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Modeling batch annealing process using data mining techniques for cold rolled steel sheets

Saraee, M, Moghimi, M and Bagheri, A 2011, Modeling batch annealing process using data mining techniques for cold rolled steel sheets , in: The 17th Annual ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 21-24 August 2011, San Diego.

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    Abstract

    The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained.

    Item Type: Conference or Workshop Item (Paper)
    Themes: Energy
    Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
    Journal or Publication Title: Proceedings of the First International Workshop on Data Mining for Service and Maintenance
    Publisher: ACM
    Refereed: Yes
    Depositing User: Dr Mo Saraee
    Date Deposited: 28 Oct 2011 11:53
    Last Modified: 17 Sep 2013 16:12
    URI: http://usir.salford.ac.uk/id/eprint/18766

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