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Augmented reality-based virtual-real fusion commissioning: a novel approach to production commissioning

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Abstract

The installation and commissioning of production units are crucial for establishing efficient production systems. However, current approaches frequently face challenges such as lengthy commissioning cycles, low productivity, and limited intelligence. This study aims to leverage the synergistic capabilities of augmented reality (AR) technology and digital twin technology in the context of Industry 4.0 to address the challenges faced in production commissioning. Therefore, to achieve intelligent commissioning, optimize the installation and commissioning process of production systems and increase productivity. This paper proposes a novel virtual-real fusion commissioning method utilizing AR for production commissioning. Initially, a digital twin modeling and process planning simulation method is introduced for virtual-real fusion production units; secondly, real-time commissioning and fusion display of the production process are enabled through AR technology; lastly, the approach is applied to the operation and commissioning of the motor rotor–embedded wire production unit. The results demonstrate that the system can simulate the entire production logic and operation even in the absence of certain equipment while visualizing the production process and product status. Furthermore, compared to traditional physical production commissioning, this method has achieved a 43.6% reduction in the commissioning cycle and resulted in space cost savings of 21.8%. Subsequent applications validate the soundness of the proposed modeling and fusion commissioning approach and provide practical theoretical and methodological guidance for digital twin modeling and production commissioning in production systems.

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References

  1. Son YH, Kim GY, Kim HC et al (2022) Past, present, and future research of digital twin for smart manufacturing. J Comput Des Eng 9(1):1–23. https://doi.org/10.1093/jcde/qwab067

    Article  MathSciNet  Google Scholar 

  2. Liu C, Jiang P, Jiang W (2020) Web-based digital twin modeling and remote control of cyber-physical production systems. Robot Comput-Integr Manuf 64:101956. https://doi.org/10.1016/j.rcim.2020.101956

    Article  Google Scholar 

  3. Efthymiou K, Pagoropoulos A, Papakostas N et al (2012) Manufacturing systems complexity review: challenges and outlook. Procedia Cirp 3:644–649. https://doi.org/10.1016/j.procir.2012.07.110

    Article  Google Scholar 

  4. Yang J, Liu F, Dong Y et al (2022) Multiple-objective optimization of a reconfigurable assembly system via equipment selection and sequence planning. Comput Ind Eng 172:108519

    Article  Google Scholar 

  5. Yadav G, Kumar A, Luthra S et al (2020) A framework to achieve sustainability in manufacturing organisations of developing economies using industry 4.0 technologies’ enablers. Comput Ind 122:103280. https://doi.org/10.1016/j.compind.2020.103280

    Article  Google Scholar 

  6. Shahim N, Møller C (2016) Economic justification of virtual commissioning in automation industry Winter Simulation Conference (WSC). IEEE 2016:2430–2441. https://doi.org/10.1109/WSC.2016.7822282

    Article  Google Scholar 

  7. Reinhart G, Wünsch G (2007) Economic application of virtual commissioning to mechatronic production systems. Prod Eng Res Devel 1(4):371–379. https://doi.org/10.1007/s11740-007-0066-0

    Article  Google Scholar 

  8. Park SC, Chang M (2012) Hardware-in-the-loop simulation for a production system. Int J Prod Res 50(8):2321–2330. https://doi.org/10.1080/00207543.2011.575097

    Article  Google Scholar 

  9. Hoffmann P, Schumann R, Maksoud TMA et al (2010) Virtual commissioning of manufacturing systems a review and new approaches for simplification. ECMS. 1:175–181

    Article  Google Scholar 

  10. Lee CG, Park SC (2014) Survey on the virtual commissioning of manufacturing systems. J Comput Des Eng 1(3):213–222. https://doi.org/10.7315/JCDE.2014.021

    Article  Google Scholar 

  11. Lechler T, Fischer E, Metzner M et al (2019) Virtual commissioning–scientific review and exploratory use cases in advanced production systems. Procedia CIRP 81:1125–1130. https://doi.org/10.1016/j.procir.2019.03.278

    Article  Google Scholar 

  12. Kuts V, Otto T, Tähemaa T, et al (2019) Digital twin based synchronised control and simulation of the industrial robotic cell using virtual reality. J Mach Eng, 19. https://doi.org/10.5604/01.3001.0013.0464

  13. Zhang H, Yan Q, Wen Z (2020) Information modeling for cyber-physical production system based on digital twin and AutomationML. Int J Adv Manuf Technol 107(3):1927–1945. https://doi.org/10.1007/s00170-020-05056-9

    Article  Google Scholar 

  14. Cimino C, Negri E, Fumagalli L (2019) Review of digital twin applications in manufacturing. Comput Ind 113:103130. https://doi.org/10.1016/j.compind.2019.103130

    Article  Google Scholar 

  15. Eschen H, Kötter T, Rodeck R et al (2018) Augmented and virtual reality for inspection and maintenance processes in the aviation industry. Procedia Manuf 19:156–163. https://doi.org/10.1016/j.promfg.2018.01.022

    Article  Google Scholar 

  16. Dalle Mura M, Dini G (2021) An augmented reality approach for supporting panel alignment in car body assembly. J Manuf Syst 59:251–260. https://doi.org/10.1016/j.jmsy.2021.03.004

    Article  Google Scholar 

  17. Li W, Huang H, Solomon T et al (2022) Synthesizing personalized construction safety training scenarios for VR training. IEEE Trans Visual Comput Graphics 28(5):1993–2002. https://doi.org/10.1109/TVCG.2022.3150510

    Article  Google Scholar 

  18. Frydenberg SG, Nordby K (2022) Virtual fieldwork on a ship’s bridge: virtual reality-reconstructed operation scenarios as contextual substitutes for fieldwork in design education[J]. Virtual Reality, 1-12https://doi.org/10.1007/s10055-022-00655-1

  19. Dammacco L, Carli R, Lazazzera V et al (2022) Designing complex manufacturing systems by virtual reality: a novel approach and its application to the virtual commissioning of a production line. Comput Ind 143:103761. https://doi.org/10.1016/j.compind.2022.103761

    Article  Google Scholar 

  20. Dahl M, Albo A, Eriksson J, et al. Virtual reality commissioning in production systems preparation[C]//2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, 2017: 1–7. https://doi.org/10.1109/ETFA.2017.8247581

  21. Berg LP, Vance JM (2017) Industry use of virtual reality in product design and manufacturing: a survey. Virtual Reality 21(1):1–17. https://doi.org/10.1007/s10055-016-0293-9

    Article  Google Scholar 

  22. Lee J, Azamfar M, Bagheri B (2021) A unified digital twin framework for shop floor design in industry 4.0 manufacturing systems. Manuf Lett 27:87–91. https://doi.org/10.1016/j.mfglet.2021.01.005

    Article  Google Scholar 

  23. Lee J, Azamfar M, Singh J, Siahpour S (2020) Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing. IET Collab Intell Manuf 2(1):34–36. https://doi.org/10.1109/TSM.2020.2995548

    Article  Google Scholar 

  24. 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. https://doi.org/10.1016/j.rcim.2019.101837

    Article  Google Scholar 

  25. Kong T, Hu T, Zhou T et al (2020) Data construction method for the applications of workshop digital twin system. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2020.02.003

    Article  Google Scholar 

  26. Kuhn T (2017) Digitaler Zwilling. Informatik-Spektrum 40(5):440–444. https://doi.org/10.1007/s00287-017-1061-2

    Article  Google Scholar 

  27. Tao F, Sun X, Jang C et al (2023) makeTwin: a reference architecture for digital twin software platform. Chin J Aeronaut. https://doi.org/10.1016/j.cja.2023.05.002

    Article  Google Scholar 

  28. Xu J, Guo T (2021) Application and research on digital twin in electronic cam servo motion control system. Int J Adv Manuf Technol 112(3):1145–1158. https://doi.org/10.1007/s00170-020-06553-7

    Article  MathSciNet  Google Scholar 

  29. 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 

  30. Son YH, Park KT, Lee D, Jeon SW, Do Noh S (2021) Digital twin–based cyber-physical system for automotive body production lines. Int J Adv Manuf Technol 115(1):291–310. https://doi.org/10.1007/s00170-021-07183-3

    Article  Google Scholar 

  31. Tao F, Zhang M, Nee AYC (2019) Digital twin driven smart manufacturing[M]. Academic Press. 10https://doi.org/10.1016/B978-0-12-817630-6.00002-3

  32. Sanglub A, Nilsook P, Wannapiroon P (2019) Imagineering on augmented reality and digital twin for digital competence. Int J Inform Educ Technol 9(3):213–217. https://doi.org/10.18178/ijiet.2019.9.3.1201

    Article  Google Scholar 

  33. Zhu Z, Liu C, Xu X (2019) Visualisation of the digital twin data in manufacturing by using augmented reality. Procedia CIRP 81:898–903. https://doi.org/10.1016/j.procir.2019.03.223

    Article  Google Scholar 

  34. Liu S, Lu S, Li J, Sun X, Lu Y, Bao J (2021) Machining process-oriented monitoring method based on digital twin via augmented reality. Int J Adv Manuf Technol 113(11):3491–3508. https://doi.org/10.1007/s00170-021-06838-5

    Article  Google Scholar 

  35. 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 

  36. Kang Y, Rong Y, Yang JA (2003) Geometric and kinetic model based computer-aided fixture design verification. J Comput Inform Sci Eng 3(3):187–199. https://doi.org/10.1115/1.1607352

    Article  Google Scholar 

  37. Angulo C, Chacón A, Ponsa P (2022) Towards a cognitive assistant supporting human operators in the artificial intelligence of things[J]. Internet of Things, 100673. https://doi.org/10.1016/j.iot.2022.100673

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Acknowledgements

The authors are very much thankful to all reviewers for their constructive criticisms and suggestions that helped to improve this paper

Funding

This work was supported by the National Key R&D Program of China (2018YFB1701303). Author Hanzhong Xu has received research support from Shanghai Electrical Apparatus Research Institute.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, 200240, China

    Hanzhong Xu, Dianliang Wu, Yu Zheng, Qihang Yu & Yue Zhao

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  1. Hanzhong Xu
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  2. Dianliang Wu
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  3. Yu Zheng
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  4. Qihang Yu
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Contributions

Hanzhong Xu: conceptualization, methodology, software, writing—original draft, writing—review and editing, resources, and data curation. Dianliang Wu: conceptualization and financial support. Yu Zheng: conceptualization and methodology. Qihang Yu: conceptualization, methodology, and supervision. Yue Zhao: software, methodology, and supervision.

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Correspondence to Dianliang Wu.

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Xu, H., Wu, D., Zheng, Y. et al. Augmented reality-based virtual-real fusion commissioning: a novel approach to production commissioning. Int J Adv Manuf Technol 131, 5527–5541 (2024). https://doi.org/10.1007/s00170-023-12067-9

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