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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