Automatic Hole Recognition and Classification for a Set of B-rep Models Representing an Injection Mold

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ISSN: 2278-0149 (Online)
Abbreviated Title: Int. J. Mech. Eng. Robot. Res.
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DOI: 10.18178/ijmerr
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Prof Richard (Chunhui) Yang

Professor of School of Engineering, Design and Built Environment, Western Sydney University, Australia. His research interests cover Industry 4.0, Additive Manufacturing, Advanced Engineering Materials and Structures (Metals and Composites), Multi-scale Modelling of Materials and Structures, Metal Forming and Metal Surface Treatment.

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Home > Published Issues > 2020 > Volume 9, No. 8, August 2020 >

Jiing-Yih Lai 1, Pei-Pu Song 1, Yao-Chen Tsai 2, and Chia-Hsiang Hsu 2

1. Mechanical Engineering Dept., National Central University, Taoyuan, Taiwan
2. CoreTech System (Moldex3D) Co., Ltd., Hsinchu, Taiwan

Abstract—A hole recognition algorithm for all hole types in a mold set is proposed. The algorithm can recognize holes of both circular and non-circular types, and blind and through types. It can also detect ladder holes, each of which is composed of a series of holes connected in sequence, and related holes that pass across different CAD models. The proposed hole recognition algorithm is divided into five steps: (1) facial composition is searched for each hole, (2) the holes are evaluated as either through or blind, (3) related holes are detected on each CAD model, (4) related holes passing across different CAD models are detected, and (5) data is output. Three mold sets were used to demonstrate the feasibility of the proposed method, and the results were compared with those from commercial software.

Index Terms—Hole recognition, Feature recognition, Feature classification, Injection mold, B-rep model

Cite: Jiing-Yih Lai, Pei-Pu Song, Yao-Chen Tsai, and Chia-Hsiang Hsu, "Automatic Hole Recognition and Classification for a Set of B-rep Models Representing an Injection Mold," International Journal of Mechanical Engineering and Robotics Research, Vol. 9, No. 8, pp. 1189-1197, August 2020. DOI: 10.18178/ijmerr.9.8.1189-1197

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.