Abstract
In this paper, a neural network-based algorithm is proposed to explore the order of the measured data points in surface fitting. In computer-aided design, the ordered points serves as the input to fit smooth surfaces so that a reverse engineering (i.e. RE) system can be established for 3D sculptured surface design. The geometry feature recognition capability of back-propagation neural networks is explored in this paper. Scan or measuring number and 3D coordinates are used as the inputs of the proposed neural networks to determine the curve to which each data point belongs and the order number of data point in the same curve. In the segmentation process, the neural network output is segment number; while the segment number and sequence number in the same curve are the outputs when sequencing the points in the same curve. After evaluating a large number of trials with various neural network architectures, two optimal models are selected for segmentation and sequence. The proposed model can easily adapt for new data from another sequence for surface fitting. In comparison to Lin et al.’s (1998) method, the proposed algorithm neither needs to calculate the angle formed by each point and its two previous ones nor causes any chaotic phenomenon.
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References
Yau, H.T., Haque, S., Menq, C.H.: Reverse engineering in the design of engine intake and exhaust ports. In: Proceedings of the Symposium on Computer-Controlled Machines for Manufacturing, SAME Winter Annual Meeting, New Orleans, LA, USA (1993)
Yau, H.T., Menq, C.H.: Computer-aided coordinate metrology. In: 13th Annual ASME International Computers in Engineering Conference and Exposition, San Diego, CA, USA (August 1993)
Sarkar, B., Menq, C.H.: Smooth-surface approximation and reverse engineering. Computer-Aided Design 23(9), 623–628 (1991)
Chivate, T.N., Jablokow, A.G.: Solid-model generation from measured point data. Computer-Aided Design 25(9), 587–600 (1993)
Milroy, M.J., Bradley, C., Vickers, G.W., Weir, D.J.: G1 continuity of B-spline surface patches in reverse engineering. Computer-Aided Design 27(6), 471–478 (1995)
Du, W.H., Schmitt, F.J.M.: On the G1 continuity of piecewise Bezier surfaces: a review with new results. Computer-Aided Design 22(9), 556–573 (1990)
Varady, T., Martin, R.R., Cox, J.: Reverse engineering of geometric models—an introduction. Computer-Aided Design 29(4), 255–268 (1997)
Lin, A.C., Lin, S.-Y., Fang, T.-H.: Automated sequence arrangement of 3D point data for surface fitting in reverse engineering. Computer in Industry 35, 149–173 (1998)
Tai, C.-C., Huang, M.-C.: The processing of data points basing on design intent inreverse engineering. International Journal of Machine Tools & Manufacture 40, 1913–1927 (2000)
Woo, H., Kang, E., Wang, S., Lee, K.H.: A new segmentation method for point cloud data. International Journal of Machine Tools & Manufacture 42, 167–178 (2002)
Prabhakar, S., Henderson, M.R.: Automatic form-feature recognition using neural-network-based techniques on boundary representations of solid models. Computer-Aided Design 24(7), 381–393 (1992)
Gu, P., Yan, X.: Neural network approach to the reconstruction of freeform surfaces for reverse engineering. Computer-Aided Design 27(1), 59–64 (1995)
Nezis, K., Vosniakos, G.: Recognising 2.5D shape features using a neural network and heuristics. Computer-Aided Design 29(7), 523–539 (1997)
Ward Systems Group, Inc. (Frederick, MD), NeuroShell 2 Help (1998)
Barhak, J., Fisher, A.: Parameterization and reconstruction from 3D Scattered points based on neural network and PDE techniques. IEEE Transactions on visualization and computer graphics 7(1), 1–16 (2001)
Barhak, J., Fisher, A.: Adaptive reconstruction of freeform objects with 3D SOM neural network girds. Computers & Graphics 26, 745–751 (2002)
Weiss, V., Andor, L., Renner, G., Várady, T.: Advanced surface fitting techniques. Computer Aided Geometric Design 19, 19–42 (2002)
Floater, M.S., Reimers, M.: Meshless parameterization and surface reconstruction. Computer Aided Geometric Design 18, 77–92 (2001)
Yin, Z.: Reverse engineering of a NURBS surface from digitized points subject to boundary conditions. Computers &Graphics 28, 207–212 (2004)
Boissonnat, J.-D., Cazals, F.: Smooth surface reconstruction via natural neighbor interpolation of distance functions. Computational Geometry 22, 185–203 (2002)
Alrashdan, A., Motavalli, S., Fallahi, B.: Automatic segmentation of digitized data for reverse engineering application. IIE Transactions 32, 59–69 (2000)
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He, X., Li, C., Hu, Y., Zhang, R., Yang, S.X., Mittal, G.S. (2008). Automatic Order of Data Points in RE Using Neural Networks. In: Fyfe, C., Kim, D., Lee, SY., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2008. IDEAL 2008. Lecture Notes in Computer Science, vol 5326. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88906-9_17
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DOI: https://doi.org/10.1007/978-3-540-88906-9_17
Publisher Name: Springer, Berlin, Heidelberg
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