Automatic small blend recognition from B-rep models for analysis

Li, Jiping; Tang, Gang; Shi, Dianbo; Geng, Meng; Zhu, Huawei; Hagiwara, Ichiro

doi:10.1007/s00366-009-0127-4

Cited by 15 publications

(9 citation statements)

References 7 publications

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“…The commonly used EBFs have constant cross-sectional radius throughout the blend face. Many CAD systems also support variable radii for the blending (Venkataraman, Sohoni, and Elber 2001;Li et al 2009). In some commercial CAD packages, blend faces are normally called rounds or fillets.…”

Section: Introductionmentioning

confidence: 98%

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Small blend suppression from B-rep models in computer-aided engineering analysis

Lai

Wong

Huynh

et al. 2016

Journal of the Chinese Institute of Engineers

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In mechanical design, small blending is a common operation used to improve the strength and aesthetics of the workpiece. In blending operations, certain smooth faces are added to the boundary representation (B-rep) model to smooth sharp edges and vertices. These faces are termed 'blend faces. ' However, they may affect the quality of analysis; these faces should be meshed with tiny meshes, which may result in an increase in computational time and reduced accuracy of simulation results. Hence, small blend suppression is an approach for improving the quality of generated meshes. Blend suppression is a technique for reverting a blended computer-aided design model to its original unblended status. This study proposes a blend suppression algorithm for removing blend faces in computer-aided engineering (CAE) analysis. The proposed algorithm operates as follows: (1) edge blend faces and vertex blend faces are grouped individually, (2) new geometric data are computed, (3) new elements are added to the data structure of the B-rep model, (4) all data related to blend faces are deleted, and (5) all topological data in the B-rep model are updated.Several examples are presented in this paper to demonstrate the feasibility of the proposed method for blend suppression as well as its advantage in improving the quality of meshes in CAE analysis.

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Section: Introductionmentioning

confidence: 98%

“…They also proposed an automatic method for recognizing each of the chains and blend faces. Li et al (2009) transformed the input B-rep model into an aggregation of faces. Each face was then judged to be a blend or not based on a unique calculating procedure of the blend physical size and the matching conditions.…”

Section: Introductionmentioning

confidence: 99%

Small blend suppression from B-rep models in computer-aided engineering analysis

Lai

Wong

Huynh

et al. 2016

Journal of the Chinese Institute of Engineers

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“…They proposed an algorithm using smooth edges, support faces, and span angles for recognizing both EBFs and VBFs. Li et al [6] presented an algorithm using smooth edges, normal vectors, span angles, and the area of a target feature for automatically recognizing and screening BFs. Joshi et al [7] emphasized the importance of recognizing and simplifying BFs on freeform surfaces.…”

Section: Introductionmentioning

confidence: 99%

Recognition of virtual loops on 3D CAD models based on the B-rep model

Lai

Wang

You

et al. 2016

Engineering with Computers

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B-rep model can provide effective topological data of the solid model for feature recognition analysis. The vertex, edge, and face are fundamental elements in the B-rep model. Each of these elements only records the topological data of its neighboring elements. The loop is another essential element linking all edges corresponding to a face. An outer loop always exists to record the external boundary of the face. Additional inner loops may also exist to record the inner boundaries because of holes. The loop concept can be employed in feature recognition for searching edges and faces related to the target feature. However, the current data structure of the loop in the B-rep model does not provide enough information for use in feature recognition. Therefore, a new type of loop, hereafter called a virtual loop, is necessary to fulfill the task. The aim of this study was to define and record the virtual loop in the B-rep model, and to develop algorithms in accordance with the virtual loop for the recognition of several types of features.The primary limitation of the current loop data in the B-rep model is that they are restricted to a face only. Whenever a face is sliced or divided into several faces, a new set of B-rep data must be established; all the newly generated faces are considered individual faces. In addition, the loop data are modified accordingly to reflect the new face status. Often in CAD models, a feature can be recognized in accordance with specific contours surrounding the target feature. For examples, features are generally classified as concave (e.g., features such as holes and pockets) or convex (e.g., features such as bosses, extrusions, and ribs). Finding a contour corresponding to the target feature may not be the only possible method for feature recognition, but it certainly facilitates the recognition of complex features. Many practical features do not lie on only a single face; rather, they usually lie on multiple faces, which is beyond the scope of the current loop data in the B-rep model. AbstractLoops are critical elements in boundary representation (B-rep) models because they link all edges corresponding to a face. Loops can be used in feature recognition for identifying depressions or protrusions. In real 3D CAD models, however, features typically lie across multiple faces, which is beyond the data structure of current B-rep models. This study presents a virtual loop concept to account for all loop types used in CAD models, and develops algorithms for recognizing them. In accordance with the complexity of the recognition algorithm, this study defines three types of loop: single, virtual, and multivirtual. A single loop is the current loop recorded in the B-rep model. A virtual loop lies across faces that are at least G 1 continuous. Finally, a multivirtual loop lies across faces that are either G 0 or G 1 continuous. The proposed loop structure provides a more complete data structure for recognizing various types of features in feature-recognition modules. Several realistic CAD models ar...

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“…With the popularity of 3D products, the 3D technology applications in various fields are becoming more mature [1]. As one of the major researches of 3D technology, 3D Human Body modeling plays an important role, which has been widely applied in many areas such as game-making, costume design, social security, film production, etc.…”

Section: Introductionmentioning

confidence: 99%

3D human body modeling based on single Kinect

Zhang

Peng

et al. 2014

2014 7th International Conference on Biomedical Engineering and Informatics

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3D human body models have been widely used in all fields. For acquiring precise human body models, the research about their recognition and reconstruction has become a major topic. Here we present a new method for modeling 3D human body based on single Kinect from the viewpoint of cost and operability. In this method, human body 3D information is recognized and acquired by only one Kinect while 3D human body models are reconstructed by using the tools of Processing and Point Cloud Library. To achieve the reconstruction objective, the iterative closest point algorithm is adopted for registering the captured upper human body 3D point cloud data with the standard reference human body data. The experiment results demonstrate that this method is feasible.

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Automatic small blend recognition from B-rep models for analysis

Cited by 15 publications

References 7 publications

Small blend suppression from B-rep models in computer-aided engineering analysis

Small blend suppression from B-rep models in computer-aided engineering analysis

Recognition of virtual loops on 3D CAD models based on the B-rep model

3D human body modeling based on single Kinect

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