Journal of Textile Research ›› 2024, Vol. 45 ›› Issue (04): 195-203.doi: 10.13475/j.fzxb.20230204901

• Apparel Engineering • Previous Articles     Next Articles

Knowledge graph construction technology for provision of sewing process information

ZHENG Xiaohu1,2,3(), LIU Zhenghao4, LIU Bing5, ZHANG Jie1,2,3, XU Xiuliang6, LIU Xi7   

  1. 1. Institute of Artificial Intelligence, Donghua University, Shanghai 201620, China
    2. Engineering Research Center of Artificial Intelligence Technology in the Textile Industry, Ministry of Education, Shanghai 201620, China
    3. Shanghai Industrial Big Data and Intelligent Systems Engineering Technology Center, Shanghai 201620, China
    4. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    5. Hangzhou Zhongfu Technology & Innovation Research Institute Co., Ltd., Hangzhou, Zhejiang 311199, China
    6. HIKARI (Shanghai) Precise Machinery Scientific & Technology Co., Ltd., Shanghai 201599, China
    7. College of Information Science and Technology, Donghua University, Shanghai 201620, China
  • Received:2023-02-21 Revised:2024-01-03 Online:2024-04-15 Published:2024-05-13

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Abstract:

Objective The sewing process is characterized by long processing chains, diverse production elements and scattered processing information. Using knowledge graph technology for the management of design, operation and maintenance data generated during the sewing process, this research proposed a knowledge graph construction method for sewing process information management to achieve standardized knowledge representation.

Method Modelling methods for the organisation of sewing process information were investigated. The process information generated during the fabric sewing process was classified, and a sewing process knowledge ontology model was established based on the classification results to realise the construction of a knowledge graph. The process recommendation method was established based on the graph. Experiments were carried out on fabric structure, fabric mechanical parameters and fabric sewing process to establish a knowledge system and to analyse the mechanical properties of fabrics before and after sewing. Based on the analysis, a regression model of fabric mechanical properties and sewing flatness and a theoretical model of fabric sewing shrinkage were established. An ontology model of the sewing parameter knowledge system was created for sewing parameter recommendation based on knowledge graph.

Results According to the requirements of sewing process corpus and knowledge graph, a process recommendation method based on knowledge graph was established by combining the characteristics of industry knowledge structure and knowledge management requirements.The developed ontology and knowledge graph contains a total of 2 865 entities and 52 relations, with wide knowledge coverage and strong generalization, facilitating the standardized representation of unstructured knowledge. The relationship between mechanical parameters and sewing parameters were modelled for common fabrics in the flat sewing process, the flatness of the sewn fabric and the maximum sewing shrinkage were predicted and recommendations for sewing parameters, bonding parameters and processing instructions for the corresponding fabrics were achieved. The technical architecture for intelligent recommendation of sewing parameters was established. The knowledge system was interconnected with other sewing process knowledge and enabled integration of process information.

Conclusion The established knowledge graph is characterized by strong integration and interconnection of sewing process knowledge, which enables data integration and facilitates the maintenance and expansion of knowledge at a later stage. The research provides a useful supplementary case for process information management paths in the sewing industry, showing that knowledge graph technology has good application prospects in the sewing industry and has a certain reference value.

Key words: clothing, sewing, knowledge graph, process knowledge management, flat seam, knowledge recommendation, sewing parameter

CLC Number: 

  • TS941

Fig.1

Sewing process knowledge graph construction and application architecture diagram"

Fig.2

Example of ontology type"

Fig.3

Structure diagram of sewing process knowledge ontology model"

Fig.4

Ideas for recommended methods of sewing processes"

Tab.1

Basic specifications for individual fabrics"

面料
编号		 厚度/
mm		 面密度/
(g·m-2)		 经密/
(根·(10 cm)-1)		 纬密/
(根·(10 cm)-1)
100317513 0.223 3 252.058 376 370
100317559 0.170 0 210.533 365 360
100318006 0.130 0 166.092 472 326
100319037 0.203 3 240.542 390 376

Fig.5

Fabric level classification method"

Tab.2

Pre experimental results"

面料类别 平整度 缝缩率/%
层次编码 种类 经向 纬向 经向 纬向
221112 1 4.85 4.82 7.37 7.41
2 4.83 4.85 7.35 7.42
3 4.87 4.86 7.41 7.38
113321 1 4.96 4.93 6.82 6.85
2 4.93 4.92 6.85 6.82
3 4.94 4.91 6.84 6.81

Fig.6

Fabric basics ontology model"

Fig.7

Test content of fabric mechanical property parameters"

Tab.3

Basic specifications for individual fabrics"

因子 主要载荷变量 因子命名
因子1 经向剪切滞后力、纬向剪切滞后力、经向剪切刚度、纬向剪切刚度 Y1剪切因子
因子2 纬向弯曲刚度、经向滞后距、纬向滞后距、经向弯曲刚度 Y2弯曲因子
因子3 纬向拉伸回弹性、纬向拉伸能量、经向拉伸能量、经向拉伸回弹性 Y3拉伸能量因子
因子4 压缩比功、压缩回弹性、压缩线性度 Y4压缩因子
因子5 纬向表面粗糙度、纬向摩擦因数平均偏差 Y5表面因子1
因子6 纬向动摩擦平均因数、经向动摩擦平均因数 Y6表面因子2
因子7 经向表面粗糙度、经向摩擦因数平均偏差 Y7表面因子3
因子8 纬向拉伸线性度、经向拉伸线性度 Y8拉伸线性度因子

Tab.4

Sewing experimental parameters"

面料种类 针号 配用缝线
线密度/tex		 针距/
(针·(3 cm)-1)
2111\2141\2142 9 17.4 16
1112\1132\2111\2251 11 21.9 14
1212\1222\2131\2132\
2211\2212\2231		 14 41.7 12
2222\2232 16 41.7 11

Fig.8

Recommended knowledge system ontology model"

Fig.9

Part of sewing process knowledge graph"

Fig.10

Intelligent recommendation interface for sewing parameters"

Fig.11

Intelligent recommended technical architecture diagram of sewing parameters"

[1] 吴丹. 人工智能技术在服装制造中的应用[J]. 纺织报告, 2021, 40(10):35-36.
WU Dan. Application of artificial intelligence technology in clothing manufacturing[J]. Textile Reports, 2021, 40(10): 35-36.
[2] 丛洪莲, 赵博宇, 董智佳. 智能针织产品开发现状与应用前景[J]. 纺织导报, 2020(5):20-24.
CONG Honglian, ZHAO Boyu, DONG Zhijia. Development status and application prospect of intelligent knitting products[J]. China Textile Leader, 2020(5):20-24.
[3] 张永军, 程鑫, 李彦胜, 等. 利用知识图谱的国土资源数据管理与检索研究[J]. 武汉大学学报(信息科学版), 2022, 47(8):1165-1175.
ZHANG Yongjun, CHENG Xin, LI Yansheng, et al. Research on land and resources management and retrieval using knowledge graph[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1165-1175.
[4] 张云中, 祝蕊. 面向知识问答系统的图情学术领域知识图谱构建:多源数据整合视角[J]. 情报科学, 2021, 39(5): 115-123.
ZHANG Yunzhong, ZHU Rui. The Construction of knowledge graph in the LIS academic field of knowledge question answering system: a multi-source data integration perspective[J]. Information Science, 2021, 39(5): 115-123.
[5] AMERI F, BERNSTEIN W. A Thesaurus-guided framework for visualization of unstructured manufacturing capability data[C]// IFIP International Conference on Advances in Production Management Systems. Hamburg, Germany: Springer, 2017: 202-212.
[6] HU Yang, DING Yiwen, XU Feng, et al. Knowledge recommendation system for human-robot collaborative disassembly using knowledge graph[C]// International Manufacturing Science and Engineering Conference. Erie, American: American Society of Mechanical Engineers, Virtual, Online, 2021. DOI: 10.1115/MSEC2021-64642.
[7] 陶家琦, 李心雨, 郑湃, 等. 制造领域知识图谱的应用研究现状与前沿[J/OL]. 计算机集成制造系统:1-32[2022-11-09]. http://kns.cnki.net/kcms/detail/11.5946.tp.20220328.1707.013.html.
TAO Jiaqi, LI Xinyu, ZHENG Pai, et al. State-of-the-art and frontier of manufacturing knowledge graph application[J/OL]. Computer Integrated Manufacturing Systems: 1-32 [2022-11-09]. http://kns.cnki.net/kcms/detail/11.5946.tp.20220328.1707.013.html.
[8] 张栋豪, 刘振宇, 郏维强, 等. 知识图谱在智能制造领域的研究现状及其应用前景综述[J]. 机械工程学报, 2021, 57(5):90-113.
doi: 10.3901/JME.2021.05.090
ZHANG Donghao, LIU Zhenyu, JIA Weiqiang, et al. Review on knowledge graph and its application prospects to intelligent manufacturing[J]. Journal of Mechanical Engineering, 2021, 57(5):90-113.
doi: 10.3901/JME.2021.05.090
[9] KIM M, SUL I H, KIM S. Development of a sewing machine controller for seam pucker reduction using online measurement feedback system[J]. Journal of Engineered Fibers and Fabrics, 2017, 12(2): 67-72.
[10] PAN R, GAO W, LI W, et al. Image analysis for seam-puckering evaluation[J]. Textile Research Journal, 2017, 87(20): 2513-2523.
[11] FERNANDO S, JAYAWARDENA T. Measurement of seam puckering and influence of its causes[J]. Journal of Engineering, 2014(4): 1-7.
[12] SARKAR Joy, AL Faruque Md Abdullah, MONDAL Moni Sankar. Modeling the seam strength of denim garments by using fuzzy expert system[J]. Journal of Engineered Fibers and Fabrics, 2021. DOI:10.1177/1558925021988976.
[13] 顾朝晖, 雒少娜. 针织服装面料缝制工艺参数的确定[J]. 纺织学报, 2015, 36(9):94-99.
GU Zhaohui, LUO Shaona. Determination of sewing parameters for knitted fabric[J]. Journal of Textile Research, 2015, 36(9): 94-99.
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