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Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Warianty tytułu
Języki publikacji
Recently, Industry 4.0 introduced a breakthrough in the textile industry to meet customer demands. This study aimed to accurately estimate the production rate of a knitting machine through an online monitoring system using the Internet of Things (IoT) and machine learning (ML) concepts. Experimentally, a double knitting machine was attached with sensors for gathering data of the machine speed, yarn feeder speed and stitch length while other production variables remained constant. Two prediction models were introduced since correlation results revealed multicollinearity issues among the parameters measured. The second model achieved a prediction accuracy of 100 %. Thus, it presents a novel formula of production calculation.
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
  • Textile Engineering Department, Faculty of Engineering, Alexandria University, Egypt
  • Electrical Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
  • Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt
  • School of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
  • Electrical (Power) Engineering Department, Faculty of Engineering, Alexandria University, Egypt
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