使用目标多特征识别的纳米纤维制造在线监测系统

刘大利; 郭俊; 方淑慧; 井长娟; 刘媛媛; 胡庆夕

doi:10.3788/OPE.20122002.0360

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使用目标多特征识别的纳米纤维制造在线监测系统

微纳技术与精密机械 | 更新时间：2020-08-12

- 使用目标多特征识别的纳米纤维制造在线监测系统
- On-line monitor system for nanoscale fiber manufacturing based on multi-featured pattern recognition
- 光学精密工程 2012年20卷第2期页码：360-368
- 作者机构：
  
  1. 上海大学快速制造工程中心上海,200444
  2. 上海大学机械自动化及机器人上海市重点实验室上海,200444
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.51075253,51105239)()
- DOI：10.3788/OPE.20122002.0360
  中图分类号： TQ34;TP67
- 收稿日期：2011-09-27，
  
  修回日期：2011-11-09，
  
  网络出版日期：2012-02-25，
  
  纸质出版日期：2012-02-25
- 稿件说明：
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刘大利, 郭俊, 方淑慧, 井长娟, 刘媛媛, 胡庆夕. 使用目标多特征识别的纳米纤维制造在线监测系统[J]. 光学精密工程, 2012,20(2): 360-368

LIU Da-li, GUO Jun, FANG Shu-hui, JING Chang-juan, LIU Yuan-yuan, HU Qing-xi. On-line monitor system for nanoscale fiber manufacturing based on multi-featured pattern recognition[J]. Editorial Office of Optics and Precision Engineering, 2012,20(2): 360-368
刘大利, 郭俊, 方淑慧, 井长娟, 刘媛媛, 胡庆夕. 使用目标多特征识别的纳米纤维制造在线监测系统[J]. 光学精密工程, 2012,20(2): 360-368 DOI： 10.3788/OPE.20122002.0360.

LIU Da-li, GUO Jun, FANG Shu-hui, JING Chang-juan, LIU Yuan-yuan, HU Qing-xi. On-line monitor system for nanoscale fiber manufacturing based on multi-featured pattern recognition[J]. Editorial Office of Optics and Precision Engineering, 2012,20(2): 360-368 DOI： 10.3788/OPE.20122002.0360.

摘要

针对电纺丝成型纳米级纤维工艺中因供料速度与电压值不匹配而出现的不稳定现象-流涎

提出了基于目标多特征识别的纳米纤维制造在线检测系统来提高纳米纤维成型质量的稳定性并实现纤维直径的可控性。首先

使用工业CCD对喷头出口处的泰勒锥进行连续图像采集

并对其进行锐化、滤波、阈值分割等预处理;然后

用目标多特征识别算法对泰勒锥的周长、面积和锥高等特征进行识别

实现对泰勒锥的形态和大小实时判别;最后

以判别结果作为控制系统的反馈信号

实时在线调节供料模块或电源模块驱动供料速度与电压达到匹配。实验结果表明

当电压为8 kV

系统能在0.43 s后达到稳定

从而维持泰勒锥形状稳定

无流涎现象

得到直径均匀的纳米纤维。提出的方法有效地解决了电纺纳米纤维直径不均匀和不可控制的难题。

Abstract

To solve the problem of salivate

an unsteady phenomenon caused by the mismatch of feed velocity and high-voltage in the electrospinning to fabricate nanoscale fibers

this paper proposes an on-line monitor system based on the multi-featured pattern recognition for realizing both the stability of nanofiber manufacturing and the controllability of fiber diameter. Firstly

an industry CCD is used to collect the image of the Taylor cone continously

and then these images are preprocessed by sharpening

filtering and threshold segmentation. The perimeter

area and height of the Taylor cone are recognized by the multi-featured pattern recognition algorithm to judge the shape and size of the Taylor cone. Finally

the result of pattern recognition is viewed as the feedback signal to adjust the feed module and high-voltage module. Experimental results indicate that when the voltage is 8 kV

the system can be stabilized in 0.43 s

and it maintains a stable shape of Taylor cone without the salivate phenomenon.Moreover

obtained nanoscale fiber shows a uniform diameter. In conclusion

this method has reliably and effectively solved the instability and uncontrollability of nanoscale fiber manufarturing.

关键词

Keywords

references

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