ANN-based 3D part search with different levels of detail (LOD) in negative feature decomposition

被引：11

作者：

Chu, Chih-Hsing ^{[1
]}

Cheng, Han-Chung ^{[1
]}

Wang, Eric ^{[2
]}

Kim, Yong-Se ^{[2
]}

机构：

[1] Natl Tsing Hua Univ, Dept Ind Engn & Engn Management, Hsinchu 300, Taiwan

[2] Sungkyunkwan Univ, Sch Mech Engn, Suwon, South Korea

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2009年 / 36卷 / 08期

关键词：

Similarity assessment; Levels of detail (LOD); Negative feature; Feature recognition; Part search; Design retrieval; SIMILARITY ASSESSMENT; FEATURE RECOGNITION;

D O I：

10.1016/j.eswa.2009.02.011

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Duplicate designs consume a large amount of enterprise resources during product development. Automatic search for similar parts is an effective solution for design reuse. Previous studies have only concerned similarity assessment based on complete 3D models, which may produce unsatisfactory result in practice. This paper proposes a novel scheme which incorporates the concept of LOD (levels of detail) into 3D part search. The scheme allows searching with different LOD variants created from the negative feature tree (NFT) of a solid model. A back-propagation artificial neural network is established to combine the D2-based similarity evaluation at each level of NFT. A human cognition model (HCM) is obtained by training the network with a set of data generated from a human experiment of similarity ranking. Search examples based on HCM show that the proposed scheme provides a practical tool for retrieval of similar part models. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：10905 / 10913

页数：9

共 24 条

[1]

[Anonymous], ACM 4 S SOL MOD APPL

[2]

[Anonymous], INT C ENG DES

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