Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Sherien Elkateb; Ahmed Métwalli; Abdelrahman Shendy; Karim Moussa; Ahmed E.B. Abu-Elanien

doi:10.2478/ftee-2023-0035

Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Sherien Elkateb^*, Ahmed Métwalli, Abdelrahman Shendy, Karim Moussa, Ahmed E.B. Abu-Elanien

^*Corresponding author for this work

School of Internet of Things

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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.

Original language	English
Pages (from-to)	46-52
Number of pages	7
Journal	Fibres and Textiles in Eastern Europe
Volume	31
Issue number	4
DOIs	https://doi.org/10.2478/ftee-2023-0035
Publication status	Published - 2023

Keywords

internet of things
knitting machine productivity
machine learning
multicollinearity
Online monitoring
prediction accuracy

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10.2478/ftee-2023-0035

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title = "Online Monitoring-Based Prediction Model of Knitting Machine Productivity",

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.",

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author = "Sherien Elkateb and Ahmed M{\'e}twalli and Abdelrahman Shendy and Karim Moussa and Abu-Elanien, {Ahmed E.B.}",

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AU - Elkateb, Sherien

AU - Métwalli, Ahmed

AU - Shendy, Abdelrahman

AU - Moussa, Karim

AU - Abu-Elanien, Ahmed E.B.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - internet of things

KW - knitting machine productivity

KW - machine learning

KW - multicollinearity

KW - Online monitoring

KW - prediction accuracy

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JF - Fibres and Textiles in Eastern Europe

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ER -

Online Monitoring-Based Prediction Model of Knitting Machine Productivity

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Keywords

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