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dc.contributor.authorChan, Kit Yan
dc.contributor.authorKwong, C.
dc.contributor.authorTsim, Y.
dc.date.accessioned2017-01-30T13:44:21Z
dc.date.available2017-01-30T13:44:21Z
dc.date.created2010-03-25T20:02:41Z
dc.date.issued2009
dc.identifier.citationChan, K.Y. and Kwong, C.K. and Tsim, Y.C. 2009. Modelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms. Engineering Applications of Artificial Intelligence. 23 (1): pp. 18-26.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/34568
dc.identifier.doi10.1016/j.engappai.2009.09.009
dc.description.abstract

Determination of process conditions for a fluid dispensing process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach. Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid dispensing processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid dispensing process for microchip encapsulation. An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modeling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modeling the fluid dispensing. It is then followed by the determination of process conditions of the process based on a genetic algorithm approach. Validation tests were conducted. Results of them indicate that process conditions determined based on the proposed approaches can achieve the specified quality requirements.

dc.publisherElsevier B. V.
dc.subjectgenetic algorithm
dc.subjectneural fuzzy networks
dc.subjectmicrochip encapsulation
dc.subjectFluid dispensing
dc.titleModelling and optimization of fluid dispensing for electronic packaging using neural fuzzy networks and genetic algorithms
dc.typeJournal Article
dcterms.source.volume23
dcterms.source.number1
dcterms.source.startPage18
dcterms.source.endPage26
dcterms.source.issn0952-1976
dcterms.source.titleEngineering Applications of Artificial Intelligence
curtin.note

The link to the journal’s home page is: http://www.elsevier.com/wps/find/journaldescription.cws_home/975/description#description. Copyright © 2009 Elsevier B.V. All rights reserved

curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusOpen access
curtin.facultyCurtin Business School
curtin.facultyThe Digital Ecosystems and Business Intelligence Institute (DEBII)


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