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Business Intelligence and Performance Management pp 191–208Cite as

Design and Automation for Manufacturing Processes: An Intelligent Business Modeling Using Adaptive Neuro-Fuzzy Inference Systems

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Abstract

The design and automation of a steel making process is getting more complex as a result of the advances in manufacturing and becoming more demanding in quality requirements. It is essential to have an intelligent business process model which brings consistent and outstanding product quality thus keeping the trust with the business stakeholders. Hence, schemes are highly needed for improving the nonlinear process automation. The empirical mathematical model for steel making process is usually time consuming and may require high processing power. Fuzzy neural approach has recently proved to be very beneficial in the identification of such complex nonlinear systems. In this chapter, we discuss the applicability of an Adaptive Neuro-Fuzzy Inference System (ANFIS) to model the dynamics of the hot rolling industrial process including: roll force, roll torque and slab temperature. The proposed system was developed, tested as well as compared with other existing systems. We have conducted several simulation experiments on real data and the results confirm the effectiveness of the ANFIS based algorithms.

Keywords

  • Mean Square Error
  • Fuzzy Inference System
  • Minimum Mean Square Error
  • Rolling Process
  • Roll Force

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgements

Authors would like to thank Prof. Dr. Can Özsoy and the Ereg̃li Iron and Steel Factory for providing the required technical assistant and the data for this developed research work.

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Authors and Affiliations

  1. Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia

    Alaa F. Sheta & Mehedi Masud

  2. Electronic, Electrical and Computer Engineering Department, University of Birmingham, Birmingham, Edgbaston, UK

    Malik Braik

  3. Marine Engineering Operations Department, Faculty of Naval Architecture and Maritime, Yildiz Technical University, Istanbul, Turkey

    Ertan Öznergiz

  4. Faculty of Technology, De Montfort University, Leicester, LE1 9BH, UK

    Aladdin Ayesh

Authors
  1. Alaa F. Sheta
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  2. Malik Braik
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  3. Ertan Öznergiz
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  4. Aladdin Ayesh
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  5. Mehedi Masud
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Corresponding author

Correspondence to Alaa F. Sheta .

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Editors and Affiliations

  1. Department of Computer Science, Georg Simon Ohm Univ. of Applied Science, Philipp-Kittler-Str. 18, Nuremberg, 90480, Germany

    Peter Rausch

  2. Department of Computer Science, Taif University, Makkah, Al-huwayah, Taif, PO888, Saudi Arabia

    Alaa F. Sheta

  3. Faculty of Technology, De Montfort University, The Gateway, Leicester, LE1 9BH, Leicestershire, United Kingdom

    Aladdin Ayesh

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Sheta, A.F., Braik, M., Öznergiz, E., Ayesh, A., Masud, M. (2013). Design and Automation for Manufacturing Processes: An Intelligent Business Modeling Using Adaptive Neuro-Fuzzy Inference Systems. In: Rausch, P., Sheta, A., Ayesh, A. (eds) Business Intelligence and Performance Management. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-4866-1_13

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  • DOI: https://doi.org/10.1007/978-1-4471-4866-1_13

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