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

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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.


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

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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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Correspondence to Alaa F. Sheta .

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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.

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