Please use this identifier to cite or link to this item: https://doi.org/10.1007/BF00128649
Title: Generic form feature recognition and operation selection using connectionist modelling
Authors: Gu, Z.
Zhang, Y.F. 
Nee, A.Y.C. 
Issue Date: Aug-1995
Source: Gu, Z., Zhang, Y.F., Nee, A.Y.C. (1995-08). Generic form feature recognition and operation selection using connectionist modelling. Journal of Intelligent Manufacturing 6 (4) : 263-273. ScholarBank@NUS Repository. https://doi.org/10.1007/BF00128649
Abstract: For the purpose of enhancing the adaptability of computer-aided process planning systems, two connectionist modelling methods, namely neocognitron (i.e. neural network modelling for pattern recognition) and fuzzy associative memories (FAM), are applied to the phases of feature recognition and operation selection respectively in order to provide the system with the ability of self-learning and the ability to integrate traditional expert planning systems with connectionism-based models. In this paper, the attributed adjacency graph (AAG) extracted from a (B-Rep) solid model is converted to attributed adjacency matrices (AAM) that can be used as input data for the neocognitron to train and recognize feature patterns. With this technique, the system can not only self-reconstruct its recognition abilities for new features by learning without a priori knowledge but can also recognize and decompose intersection features. A fuzzy connectionist model, which is created using the Hebbian fuzzy learning algorithm, is employed subsequently to map the features to the appropriate operations. As the algorithm is capable of learning from rules, it is much easier to integrate the proposed model with conventional expert CAPP systems so that they become more generic in dealing with uncertain information processing and perform knowledge updating. mg]Keywords mw]Computer-aided process planning mw]feature recognition mw]neural networks mw]fuzzy neural networks mw]operation selection mw]connectionist model mw]fuzzy associative memories © 1995 Chapman & Hall.
Source Title: Journal of Intelligent Manufacturing
URI: http://scholarbank.nus.edu.sg/handle/10635/58334
ISSN: 09565515
DOI: 10.1007/BF00128649
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