Skip to main content

Advertisement

Log in

Adaptive planning of human–robot collaborative disassembly for end-of-life lithium-ion batteries based on digital twin

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Increasing numbers of lithium-ion batteries for new energy vehicles that have been retired pose a threat to the ecological environment, making their disassembly and recycling methods a research priority. Due to the variation in models and service procedures, numerous lithium-ion battery brands, models, and retirement states exist. This uncertainty contributes to the complexity of the disassembly procedure, which calls for a great deal of adaptability. Human–Robot Collaboration Disassembly (HRCD) mode maximizes the advantages of both humans and robots, progressively replacing single-person disassembly and single-machine disassembly to become the standard method for disassembling end-of-life lithium-ion batteries (LIBs). However, the HRCD process has more dimensions and uncertainties. In light of the obstacles above, this paper developed an HRCD environment with virtual and real interaction functions, which recommended real-time cooperation strategies in the dynamic production process and significantly enhanced the flexibility of disassembly operations. Based on the genetic algorithm (GA), the Disassembly Sequence Planning (DSP) is developed for waste LIBs in the source domain and imported into the knowledge base. Then, the rapid adaptive generation of HRCD task strategy for LIBs is generated, utilizing the transfer learning approach in the target domain. Two types of end-of-life automobile LIBs are analyzed as case study products. The results demonstrated that the proposed method could plan an effective action sequence, effectively reduce the design time of the target domain disassembly strategy, and enhance the flexibility of HRCD.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  • Bänziger, T., Kunz, A., & Wegener, K. (2020). Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions. Journal of Intelligent Manufacturing, 31(7), 1635–1648.

    Article  Google Scholar 

  • Bilberg, A., & Malik, A. A. (2019). Digital twin driven human–robot collaborative assembly. CIRP Annals, 68(1), 499–502.

    Article  Google Scholar 

  • Che, Z. H., Chiang, T. A., & Lin, T. T. (2021). A multi-objective genetic algorithm for assembly planning and supplier selection with capacity constraints. Applied Soft Computing, 101, 107030.

    Article  Google Scholar 

  • Chhim, P., Chinnam, R. B., & Sadawi, N. (2019). Product design and manufacturing process based ontology for manufacturing knowledge reuse. Journal of Intelligent Manufacturing, 30(2), 905–916.

    Article  Google Scholar 

  • Ge, W., & Yu, Y. (2017). Borrowing treasures from the wealthy: Deep transfer learning through selective joint fine-tuning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1086–1095).

  • Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. White Paper, 1(2014), 1–7.

    Google Scholar 

  • Guo, M. H., Cai, J. X., Liu, Z. N., Mu, T. J., Martin, R. R., & Hu, S. M. (2021). Pct: Point cloud transformer. Computational Visual Media, 7(2), 187–199.

    Article  Google Scholar 

  • Hu, H., Li, Z., Qin, S., & Ma, L. (2021b). Construction of feature tensor descriptor and self-similarity analysis for 3d point cloud models. Journal of Computer-Aided Design & Computer Graphics, 33(4), 590–600.

    Article  Google Scholar 

  • Hu, Y., Wang, Y., Hu, K., & Li, W. (2021a). Adaptive obstacle avoidance in path planning of collaborative robots for dynamic manufacturing. Journal of Intelligent Manufacturing, 1, 1–19.

    Google Scholar 

  • Huang, J., Pham, D. T., Li, R., Qu, M., Wang, Y., Kerin, M., ... & Zhou, Z. (2021). An experimental human-robot collaborative disassembly cell. Computers & Industrial Engineering, 155, 107189.

  • IEA, Global electric car stock, 2010–2021, IEA, Paris https://www.iea.org/data-and-statistics/charts/global-electric-car-stock-2010-2021

  • Ji, Y., Yang, Y., Shen, H. T., & Harada, T. (2021). View-invariant action recognition via Unsupervised AttentioN Transfer (UANT). Pattern Recognition, 113, 107807.

    Article  Google Scholar 

  • Keselman, L., Iselin Woodfill, J., Grunnet-Jepsen, A., & Bhowmik, A. (2017). Intel realsense stereoscopic depth cameras. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 1–10).

  • Lander, L., Cleaver, T., Rajaeifar, M. A., Nguyen-Tien, V., Elliott, R. J., Heidrich, O., ... & Offer, G. (2021). Financial viability of electric vehicle lithium-ion battery recycling. Iscience, 24(7), 102787.

  • Laradji, I. H., & Babanezhad, R. (2020). M-ADDA: Unsupervised domain adaptation with deep metric learning. In Domain adaptation for visual understanding (pp. 17–31). Springer, Cham.

  • Lee, M. L., Behdad, S., Liang, X., & Zheng, M. (2020, July). Disassembly sequence planning considering human-robot collaboration. In 2020 American Control Conference (ACC) (pp. 2438–2443). IEEE.

  • Lee, M. L., Behdad, S., Liang, X., & Zheng, M. (2022). Task allocation and planning for product disassembly with human–robot collaboration. Robotics and Computer-Integrated Manufacturing, 76, 102306.

    Article  Google Scholar 

  • Liu, S., Bao, J., & Zheng, P. (2023). A review of digital twin-driven machining: From digitization to intellectualization. In Journal of Manufacturing Systems, 67, 361–378.

    Article  Google Scholar 

  • Liu, S., Lu, Y., Zheng, P., Shen, H., & Bao, J. (2022a). Adaptive reconstruction of digital twins for machining systems: A transfer learning approach. Robotics and Computer-Integrated Manufacturing, 78, 102390.

    Article  Google Scholar 

  • Liu, S., Sun, Y., Zheng, P., Lu, Y., & Bao, J. (2022b). Establishing a reliable mechanism model of the digital twin machining system: An adaptive evaluation network approach. Journal of Manufacturing Systems, 62, 390–401.

    Article  Google Scholar 

  • Lv, Q., Zhang, R., Sun, X., Lu, Y., & Bao, J. (2021). A digital twin-driven human-robot collaborative assembly approach in the wake of COVID-19. Journal of Manufacturing Systems, 60, 837–851.

    Article  Google Scholar 

  • Odenthal, B., Mayer, M. P., Kabuß, W., Kausch, B., & Schlick, C. M. (2011, July). An empirical study of disassembling using an augmented vision system. In International Conference on Digital Human Modeling (pp. 399–408). Springer, Berlin.

  • Ordoñez, J., Gago, E. J., & Girard, A. (2016). Processes and technologies for the recycling and recovery of spent lithium-ion batteries. Renewable and Sustainable Energy Reviews, 60, 195–205.

    Article  Google Scholar 

  • Qian, J., Zhang, Z., Shi, L., & Song, D. (2021). An assembly timing planning method based on knowledge and mixed integer linear programming. Journal of Intelligent Manufacturing, 1, 1–25.

    Google Scholar 

  • Raatz, A., Blankemeyer, S., Recker, T., Pischke, D., & Nyhuis, P. (2020). Task scheduling method for HRC workplaces based on capabilities and execution time assumptions for robots. CIRP Annals, 69(1), 13–16.

    Article  Google Scholar 

  • Ranz, F., Hummel, V., & Sihn, W. (2017). Capability-based task allocation in human-robot collaboration. Procedia Manufacturing, 9, 182–189.

    Article  Google Scholar 

  • Rastegarpanah, A., Gonzalez, H. C., & Stolkin, R. (2021). Semi-autonomous behaviour tree-based framework for sorting electric vehicle batteries components. Robotics, 10(2), 82.

    Article  Google Scholar 

  • Raziei, Z., & Moghaddam, M. (2021). Adaptable automation with modular deep reinforcement learning and policy transfer. Engineering Applications of Artificial Intelligence, 103, 104296.

    Article  Google Scholar 

  • Ren, M., Zhang, Q., & Zhang, J. (2019). An introductory survey of probability density function control. Systems Science & Control Engineering, 7(1), 158–170.

    Article  Google Scholar 

  • Rodríguez, I., Nottensteiner, K., Leidner, D., Durner, M., Stulp, F., & Albu-Schäffer, A. (2020). Pattern recognition for knowledge transfer in robotic assembly sequence planning. IEEE Robotics and Automation Letters, 5(2), 3666–3673.

    Article  Google Scholar 

  • Singh, A., Yu, A., Yang, J., Zhang, J., Kumar, A., & Levine, S. (2020). Cog: Connecting new skills to past experience with offline reinforcement learning. arXiv preprint arXiv:2010.14500.

  • Sun, X., Zhang, R., Liu, S., Lv, Q., Bao, J., & Li, J. (2022). A digital twin-driven human–robot collaborative assembly-commissioning method for complex products. The International Journal of Advanced Manufacturing Technology, 118(9), 3389–3402.

    Article  Google Scholar 

  • Tao, F., Zhang, M., Liu, Y., & Nee, A. Y. (2018). Digital twin driven prognostics and health management for complex equipment. CIRP Annals, 67(1), 169–172.

    Article  Google Scholar 

  • Uglanov, A., Kartashev, K., Campean, F., Doikin, A., Abdullatif, A., Angiolini, E., ... & Zhang, Q. (2022). Driver Behaviour Modelling: Travel Prediction Using Probability Density Function. In UK Workshop on Computational Intelligence (pp. 545–556). Springer, Cham.

  • Vo, A. V., Truong-Hong, L., Laefer, D. F., & Bertolotto, M. (2015). Octree-based region growing for point cloud segmentation. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 88–100.

    Article  Google Scholar 

  • Vongbunyong, S., Kara, S., & Pagnucco, M. (2013). Basic behaviour control of the vision-based cognitive robotic disassembly automation. Assembly Automation., 33(1), 38–56.

    Article  Google Scholar 

  • Vongbunyong, S., Vongseela, P., & Sreerattana-aporn, J. (2017). A process demonstration platform for product disassembly skills transfer. Procedia CIRP, 61, 281–286.

    Article  Google Scholar 

  • Wu, Z., He, L., Wang, Y., Goh, M., & Ming, X. (2020). Knowledge recommendation for product development using integrated rough set-information entropy correction. Journal of Intelligent Manufacturing, 31(6), 1559–1578.

    Article  Google Scholar 

  • Xu, C., Wang, J., Zhang, J., & Li, X. (2021b). Anomaly detection of power consumption in yarn spinning using transfer learning. Computers & Industrial Engineering, 152, 107015.

    Article  Google Scholar 

  • Xu, W., Cui, J., Liu, B., Liu, J., Yao, B., & Zhou, Z. (2021a). Human-robot collaborative disassembly line balancing considering the safe strategy in remanufacturing. Journal of Cleaner Production, 324, 129158.

    Article  Google Scholar 

  • Xu, W., Tang, Q., Liu, J., Liu, Z., Zhou, Z., & Pham, D. T. (2020). Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing. Robotics and Computer-Integrated Manufacturing, 62, 101860.

    Article  Google Scholar 

  • Yao, B., Zhou, Z., Wang, L., Xu, W., Yan, J., & Liu, Q. (2018). A function block based cyber-physical production system for physical human–robot interaction. Journal of Manufacturing Systems, 48, 12–23.

    Article  Google Scholar 

  • Yu, T., Huang, J., & Chang, Q. (2020). Mastering the working sequence in human-robot collaborative assembly based on reinforcement learning. IEEE Access, 8, 163868–163877.

    Article  Google Scholar 

  • Yu, D., Huang, Z., Makuza, B., Guo, X., & Tian, Q. (2021). Pretreatment options for the recycling of spent lithium-ion batteries: A comprehensive review. Minerals Engineering, 173, 107218.

    Article  Google Scholar 

  • Zhang, R., Lv, Q., Li, J., Bao, J., Liu, T., & Liu, S. (2022). A reinforcement learning method for human-robot collaboration in assembly tasks. Robotics and Computer-Integrated Manufacturing, 73, 102227.

    Article  Google Scholar 

  • Zhu, L., & Chen, M. (2020). Research on Spent LiFePO4 Electric Vehicle Battery Disposal and Its Life Cycle Inventory Collection in China. International Journal of Environmental Research and Public Health, 17(23), 8828.

    Article  Google Scholar 

  • Zhu, W., Braun, B., Chiang, L. H., & Romagnoli, J. A. (2021). Investigation of transfer learning for image classification and impact on training sample size. Chemometrics and Intelligent Laboratory Systems, 211, 104269.

    Article  Google Scholar 

Download references

Funding

This work is financially supported by the Municipal Natural Science Foundation of Shanghai (21ZR1400800), in part by the Priming Scientific Research Foundation for the Junior Researchers of Donghua University and Graduate Student Innovation Fund of Donghua University (CUSF-DH-D-2022072).

Author information

Authors and Affiliations

Authors

Contributions

WQ: Conceptualization, Methodology, Data curation, Writing—original draft, Writing—review & editing. JL: Conceptualization, Methodology, Writing—original draft, Writing—review & editing, Funding acquisition. RZ: Conceptualization, Resources. SL: Visualization, Validation. JB: Project administration.

Corresponding author

Correspondence to Jie Li.

Ethics declarations

Conflict of interest

The authors of this paper have no relevant financial or non-financial interests to disclose.

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

Qu, W., Li, J., Zhang, R. et al. Adaptive planning of human–robot collaborative disassembly for end-of-life lithium-ion batteries based on digital twin. J Intell Manuf 35, 2021–2043 (2024). https://doi.org/10.1007/s10845-023-02081-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-023-02081-9

Keywords

Navigation