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Real-Time Monitoring of Knitting Machine Performance Using IoT and Machine Learning: Innovations and Applications |
Textile Engineering Dept., Faculty of Engineering, Alexandria University |
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Abstract Textile technologies are revolutionising with Industry 4.0. This research aims to introduce a novel real-time monitoring system in the knitting sector using the Internet of Things and machine learning technologies to measure and display productivity precisely through an interactive dashboard. Sensors were integrated into a circular knitting machine to track productivity and performance. A comparative statistical analysis through three processing phases demonstrates the high accuracy and precision of the current system, as evidenced by minimum variance and error values. The t-test results validate a non-significant difference between actual and device-measured production. Thus, it enables real-time monitoring, preventive maintenance, and cost-effective quality in knitting machines.
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Fund:Project supported by the Science, Technology & Innovation Funding Authority (STDF) under grant number 43467. |
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Cite this article: |
Sherien Elkateb,Ahmed Metwalli,Abdelrahman Shendy. Real-Time Monitoring of Knitting Machine Performance Using IoT and Machine Learning: Innovations and Applications[J]. Journal of Fiber Bioengineering and Informatics, 2023, 16(4): 297-309.
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