Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article

Research on digital twin monitoring system for large complex surface machining

Published: 21 February 2023 Publication History
  • Get Citation Alerts
  • Abstract

    With the rapid development of aerospace, the large complex curved workpiece is widely used. However, the lack of digital monitoring and detection in the current manufacturing process leads to the low efficiency of the parts produced and processed, and quality consistency cannot be guaranteed. Aiming at the problems of low degree of virtual visualization and insufficient monitoring ability of large complex surface machining, a framework of large complex surface machining monitoring system based on digital twin technology was proposed. The digital research of intelligent processing monitoring system is carried out from six dimensions. By studying the key technologies of virtual twin model construction, multi-source data acquisition and transmission, and virtual-real mapping relationship construction, a digital twin monitoring system for large complex surface machining is developed. Finally, the feasibility and effectiveness of the twin system are verified by a real scene, and it provides a reference for monitoring the machining process of large complex curved workpieces.

    References

    [1]
    AboElHassan A and Yacout S A digital shadow framework using distributed system concepts Journal of Intelligent Manufacturing 2022
    [2]
    Chen Z, Fang H, and Fang Y Machining method for coating on huge revolution surfaces China Mechanical Engineering 2014 25 23 3195-3199
    [3]
    Cimino C, Negri E, and Fumagalli L Review of digital twin applications in manufacturing Computers in Industry 2019 113 103130
    [4]
    Coronado PDU, Lynn R, Louhichi W, Parto M, Wescoat E, and Kurfess T Part data integration in the shop floor digital Twin: mobile and cloud technologies to enable a manufacturing execution system Journal of Manufacturing Systems 2018 48 25-33
    [5]
    Duan JG, Ma TY, Zhang QL, Liu Z, and Qin JY Design and application of digital twin system for the blade-rotor test rig Journal of Intelligent Manufacturing 2021
    [6]
    Eguia J, Uriarte L, and Lamikiz A Analysis, optimization and accuracy assessment of special-purpose portable machines by virtual techniques International Journal of Machine Tools and Manufacture 2016 111 31-42
    [7]
    Fan Y, Yang J, Chen J, Hu P, Wang X, Xu J, and Zhou B A digital-twin visualized architecture for flexible manufacturing system Journal of Manufacturing Systems 2021 60 176-201
    [8]
    Garg G, Kuts V, and Anbarjafari G Digital twin for fanuc robots: Industrial robot programming and simulation using virtual reality Sustainability 2021 13 18 10336
    [9]
    Garland M and Heckbert PS Surface simplification using quadric error metrics IEEE Computer Graphics and Applications. 1997 16 64
    [10]
    Grieves M and Vickers J Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems Transdisciplinary Perspectives on Complex Systems 2017
    [11]
    Guo Y, Sun Y, and Wu K Research and development of monitoring system and data monitoring system and data acquisition of CNC machine tool in intelligent manufacturing International Journal of Advanced Robotic Systems 2020 17 2 1729881419898017
    [12]
    Hüffner F, Komusiewicz C, Moser H, and Niedermeier R Fixed-parameter algorithms for cluster vertex deletion Theory of Computing Systems 2010 47 1 196-217
    [13]
    Kalvin AD and Taylor RH Superfaces: Polygonal mesh simplification with bounded error IEEE Computer Graphics and Applications 1996 16 3 64-77
    [14]
    Lee J, Lapira E, Bagheri B, and Kao HA Recent advances and trends in predictive manufacturing systems in big data environment Manufacturing Letters 2013 1 1 38-41
    [15]
    Lee RS and Lin YH Development of universal environment for constructing 5-axis virtual machine tool based on modified D-H notation and OpenGL Robotics and Computer-Integrated Manufacturing 2010 26 3 253-262
    [16]
    Liu C, Hong X, Zhu Z, and Xu X Machine tool digital twin: Modelling methodology and applications 2018 ORCA
    [17]
    Liu S, Lu S, Li J, Sun X, Lu Y, and Bao J Machining process-oriented monitoring method based on digital twin via augmented reality The International Journal of Advanced Manufacturing Technology 2021 113 11 3491-3508
    [18]
    Liu W, Kong C, Niu Q, Jiang J, and Zhou X A method of NC machine tools intelligent monitoring system in smart factories Robotics and Computer-Integrated Manufacturing 2020 61 101842
    [19]
    Michael, G. (2022). Product lifecycle management.
    [20]
    Rossignac J and Borrel P Multi-resolution 3D approximations for rendering complex scenes Modeling in computer graphics 1993 Springer 455-465
    [21]
    Syafrudin M, Alfian G, Fitriyani NL, and Rhee J Performance analysis of IoT-based sensor, big data processing, and machine learning model for real-time monitoring system in automotive manufacturing Sensors 2018 18 9 2946
    [22]
    Tao F, Qi Q, Wang L, and Nee AYC Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0: Correlation and comparison Engineering 2019 5 4 653-661
    [23]
    Tao F, Sui F, Liu A, Qi Q, Zhang M, Song B, Guo Z, Lu SCY, and Nee AY Digital twin-driven product design framework International Journal of Production Research 2019 57 12 3935-3953
    [24]
    Tao F, Zhang H, Liu A, and Nee AY Digital twin in industry: State-of-the-art IEEE Transactions on Industrial Informatics 2018 15 4 2405-2415
    [25]
    Vichare P, Zhang X, Dhokia V, Cheung WM, Xiao W, and Zheng L Computer numerical control machine tool information reusability within virtual machining systems Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2018 232 4 593-604
    [26]
    Wang G, Cao Y, and Zhang Y Digital twin-driven clamping force control for thin-walled parts Advanced Engineering Informatics 2022 51 101468
    [27]
    Wang W, Zhang X, Li Y, and Li Y Open CNC machine tool's state data acquisition and application based on OPC specification Procedia CIRP 2016 56 384-388
    [28]
    Wang Y, Zheng L, and Wang Y Event-driven tool condition monitoring methodology considering tool life prediction based on industrial internet Journal of Manufacturing Systems 2021 58 205-222
    [29]
    Wenna W, Weili D, Changchun H, Heng Z, Haibing F, and Yao Y A digital twin for 3D path planning of large-span curved-arm gantry robot Robotics and Computer-Integrated Manufacturing 2022 76 102330
    [30]
    Ye Y, Hu T, Zhang C, and Luo W Design and development of a CNC machining process knowledge base using cloud technology The International Journal of Advanced Manufacturing Technology 2018 94 9 3413-3425
    [31]
    Yu-Shun W, Ling-Song H, Gao ZQ, Jun-Feng W, and Yang-Fan C Remote monitoring for the operation status of CNC machine tools based on HTML5 Advances in Technology Innovation 2019 4 4 260-268
    [32]
    Zhang Y, Zhang C, Yan J, Yang C, and Liu Z Rapid construction method of equipment model for discrete manufacturing digital twin workshop system Robotics and Computer-Integrated Manufacturing 2022 75 102309
    [33]
    Zhu K and Zhang Y A cyber-physical production system framework of smart CNC machining monitoring system IEEE/ASME Transactions on Mechatronics 2018 23 6 2579-2586
    [34]
    Zhu L, Li H, Liang W, and Wang W A web-based virtual CNC turn-milling system The International Journal of Advanced Manufacturing Technology 2015 78 1 99-113

    Cited By

    View all

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Journal of Intelligent Manufacturing
    Journal of Intelligent Manufacturing  Volume 35, Issue 3
    Mar 2024
    458 pages

    Publisher

    Springer-Verlag

    Berlin, Heidelberg

    Publication History

    Published: 21 February 2023
    Accepted: 29 December 2022
    Received: 13 September 2022

    Author Tags

    1. Digital twin
    2. Data acquisition
    3. Virtual model
    4. Visual monitoring

    Qualifiers

    • Research-article

    Funding Sources

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 0
      Total Downloads
    • Downloads (Last 12 months)0
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 11 Aug 2024

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    View options

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media