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dc.contributor.authorChan, Kit Yan
dc.contributor.authorLing, S.
dc.contributor.authorDillon, Tharam
dc.contributor.authorKwong, C.
dc.contributor.editorChin-Teng Lin and Yau-Huang Kuo
dc.identifier.citationChan, K.Y. and Ling, S.H. and Dillon, T.S. and Kwong, C.K. 2011. Manufacturing modeling using an evolutionary fuzzy regression, in IEEE International Conference on Fuzzy Systems (FUZZ 2011), Jun 27-30 2011. Taipei, Taiwan: IEEE.

Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out.

dc.subjectmanufacturing process modelling
dc.subjectfuzzy regression
dc.titleManufacturing modeling using an evolutionary fuzzy regression
dc.typeConference Paper
dcterms.source.titleProceedings of the IEEE international conference on fuzzy systems (FUZZ 2011)
dcterms.source.seriesProceedings of the IEEE international conference on fuzzy systems (FUZZ 2011)
dcterms.source.conferenceIEEE International Conference on Fuzzy Systems (FUZZ 2011)
dcterms.source.conference-start-dateJun 27 2011
dcterms.source.conferencelocationTaipei, Taiwan
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available

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