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

Authors:
Chih-Hsing Chu;Han-Chung Cheng;Eric Wang;Yong-Se Kim
Affiliations:
Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Kuang Fu Rd., Sec 2, Hsinchu 300, Taiwan;Department of Industrial Engineering and Engineering Management, National Tsing Hua University, 101 Kuang Fu Rd., Sec 2, Hsinchu 300, Taiwan;School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea;School of Mechanical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

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.