Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/58658
Title: Recognition of overlapping machining features based on hybrid artificial intelligent techniques
Authors: Li, W.D.
Ong, S.K. 
Nee, A.Y.C. 
Keywords: Hint-based recognition
Neural networks
Overlapping feature recognition
Issue Date: 2000
Source: Li, W.D.,Ong, S.K.,Nee, A.Y.C. (2000). Recognition of overlapping machining features based on hybrid artificial intelligent techniques. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214 (8) : 739-744. ScholarBank@NUS Repository.
Abstract: A hybrid feature recognition system using feature hints, graph manipulations and artificial neural networks for the recognition of overlapping machining features is presented. Based on the enhanced attributed adjacency graph (EAAG) representation and the virtual link graph (VLG) of a designed part, the face loops (F-loops) are defined as the generalized feature hints. They are then extracted from the EAAG using vector calculations, and the relationships between the F-loops are established. Next, the F-loops are manipulated according to the six types of the relationship between F-loops to build the F-loop subgraphs (FLGs), which are potential features. Finally, these FLGs are presented to a trained artificial neural network using various overlapping feature cases to be classified into different types of feature. By utilizing the characteristics of three intelligent techniques in the different subtasks of the feature recognition process, the system can recognize complex overlapping machining features with planar faces and quadric surfaces efficiently. The system is open and has the capability to recognize new types of overlapping feature from the learning ability of the artificial neural networks. © IMechE 2000.
Source Title: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture
URI: http://scholarbank.nus.edu.sg/handle/10635/58658
ISSN: 09544054
Appears in Collections:Staff Publications

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