Manufacturing Systems, 24, 1, pp. 1-16, (2018)
[9] Tao F., Liu W., Zhang M., Et al., Five-dimension digital twin model and its ten applications, Computer Integrated Manufacturing Systems, 25, 1, pp. 1-18, (2019)
[10] Luo W., Hu T., Zhang C., Et al., Digital twin for CNC machine tool: modeling and using strategy, Journal of Ambient Intelligence and Humanized Computing, 10, 3, pp. 1129-1140, (2019)

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