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Journal cover: Journal of Manufacturing Technology Management

Journal of Manufacturing Technology Management

ISSN: 1741-038X
Previously published as: Integrated Manufacturing Systems

Online from: 2004

Subject Area: Operations and Logistics Management

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

Document Information:
Title:Application of Self Organizing Map (SOM) to model a machining process
Author(s):Mohamad Saraee, (School of Computing, Science and Engineering, University of Salford, Salford, UK), Seyed Vahid Moosavi, (Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran), Shabnam Rezapour, (Industrial Engineering Department, Amirkabir University of Technology, Tehran, Iran)
Citation:Mohamad Saraee, Seyed Vahid Moosavi, Shabnam Rezapour, (2011) "Application of Self Organizing Map (SOM) to model a machining process", Journal of Manufacturing Technology Management, Vol. 22 Iss: 6, pp.818 - 830
Keywords:Artificial neural nets, Decision trees, Self Organizing Map
Article type:Research paper
DOI:10.1108/17410381111149666 (Permanent URL)
Publisher:Emerald Group Publishing Limited
Acknowledgements:The authors would like to thank the Machining Workshop Centre of Isfahan University of Technology for their collaborations and providing the dataset for this experiment.

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.

Originality/value – To the best of the authors' knowledge, this is the first time SOM and decision tree has been applied to the WEDM process successfully.

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