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Application of Self Organizing Map (SOM) to model a machining process

Saraee, M, Moosavi, S and Rezapour, S 2011, 'Application of Self Organizing Map (SOM) to model a machining process' , Journal of Manufacturing Technology Management, 22 (6) , pp. 818-830.

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    Purpose: This paper aims to present a practical application of Self Organizing Map (SOM) and decision tree algorithms to model a multi-response machining process and to provide a set of control rules for this process. Design/methodology/approach: SOM is a powerful artificial neural network approach used for analyzing and visualizing high-dimensional data. Wire electrical discharge machining (WEDM) process is a complex and expensive machining process, in which there are a lot of factors having effects on the outputs of the process. In this work, after collecting a dataset based on a series of designed experiments, the paper applied SOM to this dataset in order to analyse the underlying relations between input and output variables as well as interactions between input variables. The results are compared with the results obtained from decision tree algorithm. Findings: Based on the analysis of the results obtained, the paper extracted interrelationships between variables as well as a set of control rules for prediction of the process outputs. The results of the new experiments based on these rules, clearly demonstrate that the paper's predictions are valid, interesting and useful.

    Item Type: Article
    Themes: Built and Human Environment
    Subjects outside of the University Themes
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Support Divisions > Research & Innovation
    Journal or Publication Title: Journal of Manufacturing Technology Management
    Publisher: Emerald
    Refereed: Yes
    ISSN: 1741-038X
    Related URLs:
    Depositing User: Dr Mo Saraee
    Date Deposited: 19 Oct 2011 11:27
    Last Modified: 23 Sep 2013 11:29

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