Journal
JOURNAL OF PROCESS CONTROL
Volume 24, Issue 6, Pages 1015-1023Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2013.12.009
Keywords
Automatic optical inspection system; Supervised classification; Parameter optimization; Imbalanced data; Synthetic Minority Over-sampling; TEchnique (SMOTE); TFT-LCD glass substrate
Funding
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science, ICT and Future Planning [2013R1A1A1A05004852]
- National Research Foundation of Korea [2013R1A1A1A05004852] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
This study addresses classification methodology for the automatic inspection of a range of defects on the surface of glass substrates in thin film transistor liquid crystal display glass substrate manufacturing. The proposed methodology consisted of four stages: (1) feature extraction by calculating the wavelet co-occurrence signature from the substrate images, (2) handling of imbalanced dataset using the Synthetic Minority Over-sampling TEchnique (SMOTE), (3) reduction of the feature's dimension by principal component analysis, and (4) finally choosing the best classifier between three different methods: Classification And Regression Tree (CART), Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM). In training the SVM and MLP classifiers, the simulated annealing algorithm was used to obtain the optimal tuning parameters for the classifiers. From the industrial case study, the proposed feature extraction algorithm could remove the defect-irrelevant image features and SMOTE increased the accuracy of all three methods. Furthermore, the optimized SVM and MLP models were more accurate than the CART model whereas a higher accuracy of 89.5% was observed for the proposed SVM model. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available