Online Monitoring-Based Prediction Model of Knitting Machine Productivity

• Autorzy: Elkateb Sherien , Métwalli Ahmed , Shendy Abdelrahman , Moussa Karim , Abu-Elanien Ahmed E. B.

Identyfikatory

DOI

10.2478/ftee-2023-0035

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

online monitoring; internet of things; machine learning; multicollinearity; knitting machine productivity; prediction accuracy

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2023
• Tom: Vol. 31, no. 4
• Strony: 46--52
• Opis fizyczny: Bibliogr. 16 poz., rys., tab.

Twórcy

autor

Elkateb Sherien

• Textile Engineering Department, Faculty of Engineering, Alexandria University, Egypt

autor

Métwalli Ahmed

• Electrical Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

autor

Shendy Abdelrahman

• Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt

autor

Moussa Karim

• School of Internet of Things, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China

autor

Abu-Elanien Ahmed E. B.

• Electrical (Power) Engineering Department, Faculty of Engineering, Alexandria University, Egypt

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