Automatic Inspection of TFT-LCD Glass Substrates Using Optimized Support Vector Machines

The visual appearance of manufactured products is often one of the important quality attributes for certain types of products, which are mainly used for display purposes or used as the exterior part of other products. TFT-LCD (thin film transistor – liquid crystal display) glass substrates can be one of the representative cases. In such cases, visual quality (i.e., visual appearance) as well as the physical or mechanical quality attributes has to be controlled or maintained. This paper presents an industrial case study of a new machine vision methodology to manufacturing of TFT-LCD glass substrates. In this case study, we developed a classification model using support vector machine (SVM), optimized via the simulated annealing (SA) algorithm. We also used parallel genetic algorithm to reduce the number of features for classification. The results show that utilization of optimized SVM approach with SA in classification of TFT-LCD glass defects could be a viable alternative to manual classification in the TFT-LCD glass substrate industry.