TY - JOUR
T1 - Application of ART neural network to development of technology for functional feature-based reference design retrieval
AU - Wang, Chin Bin
AU - Chen, Yuh Jen
AU - Chen, Yuh Min
AU - Chu, Hui Chuan
N1 - Funding Information:
The authors would like to thank the National Science Council of the Republic of China for financially supporting this research under Contract No. NSC 92-2212-E-006-065.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2005/6
Y1 - 2005/6
N2 - Engineering design is a knowledge intensive process. The execution of each task in the process requires various aspects of knowledge and experience. Therefore, organizing, storing and retrieving product design information, design intents and underlining design knowledge is one of the most important tasks in engineering knowledge management. This study develops a novel scheme for functional feature-based reference design retrieval using adaptive resonance theory (ART1) neural network to provide engineering designers with easy access to relevant design and other knowledge. This retrieval process includes the steps of functional feature-based query, case searching, and case ranking. The technology involves a binary code-based representation for functional features, ART1 neural network for functional feature-based case clustering, functional feature-based case similarity ranking, and a case-based representation for designed entities. The objective of this study can be achieved by performing the following tasks: (i) designing a functional feature-based reference design retrieval process, (ii) developing a functional feature representation, (iii) investigating ART1 neural network, (iv) implementing a functional feature-based reference design retrieval mechanism, and (v) experimenting with functional feature-based case clustering.
AB - Engineering design is a knowledge intensive process. The execution of each task in the process requires various aspects of knowledge and experience. Therefore, organizing, storing and retrieving product design information, design intents and underlining design knowledge is one of the most important tasks in engineering knowledge management. This study develops a novel scheme for functional feature-based reference design retrieval using adaptive resonance theory (ART1) neural network to provide engineering designers with easy access to relevant design and other knowledge. This retrieval process includes the steps of functional feature-based query, case searching, and case ranking. The technology involves a binary code-based representation for functional features, ART1 neural network for functional feature-based case clustering, functional feature-based case similarity ranking, and a case-based representation for designed entities. The objective of this study can be achieved by performing the following tasks: (i) designing a functional feature-based reference design retrieval process, (ii) developing a functional feature representation, (iii) investigating ART1 neural network, (iv) implementing a functional feature-based reference design retrieval mechanism, and (v) experimenting with functional feature-based case clustering.
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U2 - 10.1016/j.compind.2004.12.004
DO - 10.1016/j.compind.2004.12.004
M3 - Article
AN - SCOPUS:18844376906
SN - 0166-3615
VL - 56
SP - 428
EP - 441
JO - Computers in Industry
JF - Computers in Industry
IS - 5
ER -