Feature Extraction Based 3D Model Registration for Surface Finish Quality Evaluation

Abstract:

Engineers and designers constantly strive to design machines that have high life cycle, can run faster, and can manufacture products having higher geometrical accuracy. However, manufacturing processes often produce parts with surfaces that are unsatisfactory from the viewpoint of geometrical precision or quality of surface texture. In general, electronic, optical, and tactual methods are used for exploring and evaluating the surface texture and the surface geometry. With the advancements in the reverse engineering technology, computer based numerical analysis of the data obtained from 3D imaging devices, referred to as registration, has also emerged as a tool for shape inspection. Registration expedites shape inspection by augmenting the process of error measurement. The existing registration methods suffer from problems that limit the extensive use of the existing approaches, thereby making them application specific. In this research, we propose a novel approach for reducing the number of data points used for registration without compromising with registration accuracy. We propose a segmentation based registration approach that utilizes the features extracted from the models, thus reducing the data size. The registered data sets can be compared to each other for evaluating the differences in shape. The difference information can further be used to appropriately modify the manufacturing process parameters for obtaining highly accurate products.

• Periodical: Advanced Materials Research (Volume 1017)
• Pages: 141-146
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1017.141
• Online since: September 2014
• Authors: Nitin Sharma*, Jiang Zhu, Tomohisa Tanaka, Yoshio Saito
• Keywords: Feature Extraction, Manufacturing Accuracy, Point Cloud, Registration, Shape Inspection

References

[1] P. J. Besl, N. D. McKay, A method for registration of 3D shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, 1992, pp.239-256.

DOI: 10.1109/34.121791

[2] S. Gold, A. Rangarajan, C. P. Lu, S. Pappu and E. Mjolsness, New algorithms for 2-D and 3-D point matching: Pose estimation and correspondence, Pattern Recognition, vol. 31, no. 8, 1998, pp.1019-1031.

DOI: 10.1016/s0031-3203(98)80010-1

[3] K. Demarsin, D. Vanderstraeten, T. Volodine, D. Roose, Detection of closed sharp edges in point clouds using normal estimation and graph theory, Computer-Aided Design, vol. 39, no. 4, 2007, pp.276-283.

DOI: 10.1016/j.cad.2006.12.005

[4] Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars, Computational Geometry - Algorithms and Applications, 3rd edition, Springer, (2008).

DOI: 10.1145/369836.571192

[5] S. Gumhold, X. Wang, R. Macleod, Feature extraction from point clouds, in: Proceedings of the 10th International Meshing Roundtable, 2001, pp.293-305.

[6] D. L. Page, Y. Sun, A. F. Koschan, J. Paik, and M. A. Abidi, Normal vector voting: Crease detection and curvature estimation on large, noisy meshes, Journal of Graphical Models, vol. 64, 2002, pp.199-229.

DOI: 10.1006/gmod.2002.0574