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Online Monitoring-Based Prediction Model of Knitting Machine Productivity

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Warianty tytułu
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.
Rocznik
Strony
46--52
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
  • 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
autor
  • School of Internet of Things, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China
  • Electrical (Power) Engineering Department, Faculty of Engineering, Alexandria University, Egypt
Bibliografia
  • 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.
  • 6. Kusters D, Praß N, GloyY. Textile learning factory 4.0 – preparing germany’s textile industry for the digital future. In Procedia Manufacturing,2017; 9: 214–221.
  • 7. Chen Z, Xing M. Upgrading of textile manufacturing based on industry 4.0. In Proceedings of the 5th International Conference on Advanced Design and Manufacturing Engineering. Atlantis Press,2015.
  • 8. Manglani H, HodgeG, Oxenham W. Application of the internet of things in the textile industry. Textile Progress,2019; 51(3): 225–297.
  • 9. MQTT. [Online]. Available: https://mqtt.org/ Accessed ( September 2022).
  • 10. Dionisio R, Malhao S, Torres P. Development of a smart gateway for a label loom machine using industrial iot technologies. In International Journal of Online and Biomedical Engineering (iJOE),2020;16(4): 6 .
  • 11. Definition of MQTT. https://www.opc-router.com/what-is-mqtt/ Accessed (September 2022).
  • 12. Zhi X, Yuexin S, Jin M, Lujie Z, Zijian D. Research on the Pearson correlation coefficient evaluation method of analog signal in the process of unit peak load regulation. In 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI),2017; 522.
  • 13. Frost J.Calculations of variance-inflation-factors (VIFs). https://statisticsbyjim.com/regression/variance-inflation-factors/Accessed ( September 2022).
  • 14. Allen M. Understanding Regression Analysis. Plenum Press,New York,1993; Ch3:177-180.
  • 15. Linear regression. https://www.ibm.com/dk-en/analytics/learn/linear-regressionAccessed ( November 2022).
  • 16. Kavitha S, Varuna S, Ramya R.A comparative analysis on linear regression and support vector regression. Online International Conference on Green Engineering and Technologies (IC-GET),2016; 1-5 . doi: 10.1109/GET.2016.7916627.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8f025bc9-2522-4b2e-ae64-f1301031a4e7
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