Determination of process conditions of epoxy dispensing processes using a genetic algorithm based neural fuzzy networks
dc.contributor.author | Chan, Kit Yan | |
dc.contributor.author | Ling, S. | |
dc.contributor.author | Dillon, Tharam | |
dc.contributor.author | Kwong, C. | |
dc.contributor.editor | Chin-Teng Lin | |
dc.contributor.editor | Yau-Huang Kuo | |
dc.date.accessioned | 2017-01-30T13:30:19Z | |
dc.date.available | 2017-01-30T13:30:19Z | |
dc.date.created | 2012-02-09T20:00:50Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Chan, Kit and Ling, Sai and Dillon, Tharam and Kwong, Che. 2011. Determination of process conditions of epoxy dispensing processes using a genetic algorithm based neural fuzzy networks, in IEEE International Conference on Fuzzy Systems (FUZZ 2011), Jun 27-30 2011. Taipei, Taiwan: IEEE. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/32323 | |
dc.identifier.doi | 10.1109/FUZZY.2011.6007321 | |
dc.description.abstract |
In this paper, process conditions of epoxy dispensing processes are determined by the proposed genetic algorithm based neural fuzzy networks, which consists of two tasks: a) the approach of neural fuzzy networks, which was shown to be better than the other existing approaches, is proposed to develop models in relating between process parameters and quality characteristics for the epoxy dispensing processes; b) the approach of genetic algorithm is used to determine process parameters with respect to pre-defined quality requirements based on the developed neural fuzzy network models. The results indicate that, based on the proposed genetic algorithm based neural fuzzy network, estimated process parameters can achieve specified requirements of microchip encapsulations with high and robust qualities. | |
dc.publisher | IEEE | |
dc.subject | neural fuzzy networks | |
dc.subject | Epoxy dispensing process | |
dc.subject | microchip encapsulation | |
dc.title | Determination of process conditions of epoxy dispensing processes using a genetic algorithm based neural fuzzy networks | |
dc.type | Conference Paper | |
dcterms.source.startPage | 2253 | |
dcterms.source.endPage | 2260 | |
dcterms.source.issn | 1098-7584 | |
dcterms.source.title | Proceedings of the IEEE international conference on fuzzy systems (FUZZ 2011) | |
dcterms.source.series | Proceedings of the IEEE international conference on fuzzy systems (FUZZ 2011) | |
dcterms.source.conference | IEEE International Conference on Fuzzy Systems (FUZZ 2011) | |
dcterms.source.conference-start-date | Jun 27 2011 | |
dcterms.source.conferencelocation | Taipei, Taiwan | |
dcterms.source.place | Taiwan | |
curtin.department | Digital Ecosystems and Business Intelligence Institute (DEBII) | |
curtin.accessStatus | Fulltext not available |