Smart Manufacturing Systems Using IoT and Automation

  • Authors

    • Aiko Yamamoto Product Manager, Panasonic, Japan Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2021PI2K6N

    Published 02-04-2021

  • Smart Manufacturing, Industry 4.0, Internet Of Things (Iot), Industrial Automation, Cyber-Physical Systems, Predictive Maintenance

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    Smart Manufacturing Systems Using IoT and Automation. (2021). International Journal of Modern Research in Science & Engineering, 4(1), 01-15. https://doi.org/10.67228/30716357/IJMRSE-2021PI2K6N

  • Abstract

    Smart Manufacturing Systems (SMS) is the paradigm shift in the contemporary industrial manufacturing that can unite Internet of Things (IoT) technologies, automation, cyber-physical systems, and data-driven intelligence to improve efficiency, flexibility, qualities, and sustainability. Conventional manufacturing systems tend to be inhibited by fixed production lines, reduced real-time visibility, and fixed manual decision making systems. With the advent of Industry 4.0, manufacturers can now use the interconnected and intelligent systems that can operate autonomously, preventive maintenance, adaptive control and optimize the use of the resources. The current paper is a detailed analysis of smart manufacturing systems that utilize the IoT and automation technology. It examines the architectural solutions, it is allowing technology, communication protocol, data analytics, and automation solutions that all constitute smart factories. Through an extensive literature review, recent developments, issues, and research gaps in the context of IoT based manufacturing setting are pointed at. The methodology suggests an integrated smart manufacturing model that will integrate sensor networks, edge and cloud computing, industrial automation, and machine intelligence. Performance evaluation measures are addressed in detail like production efficiency, downtime, reduction, system optimization in energy and scalability of the system. The findings indicate a high improvement in operational performance, predictive accuracy, and decision-making ability when compared to the traditional manufacturing system. The paper ends with a statement of future research directions, which are autonomous manufacturing with artificial intelligence, digital twins, and secure industrial IoT ecosystems.

  • References

    [1] Kagermann, H., Wahlster, W., & Helbig, J., “Recommendations for implementing the strategic initiative INDUSTRIE 4.0,” Acatech – National Academy of Science and Engineering, Germany, 2013.

    [2] Lu, Y., “Industry 4.0: A survey on technologies, applications and open research issues,” Journal of Industrial Information Integration, vol. 6, pp. 1–10, 2017.

    [3] Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T., “Intelligent manufacturing in the context of Industry 4.0: A review,” Engineering, vol. 3, no. 5, pp. 616–630, 2017.

    [4] Qin, J., Liu, Y., & Grosvenor, R., “A categorical framework of manufacturing for Industry 4.0 and beyond,” Procedia CIRP, vol. 52, pp. 173–178, 2016.

    [5] Xu, L. D., He, W., & Li, S., “Internet of Things in industries: A survey,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243, 2014.

    [6] Tao, F., Qi, Q., Liu, A., & Kusiak, A., “Data-driven smart manufacturing,” Journal of Manufacturing Systems, vol. 48, pp. 157–169, 2018.

    [7] Lee, J., Bagheri, B., & Kao, H. A., “A cyber-physical systems architecture for Industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 3, pp. 18–23, 2015.

    [8] Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M., “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013.

    [9] Wan, J., Cai, H., & Zhou, K., “Industrie 4.0: Enabling technologies,” Proceedings of the International Conference on Intelligent Computing and Internet of Things, pp. 135–140, 2015.

    [10] Monostori, L., “Cyber-physical production systems: Roots, expectations and R&D challenges,” Procedia CIRP, vol. 17, pp. 9–13, 2014.

    [11] Wang, S., Wan, J., Zhang, D., Li, D., & Zhang, C., “Towards smart factory for Industry 4.0: A self-organized multi-agent system with big data based feedback and coordination,” Computer Networks, vol. 101, pp. 158–168, 2016.

    [12] Lee, J., Ardakani, H. D., Yang, S., & Bagheri, B., “Industrial big data analytics and cyber-physical systems for future maintenance & service innovation,” Procedia CIRP, vol. 38, pp. 3–7, 2015.

    [13] Zhang, Y., Ren, S., Liu, Y., & Si, S., “A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products,” Journal of Cleaner Production, vol. 142, pp. 626–641, 2017.

    [14] Humayed, A., Lin, J., Li, F., & Luo, B., “Cyber-physical systems security—A survey,” IEEE Internet of Things Journal, vol. 4, no. 6, pp. 1802–1831, 2017.

    [15] Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L., “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016.

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