Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations


  • Yuanfang Wei Yantai Institute of Science and Technology
  • Li Song Yantai Institute of Science and Technology



Internet of Manufacturing Things, Human Machine Interfaces, Machinery, Industries


The access gateway layer in the IoT interior design bridging the gap between several destinations. The capabilities include message routing, message identification, and a service. IoT intelligence can help machinery industries optimize their operations with perspectives on factory processes, energy use, and help efficiency. Automation can bring in improved operations, lower destruction, and greater manufacture. IoT barriers are exactly developed for bridging the gap between field devices and focused revenues and industrial applications, maximizing intelligent system performance and receiving and processing real-time operational control data that the network edge. The creation of powerful, flexible, and adjustable Human Machine Interfaces (HMI) can enable associates with information and tailored solutions to increase productivity while remaining safe. An innovative strategy for data-enabled engineering advances based on the Internet of Manufacturing Things (IoMT) is essential for effectively utilizing physical mechanisms. The proposed method HMI-IoMT has been gap analysis to other business processes turns into a reporting process that can be utilized for improvement. Implementing a gap analysis in production or manufacturing can bring the existing level of manpower allocation closer to an ideal level due to balancing and integrating the resources. Societal growth and connection are both aided in the built environment. Manufacturing operations are made much more productive with the help of automation and advanced machinery. Increasing the output of products and services is possible as a result of this efficiency, which allows for the fulfillment of an expanding population's necessities.

Author Biographies

Yuanfang Wei, Yantai Institute of Science and Technology

School of Ocean Engineering

Li Song, Yantai Institute of Science and Technology

School of Ocean Engineering


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Author AA, Author BB. Title of book. Edition. Place of publication: Publisher; Year of publication. Chapter number, Chapter title; p. [page numbers of chapter].

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Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 62, 738-752.

Zhou, G., Zhang, C., Li, Z., Ding, K., & Wang, C. (2020). Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. International Journal of Production Research, 58(4), 1034-1051.

Ghahramani, M., Qiao, Y., Zhou, M. C., O'Hagan, A., & Sweeney, J. (2020). AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA Journal of Automatica Sinica, 7(4), 1026-1037.

Phuyal, S., Bista, D., & Bista, R. (2020). Challenges, opportunities and future directions of smart manufacturing: a state of art review. Sustainable Futures, 2, 100023.

Morgan, J., Halton, M., Qiao, Y., & Breslin, J. G. (2021). Industry 4.0 smart reconfigurable manufacturing machines. Journal of Manufacturing Systems, 59, 481-506.

Xia, K., Sacco, C., Kirkpatrick, M., Saidy, C., Nguyen, L., Kircaliali, A., & Harik, R. (2021). A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence. Journal of Manufacturing Systems, 58, 210-230.

Leng, J., Wang, D., Shen, W., Li, X., Liu, Q., & Chen, X. (2021). Digital twins-based smart manufacturing system design in Industry 4.0: A review. Journal of manufacturing systems, 60, 119-137.

Lu, Y., Xu, X., & Wang, L. (2020). Smart manufacturing process and system automation–a critical review of the standards and envisioned scenarios. Journal of Manufacturing Systems, 56, 312-325.

Abubakr, M., Abbas, A. T., Tomaz, I., Soliman, M. S., Luqman, M., & Hegab, H. (2020). Sustainable and smart manufacturing: an integrated approach. Sustainability, 12(6), 2280.

He, B., & Bai, K. J. (2021). Digital twin-based sustainable intelligent manufacturing: A review. Advances in Manufacturing, 9(1), 1-21.

Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., ... & Chen, X. (2021). Digital twin-based designing of the configuration, motion, control, and optimization model of a flow-type smart manufacturing system. Journal of Manufacturing Systems, 58, 52-64.

Essien, A., & Giannetti, C. (2020). A deep learning model for smart manufacturing using convolutional LSTM neural network autoencoders. IEEE Transactions on Industrial Informatics, 16(9), 6069-6078.

Lee, J., Azamfar, M., Singh, J., & Siahpour, S. (2020). Integration of digital twin and deep learning in cyber‐physical systems: towards smart manufacturing. IET Collaborative Intelligent Manufacturing, 2(1), 34-36.

Chen, G., Wang, P., Feng, B., Li, Y., & Liu, D. (2020). The framework design of smart factory in discrete manufacturing industry based on cyber-physical system. International Journal of Computer Integrated Manufacturing, 33(1), 79-101.

Mahajan, H. B., Badarla, A., & Junnarkar, A. A. (2021). CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7777-7791.

Gao, K., Huang, Y., Sadollah, A., & Wang, L. (2020). A review of energy-efficient scheduling in intelligent production systems. Complex & Intelligent Systems, 6, 237-249.

Cheng, J., Zhang, H., Tao, F., & Juang, C. F. (2020). DT-II: Digital twin enhanced Industrial Internet reference framework towards smart manufacturing. Robotics and Computer-Integrated Manufacturing, 62, 101881.

Shi, Z., Xie, Y., Xue, W., Chen, Y., Fu, L., & Xu, X. (2020). Smart factory in Industry 4.0. Systems Research and Behavioral Science, 37(4), 607-617.

Li, L., Mao, C., Sun, H., Yuan, Y., & Lei, B. (2020). Digital twin driven green performance evaluation methodology of intelligent manufacturing: hybrid model based on fuzzy rough-sets AHP, multistage weight synthesis, and PROMETHEE II. Complexity, 2020, 1-24.

Arinez, J. F., Chang, Q., Gao, R. X., Xu, C., & Zhang, J. (2020). Artificial intelligence in advanced manufacturing: Current status and future outlook. Journal of Manufacturing Science and Engineering, 142(11), 110804.

Javaid, M., Haleem, A., Singh, R. P., Rab, S., & Suman, R. (2021). Upgrading the manufacturing sector via applications of Industrial Internet of Things (IIoT). Sensors International, 2, 100129.

Wang, B., Tao, F., Fang, X., Liu, C., Liu, Y., & Freiheit, T. (2021). Smart manufacturing and intelligent manufacturing: A comparative review. Engineering, 7(6), 738-757.

Wan, J., Li, X., Dai, H. N., Kusiak, A., Martinez-Garcia, M., & Li, D. (2020). Artificial-intelligence-driven customized manufacturing factory: key technologies, applications, and challenges. Proceedings of the IEEE, 109(4), 377-398.

Ma, S., Zhang, Y., Liu, Y., Yang, H., Lv, J., & Ren, S. (2020). Data-driven sustainable intelligent manufacturing based on demand response for energy-intensive industries. Journal of Cleaner Production, 274, 123155.

Li, C., Chen, Y., & Shang, Y. (2022). A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology, an International Journal, 29, 101021.




How to Cite

Yuanfang Wei, Li Song. Intelligent manufacturing: bridging the gap between the Internet of Things and machinery to achieve optimized operations. EAI Endorsed Scal Inf Syst [Internet]. 2024 Apr. 10 [cited 2024 May 18];11(5). Available from: