journal article·2021-08

Automatic weaving method for three-dimensional composite preforms using vision system

Siyuan Li, Zhongde Shan, Dong Du, Li Zhan, Zhikun Li, Yunzhi Liu

Textile Research Journal· Volume 91· Issue 15-16
Publisher page

Abstract

Three-dimensional composite preform is the main structure of fiber-reinforced composites. During the weaving process of large-sized three-dimensional composite preform, relative rotation or translation between the fiber feeder and guided array occurs before feeding. Besides, the weaving needles can be at different heights after moving out from the guided array. These problems are mostly detected and adjusted manually. To make the weaving process more precise and efficient, we propose machine vision-based methods which could realize accurate estimation and adjustment of the relative position-pose between the fiber feeder and guided array, and make the needles pressing process automatic by recognizing the position of the weaving needles. The results show that the estimation error of relative position-pose is within 5%, and the rate of unrecognized weaving needles is 2%. Our proposed methods improve the automation level of weaving, and are conducive to the development of preform forming toward digital manufacturing.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Industrial Vision Systems and Defect Detection · Optical measurement and interference techniques · Advanced Measurement and Metrology Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical Engineering, Tsinghua University, Beijing, China

Chinese Academy of Machinery Science and Technology, Beijing, China

References (37)

Show source-provided references
1. 10.1016/j.compositesb.2011.01.01010.1016/j.compositesb.2011.01.010 ↗
2. 10.1016/j.ijsolstr.2012.10.01510.1016/j.ijsolstr.2012.10.015 ↗
3. 10.1016/j.compositesa.2017.03.01710.1016/j.compositesa.2017.03.017 ↗
4. 10.1016/S1359-835X(99)00034-210.1016/S1359-835X(99)00034-2 ↗
5. 10.1177/152808371244429810.1177/1528083712444298 ↗
6. 10.1016/j.compositesb.2018.05.00710.1016/j.compositesb.2018.05.007 ↗
7. 10.1177/004051751143501310.1177/0040517511435013 ↗
8. 10.1080/0040500990869062410.1080/00405009908690624 ↗
9. 10.1177/152808371770200910.1177/1528083717702009 ↗
10. 10.1177/002199831559026710.1177/0021998315590267 ↗
11. 10.1016/j.compstruct.2016.01.05010.1016/j.compstruct.2016.01.050 ↗
12. 10.1177/004051751880379210.1177/0040517518803792 ↗
13. 10.1016/S0266-3538(99)00115-310.1016/S0266-3538(99)00115-3 ↗
14. 10.1016/j.compositesb.2017.07.02210.1016/j.compositesb.2017.07.022 ↗
15. 10.1016/j.compositesa.2018.01.00610.1016/j.compositesa.2018.01.006 ↗
16. 10.1016/j.compositesa.2015.10.01110.1016/j.compositesa.2015.10.011 ↗
17. 10.1111/str.1220610.1111/str.12206 ↗
18. 10.1016/j.cirp.2008.03.12810.1016/j.cirp.2008.03.128 ↗
19. 10.1016/j.cirp.2011.03.05610.1016/j.cirp.2011.03.056 ↗
20. Branscomb DJ, Beale DG, Broughton RM. Application of machine vision techniques for fault diagnostics and system examination of braid. In: Proceedings of the ASME Design Engineering Technical Conference. 2010, pp. 383–389.10.1115/DETC2010-29012 ↗
21. 10.1109/TIM.2010.206985010.1109/TIM.2010.2069850 ↗
22. 10.1177/00405175020720060410.1177/004051750207200604 ↗
23. 10.1177/004051751245834010.1177/0040517512458340 ↗
24. 10.1109/LSP.2018.282530910.1109/LSP.2018.2825309 ↗
25. 10.1016/j.compstruct.2018.07.02210.1016/j.compstruct.2018.07.022 ↗
26. 10.1016/j.compositesb.2018.12.06410.1016/j.compositesb.2018.12.064 ↗
27. 10.1016/j.compscitech.2019.04.03110.1016/j.compscitech.2019.04.031 ↗
28. 10.1016/j.compscitech.2011.04.01110.1016/j.compscitech.2011.04.011 ↗
29. 10.1016/j.patcog.2012.09.02010.1016/j.patcog.2012.09.020 ↗
30. 10.1145/358669.35869210.1145/358669.358692 ↗
31. Mengmeng C, Zhiquan F, Min L. Dynamic gesture recognition based on the trend of key points. In: Proceedings-2nd International Conference on Multimedia and Image Processing, ICMIP 2017 . 2017, pp. 98–102.10.1109/ICMIP.2017.7 ↗
32. 10.1109/34.88871810.1109/34.888718 ↗
33. 10.1109/TIP.2009.201736310.1109/TIP.2009.2017363 ↗
34. 10.1109/TRA.2003.81023910.1109/TRA.2003.810239 ↗
35. 10.1109/TPAMI.1981.476716610.1109/TPAMI.1981.4767166 ↗
36. 10.1109/TPAMI.1987.476794110.1109/TPAMI.1987.4767941 ↗
37. 10.1364/AO.37.00541610.1364/AO.37.005416 ↗