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dc.contributor.authorChan, Kit Yan
dc.contributor.authorDillon, Tharam
dc.contributor.authorKwong, C.
dc.identifier.citationChan, K.Y. and Dillon, T.S. and Kwong, C.K. 2011. Modeling of a liquid epoxy molding process using a particle swarm optimization based fuzzy regression approach. IEEE Transactions on Industrial Informatics. 7 (1): pp. 148-158.

Modeling of manufacturing processes is important because it enables manufacturers to understand the process behavior and determine the optimum operating conditions of the process for a high yield, low cost and robust operation. However, existing techniques in modeling manufacturing processes cannot address the whole common issues in developing models for manufacturing processes: a) manufacturing processes are usually nonlinear in nature; b) a small amount of experimental data is only available for developing manufacturing process models; c) outliers often exist in experimental data; d) explicit models in a polynomial form are often prefered by manufacturing process engineers; e) models with satisfactory prediction accuracy are required. In this paper, a modeling algorithm, namely the particle swarm optimization based fuzzy regression (PSO-FR) approach, is proposed to generate fuzzy nonlinear regression models, which seek to address all of the common issues in developing models for manufacturing processes. The PSO-FR first employs the operations of particle swarm optimization to generate the structures of the process models in nonlinear polynomial form, and then it employs a fuzzy coefficient generator to identify outliers in the original experimental data. Fuzzy coefficients of the process models are determined by the fuzzy coefficient generator in which theexperimental data excluding the outliers is used. The effectiveness of the PSO-FR approach is evaluated by modeling the manufacturing process liquid epoxy molding process which is a commonly used technology for microchip encapsulation in electronic packaging.Results were compared with those based on the commonly used modeling methods. It was found that PSO-FR can achieve better goodness-of-fitness than other methods. Also the prediction accuracy of the model developed based on the PSO-FR is better than the other methods.

dc.subjectparticle swarm optimization
dc.subjectmanufacturing process
dc.subjectprocess modeling
dc.subjectfuzzy regression
dc.subjectliquid epoxy molding
dc.titleModeling of a liquid epoxy molding process using a particle swarm optimization based fuzzy regression approach
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Industrial Informatics
curtin.departmentDigital Ecosystems and Business Intelligence Institute (DEBII)
curtin.accessStatusFulltext not available

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