Research and experimentation on constant warp tension control strategy in the weaving of corner-linked looms
Source abstract
Abstract
In the weaving process of the corner-linked loom, the stability of yarn tension has a significant effect on the quality of the fabric and production efficiency. Focusing on the beam installation structure of the let-off system, the special weaving process of 30-layer corner-linked fabric, and the characteristics of the let-off motion, in this paper we analyze the main factors affecting the warp yarn tension of the corner-linked loom and two control modes for constant warp yarn tension. We demonstrate the theoretical basis of the position mode, presenting parameter derivation calculations. We propose that the direct control method can be used to control the let-off amount per weft, and that the beam diameter estimation method can be used to ensure sufficient let-off amount. The principle of the integral separation proportional–integral–differential (PID) control algorithm and the tension control transfer function model are elaborated, with comparative simulation analyses. Finally, the software and hardware design of the actual warp yarn system and experimental test data analysis are presented. Both simulation and physical experiment results indicate that, in the continuous weaving process, frequent tension fluctuations can be detected by the tension pendulum rod and angle sensor. The combined PID control algorithm can significantly improve the control accuracy and stability of warp yarn tension. The experimental data curve shows that, under step disturbances, the fluctuation amplitude of the tension pendulum rod remains within ±1°, with no overshoot, fully meeting the special weaving requirements of the corner-linked loom.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Mechanical Behavior of Composites · Vibration and Dynamic Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Zhejiang Polytechnic University of Mechanical and Electrical Engineering, Hangzhou, China
Funding
Zhejiang Polytechnic University of Mechanical and Electrical Engineering
References (30)
Show source-provided references
1. Xia P, Liu W. H∞ network control of warp tension in carbon fiber angle loom. Sci Technol Eng 2022; 22: 8775–8779.
2. Chen X, Wu G, Lin H. Research on constant tension control of multi-wire cutter based on PID integral separation algorithm. Comb Mach Tool Autom Process Technol 2012; 1: 82–85.
3. Beck T, Lenz C, Hambach LD, et al. Adaptive yarn supply system with controllable tension. Melliand Int Text Rep 2018; 46: 11–13.
4. Huang X. Design of tension controller based on integral separation PID algorithm. Ind Control Comput 2011; 24: 35–39.
5. Li P. Application of integral separation PID control algorithm in PLC furnace temperature control system. Electron Des Eng 2013; 21: 53–57.
6. Shi X, Ji G, Qian J, et al. PID-based ROV motion control simulation. China Ocean Platf 2024; 39: 27–34.
7. Lu X, Yang J. Calculation and control algorithm of warp tension of multi-warp beam let-off mechanism of carbon fiber multi-layer corner loom. FRP/Compos 2017; 12: 15–19.
8. Han T, Zhou Q, Chen G, et al. Design of warp tension detection system for three-dimensional loom based on Lab VIEW. J Donghua Univ (Nat Sci Ed) 2018; 44: 763–767.
9. Chen J, Zhao B, Zhang YH. Warp tension detection and control technology of loom. Text Mach 2013; 2: 32–35.
10. Tong S, Liu S, Li Y. Fuzzy adaptive PID tracking control for airborne optoelectronic tracking platform. Electron Opt Control 2014; 21: 66–67.
11. Gong X, Xu S, Zhang L. Fuzzy adaptive compensation speed control system for permanent magnet synchronous motor. Comput Simul 2014; 31: 357–359.
12. Li X, Zhang Q. Adaptive active control of yarn dynamic tension on high speed warp knitting machine. Knitt Ind 2022; 5: 14–16.
13. Peng L, Luo C, Niu C, et al. Control technology for spandex yarn delivery in weft knitting. J Text Res 2021; 42: 163–168.
14. Dai N, Peng L, Hu X, et al. Control technology for active dynamical yarn feeding for circular weft knitting machine. J Text Res 2018; 39: 154–159.
15. Cao X, Guan W, Luo R. Brushless motor torque ripple suppression control system based on SVPWM and ADRC algorithm. Meas Control Technol 2021; 40: 152–157.
16. Zheng L. Research on control technology of high-precision dynamic yarn feeding stepping motor for circular weft knitting machine. Doctoral dissertation, Xi’an Polytechnic University, China, 2021, pp. 14–15.
17. Ozge C, Recep E. Experimental investigation of effect of balloon length on yarn tension during unwinding. Int J Clothing Sci Technol 2019; 31: 496–505.
18. Wang Y, Wu Y, Lv Z, et al. Research on fuzzy ADRC control strategy of constant tension yarn feeder. Foreign Electron Meas Technol 2021; 40: 53–57.
19. Chen C, Wang C, Lyu WT, et al. Research progress on yarn tension sensors. Adv Text Technol 2022; 30: 2–8.
20. Han J. From PID technology to ADRC technology. Control Eng China 2002; 9: 14–17.
21. Li Y, Peng L, Liu J, et al. Measurement of yarn tension in axial direction based on transverse vibration frequency. J Text Res 2023; 44: 73–78.
22. Zhang D, Gan X, Yang C, et al. Research progress of non-contact fiber tension detection technology in spinning process. J Text Res 2022; 43: 187–192.
23. Deng M, Gao J, Yang Y, et al. Design and data analysis of yarn tension test device. Light Ind Sci Technol 2022; 38: 19–22.
24. Chen E. Characteristics and compensation technology of the dynamic tension of warp knitted yarn. Manage Technol SME 2020; 5: 177–179.
25. Sun S, Miao X, Zhang Q, et al. Yarn tension fluctuation on high-speed warp knitting machine. J Text Res 2020; 41: 52–54.
26. 10.1016/j.ymssp.2005.02.00510.1016/j.ymssp.2005.02.005 ↗
27. Nurwaha D, Wang X. Prediction of rotor spun yarn strength using adaptive neuro-fuzzy inference system and linear multiple regression methods. J Donghua Univ 2023; 25: 49–53.
28. Mikolajczyk Z. Model of the feeding process of anisotropic warp knitted fabrics. Fibres Text East Eur 2023; 11: 59–63.
29. Xie Z. Research on the dynamic testing method of noncontact yarn winding tension. Doctoral dissertation, Donghua University, China, 2009, pp. 36–42.
30. Mwasiagi JI, Huang X, Wang X, et al. Performance of neural network algorithms during the prediction of yarn breaking elongation. Fibers Polym 2022; 9: 81–84.