Logo

 
CSME 2018/04
Volume 39 No.2 : 145-152
 
A Novel Feature Detection Method U sing Multi Dimensional I mage Fusion for Automated Optical Inspection on Critical Dimension

Liang-Chia Chena, Ching Wen Liang b, Dinh-Cuong Hoangc, Duc-Hieu Duongb, Chin-Sheng Chenb and Shyh-Tsong Lind
aDepartment of Mechanical Engineering, National Taiwan University and Institute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan, 10617, R.O.C.
bInstitute of Automation Technology, National Taipei University of Technology, Taipei, Taiwan, 10608, R.O.C.
cDepartment of Mechanical Engineering, National Taiwan University, Taipei, Taiwan, 10617, R.O.C.
dDepartment of Electro-Optical Engineering, National Taipei University of Technology, Taipei, Taiwan, 10608, R.O.C.


Abstract: This paper presents a novel approach which is based on multi-dimension image fusion to effective extraction and segmentation of edge features for accurately measuring critical dimension on objects having complicated surface patterns or random reflectance. In the approach, coarse estimation of edge points is firstly performed by using the 3D edge detector to identify correct image regions of interest (ROI) for object segmentation. 2D image processing algorithms are performed on the ROI to segment the precise object edges for critical dimension (CD) measurement. To verify the effectiveness of the strategy, the developed method has been verified through measurement of aerospace composite parts for its edge detection and critical dimension accuracy. The measurement repeatability error of this critical dimension can be kept below 1.1% of the measured CD while the standard deviation can be kept less than 0.137 mm. Experimental results have demonstrated the feasibility and applicability of the developed method.

Keywords:  Automated optical inspection (AOI) object segmentation, image fusion , point clouds , critical dimension (CD)

Download PDF
*Corresponding author; e-mail: lchen@ntu.edu.tw
© 2018  CSME , ISSN 0257-9731 





TOP