Skip to main content
Log in

Reconfigured lightweight model design method for DT-based mechatronics equipment

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Digital twin (DT) technology, as one of the top strategic technology trends for 2020, has received widespread attention and has gradually been widely used in the smart manufacturing field. DT-based mechatronics equipment emphasizes the timeliness of online simulation decision-making and the promptness of model response. However, the established DT model covering all elements of mechatronics equipment involves multi-domain, multi-scale, and multi-dimensional. If this model is directly used for simulation analysis of specific applications, it will make the solution tedious and complicated and bring a waste of computational resources. Motivated by this need, an application-oriented reconfigured lightweight DT model design method for mechatronics equipment is studied in this paper. The method starts with an application-oriented analysis of the system decomposition, decomposition scheme evaluation, and core module identification criteria. Then, the above criteria are used as a guide to identifying the core system modules for this application, and the core system modules are optimized based on the idea of inheritance. Finally, the optimal system modules are validated and analyzed to ensure that the model-solving efficiency is improved as much as possible without losing this model’s necessary behavioral characteristics and dominant effects. A case study of virtual machining dynamic performance test bench (VM-TB) design scheme validation is carried out to show the implementation flow of the proposed method and verify its operability and effectiveness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The data used or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

Not applicable.

References

  1. Schleich B, Anwer N, Mathieu L, Wartzack S (2017) Shaping the digital twin for design and production engineering. CIRP Ann 66(1):141–144. https://doi.org/10.1016/j.cirp.2017.04.040

    Article  Google Scholar 

  2. Grieves M (2014) Digital twin: manufacturing excellence through virtual factory replication. White Pap 1:1–7

    Google Scholar 

  3. Qi Q et al (2021) Enabling technologies and tools for digital twin. J Manuf Syst 58:3–21. https://doi.org/10.1016/j.jmsy.2019.10.001

    Article  Google Scholar 

  4. Wei Y, Hu T, Wang Y, Wei S, Luo W (2022) Implementation strategy of physical entity for manufacturing system digital twin. Robot Comput Integr Manuf 73:102259. https://doi.org/10.1016/j.rcim.2021.102259

    Article  Google Scholar 

  5. Qiao Q, Wang J, Ye L, Gao RX (2019) Digital twin for machining tool condition prediction. Procedia CIRP 81:1388–1393. https://doi.org/10.1016/j.procir.2019.04.049

    Article  Google Scholar 

  6. Zhuang C, Liu J, Xiong H (2018) Digital twin-based smart production management and control framework for the complex product assembly shop-floor. Int J Adv Manuf Technol 96(1):1149–1163. https://doi.org/10.1007/s00170-018-1617-6

    Article  Google Scholar 

  7. Leng J et al (2021) Digital twin-driven joint optimisation of packing and storage assignment in large-scale automated high-rise warehouse product-service system. Int J Comput Integr Manuf 34(7–8):783–800. https://doi.org/10.1080/0951192X.2019.1667032

    Article  Google Scholar 

  8. Lu Y, Liu C, Wang KIK, Huang H, Xu X (2020) Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot Comput Integr Manuf 61:101837. https://doi.org/10.1016/j.rcim.2019.101837

    Article  Google Scholar 

  9. Liu Q, Zhang H, Leng J, Chen X (2019) Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system. Int J Prod Res 57(12):3903–3919. https://doi.org/10.1080/00207543.2018.1471243

    Article  Google Scholar 

  10. Wang XV, Wang L (2019) Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0. Int J Prod Res 57(12):3892–3902. https://doi.org/10.1080/00207543.2018.1497819

    Article  Google Scholar 

  11. Zhang J, Li L, Lin G, Fang D, Tai Y, Huang J (2020) Cyber resilience in healthcare digital twin on lung cancer. IEEE Access 8:201900–201913. https://doi.org/10.1109/ACCESS.2020.3034324

    Article  Google Scholar 

  12. Jimenez JI, Jahankhani H, Kendzierskyj S (2020) Health care in the cyberspace: medical cyber-physical system and digital twin challenges. In: Digital twin technologies and smart cities. Springer, pp 79–92. https://doi.org/10.1007/978-3-030-18732-3_6

  13. Khajavi SH, Motlagh NH, Jaribion A, Werner LC, Holmström J (2019) Digital twin: vision, benefits, boundaries, and creation for buildings. IEEE Access 7:147406–147419. https://doi.org/10.1109/ACCESS.2019.2946515

    Article  Google Scholar 

  14. ansys-twin-builder (2022). Available: https://www.ansys.com/products/systems/ansys-twin-builder/twin-builder-capabilities

  15. Babcock C (2016) GE plans software platform for creating 'digital twins

  16. Wright L, Davidson S (2020) How to tell the difference between a model and a digital twin. Adv Model Simul Eng Sci 7(1):13. https://doi.org/10.1186/s40323-020-00147-4

    Article  Google Scholar 

  17. Luo W, Hu T, Ye Y, Zhang C, Wei Y (2020) A hybrid predictive maintenance approach for CNC machine tool driven by digital twin. Robot Comput Integr Manuf 65:101974. https://doi.org/10.1016/j.rcim.2020.101974

    Article  Google Scholar 

  18. Sheng Z, Liu C, Song J, Xie H (2015) Module division and configuration modeling of CNC product–service system. Proc Inst Mech Eng C J Mech Eng Sci 231(3):494–506. https://doi.org/10.1177/0954406215616424

    Article  Google Scholar 

  19. Leng J et al (2020) Digital twin-driven rapid reconfiguration of the automated manufacturing system via an open architecture model. Robot Comput Integr Manuf 63:101895. https://doi.org/10.1016/j.rcim.2019.101895

    Article  Google Scholar 

  20. Zhang C, Xu W, Liu J, Liu Z, Zhou Z, Pham DT (2021) Digital twin-enabled reconfigurable modeling for smart manufacturing systems. Int J Comput Integr Manuf 34(7–8):709–733. https://doi.org/10.1080/0951192X.2019.1699256

    Article  Google Scholar 

  21. Zhang C, Xu W, Liu J, Liu Z, Zhou Z, Pham DT (2019) A reconfigurable modeling approach for digital twin-based manufacturing system. Procedia CIRP 83:118–125. https://doi.org/10.1016/j.procir.2019.03.141

    Article  Google Scholar 

  22. Cai Y, Wang Y, Burnett M (2020) Using augmented reality to build digital twin for reconfigurable additive manufacturing system. J Manuf Syst 56:598–604. https://doi.org/10.1016/j.jmsy.2020.04.005

    Article  Google Scholar 

  23. Li F, Li X, Xie H (2014) Modular design research of computer numerical control machine tools oriented to customer requirements. Adv Mech Eng 12(4):1687814020916574. https://doi.org/10.1177/1687814020916574

    Article  Google Scholar 

  24. Wei Y, Hu T, Yue P, Luo W, Ma S (2022) Study on the construction theory of digital twin mechanism model for mechatronics equipment. Int J Adv Manuf Technol. https://doi.org/10.1007/s00170-022-09144-w

  25. Wei Y, Hu T, Zhou T, Ye Y, Luo W (2021) Consistency retention method for CNC machine tool digital twin model. J Manuf Syst 58:313–322. https://doi.org/10.1016/j.jmsy.2020.06.002

    Article  Google Scholar 

  26. Driver HE, Kroeber AL (1932) Quantitative expression of cultural relationships (no. 4). University of California Press, Berkeley

Download references

Funding

This work is financially supported by the National Key Research and Development Program of China (Grant No. 2020YFB1708400), the National Natural Science Foundation of China (Grant No. 51875323), and the Taishan Scholarship special funding support.

Author information

Authors and Affiliations

Authors

Contributions

All authors have contributed to the creation of this manuscript for important intellectual content and read and approved the final manuscript.

Corresponding author

Correspondence to Tianliang Hu.

Ethics declarations

Ethical approval

The authors consciously assure that the manuscript has not been published and is not under consideration for publication elsewhere.

Consent to participate

All the authors consent to participate in this research and contribute to the research.

Consent for publication

All the authors consent to publish the research. There are no potential copyright/plagiarism issues involved in this research.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, Y., Hu, T., Yue, P. et al. Reconfigured lightweight model design method for DT-based mechatronics equipment. Int J Adv Manuf Technol 131, 5437–5455 (2024). https://doi.org/10.1007/s00170-022-10707-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-022-10707-0

Keywords

Navigation