Online Monitoring-Based Prediction Model of Knitting Machine Productivity-Reference-Cited by-同舟云学术

Online Monitoring-Based Prediction Model of Knitting Machine Productivity

Published:2023-10-01 Issue:4 Volume:31 Page:46-52
ISSN:2300-7354
Container-title:Fibres & Textiles in Eastern Europe
language:en
Short-container-title:

Author:

Elkateb Sherien¹,Métwalli Ahmed²,Shendy Abdelrahman³,Moussa Karim⁴,Abu-Elanien Ahmed E. B.⁵

Affiliation:

1. 1 Textile Engineering Department, Faculty of Engineering, Alexandria University , Egypt.

2. 2 Electrical Engineering Department, Arab Academy for Science, Technology and Maritime Transport , Alexandria , Egypt.

3. 3 Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport , Alexandria , Egypt.

4. 4 Electrical (Electronics and Communications) Engineering Department, Horus University , Damietta , Egypt.

5. 5 Electrical (Power) Engineering Department, Faculty of Engineering, Alexandria University , Egypt.

Abstract

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.

Publisher

Walter de Gruyter GmbH

Subject

Industrial and Manufacturing Engineering,General Environmental Science,Materials Science (miscellaneous),Business and International Management

Link

https://www.sciendo.com/pdf/10.2478/ftee-2023-0035

Reference16 articles.

1. Giri C, Jain S, Zeng X, Bruniaux P A.Detailed Review of Artificial Intelligence Applied in the Fashion and Apparel Industry. In IEEE Access, 2019; 7: 95376-95396. doi: 10.1109/ACCESS.2019.2928979.

2. Atik C, Kut A, Birant D, Birol S. Prediction of Cloth Waste Using Machine Learning Methods in the Textile Industry. In 9th International Conference on Electrical and Electronics Engineering (ICEEE), 2022;165-169. doi: 10.1109/ICEEE55327.2022.9772517.

3. Kavre M, Gadekar A, Gadhade Y.Internet of Things (IoT): A Survey. In IEEE Pune Section International Conference (PuneCon), 2019; 1-6. doi: 10.1109/PuneCon46936.2019.9105831.

4. Karmakar A, Dey N, Baral T, ChowdhuryM, RehanM Industrial Internet of Things: A Review. In International Conference on Opto-Electronics and Applied Optics (Optronix), 2019; 1-6. doi: 10.1109/OPTRONIX.2019.8862436.

5. Rath M, Gannouni A, Luetticke D, Gries T. Digitizing a Distributed Textile Production Process using Industrial Internet of Things: A Use-Case. In 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS),2021; 315-320. doi: 10.1109/ICPS49255.2021.9468203.

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