High-speed Visual Positioning and Correction System of Thin Silicon Wafer

Zhang Mei-jie; Zhang Ping

doi:10.12052/gdutxb.170077

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

High-speed Visual Positioning and Correction System of Thin Silicon Wafer

更新时间：2026-05-27

- High-speed Visual Positioning and Correction System of Thin Silicon Wafer
- Vol. 34, Issue 1, Pages: 9-15(2018)
- 作者机构：
  
  1. 广东工业大学　机电工程学院,广东,广州,510006
  2. 佛山广工大数控装备研究院,广东,佛山,528225
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.170077
  CLC： TP391
- Online First：09 January 2018，
  
  Published：2018
- 稿件说明：
移动端阅览
张美杰, 张平. 微薄硅晶片高速视觉定位及矫正系统[J]. 广东工业大学学报, 2018, 34(1): 9-15.

Zhang Mei-jie, Zhang Ping. High-speed Visual Positioning and Correction System of Thin Silicon Wafer[J]. 2018, 34(1): 9-15.
张美杰, 张平. 微薄硅晶片高速视觉定位及矫正系统[J]. 广东工业大学学报, 2018, 34(1): 9-15. DOI： 10.12052/gdutxb.170077.

Zhang Mei-jie, Zhang Ping. High-speed Visual Positioning and Correction System of Thin Silicon Wafer[J]. 2018, 34(1): 9-15. DOI： 10.12052/gdutxb.170077.

摘要

为解决当前机械定位存在的不足

提出了一种微薄硅晶片高速视觉定位及矫正系统. 视觉定位算法采用链码跟踪以及Principal Component Analysis(PCA)获得“主元”

在此基础上采用线段聚类的直线检测算法

拟合出电池片的边缘直线；然后计算出电池片的位置和角度偏差

通过机械手进行位置和角度矫正. 实验结果验证了该系统算法的实用性. 在实际生产中

该系统能够快速、精确地对电池片进行定位和矫正

大大增加了电池片串焊的效率和精度.

Abstract

In order to solve the disadvantages of the current mechanical positioning

a high speed vision positioning and correction system for a thin silicon wafer is proposed. The visual location algorithm gets the "master yuan" using the chain code tracking and Principal Component Analysis (PCA)

on the basis of the line detection algorithm of line segment clustering

the edge line of the cell is fitted

and then the cell position and angle deviation are calculated

and finally

position and angle are corrected by using manipulator. The experimental results verify the practicability of the algorithm. In the actual production

the system can quickly and accurately locate and correct the battery

greatly increasing the efficiency and accuracy of the battery string welding.

关键词

Keywords

references

王楠. 基于视觉的硅太阳能电池检测方法的研究[D]. 保定: 河北农业大学农业机械化工程学院, 2014.

QIAN X L, ZHANG H Q, CHEN Y X, et al . Research status and prospect of solar cell surface defect detection based on machine vision [J ] . Journal of Beijing University of Technology, 2017, 43 (1): 76-85.

ABRAHAM S, HENRY F K, SUDARSHAN S. 数据库系统概念[M]. 3版. 机械工业出版社, 2008.

GUO S Y, ZHOU L Q,WEN H, et al . Linear hough transform based on binary image neighborhood weighted [J ] . Journal of Electronic Measurement and Instrument, 2014, 28 (5): 478-485.

RAFAEL C G, RICHARD E W. 数字图像处理[M]. 2版. 北京: 电子工业出版社, 2007.

JU L. Solar cell slice position error detection based on machine vision [J]. Journal of Mechanical Manufacturing and Automation, 2012, 41 (5): 166-167.

ANWAR S A, ABDULLAH M Z. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique[J]. Eurasip Journal on Image Video Processing, 2014, 2014(1): 1-17.

LI C X, YUAN H B. Solar cells positioning and defect detection system based on the VisionPro[J]. Mechanical and Electrical Integration, 2014 (12): 50-54.

YUAN Q K, ZHANG Z Y, BI Q. Application of improved RANSAC algorithm in line fitting [J]. Combination Machine Tool and Automatic Machining Technology, 2015 (1): 123-125.

DAI J G, ZHANG L, LI J W, et al . A method of line extraction combining chain code tracking with phase verification [J ] . Journal of Surveying and Mapping, 2017, 46 (2): 218-227.

DONG J, YANG X, YU Q F. Fast line segment detection based on edge connecting[J]. Journal of Optics, 2013, 33(3): 1-8.

BASTARI A, BRUNI A, CRISTALLI C. Classification of silicon solar cells using Electroluminescence texture analysis [C]// IEEE International Symposium on Industrial Electronics. [S.l.]: IEEE Xplore, 2010: 1722-1727.

WU Z F. Positioning system for solar cells based on image sensor [J]. Special Equipment of Electronics Industry, 2014, 43(7): 9-12.

LIU W, JIN W B, XIAO X Q. Approach for line detection based on principal component analysis and clustering[J]. Journal of Computer Application, 2011, 31(5): 1202-1204.

OUYANG M, WANG R H, CHEN F T. Texture feature co-occurrence matrix LAB color distance extraction based on [J]. Journal of Guangdong University of Technology, 2011, 28 (4): 48-50.

DAI Z S, PAN Q, CHANG, G L, etc. Machine vision chip pin welding defect detection based on [J]. Journal of Guangdong University of Technology, 2016, 33 (3): 65-69.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research and Implementation of a Pulsed Laser Ranging Method Based on Digital Mixing

A Study of Power Quality Regulation Strategies of Energy Storage Converters

Ground Deformation Monitoring and Influencing Factors Analysis in Fuzhou City Based on PSI Technology

Robust Design for Artificial Noise-assisted UAV Terahertz Secure Communication

Research on Foreign Object Detection in Power Transmission Lines Based on the AAGV-YOLOX Model

Related Author

No data

Related Institution

No data

AI问答

⁰