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Integrating Virtual Twin and Deep Neural Networks for Efficient and Energy-Aware Robotic Deburring in Industry 4.0

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Abstract

In the context of modern manufacturing, digitalization, real-time monitoring, and simulation are integral components that contribute to efficiency and energy awareness. This research paper aims to present a comprehensive framework of an intelligent robotic deburring system that incorporates elements from Industry 4.0 and virtual twin technology. The framework includes process planning, robot programming, and the creation of a virtual twin within a robotic deburring work cell. A key aspect of this framework is the utilization of deep neural networks for accurate burr identification, coupled with human-in-the-loop process monitoring. The integration of a virtual twin enables real-time process planning and enhances adaptability and flexibility to address dynamic changes during operation. A practical evaluation of the framework demonstrates its effectiveness, with the robotics deburring process achieving a significant 31% energy saving.

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Acknowledgements

The authors thank Visvesvaraya National Institute of Technology, Nagpur, India, for providing the necessary infrastructure and computational facility to conduct this research. However, no funding was received for conducting this study. Authors are also grateful to Mrs. Rohini Bhute, R and D developer at Dassault Systems Pune for allowing us to use algorithm developed by her during M.Tech. dissertation work at VNIT Nagpur (2019) given in Fig. 16 for this research work.

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  1. Department of Mechanical Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India

    M. R. Rahul & Shital S. Chiddarwar

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RMR (Research Scholar) collected the data, implemented the proposed methodology, analyzed the results and drafted the manuscript. Dr SSC (Supervisor) conceptualized and designed the methodology for the research work, helped interpret the results, and reviewed and edited the manuscript to its final form.

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Correspondence to Shital S. Chiddarwar.

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Rahul, M.R., Chiddarwar, S.S. Integrating Virtual Twin and Deep Neural Networks for Efficient and Energy-Aware Robotic Deburring in Industry 4.0. Int. J. Precis. Eng. Manuf. 24, 1517–1534 (2023). https://doi.org/10.1007/s12541-023-00875-8

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