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Shojaeinasab, A., Charter, T., Jalayer, M., Khadivi, M., Ogunfowora, O., & Raiyani, N., et al. (2022). Intelligent manufacturing execution systems: a systematic review. Journal of Manufacturing Systems.Search in Google Scholar
Chai, T. Y., Liu, Q., Ding, J. L., Shaowen, L. U., Song, Y. J., & Zhang, Y. J. (2022). Perspectives on industrial-internet-driven intelligent optimizedmanufacturing mode for process industries. SCIENTIA SINICA Technologica, 52(1), 14-25.Search in Google Scholar
Mitrea, D., & Tamas, L. (2018). Manufacturing execution system specific data analysis-use case with a cobot. IEEE Access, 6, 1-1.Search in Google Scholar
Liu, C., Tang, D., Zhu, H., & Nie, Q. (2021). A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system. IEEE Access, PP(99), 1-1.Search in Google Scholar
Abadi, C., Manssouri, I., & Abadi, A. (2021). An artificial - intelligent - based system to automate the design of complex mechanical products. International journal of engineering research in Africa(58-), 58.Search in Google Scholar
Wan, J., Tang, S., Hua, Q., Li, D., & Lloret, J. (2017). Context-aware cloud robotics for material handling in cognitive industrial internet of things. IEEE Internet of Things Journal, PP(99), 1-1.Search in Google Scholar
Jin, J., Yu, K., Zhang, N., & Pang, Z. (2022). Guest editorial: special section on real-time edge computing over new generation automation networks for industrial cyber-physical systems. IEEE transactions on industrial informatics.Search in Google Scholar
Ji, Z., Yanhong, Z., Wang, B., & Jiyuan, Z. (2019). Human–cyber–physical systems (hcpss) in the context of new-generation intelligent manufacturing. Engineering.Search in Google Scholar
Xu, J. (2021). Big nb-iot data: enhancing portability of handheld narrow-band internet of things performance on big data technology. Mobile Information Systems, 2021(5), 1-6.Search in Google Scholar
Jonathan, C., Riccardo, C., Alberto, F., & Riccardo, G. (2023). Combining human guidance and structured task execution during physical human–robot collaboration. Journal of Intelligent Manufacturing(7), 34.Search in Google Scholar
Wang, J., Gao, S., Tang, Z., Tan, D., Cao, B., & Fan, J. (2023). A context-aware recommendation system for improving manufacturing process modeling. Journal of Intelligent Manufacturing.Search in Google Scholar
Liu, Q., Liu, M., Zhou, H., Yan, F., Ma, Y., & Shen, W. (2022). Intelligent manufacturing system with human-cyber-physical fusion and collaboration for process fine control. Journal of Manufacturing Systems.Search in Google Scholar
Zhi-Hao, W., Yi-Ting, L., & Yu-Chan, W. (2023). Design of intelligent manufacturing iot sensing system for polymer process monitoring. The International Journal of Advanced Manufacturing Technology(7/8), 129.Search in Google Scholar
Zhu, Q., Huang, S., Wang, G., Moghaddam, S. K., Lu, Y., & Yan, Y. (2022). Dynamic reconfiguration optimization of intelligent manufacturing system with human-robot collaboration based on digital twin. Journal of Manufacturing Systems.Search in Google Scholar
Li, B., Chen, R. S., & Liu, C. Y. (2021). Using intelligent technology and real-time feedback algorithm to improve manufacturing process in lot semiconductor industry. Journal of supercomputing(5), 77.Search in Google Scholar
Yao, FengYao, YipingXing, LiningChen, HuangkeLin, ZhongweiLi, Tianlin. (2019). An intelligent scheduling algorithm for complex manufacturing system simulation with frequent synchronizations in a cloud environment. Memetic computing, 11(4).Search in Google Scholar