纺织学报 ›› 2024, Vol. 45 ›› Issue (04): 195-203.doi: 10.13475/j.fzxb.20230204901

• 服装工程 • 上一篇    下一篇

面向平缝工艺信息融合的知识图谱构建方法

郑小虎1,2,3(), 刘正好4, 刘冰5, 张洁1,2,3, 徐修亮6, 刘希7   

  1. 1.东华大学 人工智能研究院, 上海 201620
    2.纺织工业人工智能技术教育部工程研究中心, 上海 201620
    3.上海工业大数据与智能系统工程技术研究中心, 上海 201620
    4.东华大学 机械工程学院, 上海 201620
    5.杭州中服科创研究院有限公司, 浙江 杭州 311199
    6.上海富山精密机械科技有限公司, 上海 201599
    7.东华大学 信息科学与技术学院, 上海 201620
  • 收稿日期:2023-02-21 修回日期:2024-01-03 出版日期:2024-04-15 发布日期:2024-05-13
  • 作者简介:郑小虎(1983—),男,副教授,博士。主要研究方向为人工智能技术应用。E-mail:xhzheng@dhu.edu.cn。
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(2232021D-15);国家工信部项目(2021-0173-2-1);上海市科技计划项目(20DZ2251400)

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 Published:2024-04-15 Online:2024-05-13

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摘要：

针对缝纫工艺流程链路长,生产要素多样,工艺知识分散化的特点,首先,提出缝纫工艺信息组织建模方法,面向多源异构平缝工艺信息构建本体模型;其次,研究基于面料力学性能的平缝工艺推荐方法,构建面料基础性能、缝制性能知识体系,结合面料层次分类方法和实验参数分析,形成力学性能参数与缝制参数之间的数学模型;在此基础上,以专家经验文本、实验测量数据为原始数据,构建面向平缝工艺信息融合的知识图谱,开发WEB系统展开应用验证。结果表明:所构建的工艺推荐方法实现了对面料平整度和最大缝缩率的预测,知识融合系统实现了缝纫工艺知识的智能搜索和缝制参数的智能推荐,为工艺数据集成、装备故障运维、工艺路线设计、产品质量控制提供决策参考。

关键词: 服装, 缝纫, 知识图谱, 工艺知识管理, 平缝工艺, 知识推荐, 缝制参数

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

中图分类号: 

  • TS941

图1

缝纫工艺知识图谱构建及应用架构图"

图2

本体类型示例"

图3

缝纫工艺知识本体模型结构"

图4

平缝工艺推荐方法的整体思路"

表1

部分面料的基本规格参数"

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

图5

面料层次分类方法"

表2

预实验结果"

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

图6

面料基础性能知识体系本体模型"

图7

织物力学性能参数测试内容"

表3

各因子主要载荷变量及命名"

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

表4

缝制实验的部分参数"

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

图8

缝制参数推荐知识体系本体模型"

图9

缝纫工艺知识图谱的部分内容"

图10

缝制参数智能推荐界面"

图11

缝制参数智能推荐技术架构"

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