Please use this identifier to cite or link to this item:
http://hdl.handle.net/11452/21517
Title: | Neural network based non-standard feature recognition to integrate CAD and CAM |
Authors: | Uludağ Üniversitesi/Mühendislik Fakültesi. Öztürk, Nursel Öztürk, Ferruh AAG-9336-2021 AAG-9923-2021 7005688805 56271685800 |
Keywords: | Feature recognitionneural networks Feature-based design Neural networks Cad/cam Manufacturing features Discriminant-analyis Automatic extraction Components Models Decomposition Heuristics Systems Computer aided design Computer aided manufacturing Data acquisition Data structures Database systems Nonstandard feature recognition Feature extraction |
Issue Date: | Jun-2001 |
Publisher: | Elsevier |
Citation: | Öztürk, N. ve Öztürk, F. (2001). "Neural network based non-standard feature recognition to integrate CAD and CAM" Computers in Industry, 45(2),123-135. |
Abstract: | In this paper, a neural network based feature recognition approach which is capable of extracting information from design database is proposed to automate the integration of the design and applications following design. CAD data base is converted to feature based model information which can be used by CAM applications. Multilayer perceptron neural network is provided with Boundary representation (B-rep) information to recognise simple and complex features. B-rep structure is used to process the face-score values in terms of geometry and topology of the solid model. The effectiveness of proposed approach is demonstrated with experimental results which show the validity of this method to recognise complex shape features. |
URI: | https://doi.org/10.1016/S0166-3615(01)00090-2 https://www.sciencedirect.com/science/article/pii/S0166361501000902 http://hdl.handle.net/11452/21517 |
ISSN: | 0166-3615 |
Appears in Collections: | Scopus Web of Science |
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