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Complex product manufacturing and operation and maintenance integration based on digital twin

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Abstract

To realize the deep integration of complex product manufacturing and operation and maintenance processes, while eliminate the phenomenon of information islands generated in the manufacturing and operation and maintenance links, in the light of the lack of in-depth integration in the manufacturing and operation and maintenance process of complex products. With the help of digital twin technology, this paper puts forward the overall framework manufacturing and operation and maintenance integration of a complex product based on digital twin and the system integration mode of virtual and real integration. The solutions of complex product integration of manufacturing and operations and maintenance, data fusion, modeling and simulation, and manufacturing plant operation mode based on digital twin are analyzed. The key technologies based on digital twin deep integration of manufacturing and operation and maintenance, fault prediction, and knowledge base/case library construction are discussed. Finally, combined with the fault prediction case of a certain type of electric multiple units (EMU) bogie, the associated operation mode between the operation and maintenance process of the EMU and the manufacturer is demonstrated, which proves the feasibility and effectiveness of the integration method.

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References

  1. Eswaran S, Christopher L, Helen G (2015) Managing and supporting product life cycle through engineering change management for a complex product. Res Eng Des 26(3):189–217. https://doi.org/10.1007/s00163-015-0192-1

    Article  Google Scholar 

  2. Mohammed MM, Abdulrahman MAA, Bashir S, Hisham A (2018) Requirements of the smart factory system: a survey and perspective. Machines 6(2):23. https://doi.org/10.3390/machines6020023

    Article  Google Scholar 

  3. Tao F, Zhang H, Qi QL, Zhang M, Liu WR, Cheng JF, Ma X, Zhang LC, Xue RJ (2020) Ten questions about digital twins: analysis and thinking. Comput Integr Manuf Syst 1:1–17. https://doi.org/10.13196/j.cims.2020.01.001

    Article  Google Scholar 

  4. Ren S, Zhang YF, Huang BB (2018) New pattern of lifecycle big-data-driven smart manufacturing service for complex product. Aust J Mech Eng 22:194–203. https://doi.org/10.3901/JME.2018.22.194

    Article  Google Scholar 

  5. Li H, Mi SH, Li QF, Wen XY, Qiao DP, Luo GF (2018) A scheduling optimization method for maintenance, repair and operations service resources of complex products. J Intell Manuf 2018:1–19. https://doi.org/10.1007/s10845-018-1400-4

    Article  Google Scholar 

  6. Kadir E, Fatma SÖ (2018) An integrated production scheduling and workforce capacity planning model for the maintenance and repair operations in airline industry. Comput Ind Eng 127:832–840. https://doi.org/10.1016/j.cie.2018.11.022

    Article  Google Scholar 

  7. Matias S, Harri H, Janne H (2019) Maintenance, repair, and operations inventory reduction and operational development. Int J Ind Syst Eng 1:1–31. https://doi.org/10.1504/IJISE.2019.099780

    Article  Google Scholar 

  8. Li H, Wang HQ, Cheng Y, Fei T, Hao B, Wang XC, Ji YJ, Song WY, Du WL, Wen XY, Gong XY, Li K, Zhang YF, Luo GF, Li QF (2020) Technology and application of data-driven intelligent services for complex products. Chin Mech Eng 07:757–772. https://doi.org/10.3969/j.issn.1004-132X.2020.07.001

    Article  Google Scholar 

  9. Liu WM, Liu KL, Deng TH (2020) Modelling, analysis and improvement of an integrated chance-constrained model for level of repair analysis and spare parts supply control. Int J Prod Res 10:3090–3109. https://doi.org/10.1080/00207543.2019.1629669

    Article  Google Scholar 

  10. Li L, Min L, Shen WM, Cheng GQ (2019) A novel performance evaluation model for MRO management indicators of high-end equipment. Int J Prod Res 21:6740–6757. https://doi.org/10.1080/00207543.2019.1566654

    Article  Google Scholar 

  11. Francisco GQ, Olivier C, Anne L’A, Pierre C (2016) A modeling framework for manufacturing services in Service-oriented Holonic Manufacturing Systems. Eng Appl Artif Intell 2016:26–36. https://doi.org/10.1016/j.engappai.2016.06.004

    Article  Google Scholar 

  12. Sun YD, Zhang X, Ning RX, Zhao W, Bian Y (2013) Integrated modeling method oriented to multi-disciplinary collaborative development domain. Comput Integr Manuf Syst 3:449–460. https://doi.org/10.13196/j.cims.2013.03.3.sunyd.007

    Article  Google Scholar 

  13. Enzo M, Mirko K, Michael F (2018) Data-driven production control for complex and dynamic manufacturing systems. CIRP Ann Manuf Technol 67:515–518. https://doi.org/10.1016/j.cirp.2018.04.033

    Article  Google Scholar 

  14. Li H, Tao F, Wang HQ, Song WY, Zhang ZF, Fan BB, Wu CL, Li YP, Li LL, Wen XY, Zhang XS, Luo GF (2019) Integration framework and key technologies of complex product design-manufacturing based on digital twin. Comput Integr Manuf Syst 6:1320–1336. https://doi.org/10.13196/j.cims.06.002

    Article  Google Scholar 

  15. WU Y, Yao YL, Xiong H, Zhuang CB, Zhao HR, Liu JH (2019) Quality control method of complex product assembly process based on digital twin technology. Comput Integr Manuf Syst 6:1568–1575. https://doi.org/10.13196/j.cims.2019.06.024

    Article  Google Scholar 

  16. Li LL, Li H, Gu F, Ding N, Gu XJ, Luo GF (2019) Multidisciplinary collaborative design modeling technologies for complex mechanical products based on digital twin. Comput Integr Manuf Syst 6:1307–1319. https://doi.org/10.13196/j.cims.2019.06.001

    Article  Google Scholar 

  17. Yang Y, Yan YH, Liu XJ, Ni ZH, Feng JD, Liu JS (2020) Digital twin-based smart assembly process design and application framework for complex products and its case study. J Manuf Syst 58:94–107. https://doi.org/10.1016/j.jmsy.2020.04.013

    Article  Google Scholar 

  18. Tao F, Liu WR, Zhang M, Hu TL, Qi QL, Zhang H, Sui FY, Wang T, Xu H, Huang ZG, Ma X, Zhang LC, Cheng JF, Yao NK, Yi WM, Zhu KZ, Zhang XS, Meng FJ, Jin XH, Liu ZB, He LR, Cheng H, Zhou EZ, Li Y, Lv Q, Luo YM (2019) Five-dimension digital twin model and its ten applications. Comput Integr Manuf Syst 1:1–18. https://doi.org/10.13196/j.cims.2019.01.001

    Article  Google Scholar 

  19. Tao F, Zhang M, Cheng JF, Qi QL (2017) Digital twin workshop: a new paradigm for future workshop. 1:1–9. https://doi.org/10.13196/j.cims.2017.01.001

  20. Zhuang CB, Liu JH, 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 

  21. Yi C, Binil S, Yuan SL (2017) Sensor data and information fusion to construct digital-twins virtual machine tools for cyber-physical manufacturing. Proc Manuf 10:1031–1042. https://doi.org/10.1016/j.promfg.2017.07.094

    Article  Google Scholar 

  22. Wang YR, Wu ZL (2020) Model construction of planning and scheduling system based on digital twin. Int J Adv Manuf Technol 109(7-8):2189–2203. https://doi.org/10.1007/s00170-020-05779-9

    Article  Google Scholar 

  23. Andrea C, Luca O, Francesco B, Francesca C, Mehmet A, Stefano S (2019) Data-driven ship digital twin for estimating the speed loss caused by the marine fouling. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2019.05.045.186

  24. Zhang XH, Zhang YM, Wang Y, Du YY, Wang MY, Xie N, Ju JS (2020) DT-driven aided guidance of equipment maintenance using MR. Comput Integr Manuf Syst. [2020-12-10] http://kns.cnki.net/kcms/detail/11.5946.TP.20200312.1004.002.html

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Acknowledgements

The authors wish to acknowledge support from the Staff of Industrial Engineering Project Team, School of Mechanical Engineering, Xi’an University of Science and Technology.

Funding

This work was supported by the National Key Research and Development Program Project Fund of China #1 under Grant number 2018YFB1703402, the National Natural Science Foundation of China #2 under Grant number 51705417, and Shaanxi Provincial Natural Science Fund #3 under Grant number 2019JQ-086.

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  1. School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China

    Yunrui Wang, Wenzhe Ren, Yan Li & Chuanwei Zhang

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  1. Yunrui Wang
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  2. Wenzhe Ren
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  3. Yan Li
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  4. Chuanwei Zhang
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All authors contributed equally to the generation and analysis of experimental data, and the development of the manuscript.

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Correspondence to Yunrui Wang.

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Wang, Y., Ren, W., Li, Y. et al. Complex product manufacturing and operation and maintenance integration based on digital twin. Int J Adv Manuf Technol 117, 361–381 (2021). https://doi.org/10.1007/s00170-021-07350-6

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  • DOI: https://doi.org/10.1007/s00170-021-07350-6

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