纺织学报 ›› 2022, Vol. 43 ›› Issue (12): 173-180.doi: 10.13475/j.fzxb.20210805708

• 机械与器材 • 上一篇    下一篇

基于延展性的机器人面料缝制张力预测方法

宋洁心1,2, 付天宇1,2, 李凤鸣1,2, 宋锐1,2(), 李贻斌1,2   

  1. 1.山东大学 控制科学与工程学院, 山东 济南 250100
    2.山东大学 智能无人系统教育部工程研究中心, 山东 济南 250100
  • 收稿日期:2021-08-12 修回日期:2022-07-23 出版日期:2022-12-15 发布日期:2023-01-06
  • 通讯作者: 宋锐
  • 作者简介:宋洁心(1999—),女,硕士生。主要研究方向为机器人柔性作业、机器人缝制。
  • 基金资助:
    山东省重大科技创新工程项目(2019JZZY010430);国家自然科学基金面上项目(61973196);NSFC-深圳机器人基础研究中心项目(U2013204)

Prediction method for tension of fabric sewn by robot based on extensibility

SONG Jiexin1,2, FU Tianyu1,2, LI Fengming1,2, SONG Rui1,2(), LI Yibin1,2   

  1. 1. School of Control Science and Engineering, Shandong University, Ji'nan, Shandong 250100, China
    2. Engineering Research Center of Ministry of Education for Intelligent Unmanned Systems, Shandong University, Ji'nan, Shandong 250100, China
  • Received:2021-08-12 Revised:2022-07-23 Published:2022-12-15 Online:2023-01-06
  • Contact: SONG Rui

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摘要：

针对机器人缝制过程中未知面料期望张力导致面料变形的问题,根据面料的延展性分类训练支持向量机(SVM)模型,通过线性支持向量机模型预测未知面料的延展性;其次,采用模糊逻辑控制系统确定面料特性与期望张力之间的非线性关系;最后,利用毛呢、绸缎、天鹅绒布、摇粒绒布4种面料对SVM模型进行测试,通过模糊逻辑得到面料期望张力查询表,根据期望张力对毛呢和竹节棉麻进行缝制实验。结果表明:在拉伸面料的过程中,线性支持向量机模型预测的延展性最终趋于面料实际的延展性,基于模糊逻辑根据面料延展性和种类可实现对任意面料期望张力的预测,预测张力可满足智能化缝制加工的需要。该研究为避免面料形变从而提高缝制质量提供了前提条件。

关键词: 工业机器人, 面料缝制, 延展性, 期望张力, 模糊控制

Abstract:

In order to better understand the effect the sewing tension on fabric sewing flatness in the robotic sewing process, the support vector machine (SVM) model was trained according to the extensibility of the fabric, and the extensibility of a new fabric was predicted by the linear SVM model. A fuzzy logic control system was used to determine the nonlinear relationship between the cloth characteristics and the applied tension. The SVM model was tested with four fabrics, i.e., wool, silk, velvet, and flannel, among which silk was selected to output the expected tension of the fabric through fuzzy logic relations. The results show that in the process of fabric stretching, the extensibility predicted by the linear SVM model eventually tends to converge towards the actual extensibility of the fabric. Based on fuzzy logic, the expected tension of any fabric can be predicted according to the extensibility and fabric type. This research provides a prerequisite for avoiding fabric deformation and improving sewing quality.

Key words: industrial robot, fabric sewing, extensibility, expected tension, fuzzy control

中图分类号: 

  • TP242.2

图1

基于面料延展性的期望张力预测方法图"

表1

期望张力控制规则表"

面料种类T 面料延展性E 面料期望张力 F q
低 非常低 非常低
低 低 非常低
低 中等 低
低 高 中等
低 非常高 高
中等 非常低 低
中等 低 低
中等 中等 中等
中等 高 高
中等 非常高 非常高
高 非常低 中等
高 低 中等
高 中等 高
高 高 非常高
高 非常高 非常高

图2

隶属度函数曲线图"

图3

实验平台图"

图4

竹节棉麻面料的张力图"

图5

S-F曲线图"

图6

不同核函数的ROC曲线图"

图7

面料延展性的SVM预测图"

图8

绸缎的期望张力重心去模糊化图"

表2

面料期望张力查询表"

面料种类T 面料延展性E 面料期望张力 F q
0.2 0.2 0.199
0.2 0.4 0.293
0.2 0.6 0.466
0.2 0.8 0.642
0.4 0.2 0.256
0.4 0.4 0.358
0.4 0.6 0.534
0.4 0.8 0.707
0.6 0.2 0.371
0.6 0.4 0.466
0.6 0.6 0.642
0.6 0.8 0.774
0.8 0.2 0.429
0.8 0.4 0.534
0.8 0.6 0.707
0.8 0.8 0.801

图9

毛呢缝制张力图"

图10

毛呢缝制张力不同时的线迹图"

图11

竹节棉麻面料缝制张力图"

图12

竹节棉麻面料缝制张力不同时的线迹图"

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