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Automatic grading of TFT – LCD glass substrates using optimized support vector machines / Ali Yousefian Jazi in Industrial & engineering chemistry research, Vol. 51 N° 33 (Août 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 33 (Août 2012) . - pp. 10887-10894 Titre : Automatic grading of TFT – LCD glass substrates using optimized support vector machines Type de document : texte imprimé Auteurs : Ali Yousefian Jazi, Auteur ; Seongkyu Yoon, Auteur ; J. Jay Liu, Auteur Année de publication : 2012 Article en page(s) : pp. 10887-10894 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Glass Résumé : The visual appearance of manufactured products is often one of the major quality attributes for certain types of products, which are used mainly for display purposes or used as the exterior part of other products. TFT―LCD (thin film transistor―liquid crystal display) glass substrates can serve as a representative case. 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 machine vision methodology for the automatic grading of TFT―LCD glass substrates. In this case study, a classification model was developed using support vector machine (SVM), optimized via the simulated annealing (SA) algorithm. Parallel genetic algorithm (PGA) was also used to reduce the number of features for classification. The results show that utilization of an 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. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26286463 [article] Automatic grading of TFT – LCD glass substrates using optimized support vector machines [texte imprimé] / Ali Yousefian Jazi, Auteur ; Seongkyu Yoon, Auteur ; J. Jay Liu, Auteur . - 2012 . - pp. 10887-10894. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 33 (Août 2012) . - pp. 10887-10894 Mots-clés : Glass Résumé : The visual appearance of manufactured products is often one of the major quality attributes for certain types of products, which are used mainly for display purposes or used as the exterior part of other products. TFT―LCD (thin film transistor―liquid crystal display) glass substrates can serve as a representative case. 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 machine vision methodology for the automatic grading of TFT―LCD glass substrates. In this case study, a classification model was developed using support vector machine (SVM), optimized via the simulated annealing (SA) algorithm. Parallel genetic algorithm (PGA) was also used to reduce the number of features for classification. The results show that utilization of an 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. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26286463