Face clustering of a large-scale CAD model for surface mesh generation

被引:37
|
作者
Inoue, K
Itoh, T
Yamada, A
Furuhata, T
Shimada, K
机构
[1] IBM Japan Ltd, Tokyo Res Lab, Kanagawa 2428502, Japan
[2] IBM Japan Ltd, Yamato Software Dev Lab, Kanagawa 2428502, Japan
[3] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
关键词
mesh generation; face clustering; mesh simplification; re-parameterisation; finite element analysis;
D O I
10.1016/S0010-4485(00)00124-X
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A detailed CAD model needs manual clean-up, or simplifying operations, before a finite element mesh can be automatically generated because such a model consists of hundreds or thousands of faces many of which may be smaller than a desired mesh element size. We propose an automated face clustering method used as a pre-process of surface mesh generation. By decomposing a model into face clusters so that each region can be projected onto a simple parametric surface such as a plane, we obtain a final mesh as an aggregate of sub-meshes for respective clusters without time-consuming manual preparation work. The projection onto a surface realises re-parameterisation as well as suppression of small details. The main contribution of this work is the integration of (1) a greedy algorithm for combining faces into clusters, and (2) geometric indices that reflect various aspects of a preferable shape for a cluster. The validity of the approach is demonstrated with results of clustering and mesh generation for a realistic-scale CAD model. (C) 2001 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:251 / 261
页数:11
相关论文
共 23 条
  • [1] Acceleration of Dynamic Bubble Mesh Generation for Large-Scale Model
    Noguchi, So
    Nobuyama, Fumiaki
    Igarashi, Hajime
    IEEE TRANSACTIONS ON MAGNETICS, 2014, 50 (02) : 453 - 456
  • [2] Surface mesh generation of large-scale digital rock images in 3D
    Liu, Yan
    Xing, H. L.
    2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, 2013, 18 : 1208 - 1216
  • [3] Efficient large-scale face clustering using an online Mixture of Gaussians
    Montero, David
    Aginako, Naiara
    Sierra, Basilio
    Nieto, Marcos
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2022, 114
  • [4] Large-scale Face Clustering Method Research Based on Deep Learning
    Wen, Zixin
    2021 3RD INTERNATIONAL CONFERENCE ON MACHINE LEARNING, BIG DATA AND BUSINESS INTELLIGENCE (MLBDBI 2021), 2021, : 731 - 734
  • [5] Computational Performance of an Embedded Reinforcement Mesh Generation Method for Large-Scale RC Simulations
    Markou, George
    INTERNATIONAL JOURNAL OF COMPUTATIONAL METHODS, 2015, 12 (03)
  • [6] Automatic surface mesh generation for CAD models with dirty geometry
    Zhang, Hanbing
    Wu, Meng
    Jiang, Luo
    Fan, Jinhua
    ENGINEERING WITH COMPUTERS, 2025,
  • [7] Three-dimensional (3D) CAD model lightweight scheme for large-scale assembly and simulation
    Liu, Wei
    Zhou, Xionghui
    Zhang, Xiaobing
    Niu, Qiang
    INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2015, 28 (05) : 520 - 533
  • [8] A computational model for organism growth based on surface mesh generation
    Leung, CH
    Berzins, M
    JOURNAL OF COMPUTATIONAL PHYSICS, 2003, 188 (01) : 75 - 99
  • [9] Scalable generation of large-scale unstructured meshes by a novel domain decomposition approach
    Chen, Jianjun
    Xiao, Zhoufang
    Zheng, Yao
    Zou, Jianfeng
    Zhao, Dawei
    Yao, Yufeng
    ADVANCES IN ENGINEERING SOFTWARE, 2018, 121 : 131 - 146
  • [10] A large-scale parallel hybrid grid generation technique for realistic complex geometry
    Zhao, Zhong
    Zhang, Yang
    He, Lei
    Chang, Xinghua
    Zhang, Laiping
    INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS, 2020, 92 (10) : 1235 - 1255