B-rep model simplification using selective and iterative volume decomposition to obtain finer multi-resolution models

被引:21
作者
Kwon, Soonjo [1 ]
Mun, Duhwan [2 ]
Kim, Byung Chul [3 ]
Han, Soonhung [4 ]
Suh, Hyo-Won [5 ]
机构
[1] Korea Adv Inst Sci & Technol, Mech Engn Res Inst, 291 Daehak Ro, Daejeon 34141, South Korea
[2] Kyungpook Natl Univ, Dept Precis Mech Engn, 2559 Gyeongsang Daero, Sangu Si 37224, Gyeongsangbuk D, South Korea
[3] Korea Univ Technol & Educ, Sch Mech Engn, 1600 Chungjeol Ro, Cheonan Si 31253, Chungcheongnam, South Korea
[4] Korea STEP Ctr, 291 Daehak Ro, Daejeon 34141, South Korea
[5] Korea Adv Inst Sci & Technol, Dept Ind & Syst Engn, 291 Daehak Ro, Daejeon 34141, South Korea
关键词
B-rep model; Selective and iterative volume decomposition; Feature recognition; Feature-based simplification; Level of detail; Multi-resolution; FEATURE RECOGNITION; SOLID MODELS; TOPOLOGY; FEATURES; SHIP; PART;
D O I
10.1016/j.cad.2019.03.003
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
A model simplification technique is required to control the level of detail (LOD) of a three-dimensional (3D) computer-aided design (CAD) model. In particular, to simplify a B-rep model, volume decomposition operations are performed to decompose the model into volumes, and the decomposed volumes are then sequentially removed according to their importance. Volume decomposition operations can be categorized into wrapping and cutting decompositions, depending on the type of the acquired delta volume. If both wrapping and cutting decompositions can be applied to the same region, a criterion should be established for selecting the appropriate operation for that region. In this paper, we propose selective and iterative volume decomposition that combines four wrapping and two cutting decomposition operations. In addition, we propose volume and area ratios as the selection criteria for wrapping and cutting. We conducted experiments involving two part models and two assembly models to validate the proposed method. The experimental results showed that the proposed method produces a larger number of decomposed volumes with various volume distributions compared to previous methods. In summary, the proposed method is a highly effective approach for achieving incremental model simplification and obtaining multi-resolution models. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:23 / 34
页数:12
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