Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16731
Title: Application of artificial neural network and fuzzy logic in a case-based system for initial process parameter setting of injection molding
Authors: Mok, SL
Kwong, CK 
Keywords: Case-based reasoning
Artificial neural network
Fuzzy logic
Initial process parameter setting of injection molding
Issue Date: 2002
Publisher: Kluwer Academic Publishers
Source: Journal of intelligent manufacturing, 2002, v. 13, no. 3, p. 165-176 How to cite?
Journal: Journal of intelligent manufacturing 
Abstract: Determination of initial process ’meters for injection molding is a highly skilled job and based on skilled operator’s “know-how” and intuitive sense acquired through long-term experience rather than a theoretical and analytical approach. Facing with the global competition, the current trial-and-error practice becomes inadequate. In this paper, application of artificial neural network and fuzzy logic in a case-based system for initial process ’meter setting of injection molding is described. Artificial neural network was introduced in the case adaptation while fuzzy logic was employed in the case indexing and similarity analysis. A computer-aided system for the determination of initial process ’meter setting for injection molding based on the proposed techniques was developed and validated in a simulation environment. The preliminary validation tests of the system have indicated that the system can determine a set of initial process ’meters for injection molding quickly without relying on experienced molding personnel, from which good quality molded parts can be produced.
URI: http://hdl.handle.net/10397/16731
ISSN: 0956-5515
DOI: 10.1023/A:1015730705078
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