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Published in: Engineering with Computers 4/2018

19-12-2017 | Original Article

Automatic recognition and decomposition of rib features in thin-shell parts for mold flow analysis

Authors: Jiing-Yih Lai, Ming-Hsuan Wang, Pei-Pu Song, Chia-Hsiang Hsu, Yao-Chen Tsai

Published in: Engineering with Computers | Issue 4/2018

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Abstract

In mold flow analysis, for greater accuracy, convergence, and application specificity, it is beneficial to apply a hybrid combination of pyramidal, prismatic, and hexahedral meshes to replace traditional tetrahedral meshes. This, however, requires the recognition of specific features from the computer-aided design (CAD) model and decomposition into regions, so that high-quality meshes can be applied. The purpose of this study is to propose a rib recognition and decomposition algorithm for detecting all the types of ribs on thin-shell plastic parts, and decomposing them into rib regions. Conventional rib recognition methods emphasizing only the recognition of the constructing faces are not suitable for high-quality mesh generation, because they cannot provide sufficient information for region decomposition. We employ a concept of parallel pairs of edges to identify regions of regular shape on a rib structure. This not only recognizes the constructing faces, but also evaluates the constructing edges on each regular region. With the edge data on regular regions, the transition regions are recognized and the associated face and edge data are evaluated. This study focuses on the development of a feasible algorithm that can be used to deal with industrial CAD models with various kinds of geometric conditions. Several CAD examples are presented to demonstrate the feasibility of the proposed algorithm.

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Metadata
Title
Automatic recognition and decomposition of rib features in thin-shell parts for mold flow analysis
Authors
Jiing-Yih Lai
Ming-Hsuan Wang
Pei-Pu Song
Chia-Hsiang Hsu
Yao-Chen Tsai
Publication date
19-12-2017
Publisher
Springer London
Published in
Engineering with Computers / Issue 4/2018
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-017-0574-2

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