Industry 5.0-Oriented Human-Centric Robotic Manufacturing Systems

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India. Author
    • P. K. Iyengar Scientific Computing Researcher, BARC, India. Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2024PI3R8K

    Published 05-04-2024

  • Industry 5.0, Human-Centric Manufacturing, Collaborative Robots, Intelligent Automation, Artificial Intelligence, Digital Twin, Cyber-Physical Systems, IIoT, Smart Manufacturing, Adaptive Robotics

    Issue

    Section

    Articles

    How to Cite

    [1]
    N. Karmarkar and I. P. K, “Industry 5.0-Oriented Human-Centric Robotic Manufacturing Systems”, IJIARE, vol. 7, no. 1, pp. 01–09, May 2024, doi: 10.67228/30715725/IJIARE-2024PI3R8K.
  • Abstract

    Industry 5.0 represents a paradigm shift from fully automated production toward intelligent, human-centric manufacturing environments where collaborative robots, artificial intelligence (AI), edge computing, digital twins, Industrial Internet of Things (IIoT), and cyber-physical systems (CPS) operate in harmony with human workers. Unlike Industry 4.0, which primarily emphasized automation and productivity, Industry 5.0 focuses on resilience, sustainability, worker well-being, and personalized manufacturing. This study presents a comprehensive research framework for Industry 5.0-oriented human-centric robotic manufacturing systems by integrating collaborative robotics, AI-assisted decision-making, adaptive sensing, real-time monitoring, and intelligent manufacturing analytics. The proposed framework enables seamless interaction between human operators and robotic systems while ensuring operational safety, productivity, flexibility, and energy efficiency. A detailed literature review identifies current advancements, research gaps, and technological challenges associated with human-robot collaboration. The research methodology introduces an intelligent architecture incorporating multi-modal sensing, AI-based decision support, digital twin simulation, and adaptive robotic control. Comparative performance metrics demonstrate improvements in production efficiency, safety compliance, response time, and system adaptability. The findings indicate that Industry 5.0 technologies significantly enhance manufacturing performance while promoting sustainable and worker-centered industrial environments. The study concludes with future research directions involving explainable artificial intelligence, federated learning, autonomous collaborative robots, and resilient cyber-physical manufacturing ecosystems.

  • References

    [1] Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability, 11(16), 4371.

    [2] Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial AI: Applications with sustainable performance. Manufacturing Letters, 18, 16–20.

    [3] Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 15(4), 2405–2415.

    [4] Villani, V., Pini, F., Leali, F., & Secchi, C. (2018). Survey on human–robot collaboration in industrial settings. Mechatronics, 55, 248–266.

    [5] Wang, L., Törngren, M., & Onori, M. (2022). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 62, 101–118.

    [6] European Commission. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union.

    [7] Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inheritance and future research directions. Journal of Manufacturing Systems, 61, 530–535.

    [8] Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2022). Understanding the adoption of Industry 5.0 technologies. Sustainable Operations and Computers, 3, 83–95.

    [9] Longo, F., Padovano, A., & Umbrello, S. (2020). Value-oriented and ethical technology engineering in Industry 5.0. Applied Sciences, 10(12), 4182.

    [10] Peres, R. S., Barata, J., Leitao, P., & Garcia, G. (2020). Multidisciplinary approaches in smart manufacturing and collaborative robotics. Computers & Industrial Engineering, 139, 105859.

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