Browse > Article

Inspection of Automotive Oil-Seals Using Artificial Neural Network and Vision System  

노병국 (한성대학교 기계시스템공학과)
김기대 (대구가톨릭대학교 기계자동차공학부)
Publication Information
Journal of the Korean Society for Precision Engineering / v.21, no.8, 2004 , pp. 83-88 More about this Journal
Abstract
The Classification of defected oil-seals using a vision system with the artificial neural network is presented. The artificial neural network fur classification consists of 27 input nodes, 10 hidden nodes, and one output node. The selection of the number of the input nodes is based on an observation that the difference among the defected, non-defected, and smeared oil-seals is greatly pronounced in the 26 step gray-scale level thresholding. The number of the hidden nodes is chosen as a result of a trade-off between accuracy and computing time. The back-propagation algorithm is used for teaching the network. The proposed network is capable of successfully classifying the defected from the smeared oil-seals which tend to be classified as the defected ones using the binary thresholding. It is envisaged that the proposed method improves the reliability and productivity of the automotive vision inspection system.
Keywords
Artificial neural network; Vision System; Oil-seal; Smear; Level distribution;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Moganti, M., Ercal, F., Dagli, C., Tsunekawa, S., 'Automatic PCB Inspection Algorithms: A Survey,' Computer Vision and Image Understanding, Vol. 63, No. 2, pp. 287-313, 1996   DOI   ScienceOn
2 Baykut, A., Atalay, A., Ercil, A., Guler, M., 'Real-time Inspection of Textured Surfaces,' Real-Time Imaging, Vol.6, pp.17-27, 2000   DOI   ScienceOn
3 Tridic, F., Sirok, B., Bullen, P.R., Philpott, D.R., 'Monitoring Mineral Wool Production Using Real-Time Machine Vision,' Real-Time Imaging, Vol. 5, pp. 125-140, 1999   DOI   ScienceOn
4 Han, S. H., Jang, G. J., Yoon, K. J., Cha, J. H., Roh, K. S., Kweon, I. S., 'Recent Developments in Machine Vision Research,' Journal of the Korean Society of Precision Engineering, Vol.18, No.3, pp.23-34, 2001
5 Zurada, Jacek M., 'lntroduction to Artificial Neural Network,' 1992 West Publishing Company
6 Bishop, Christopher M., 'Neural Networks for Pattern Recognition,' Clarendon Press, 1995
7 Rajeswari, M., Rodd, M.G., 'Real-Time Analysis of an IC Wire-bonding Inspection System,' Real-Time Imaging, Vol.5, pp.409-421, 1999   DOI   ScienceOn
8 Park, H. J., Hwang, Y. M., 'Dimensional Measurement Using the Machine Vision,' Journal of the Korean Society of Precision Engineering, Vol.18, No.3, pp. 10-17, 2001
9 Kwon, O. D., 'A study on the development of Cutting Tool Inspection System Using Computer Vision,' Ph.D.dissertation, KAIST, 1996
10 Lee, C. H., Cho, T. D., 'A Study on the End Mill Wear Detection by the Pattern Recognition Method in the Machine Vision,' Journal of the Korean Society of Precision Engineering, Vol.20, No.4, pp.223-229, 2003