NDLI: A Machine Learning-Based Framework for Automatic Visual Inspection of Microdrill Bits in PCB Production

Content Provider	IEEE Xplore Digital Library
Author	Guifang Duan Hongcui Wang Zhenyu Liu Yen-Wei Chen
Copyright Year	1998
Abstract	In this paper, an automatic visual inspection scheme with phase identification of microdrill bits in printed circuit board (PCB) production is proposed. Different from conventional methods in which the geometric quantities of microdrill bits are measured to compare with the prior standards, the proposed method adopts a strategy of machine learning. Thus, it lowers the requirement for the enlargement of lens and the resolution of charge-coupled device; therefore, the cost of inspecting instrument can be relatively reduced. Our method mainly includes two procedures: First, the statistical shape models of microdrill bit are built to get the shape subspace, and then the phase identification is performed in the shape subspace using some pattern recognition techniques. In this paper, we compared the performance of two statistical model methods, principal component analysis (PCA) and linear discriminate analysis, together with three classifiers, support vector machines (SVMs), neural networks, and k-nearest neighbors, respectively, for phase identification of microdrill bits. The experimental results demonstrate that using low enlargement and resolution microdrill bit images the proposed method can measure up to high inspection accuracy, and provide a conclusion that the highest identification rates are obtained by PCA-SVMs, which are higher than that of the conventional method.
Starting Page	1679
Ending Page	1689
Page Count	11
File Size	790110
File Format	PDF
ISSN	10946977
Volume Number	42
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Integrated circuit layout Visualization Principal component analysis Printed circuits support vector machines (SVMs) Automatic visual inspection microdrill bit phase identification statistical shape model
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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