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Design of intelligent manufacturing IoT sensing system for polymer process monitoring

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Abstract

In many factories, production lines are rapidly expanding, and methods of signal acquisition are gaining more and more attention. Enterprises hope to establish control centers to monitor production information in real time. Melt viscosity is a very important key monitoring metric in plastic extrusion process. At present, the traditional extrusion industry mostly relies on the experience of thermometers, pressure gauges, and technicians to control the quality of manufacturing. This is not a good quality control scheme for scientific manufacturing. Thus, this project improves the signal acquisition structure by combining LoRa and Wi-Fi with heterogeneous IoT. Utilize soft sensing technology to provide more advanced signal measurements as the basis for intelligent manufacturing, including melt temperature and melt viscosity. According to the experimental results, the introduction of AMNT (Adaptive Model of Network Topology) algorithm, AMTP (Adaptive Model of Transmission Path) algorithm, and BNC (Butterfly Networking Coding) algorithm in heterogeneous IoT can optimize the transmission performance. Soft sensors based on random forests and convolutional neural networks can finally output melt characteristic scatter plot (viscosity, shear rate, and temperature).

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Funding

This work was supported by the grants from the Teaching Practice Research Program, Grant No. PSK1122933 and the Southern Taiwan University of Science and Technology (STUST) of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Authors and Affiliations

  1. Department of Information Management, Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Zhi-Hao Wang, Yi-Ting Li & Yu-Chan Wu

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  1. Zhi-Hao Wang
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  2. Yi-Ting Li
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Contributions

Z-HW: conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, and writing—original draft; Y-TL: software, supervision, and validation; and Y-CW: visualization and writing—review and editing.

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Correspondence to Zhi-Hao Wang.

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Wang, ZH., Li, YT. & Wu, YC. Design of intelligent manufacturing IoT sensing system for polymer process monitoring. Int J Adv Manuf Technol 129, 2933–2947 (2023). https://doi.org/10.1007/s00170-023-12510-x

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