Surface quality optimization based on mutative scale chaos algorithm

被引：0
作者：
Xu X. [1 ]
Yan G. [1 ]
Lei Y. [2 ]
机构：
[1] School of Mechanical Engineering, Beihang University, Beijing
[2] CAXA Technology Co., Ltd., Beijing
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2023年 / 49卷 / 12期
关键词：
chaos optimization; diagnostic shade; high-quality surface; mutative scale; surface reconstruction;
D O I：
10.13700/j.bh.1001-5965.2022.0070
中图分类号：
学科分类号：
摘要：
Surface quality optimization is a common problem in surface reconstruction. In the design of high-end products such as aerospace and automobile, if the reconstructed surfaces are required to have high-order continuity, a lot of optimization work is often needed. In order to obtain smooth and high-quality surfaces conveniently, an optimization method of surface quality based on a mutative scale chaos algorithm is proposed. Adjustable parameters are introduced. The target surface can be deformed by flexibly adjusting a number of parameters under the G1 continuity constraint between neighboring NURBS patches. A mathematical model of mutative scale chaos optimization is established, and the optimal solution of the adjustable parameters is calculated to obtain a high-quality surface with the smallest deformation compared with the original surface. The robustness and practicability of this method are verified by case analysis. The isolux analysis of the optimized surface is carried out. The outcomes demonstrate that the mutative scale chaotic algorithm-based surface quality optimization technique may guarantee the surface's quality and enhance the effectiveness of surface reconstruction. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.
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页码：3328 / 3334
页数：6
相关论文
共 16 条
  • [1] HE M F., Research on key technologies of surfaces reconstruction based on scattered point cloud data, pp. 4-7, (2006)
  • [2] BERGER M, TAGLIASACCHI A, SEVERSKY L M, Et al., A survey of surface reconstruction from point clouds, Computer Graphics Forum, 36, 1, pp. 301-329, (2017)
  • [3] SONG J, LEE J, KO K, Et al., Unorganized point classification for robust NURBS surface reconstruction using a point-based neural network, Journal of Computational Design and Engineering, 8, 1, pp. 392-408, (2021)
  • [4] NIE Z W, XIONG D D., Target surface research of aero-engine blade adaptive repairing driven by image model, Computer Integrated Manufacturing Systems, 25, 1, pp. 53-60, (2019)
  • [5] QIAO X P, ZHU X J, ZHANG H S, Et al., Research on surface fitting algorithm based on surface patches splicing, Journal of Vibration, Measurement & Diagnosis, 33, pp. 160-163, (2013)
  • [6] YANG B, WANG F G, LAI L Z, Et al., Research on the surface reconstruction technology in CATIA, Machine Design and Manufacturing Engineering, 46, 2, pp. 51-54, (2017)
  • [7] KARCIAUSKAS K, PETERS J., Refinable G<sup>1</sup> functions on G<sup>1</sup> free-form surfaces, Computer Aided Geometric Design, 54, pp. 61-73, (2017)
  • [8] WANG M M., Research on optimization method of curve and surface based on improved particle swarm optimization, pp. 49-52, (2014)
  • [9] ZHANG T, WANG H W, WANG Z C., Mutative scale chaos optimization algorithm and its application, Control and Decision, 14, 3, pp. 285-288, (1999)
  • [10] KAHMANN J., Continuity of curvature between adjacent Bézier patches, surfaces in computer aided geometric design, pp. 65-75, (1983)
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