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OPC UA Discovery-Driven Dynamic Reconfiguration of Robotic Manufacturing Systems: Method and Deployment

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Advances in Remanufacturing (IWAR 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

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Abstract

In recent years, the diversification and individuation of market demand are becoming more and more obvious, which puts forward higher requirements for the flexibility of the manufacturing system. For cyber-physical systems, flexibility refers to the ability to be scalable and reconfigurable. The traditional industrial reconfiguration method is inefficient and lacks practical deployment, it can no longer achieve the flexibility required in practical manufacturing processes. To address these problems, this paper proposes a reconfiguration solution based on an optimization algorithm and the OPC UA discovery mechanism. The optimization algorithm is used to fast generate the optimal combination of system resources for minimizing the makespan. OPC UA discovery enables the plug-and-produce functionality that can accelerate new device integration. The proposed solution can provide decision-making support for the rapid reconfiguration of the manufacturing system and also allow for the dynamic integration of newly inserted devices into the manufacturing system. Finally, the feasibility of the solution is verified by a demonstration of reconfiguration in robotic manufacturing systems.

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Acknowledgements

This research is supported by DITDP (Grant No. JCKY2021603B006), Hubei Provincial Key Research and Development Program of China (Grant No. 2023BAB012), Fundamental Research Funds for the Central Universities (WUT: 233109002), and the Young Top-notch Talent Cultivation Program of Hubei Province.

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Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Zhijian Yang, Xun Ye, Ruifang Li & Wenjun Xu

  2. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China

    Zhijian Yang, Xun Ye, Ruifang Li & Wenjun Xu

Authors
  1. Zhijian Yang
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  2. Xun Ye
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  3. Ruifang Li
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  4. Wenjun Xu
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Corresponding author

Correspondence to Xun Ye .

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Editors and Affiliations

  1. Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, Italy

    Marcello Fera

  2. Department of Industrial Engineering, University of Salerno, Fisciano, Italy

    Mario Caterino

  3. Department of Engineering, University of Campania Luigi Vanvitelli, Aversa, Italy

    Roberto Macchiaroli

  4. Department of Mechanical Engineering, The University of Birmingham, Birmingham, UK

    Duc Truong Pham

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Yang, Z., Ye, X., Li, R., Xu, W. (2024). OPC UA Discovery-Driven Dynamic Reconfiguration of Robotic Manufacturing Systems: Method and Deployment. In: Fera, M., Caterino, M., Macchiaroli, R., Pham, D.T. (eds) Advances in Remanufacturing. IWAR 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-52649-7_13

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  • DOI: https://doi.org/10.1007/978-3-031-52649-7_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-52648-0

  • Online ISBN: 978-3-031-52649-7

  • eBook Packages: EngineeringEngineering (R0)

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