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3D part similarity comparison based on levels of detail in negative feature decomposition using artificial neural network
Journal article   Open access   Peer reviewed

3D part similarity comparison based on levels of detail in negative feature decomposition using artificial neural network

Han-Chung Cheng, Chih-Hsing Chu, Eric Wang and Yong-Se Kim
Computer-Aided Design and Applications, Vol.4(5), pp.619-628
2007

Abstract

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

Duplicate designs consume a significant amount of company resources during product development. Search for similar parts for a given query part, which facilitates design reuse, is crucial to avoiding this problem. Previous studies have only compared parts on a complete scale, not on a partial scale. This paper proposes a novel scheme which incorporates the concept of LOD (Levels of Detail) into 3D part comparison in order to assess partial similarity. Different LOD variants are generated from negative feature decomposition of a solid model. A human comparison behavior model (HCBM), mainly consisting of a back-propagation artificial neural network (ANN), is established by training with the result of a similarity ranking experiment. It combines the dissimilarity value at each LOD based on a modified D2 distribution. Test examples show that the proposed scheme is effective in 3D part search with LODs.

url
https://doi.org/10.1080/16864360.2007.10738496View
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