Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/30883
Title: Joint optimization for knowledge mining : evaluating parameters of manufacturing processes
Authors: Tang, CXH
Lau, HCW
Keywords: Genetic algorithms
Knowledge management
Issue Date: 2009
Publisher: IEEE
Source: International Conference on Information Management and Engineering, 2009 : ICIME '09, 3-5 April 2009, Kuala Lumpur, p. 689-693 How to cite?
Abstract: In various kinds of manufacturing production, predicting the influence of process parameters in terms of machine performance is a necessity as they may have a serious impact on product quality as well as on the probability of machine failure. To address this issue, this paper presents a novel knowledge-based algorithm embedded with artificial intelligence for evaluating the overall suitability of adopting the predicted control parameters suggested by domain experts. The originality of this research is that the proposed knowledge-based system is equipped with fuzzy-guided genetic algorithm, enabling the identification of the best set of process parameters. Simulation using the RIE machine is provided to validate the practicability of the proposed approach.
URI: http://hdl.handle.net/10397/30883
ISBN: 978-0-7695-3595-1
DOI: 10.1109/ICIME.2009.119
Appears in Collections:Conference Paper

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