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Online Monitoring-Based Prediction Model of Knitting Machine Productivity Cover

Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Open Access
|Oct 2023

Abstract

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.

DOI: https://doi.org/10.2478/ftee-2023-0035 | Journal eISSN: 2300-7354 | Journal ISSN: 1230-3666
Language: English
Page range: 46 - 52
Published on: Oct 18, 2023
Published by: Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2023 Sherien Elkateb, Ahmed Métwalli, Abdelrahman Shendy, Karim Moussa, Ahmed E. B. Abu-Elanien, published by Łukasiewicz Research Network, Institute of Biopolymers and Chemical Fibres
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.