Research on the knitting needle detection system of a hosiery machine based on machine vision
Source abstract
Abstract
Problems with knitting needles are one of the main causes of production loss of fabric. In order to detect problems with knitting needles quickly and accurately, this paper proposes a hosiery knitting needle detection system based on machine vision. Meanwhile, according to the working condition of real hosiery knitting needles, a simulated needle cylinder rotary platform is built. The system can detect knitting needle problems, and the needle causing the issue can be identified as the beginning of the fabric defect appears. The production losses caused by the bending and fracture of knitting needles are reduced at the source. In the image processing, the vertical projection algorithm is introduced in the system, and the defect of image binarization is improved. At the same time, the multi-classification support vector machine based on the decision tree is used to realize the image classification. The experimental results show that the classification accuracy of the two levels classifiers in the system can reach 100% and 98%, and the best time of system detection is 0.348 s.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Textile materials and evaluations · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical and Electrical Engineering, Xi’an Polytechnic University, China
Xi’an key laboratory of modern intelligent textile equipment, School of Mechanical and Electrical Engineering, Xi'an Polytechnic University, China
Funding
National Natural Science Funds of China · Graduate Innovation funds of Xi'an Polytechnic University
References (19)
Show source-provided references
1. 10.1080/0040500760863055110.1080/00405007608630551 ↗
2. 10.1080/0040500760863055210.1080/00405007608630552 ↗
3. 10.1080/0040500760863055310.1080/00405007608630553 ↗
4. Letzgus W and Cheng Q. Memminger-IRO: latest weft knitting machine accessories. Int Text Rep 2008; 12: 31–32+34.
5. Liu X. Protechna: digital knitting needle detection and location apparatus. Int Text Rep 2012; 40: 63–63.10.1353/aph.2012.0042 ↗
6. Yan X and Chen D. Non-contact real-time needle deformation monitor system. Knit Ind 2016; 44: 1–3.
7. Huang D. Automatic detection device for lacked needles and broken needles of single jersey circular knitting machines. Knit Ind 2010; 38: 9–10.
8. Liu L, Liao X and Zhu Y. The research of knitting needle status monitoring setup. In: international symposium on photoelectronic detection and imaging 2013: laser sensing and imaging and applications (ed. F Amzajerdian, A Aksnes, W Chen et al.), Beijing, China, 25 June, 2013, Vol. no. 8905, pp. 89052B1-89052B-7. Beijing, China: SPIE.10.1117/12.2034928 ↗
9. Yan X, Chen D, Wang T, et al. An investigation of knitting needle hook's reflection of laser signal . Shanghai, China: Shanghai Textile Science & Technology, 2015.
10. Wu X, Huang Z, et al. Principle and structure design of magnetic levitation driving knitting needle. Knit Ind 2012; 40: 10–13.
11. 10.1007/s12206-019-0733-z10.1007/s12206-019-0733-z ↗
12. Elragal HM. Neuro-fuzzy fabric defect detection and classification for knitting machine. In: proceedings of the twenty third national radio science conference, 2006 (NRSC 2006) , Faculty of electrical engineering menoufia university, Egypt, 14-16 March, 2006, pp.1–8. Menoufia governorate egypt: IEEE. DOI: 10.1109/NRSC.2006.386359.10.1109/NRSC.2006.386359 ↗
13. Torun TK · Tekstil Ve Konfeksiyon · 2011 · 21 · 164
14. Hanbay K, Talu MF, Ozguven OF, et al. Fabric defect detection methods for circular knitting machines . In: signal processing and communications applications conference , malatya, turkey, 16–19 May, 2015, pp.735–738. malatya, turkey: IEEE. DOI: 10.1109/SIU.2015.7129932.10.1109/SIU.2015.7129932 ↗
15. Wang M, Feng JI, Bai R, et al. Machine vision detection method of fabric defects of warp knitting machine. Comput Eng Appl 2015; 51: 185–190.
16. 10.1186/s40064-016-2452-610.1186/s40064-016-2452-6 ↗
17. Jia L · J Franklin Inst · 2017 · 238 · 84
18. Zhang D, Gao G and Li C. Fabric defect detection algorithm based on Gabor filter and low-rank decomposition. In: eighth international conference on digital image process (ed CM Falco and X Jiang), Chengu, China, 29 August, 2016, 100330L. Chengu, China: International Society for Optics and Photonics. DOI: 10.1117/12.2244861.10.1117/12.2244861 ↗
19. Liu H. The research on vision-based inspectors for liquid filling line . Hunan University, 2007.