journal article·2026-01

Optimized division algorithm applied to the pattern design of the segment filling insertion technique

Jiahui Fang, Yordan Kostadinov Kyosev, Wanqi Zhao, Yuling Li, Yanxue Ma

Textile Research Journal· Volume 96· Issue 1-2
Publisher page

Abstract

Abstract With advances in the automated segment filling insertion technique, there is a need for a jacquard CAD/CAM system that can be applied to the segment filling insertion technique. Automatically generating pattern designs in pattern information graphics requires overcoming the primary challenge of automating the rational and efficient division of pattern designs. This process is guided by the principles of the segment filling insertion technique, which involves a unique approach. Existing CAD systems only use simple division algorithms for different colors. These algorithms cannot handle the complex requirements of automatic weaving in the segment filling intersection technique. In addition, the divisions produced by these algorithms often result in numerous floating yarns and entanglement problems between wefts, which adversely affect fabric quality. An algorithmic approach to classifying optimal regions within pattern designs is presented in this research. A modified depth-first search algorithm was used to divide the pattern design into contiguous regions. A longest vertical growth algorithm was then introduced to further subdivide these contiguous regions, providing the theoretical basis for the automated generation of pattern information graphics containing critical parameters for controlling weaving machine operations. The need for the algorithm described in this article was been valuated through comparative experiments. Its effectiveness was validated through weaving patterns, and its feasibility and applicability were evaluated through several types of pattern designs. The proposed algorithm provides a theoretical base for future tasks and a novel concept for the subsequent development of CAD/CAM for segment filling insertion fabrics.
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Research context

OpenAlex topics: Image and Object Detection Techniques · Medical Image Segmentation Techniques · Advanced Numerical Analysis Techniques

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

Affiliations

Donghua University

Technische Universität Dresden, Germany

Funding

Chinese Government Scholarship

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