纺织学报 ›› 2022, Vol. 43 ›› Issue (04): 153-159.doi: 10.13475/j.fzxb.20210305407

• 机械与器材 • 上一篇    下一篇

涤纶工业长丝毛丝在线检测系统的研究

张荣根1, 冯培1,2(), 刘大双1, 张俊平1, 杨崇倡1,2   

  1. 1.东华大学 机械工程学院, 上海 201620
    2.东华大学 纺织装备教育部工程研究中心, 上海 201620
  • 收稿日期:2021-03-15 修回日期:2021-10-25 出版日期:2022-04-15 发布日期:2022-04-20
  • 通讯作者: 冯培
  • 作者简介:张荣根(1990—),男,博士生。主要研究方向为机器视觉智能检测。
  • 基金资助:
    中央高校基本科研业务费专项资金资助项目(2232019G-05);国家自然科学基金青年基金项目(52103355)

Research on on-line detection system of broken filaments in industrial polyester filament

ZHANG Ronggen1, FENG Pei1,2(), LIU Dashuang1, ZHANG Junping1, YANG Chongchang1,2   

  1. 1. College of Mechanical Engineering, Donghua University, Shanghai 201620, China
    2. Engineering Research Center of Advanced Textile Machinery, Donghua University, Shanghai 201620, China
  • Received:2021-03-15 Revised:2021-10-25 Published:2022-04-15 Online:2022-04-20
  • Contact: FENG Pei

RichHTML

17

PDF (PC)

132

摘要：

涤纶工业长丝毛丝在线检验工序一直制约着生产全流程智能制造的实现,为解决涤纶工业长丝毛丝检测问题,完善在线质量检测体系,提出一种借助机器视觉智能检测技术识别毛丝和数量统计的方法。以毛丝长度作为判断依据,基于LabVIEW图像处理技术,采用图像增强、二值化处理、数学形态学等方法获取毛丝图像,并提取长度信息。通过试验得到毛丝长度检测阈值,当图像中毛丝长度超过检测阈值就可判断毛丝的存在,同时累加毛丝数量。结果表明:该检测方案检测准确率达到90%以上,设计合理,成本低廉,对提高涤纶长丝品质和降低企业成本具有很大的实用价值。

关键词: 涤纶工业长丝检测, 毛丝检测, 图像处理, LabVIEW图像处理技术, 在线质量检测

Abstract:

The on-line detection process of polyester filaments hinders intelligent manufacturing in the whole production process. In order to solve the problem in inspecting polyester broken filament yarns and to improve the on-line quality detection system, a method of identifying broken filament yarns and counting the number by means of machine vision intelligent detection technology is proposed. Based on LabVIEW image processing technology,the yarn length is taken as the judgment basis. Image enhancement,binarization,digital morphology and other methods are used to obtain the filament image and to extract the length information. Through experiments, the detection threshold of filament length is obtained. When the filament length in the image exceeds the detection threshold,the existence of the filament will be recognized,and the number of such filaments can be accumulated. The experimental results show that the accuracy of the detection scheme is over 90%,the design is reasonable,the cost is low,and it has great practical value for improving polyester filament quality and reducing enterprise cost.

Key words: industrial polyester filament, filament detection, image processing, LabVIEW image processing technology, on-line quality detection

中图分类号: 

  • TP391.41

图1

不同光强和运动速度下拍摄的长丝照片"

图2

不同灰度级变换函数处理的长丝图像"

图3

直方图处理函数处理的长丝图像"

图4

空域滤波函数处理的长丝图像"

图5

频域增强函数处理的长丝图像"

图6

不同阈值的二值化函数处理的长丝图像"

图7

数学形态学函数处理的长丝图像"

图8

不同模板的开函数与适当开函数处理长丝图像的对比图"

图9

提取毛丝疵点的处理过程"

表1

1 mm长丝宽度像素点数"

长丝样本编号 X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
像素点数 27 28 35 30 37 24 29 28 29 34

表2

实验验证数据"

长丝毛丝样
本编号		 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10
毛丝像素点数 195 189 211 128 213 191 199 184 193 210
[1] 李晓东. 浅析涤纶工业长丝生产过程中毛丝形成的原因及解决措施[J]. 黑龙江纺织, 2007(2):12-15.
LI Xiaodong. Reasons for fuzz formation during superfine PET industry yarn production and the solution mea-sures[J]. Heilongjiang Textile, 2007(2): 12-15.
[2] 史新涛. 应用于PA66中细旦丝生产的Fraytec Fv毛丝检测系统[J]. 河南科技, 2013(8):43-68.
SHI Xintao. Fraytec Fv lousiness inspection system used in the production of PA66 medium and fine denier[J]. Henan Science & Technology, 2013(8): 43-68.
[3] 穆征, 张冶. CCD摄像传感器在纺织上的应用[J]. 上海纺织科技, 2005, 33(8):8-11.
MU Zheng, ZHANG Zhi. The application of CCD photography sensor in textile industry[J]. Shanghai Textile Science & Technology, 2005, 33(8): 8-11.
[4] 张凤生, 刘冲. 高精度激光衍射测量系统[J]. 仪器仪表学报, 2001, 22(3): 149-151.
ZHANG Fengsheng, LIU Chong. Super precision laser diffraction measuring system for fine diameter[J]. Chinese Journal of Scientific Instrument, 2001, 22(3): 149-151.
[5] CHEN Z K, WITTER G. Electrical contacts for automotive applications: a review[J]. IEICE Transactions on Electronics, 2004, 87 (8):1248-1254.
[6] 李若仲, 齐跃虎, 李兆展. 光电检测系统中弱信号的检测[J]. 空军工程大学学报(自然科学版), 2002(4): 33-35.
LI Ruozhong, QI Yuehu, LI Zhaozhan. Measure and test of weak signal in the photoelectric detecting system[J]. Journal of Air Force Engineering University(Natural Science Edition), 2002(4): 33-35.
[7] LIN Jengjong, LIN Chunghua, TSAI I Shou. Applying expert system and fuzzy logic to an intellect diagnosis system for fabric inspection[J]. Textile Research Journal, 1995(12): 697-709.
[8] TSAI I S. Automatic inspection of fabric defects using and artificial neural network technique[J]. Textile Research Journal, 1996(7): 474-482.
[9] TSAI I S. Applying an artifical neural network to pattern recognition in fabric defects[J]. Textile Research Journal, 1995(3): 123-130.
[10] 余恒炜, 谭雅岚, 侯群, 等. 图像处理技术在粘胶长丝毛丝自动检测系统中的应用[J]. 自动化技术与应用, 2017, 36(9): 90-92.
YU Hengwei, TAN Yalan, HOU Qun, et al. Application of image processing technology in automatic detection of broken filament of viscose filament[J]. Techniques of Automation & Applications, 2017, 36(9): 90-92.
[11] HUANG Shihchia, CHENG Fanchieh, CHIU Yisheng. Efficient contrast enhancement using adaptive gamma correction with weighting distribution[J]. IEEE Transactions on Image Processing, 2013, 22(3): 1032-1041.
doi: 10.1109/TIP.2012.2226047 pmid: 23144035
[12] 毛磊, 连文浩, 范振钦, 等. 数字图像处理算法概述[J]. 科技与创新, 2020(19): 29-30.
MAO Lei, LIAN Wenhao, FAN Zhenqin, et al. Overview of digital image processing[J]. Algorithms Science and Technology & Innovation, 2020(19): 29-30.
[13] GE Yunwang. Summarization of intelligentized and networked induction hardening control system[J]. Heat Treatment of Metals, 2007, 32(9): 96-100.
[14] HU Guanghua, WANG Qinghui. Defect detection via un-decimated wavelet decomposition and gumbel distribution model[J]. Journal of Engineered Fibers and Fabrics, 2018, 13(1): 15-32.
[15] STERNBERG S R. Grayscale morphology[J]. Computer Vision,Graphics,and Image Processing, 1986, 35(1):333-335.
doi: 10.1016/0734-189X(86)90004-6
[1] 熊晶晶, 杨雪, 苏静, 王鸿博. 基于图像技术的织物导湿性能测试方法[J]. 纺织学报, 2021, 42(12): 70-75.
[2] 吕文涛, 林琪琪, 钟佳莹, 王成群, 徐伟强. 面向织物疵点检测的图像处理技术研究进展[J]. 纺织学报, 2021, 42(11): 197-206.
[3] 夏旭文, 孟朔, 潘如如, 高卫东. 基于改进帧间差分法的经纱撞筘拥纱在线检测[J]. 纺织学报, 2021, 42(06): 91-96.
[4] 江燕婷, 严庆帅, 辛斌杰, 高琮, 施楣梧. 纺织品单向导水性能测试方法分析[J]. 纺织学报, 2021, 42(05): 51-58.
[5] 李东洁, 郭帅, 杨柳. 基于改进图像阈值分割算法的纱线疵点检测[J]. 纺织学报, 2021, 42(03): 82-88.
[6] 唐千惠, 王蕾, 高卫东. 基于图像处理的织物保形性检测[J]. 纺织学报, 2021, 42(03): 89-94.
[7] 孟朔, 夏旭文, 潘如如, 周建, 王蕾, 高卫东. 基于卷积神经网络的机织物密度均匀性检测[J]. 纺织学报, 2021, 42(02): 101-106.
[8] 张铮烨, 辛斌杰, 邓娜, 陈阳, 邢文宇. 基于边界跟踪测量麻纤维横截面参数的算法研究与应用[J]. 纺织学报, 2020, 41(02): 39-43.
[9] 吴义伦, 李忠健, 潘如如, 高卫东, 张宁. 应用色纺纱图像的纬编针织物外观模拟[J]. 纺织学报, 2019, 40(06): 111-116.
[10] 黄嘉俊, 柯薇, 王静, 邓中民. 基于计算机视觉的牛仔服装色差检测评级系统[J]. 纺织学报, 2019, 40(05): 163-169.
[11] 王文帝, 辛斌杰, 邓娜, 李佳平, 刘宁娟. 单一视角下自适应阈值法的纱线毛羽识别及其应用[J]. 纺织学报, 2019, 40(05): 150-156.
[12] 蔡逸超, 周晓, 宋明峰, 牟新刚. 应用多尺度多方向模板卷积的筒子纱缺陷检测[J]. 纺织学报, 2019, 40(04): 152-157.
[13] 巫莹柱　单颖法　黄伯熹　林广茂　梁家豪　张晓利. 聚对苯二甲酸丙二醇酯与聚对苯二甲酸丁二醇酯混纺纤维的智能识别[J]. 纺织学报, 2018, 39(09): 169-175.
[14] 陆奕辰 王蕾 唐千惠 潘如如 高卫东. 应用图像处理的纱线黑板毛羽量检测与评价[J]. 纺织学报, 2018, 39(08): 144-149.
[15] 王雯雯 高畅 刘基宏. 应用卷积神经网络的细纱断纱锭位识别[J]. 纺织学报, 2018, 39(06): 136-141.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号　 京公网安备11010502044800号
版权所有 © 2021 《纺织学报》编辑部
地址： 北京市朝阳区延静里中街3号主楼6层《纺织学报》杂志社　邮政编码：100025　
电话：010-65017711,65917740　传真：010-65016539　Email:fangzhixuebao@vip.126.com
本系统由北京玛格泰克科技发展有限公司设计开发