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Neural networks in surface roughness estimation

Wybrane pełne teksty z tego czasopisma
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Języki publikacji
EN
Abstrakty
EN
In-process inspection systems are necessary for the automatic control of surface quality in a sensor- based manufacturing environment. The machined surface digital image has been applied for the estimation of surface roughness. The measuring system has been built for machined surface digital image acquisition. This image has been analyzed and optimized 4-element feature versus has been prepared. The optimized neural network has been applied for machined surface parameters estimation. The estimation results proved that it is possible to apply the machined surface digital image for the surface roughness on-line monitoring.
Twórcy
  • Mechanical Department Technical University of Koszalin, Raclawicka 15-17, 75-620 Koszalin
Bibliografia
  • [1] S. Aksoy, RM. Haralick, Feature Normalisation and Likelihood-based Similarity Measures for Image Retrieval, Pattern Recognition Leiiers, Special Issue on Image and Video Retrieval, 26.04.2000 1-27
  • [2] G.L. Foresti, Invariant feature extraction and neural trees for range surface classification, IEEE Transactions on Systems Man and Cybernetics Part B - Cybernetics, 2002, Vol.32, 3, 356-366
  • [3] R.M. Haralick, Statistical and Structural Approaches to Texture, Proceedings of the IEEE, 67(5), 1979, 786-804 [4 l RM. Haralick, K Shanmugam, L Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man and Cybernetics, 3(6), 1973, 610-621
  • [5] S.Y. Ho, KC. Lee, S.S. Chen, S.J. Ho, Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system, International Journal of Machine Tools f3 ManuJacture, 2002, Vol. 42, 13, 1441-1446
  • [6] B.Y. Lee, H. Juan, S,F, Yu, A study of computer vision for measuring surface roughness in the turning process, International Journal oj Advanced ManuJacturing Technology, 2002, Vol. 19, 4, 295-301
  • [7] A. Majumar, C.L. Tien, Fractal Characterization and Simulation of Rough Surfaces, 1990, Wear, Vol. 136, 313-327
  • [8] B. Mandelbrot, Fractal Geometry oj Nature, W.H. Freeman, New York, 1982
  • [9] A. Przelaskowski, Efektywne metody kompresji obrazów medycznych, PhD thesis, Politechnika Warszawska, 1995
  • [10] W. Skarbek, Multimedia, Algorytmy i standardy kompresji, Akademicka Oficyna Wydawnicza PLJ, Warszawa 1998
  • [11] B. Storch, Podstawy obróbki skrawaniem, Technical University of Koszalin Academic Press, Koszalin, 2001.
  • [12] A.K Johan Suykens, P.L. Joos Vandewalle, L.R Bart De Moor, Artificial Neural Networks Jor Modeling and Control oj NonLinear Systems, Kluwer Academic Publishers, 1996
  • [13] C. Svarer, Neural Networks for Signal Processing, Ph.D. Thesis (91-0112-134), Technical University of Denmark, 1995
  • [14] R.F. Walker, P. Jackway, 1.D. Longstaff, Improving Co-occurrence Matrix Feature Discrimination, Proceedings oj DICTA '95, The 3rd Conference on Digital Image Computing, Brisbane, 1995, 643-648
  • [15] H. Wechsler, Texture Analysis - A Survey, Signal Processing 2, 1980
  • [16] D. Whitehouse, Handbook of Surface Metrology, Institute of Physics Publishing, Bristol and Philadelphia, 1994
  • [17] A. Zawada-Tomkiewicz, Wykorzystanie wskaźników cyfrowej reprezentacji obrazu powierzchni obrobionej do monitorowania zużycia ostrza w procesie toczenia, PhD thesis, Politechnika Koszalińska, Koszalin, 2002
  • [18] A. Zawada-Tomkiewicz, ~. Storch, Classifying the wear of turning tools wit h neural networks, Journal oj Materials Processing Technology 109, 2001, 300-304
  • [19] A. Zawada-Tomkiewicz, B. Storch, The application of image processing techniques in the to ol wear estimation, Computational M ethods in Contact M echanics VI, WIT Press, Crete, Greece, 2003, 201-210
  • [20] A. Zawada-Tomkiewicz, B. Storch, I. Wierucka, Machine vision - anovel quality in monitoring systems, in Manufacturing Flexibility Design and Development, Machine Engineering, 2003, Vol. 3 No. 1-2, 242-248
  • [21] J.G. Zhang, T.N. Tan, Brief review of invariant texture analysis methods, Pattern Recognition, 2002,Vol. 35, 3, 735-747
  • [22] H. Zheng, L.X. Kong, S. Nahavandi, Automatic inspection of metallic surface defects using genetic algorithms, Journal of Materials Processing Technology, 2002, Vol. 125, 427-433
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0003-0047
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