Integrated Clustering Approach To Developing Technology For Functional Feature And Engineering Specification-Based Reference Design Retrieval  

Oleh:

Chen, Yuh-Jen ; Chen, Yuh-Min ; Chu, Hui-Chuan ; Wang, Chin-Bin ; Tsaih, Der-Chian ; Yang, Hui-Mei

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Concurrent Engineering vol. 13 no. 4 (Dec. 2005), page 257-267.
Topik: engineering knowledge management; reference design retrieval; adaptive resonance theory (ART); genetic algorithm (GA); fuzzy adaptive resonance theory (fuzzy ART).
Fulltext: 257.pdf (1.08MB)

Isi artikel

Engineering design is a complex activity, and is heavily reliant on the know-how of engineering designers. Hence, capturing, storing, and reusing design information, design intent, and underlining design knowledge to support design activities is a key issue in engineering knowledge management. To meet the demand for engineering designers regarding functional feature and engineering specification-based knowledge resources, this study proposes a novel scheme for functional feature and engineering specification-based reference design retrieval using an integrated clustering approach for providing engineering designers with easy access to relevant reference design and associated knowledge. The research objectives can be achieved by performing the following five tasks: (i) designing a functional feature and engineering specificationbased reference design retrieval process, (ii) developing a functional feature and engineering specification representation, (iii) investigating and integrating ART1 (adaptive resonance theory 1) neural network, GA (genetic algorithm), and fuzzy ART (fuzzy adaptive resonance theory) clustering techniques, and (iv) implementing a functional feature and engineering specification-based reference design retrieval mechanism and experimenting with an example. The retrieval process involves three steps: functional feature and engineering specification-based query, similar design case search and retrieval, and similar design case ranking. The techniques involved include: (i) a binary code-based representation for functional feature and an EXPRESS language-based representation for engineering specification, (ii) ART1 neural network and genetic algorithm for functional feature-based similar design case clustering, (iii) fuzzy ART for engineering specification-based similar design case clustering, (iv) similarity calculation for ranking similar design cases, and (v) a case-based representation for designed entities.

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