Factory Simulation Construction Method and Implementation of Intelligent Manufacturing

Bin Zheng; Yan Bai; Soo Siang Yang; Ming Keng Tan; Jing Song Zhang

doi:10.24191/jcrinn.v10i2.522

Factory Simulation Construction Method and Implementation of Intelligent Manufacturing

Authors

Bin Zheng Faculty of Engineering, University Malaysia Sabah, Malaysia.
Yan Bai Faculty of Mechanical Engineering, Beihua University, Jilin, 132013, China.
Soo Siang Yang Faculty of Engineering, University Malaysia Sabah, Kota Kinabalu, Sabah,88400, Malaysia.
Ming Keng Tan Faculty of Engineering, University Malaysia Sabah, Kota Kinabalu, Sabah,88400, Malaysia.
Jing Song Zhang Faculty of Engineering, University Malaysia Sabah, Kota Kinabalu, Sabah,88400, Malaysia.

DOI:

https://doi.org/10.24191/jcrinn.v10i2.522

Keywords:

Factory Simulation, Digital Twin, Intelligent Manufacturing, Simulation Modeling, Plant Simulation

Abstract

Construction methods and implementation of factory simulation of intelligent manufacturing discusses the key construction methods and realization ways of factory simulation in the field of intelligent manufacturing. As the core of the digital twin industry, it plays an important role in optimizing production and improving resource utilization. This paper expounds the construction method of factory simulation, including digital modeling, simulation parameter setting, advanced algorithm application and real-time data fusion. At the same time, it points out the challenges of model complexity and data accuracy in the simulation construction process and proposes corresponding solutions. This paper also takes Plant Simulation software as an example to analyze the implementation steps of factory simulation, such as model building, parameter configuration and process planning, emphasizing its value in optimizing factory layout, improving production process, and enhancing the scientific nature of decision-making. This paper comprehensively shows the key technical points and future development trend of factory simulation and provides theoretical support and practical guidance for the technological progress and industrial upgrading of intelligent manufacturing industry.

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References

Chander, B., Pal, S., De, D., & Buyya, R. (2022). Artificial intelligence-based internet of things for industry 5.0. In: Pal, S., De, D., Buyya, R. (Eds), Artificial intelligence-based internet of things systems (pp. 3-45). Springer. https://doi.org/10.1007/978-3-030-87059-1_1

Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: A review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96. https://doi.org/10.1631/FITEE.1601885

Li, L., Lei, B., & Mao, C. (2022). Digital twin in smart manufacturing. Journal of Industrial Information Integration, 26, 100289. https://doi.org/10.1016/j.jii.2021.100289

Liu, Y., Feng, J., Lu, J., & Zhou, S. (2024). A review of digital twin capabilities, technologies, and applications based on the maturity model. Advanced Engineering Informatics, 62, 102592. https://doi.org/10.1016/j.aei.2024.102592

Peng, M., Li, Y., Zhao, Z., & Wang, C. (2015). System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE network, 29(2), 6-14. https://doi.org/10.1109/MNET.2015.7064897

Qu, T., Lei, S. P., Wang, Z. Z., Nie, D. X., Chen, X., & Huang, G. Q. (2016). IoT-based real-time production logistics synchronization system under smart cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 84, 147-164. https://doi.org/10.1007/s00170-015-7220-1

Segovia, M., & Garcia-Alfaro, J. (2022). Design, modeling and implementation of digital twins. Sensors, 22(14), 5396. https://doi.org/10.3390/s22145396

Soori, M., Arezoo, B., & Dastres, R. (2023). Internet of things for smart factories in industry 4.0, a review. Internet of Things and Cyber-Physical Systems, 3, 192-204. https://doi.org/10.1016/j.iotcps.2023.04.006

Stallings, W. (2015). Foundations of modern networking: SDN, NFV, QoE, IoT, and Cloud. Addison-Wesley Professional.

Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of manufacturing systems, 48, 157-169. https://doi.org/10.1016/j.jmsy.2018.01.006

Wang, P., & Luo, M. (2021). A digital twin-based big data virtual and real fusion learning reference framework supported by industrial internet towards smart manufacturing. Journal of manufacturing systems, 58, 16-32. https://doi.org/10.1016/j.jmsy.2020.11.012

Wahab, H. A. Numerical study of brick using hybrid genetic algorithm. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://cdnx.uobabylon.edu.iq/undergrad_projs/D9MwvDloV0qWV1mXgRGyg.pdf

Wen, M., Li, Q., Kim, K. J., López-Pérez, D., Dobre, O. A., Poor, H. V., ... & Tsiftsis, T. A. (2021). Private 5G networks: Concepts, architectures, and research landscape. IEEE Journal of Selected Topics in Signal Processing, 16(1), 7-25. https://doi.org/10.1109/JSTSP.2021.3137669

Wu, Y., Dai, H. N., Wang, H., Xiong, Z., & Guo, S. (2022). A survey of intelligent network slicing management for industrial IoT: Integrated approaches for smart transportation, smart energy, and smart factory. IEEE Communications Surveys & Tutorials, 24(2), 1175-1211. https://doi.org/10.1109/COMST.2022.3158270