Defect Analysis Using Artificial Neural Network
Автор: S. Bhuvaneswari, J. Sabarathinam
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 5 vol.5, 2013 года.
Бесплатный доступ
This paper deals with detection of defects in the manufactured ceramic tiles to ensure high density quality. The problem is concerned with the automatic inspection of ceramic tiles using Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally on samples. Architecture of the system involves binary matrix processing and utilization of Artificial Neural Network (ANN) to detect defects. The above automatic inspection procedures have been implemented and tested on company floor tiles. The results obtained confirmed the efficiency of the methodology in defect detection in raw tile and its relevance as a promising approach on matrix, as well as included in quality control and inspection programs.
Ceramic Tiles, Defect Detection, Neural Network
Короткий адрес: https://sciup.org/15010419
IDR: 15010419
Список литературы Defect Analysis Using Artificial Neural Network
- Costa, C., and Petrou, M., 2000, “Automatic registration of ceramic tiles for the purpose of fault detection”, Machine Vision and Applications, 11:225-230.
- Sezzi, G. , 2006, “World Production and Consumption of Ceramic Tile”, Ceramic World Review, Vol. 14, No. 58, pp. 54-71.
- Riedmiller, M., 1993, “Untersuchungen zu Konvergen und Generalisierungs-verhalen überwachter Lernverfahren mit dem SNNS”, in A. Zell, editor, SNNS 1999 Workshop Proceedings, Stuttgart, September.
- Riedmiller, M., 1994, “Advanced supervised learning in multi-layer perceptrons-from Backpropagation to adaptive learning algorithms”, International Journal on Computer Standards and Interfaces, Vol. 16, pp. 265-278.
- Riedmiller, M., 1994. “Rprop-description and implementation details”, Technical Report, University of Kalsruhe, January.
- Riedmiller, M., Braun, H., 1993, “A direct adaptive method for faster backpropagation learning: The RPROP algorithm, in H. Ruspini, editor, Proceedings of the IEEE International Conference on Neural Networks (ICNN), San Francisco, USA, pp. 586-591.