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    A genetic programming based fuzzy regression approach to modelling manufacturing processes

    154599_154599.pdf (195.1Kb)
    Access Status
    Open access
    Authors
    Chan, Kit Yan
    Kwong, C.
    Tsim, Y.
    Date
    2010
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Chan, K.Y. and Kwong, C.K. and Tsim, Y.C. 2010. A genetic programming based fuzzy regression approach to modelling manufacturing processes. International Journal of Production Research. 48 (7): pp. 1967-1982.
    Source Title
    International Journal of Production Research
    DOI
    10.1080/00207540802644845
    ISSN
    00207543
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/23520
    Collection
    • Curtin Research Publications
    Abstract

    Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model.To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods.

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