Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12719
Title: A hybrid neural network and genetic algorithm approach to the determination of initial process parameters for injection moulding
Authors: Mok, SL
Kwong, CK 
Lau, WS
Keywords: Genetic algorithm
Hybrid system
Initial process parameter setting
Injection moulding
Neural network
Issue Date: 2001
Publisher: Springer
Source: International journal of advanced manufacturing technology, 2001, v. 18, no. 6, p. 404-409 How to cite?
Journal: International journal of advanced manufacturing technology 
Abstract: Determination of the initial process parameters for injection moulding is highly skilled task and is based on a skilled operator's "know-how" and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. In the face of global competition, the current trial-and-error practice is inadequate. In this paper, a hybrid neural network and genetic algorithm approach is described to determine a set of initial process parameters for injection moulding. A hybrid neural network and genetic algorithm system for the determination of initial process parameter settings for injection moulding based on the proposed appraoch was developed and validated. The preliminary validation test of the system has indicated that the system can determine a set of initial process parameters for injection moulding quickly from which good quality moulded parts can be produced without relying on experienced moulding personnel.
URI: http://hdl.handle.net/10397/12719
ISSN: 0268-3768
EISSN: 1433-3015
DOI: 10.1007/s001700170050
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