Skip to main content
SpringerLink
Log in

A modelling and updating approach of digital twin based on surrogate model to rapidly evaluate product performance

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

Abstract

As a technology for the interaction and integration of products and simulation models, the digital twin can achieve accurate prediction and evaluation of product performance. However, the accurate model base is computationally complex, has a long iteration time, and is unable to perceive changes in the operating state in time. This leads to poor adaptability of the model and low efficiency of performance evaluation. The surrogate model can simplify the above model and improve computational efficiency. Based on this, this paper proposes a digital twin modelling and updating approach. The surrogate model is applied to the digital twin modelling process, which can accurately describe the physical mechanism and achieve interaction with the physical world. Then, this paper defines the consistency metric function, which achieves the rapid perception of the operation state and follows the physical world. Meanwhile, an improved LHS-Adam model update algorithm is used to adaptively update the model structure, improving the efficiency of the model parameters adjustment. Finally, experiments are conducted on the bogie suspension system to verify the feasibility and effectiveness of the update method in practical applications. The experimental results show that the established digital twin model has good updating performance and more efficient performance evaluation capability.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Algorithm 1
Fig. 4
Fig. 5
Fig. 6
Fig.7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

N :

The number of units in the input layer

p :

The number of units in the hidden layer

\(\left\| {x - c_{p} } \right\|\) :

The input of the pth unit

\(\sigma_{p}\) :

The flatness of the pth Gaussian function

w :

Weighting coefficient

\(d_{i}\) :

Status input parameters

\(\lambda_{i}\) :

Range of values of \(d_{i}\)

\(U_{r}\) :

Physical space

\(U_{si}\) :

Simulation model

\(\max C( \, \cdot { ,} \cdot \, )\) :

The current local search output parameter

\((x_{i\_new} ,y_{i\_new} )\) :

Sample set after resampling for \(U_{si}\)

\(m_{t}\) :

Weight update of the first-order moment estimation

β 1, β 2 :

The decay rate of the hyperparameter control shift mean

\(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{v}_{t}\) :

The update of the corrected second-order moment estimation weights

t :

The number of iterations

ε:

Smooth term of RBF

θ:

The threshold of RBF

\(T_{k}\) :

The consistency metric function

\(y_{k}\) :

Surrogate model predictions at moment k

p k :

Physical space actuals at moment k

\(H_{k}\) :

Predefined thresholds of \(T_{k}\)

\({\text{LHS}}(n,m)\) :

Latin cubic matrix

\(A_{i}\) :

Global search results of \({\text{LHS}}(n,m)\)

\(A_{i}^{(\max )}\) :

Optimal Output Parameters of \({\text{LHS}}(n,m)\)

\(B_{i - 1}^{(i)} ,B_{i + 1}^{(i)}\) :

Greedy Algorithm Solving of \(T_{k}\)

Z :

parameter solving set

\(y_{i}\) :

Twin model output predictions

\(v_{t}\) :

Weight update of the second-order moment estimation

\(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{m}_{t}\) :

The update of the corrected first-order moment estimation weights

α:

Learning rate

\(\mu\) :

The equivalent taper

References

  1. Yu J, Song Y, Tang D, Dai J (2021) A digital twin approach based on nonparametric bayesian network for complex system health monitoring. J Manuf Syst 58:293–304

    Article  Google Scholar 

  2. Zhang W, Wang S, Hou L, Jiao RJ (2021) Operating data-driven inverse design optimization for product usage personalization with an application to wheel loaders. J Ind Inf Integr 23:100212

    Google Scholar 

  3. Lützenberger J, Klein P, Hribernik K, Thoben K (2016) Improving product-service systems by exploiting information from the usage phase. A case study. Procedia Cirp 47:376–381

    Article  Google Scholar 

  4. Xin Y, Chen Y, Li W, Li X, Wu F (2022) Refined simulation method for computer-aided process planning based on digital twin technology. Micromachines (Basel) 13:620

    Article  Google Scholar 

  5. Napoleone A, Macchi M, Pozzetti A (2020) A review on the characteristics of cyber-physical systems for the future smart factories. J Manuf Syst 54:305–335

    Article  Google Scholar 

  6. Negri E, Berardi S, Fumagalli L, Macchi M (2020) Mes-integrated digital twin frameworks. J Manuf Syst 56:58–71

    Article  Google Scholar 

  7. Segovia M, Garcia-Alfaro J (2022) Design, modeling and implementation of digital twins. Sensors (Basel) 22:5396

    Article  Google Scholar 

  8. Magargle R, Johnson L, Mandloi P, Davoudabadi P, Kesarkar O, Krishnaswamy S, Batteh J, Pitchaikani A (2017) A simulation-based digital twin for model-driven health monitoring and predictive maintenance of an automotive braking system. Modelica, pp 132–133

    Google Scholar 

  9. Zhu D, Li Z, Hu N (2022) Multi-body dynamics modeling and analysis of planetary gearbox combination failure based on digital twin. Applied Sciences 12:12290

    Article  Google Scholar 

  10. Alam KM, El Saddik A (2017) C2ps: a digital twin architecture reference model for the cloud-based cyber-physical systems. Ieee Access 5:2050–2062

    Article  Google Scholar 

  11. Mack Y, Goel T, Shyy W, Haftka R (2007) Surrogate model-based optimization framework: a case study in aerospace design. Evolutionary computation in dynamic and uncertain environments (Springer) 51:323–342

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

    Google Scholar 

  13. Glaessgen E, Stargel D (2012) The digital twin paradigm for future nasa and us air force vehicles. 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials Conference, 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, pp 1818. https://doi.org/10.2514/6.2012-1818

  14. Agnusdei GP, Elia V, Gnoni MG (2021) A classification proposal of digital twin applications in the safety domain. Comput Ind Eng 154:107137

    Article  Google Scholar 

  15. Voropai NI, Stennikov VA, Barakhtenko EA (2018) Methodological principles of constructing the integrated energy supply systems and their technological architecture. Journal of Physics: Conference Series. IOP Publishing, pp 12001. https://iopscience.iop.org/article/10.1088/1742-6596/1111/1/012001/meta

  16. Liu S, Bao J, Lu Y, Li J, Lu S, Sun X (2021) Digital twin modeling method based on biomimicry for machining aerospace components. J Manuf Syst 58:180–195

    Article  Google Scholar 

  17. Coelho F, Relvas S, Barbosa-Póvoa AP (2021) Simulation-based decision support tool for in-house logistics: the basis for a digital twin. Comput Ind Eng 153:107094

    Article  Google Scholar 

  18. Qin C, Tao J, Liu C (2019) A novel stability prediction method for milling operations using the holistic-interpolation scheme. Proc Inst Mech Eng Part C: J Mech Eng Sci 233:4463–4475

    Article  Google Scholar 

  19. Lu Y, Liu C, Kevin I, Wang K, Huang H, Xu X (2020) Digital twin-driven smart manufacturing: connotation, reference model, applications and research issues. Robot Comput Integr Manuf 61:101837

    Article  Google Scholar 

  20. Shevlyugin MV, Korolev AA, Golitsyna AE, Pletnev DS (2019) Electric stock digital twin in a subway traction power system. Russ Electr Eng 90:647–652

    Article  Google Scholar 

  21. Meng Z, Tang T, Wei G, Yuan L (2020) Digital twin based comfort scenario modeling of ato controlled train. Journal of Physics: Conference Series. IOP Publishing, pp 12071. https://iopscience.iop.org/article/10.1088/1742-6596/1654/1/012071/meta

  22. Liu L, Zhang X, Wan X, Zhou S, Gao Z (2022) Digital twin-driven surface roughness prediction and process parameter adaptive optimization. Adv Eng Inform 51:101470

    Article  Google Scholar 

  23. Sisson W, Karve P, Mahadevan S (2022) Digital twin approach for component health-informed rotorcraft flight parameter optimization. Aiaa J 60:1923–1936

    Article  Google Scholar 

  24. Wang M, Wang C, Hnydiuk-Stefan A, Feng S, Atilla I, Li Z (2021) Recent progress on reliability analysis of offshore wind turbine support structures considering digital twin solutions. Ocean Eng 232:109168

    Article  Google Scholar 

  25. Angione C, Silverman E, Yaneske E (2022) Using machine learning as a surrogate model for agent-based simulations. Plos One 17:e263150

    Article  Google Scholar 

  26. Li Y, Zhang W, Xiong Q, Lu T, Mei G (2016) A novel fault diagnosis model for bearing of railway vehicles using vibration signals based on symmetric alpha-stable distribution feature extraction. Shock Vib. https://doi.org/10.1155/2016/5714195

  27. Kreuzer M, Schmidt A, Kellermann W (2021) Novel features for the detection of bearing faults in railway vehicles. INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Institute of Noise Control Engineering 263(3):3833–3844

  28. Hesser DF, Altun K, Markert B (2022) Monitoring and tracking of a suspension railway based on data-driven methods applied to inertial measurements. Mech Syst Signal Process 164:108298

    Article  Google Scholar 

  29. Jesussek M, Ellermann K (2014) Fault detection and isolation for a full-scale railway vehicle suspension with multiple kalman filters. Veh Syst Dyn 52:1695–1715

    Article  Google Scholar 

  30. Zhao Y, Liang B, Iwnicki S (2014) Friction coefficient estimation using an unscented kalman filter. Veh Syst Dyn 52:220–234

    Article  Google Scholar 

  31. Bernal E, Spiryagin M, Vollebregt E, Oldknow K, Stichel S, Shrestha S, Ahmad S, Wu Q, Sun Y, Cole C (2022) Prediction of rail surface damage in locomotive traction operations using laboratory-field measured and calibrated data. Eng Fail Anal 135:106165

    Article  Google Scholar 

  32. Karttunen K, Kabo E, Ekberg A (2014) Numerical assessment of the influence of worn wheel tread geometry on rail and wheel deterioration. Wear 317:77–91

    Article  Google Scholar 

  33. Wang J, Ren Z, Chen J, Chen L (2017) Study on rail profile optimization based on the nonlinear relationship between profile and wear rate. Math Probl Eng. https://doi.org/10.1155/2017/6956514

  34. Ye Y, Sun Y, Dongfang S, Shi D, Hecht M (2021) Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study. Multibody Syst Dyn 51:91–122

    Article  MathSciNet  MATH  Google Scholar 

  35. Tsunashima H (2019) Condition monitoring of railway tracks from car-body vibration using a machine learning technique. Appl Sci 9:2734

    Article  Google Scholar 

  36. Li C, He Q, Wang P (2022) Estimation of railway track longitudinal irregularity using vehicle response with information compression and bayesian deep learning. Comput-Aided Civil Infrastruct Eng 37:1260–1276

    Article  Google Scholar 

  37. Yao Y, Li G, Wu G, Zhang Z, Tang J (2020) Suspension parameters optimum of high-speed train bogie for hunting stability robustness. Int J Rail Transp 8:195–214

    Article  Google Scholar 

  38. Zhang Z (2018) Improved adam optimizer for deep neural networks. 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS). Ieee, pp 1–2. https://ieeexplore.ieee.org/abstract/document/8624183

  39. Bosso N, Magelli M, Trinchero R, Zampieri N (2023) Application of machine learning techniques to build digital twins for long train dynamics simulations. Veh Syst Dyn:1–20. https://doi.org/10.1080/00423114.2023.2174885

  40. Attivissimo F, Danese A, Giaquinto N, Sforza P (2007) A railway measurement system to evaluate the wheel–rail interaction quality. Ieee Trans Instrum Meas 56:1583–1589

    Article  Google Scholar 

Download references

Funding

The authors gratefully acknowledge the financial support of the National Key Research and Development Program of China (Grant number 2020YFB1708003), Shandong Natural Science Foundation (Grant number ZR2020QE295), and the Taishan Scholars Program of Shandong Province (ts20190914).

Author information

Authors and Affiliations

  1. School of Engineering, Ocean University of China, Qingdao, 266000, China

    Xinyu Liu, Xu Han, Honghui Wang & Guijie Liu

  2. Department of Mechanical and Electrical Engineering, Ocean University of China, Qingdao, 266100, China

    Honghui Wang & Guijie Liu

Authors
  1. Xinyu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Honghui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guijie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to this study, including original ideas, technical articulation, validation, and writing.

Corresponding authors

Correspondence to Honghui Wang or Guijie Liu.

Ethics declarations

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

Liu, X., Han, X., Wang, H. et al. A modelling and updating approach of digital twin based on surrogate model to rapidly evaluate product performance. Int J Adv Manuf Technol 129, 5059–5074 (2023). https://doi.org/10.1007/s00170-023-12646-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-023-12646-w

Keywords

Search

Navigation