0
Research Papers

Algorithm to Construct Line-Shaped Feature Point Clouds of Single-Value Ruled Blades for Gas Turbines

[+] Author and Article Information
Yu Zhou

School of Transportation Science
and Engineering,
Beihang University,
Beijing 100191, China
e-mail: zy0741@sina.com

Hao-chen Wang

School of Transportation Science
and Engineering,
Beihang University,
Beijing 100191, China
e-mail: chichi123137@126.com

Fa-rong Du

School of Energy and Power Engineering,
Beihang University,
Beijing 100191, China
e-mail: dfr@buaa.edu.cn

Shui-ting Ding

School of Energy and Power Engineering,
Beihang University,
Beijing 100191, China
e-mail: dst@buaa.edu.cn

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received July 31, 2013; final manuscript received May 13, 2014; published online June 24, 2014. Assoc. Editor: Xiaoping Qian.

J. Manuf. Sci. Eng 136(4), 041022 (Jun 24, 2014) (6 pages) Paper No: MANU-13-1327; doi: 10.1115/1.4027719 History: Received July 31, 2013; Revised May 13, 2014

Using the geometric features of surfaces, we proposed an algorithm to construct line-shaped feature point clouds for the reverse design of a blade surface. Blade surfaces in the gas turbines field require high accuracy and have complex shapes. By quickly recognizing the border of the scattered point clouds of blades, the back-project method was used to extract the ruled generatrix vector. By solving the multi-objective optimization problem of border point clouds, the annealing algorithm was applied to match border point clouds nonrigidly. Equally divided points were constructed based on the point clouds registration results to get inerratic-orderly line-shaped feature point clouds that are beneficial for generating high quality blade surfaces quickly. The proposed algorithm is very effective for dealing with the point clouds of single-value ruled blades. It is also theoretically significant and may be applied for reconstructing the surface of point clouds of single-value ruled blade surfaces. Experiments verify the feasibility of the proposed algorithm.

FIGURES IN THIS ARTICLE
<>
Copyright © 2014 by ASME
Your Session has timed out. Please sign back in to continue.

References

Julien, C. J., Gérard, P., and Emmanuel, D., 2009, “New Approach to 5-Axis Flank Milling of Free-Form Surfaces: Computation of Adapted Tool Shape,” Comput.-Aided Des., 41(12), pp. 918–929. [CrossRef]
Xu, H. X., and Wang, G. J., 2009, “Approximating Rational Triangular Bézier Surfaces by Polynomial Triangular Bézier Surfaces,” J. Comput. Appl. Math., 228(1), pp. 287–295. [CrossRef]
Min-jae, O., Kittichai, S., and Sung Ha, P., 2012, “Constructing Bézier Surfaces Over a Boundary Curve Network With T-Junctions,” Comput.-Aided Des., 44(7), pp. 671–686. [CrossRef]
Lin, K., Huang, C., Lai, J., Tsai, Y., and Ueng, W., 2012, “Automatic Reconstruction of B-Spline Surfaces With Constrained Boundaries,” Comput. Indus. Eng., 62(1), pp. 226–244. [CrossRef]
Simon, F., 2009, “Fitting Curves and Surfaces to Point Clouds in the Presence of Obstacles,” Comput. Aided Geom. Des., 26(2), pp. 192–202. [CrossRef]
Xie, W. C., Zou, X. F., Yang, J. D., and Yang, J. B., 2012, “Iteration and Optimization Scheme for the Reconstruction of 3D Surfaces Based on Non-Uniform Rational B-Splines,” Comput.-Aided Des., 44(11), pp. 1127–1140. [CrossRef]
Akemi, G., and Andrés, I., 2012, “Particle Swarm Optimization for Non-Uniform Rational B-Spline Surface Reconstruction From Clouds of 3D Data Points,” Inf. Sci., 192(0), pp. 174–192. [CrossRef]
Carminelli, A., and Catania, G., 2008, “Curve and Surface Fitting by Means of Rational B-Spline Functions,” ASME International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, Boston, MA, Oct. 31–Nov. 6, pp. 95–100.
Hofer, M., Odehnal, B., Pottmann, H., Steiner, T., and Wallner, J., 2005, “3D Shape Recognition and Reconstruction Based on Line Element Geometry,” Tenth IEEE International Conference on Computer Vision, 2005, ICCV 2005, IEEE, pp. 1532–1538.
Pottmann, H., Hofer, M., Odehnal, B., and Wallner, J., 2004, Line Geometry for 3D Shape Understanding and Reconstruction, Springer, Berlin/Heidelberg.
Matt, O., Ramsay, D., Zhang, H., and Alla, S., 2011, “Point Set Silhouettes via Local Reconstruction,” Comput. Graphics, 35(3), pp. 500–509. [CrossRef]
Erik, B., Henk, A. D., Jonas, T., and Wubs, W. F., 2010, “The Application of Jacobian-Free Newton–Krylov Methods to Reduce the Spin-Up Time of Ocean General Circulation Models,” J. Comput. Phy., 229(21), pp. 8167–8179. [CrossRef]
Berenguer, L., Dufaud, T., and Tromeur-Dervout, D., 2013, “Aitken's Acceleration of the Schwarz Process Using Singular Value Decomposition for Heterogeneous 3D Groundwater Flow Problems,” Comput. Fluids, 80, pp. 320–326. [CrossRef]
Gao, H. S., Han, Y. Z., Zhang, J., and Yue, Z. F., 2008, “Aerodynamic Optimization Based on Approximation Method for Turbine Blade,” Multidiscip. Model. Mater. Struct., 4(4), pp. 385–392. [CrossRef]
Arno, K., Jesper, A., Babak, A. A., Ashburner, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., Song, J. H., Jenkinson, M., Lepage, C., Rueckert, D. Thompson, P., Vercauteren, T., Woods, R. P., Mann, J. J., and Parsey, R. V., 2009, “Evaluation of 14 Nonlinear Deformation Algorithms Applied to Human Brain MRI Registration,” Neuroimage, 46(3), pp. 786–802 [CrossRef] [PubMed]
Chui, H. L., and Rangarajan, A., 2003, “A New Point Matching Algorithm for Non-Rigid Registration,” Comput. Vision Image Understanding, 89(2–3), pp. 114–141. [CrossRef]
Qiu, Z. Y., Tang, H. Y., and Tian, D. S., 2009, “Non-Rigid Medical Image Registration Based on the Thin-Plate Spline Algorithm,” WRI World Congress on Computer Science and Information Engineering, pp. 522–527.
Wu, H. C., 2009, “The Karush–Kuhn–Tucker Optimality Conditions in MultiObjective Programming Problems With Interval-Valued Objective Functions,” Eur. J. Oper. Res., 196(1), pp. 49–60. [CrossRef]
Xie, Q., and Xie, X. Y., 2011, “Point Cloud Data Reduction Methods of Octree-Based Coding and Neighborhood Search,” International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), IEEE, pp. 3800–3803.

Figures

Grahic Jump Location
Fig. 1

Feature boundary of the blade surface

Grahic Jump Location
Fig. 2

Blade boundary and ruled generatrix

Grahic Jump Location
Fig. 3

The geometry model of ruled generatrix extraction

Grahic Jump Location
Fig. 4

Skin surface based on equally divided linear point cloud

Grahic Jump Location
Fig. 5

Turbine blade and compressor blade calculation results in different stages of the algorithm: (a) measure data of turbine blade, (b) extracted boundary point cloud of turbine blade, (c) extracted straight generatrix vectors of turbine blade, (d) equally divided LPC of turbine blade, (e) reverse design results of the turbine, (f) measure data of compressor wheel blade, (g) extracted boundary point cloud of compressor wheel blade, (h) extracted straight generatrix vectors of compressor wheel blade, (i) equally divided LPC of compressor wheel blade, (j) reverse design results of the compressor wheel, (k) 3D model based on triangulation, (l) result of coincidence degree based on point cloud and presented algorithm

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In