A digital implantation system for Z-direction yarn of three-dimensional preform based on flexible oriented woven process. (November 2022)

• Record Type: Journal Article
• Authors: Guo, Zitong; Huang, Hao; Shan, Zhongde; Huang, Jihua; Hou, Zhuojian; Li, Wenfeng
• Is Part Of: Engineering applications of artificial intelligence. Volume 116(2022)
• Publication Date: 2022-11
• Subjects: Flexible oriented woven process -- Digital yarn implantation -- Modified YOLOv3 -- Modified Hough transform; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105385
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The implantation of Z-direction yarn is crucial in the process of forming flexible oriented 3D woven preforms, but manual implantation of Z-direction yarn is both time-consuming and inefficient. In this study, the digital Z-direction yarn implantation process and device are developed. Firstly, in view of many targets, small diameter, and serious interference in the identification process of the guide sleeve, a modified YOLOv3 algorithm is proposed to improve the accuracy and speed of detection, completing the coarse identification of guide sleeve. Then, an algorithm with modified Hough transform is proposed to improve the accuracy and speed of circle detection of coarse identification guide sleeve. Finally, based on the 1.2 mm diameter guide sleeve, a digital yarn replacement device was built to precisely replace the guide sleeve with replacement needles and implant the Z-direction yarn into the preform. The identification error of the guide sleeve is within 0.31% and the error between the reconstructed coordinates and the actual coordinates is within 3.08%. This study is crucial to the identification and positioning of small targets under complex working conditions and the development of digital forming of preform.