Journal of Fiber Bioengineering & Informatics, 16 (2023), pp. 297-309.
Published online: 2024-09
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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.
}, issn = {2617-8699}, doi = {https://doi.org/10.3993/jfbim03031}, url = {http://global-sci.org/intro/article_detail/jfbi/23426.html} }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.