Generic form feature recognition and operation selection using connectionist modelling
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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
Keywords
Planning System Fuzzy Neural Network Connectionist Model Recognition Ability Feature RecognitionPreview
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