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Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill

  • Metallurgy and Metal Working
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Abstract

Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule-base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a reallife mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The performance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve temperature estimation; however, the best results arc obtained when fuzzy C-means algorithm, grey-box modeling and learning functions arc combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.

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Authors and Affiliations

  1. Mechanical and Electrical Engineering Faculty, Autonomous University of Nuevo Leon, 66451, San Nicolás de los Garza, Nuevo Leon, México

    José Angel Barrios (Doctor Candidate, Lectureship), Alberto Cavazos (Doctor, Professor) & Rafael Colás

  2. Hot Rolling Department, Ternium Churubusco, 64000, Monterrey, Nuevo Leon, México

    César Villanueva

Authors
  1. José Angel Barrios
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  2. César Villanueva
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  3. Alberto Cavazos
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  4. Rafael Colás
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Corresponding authors

Correspondence to José Angel Barrios or Alberto Cavazos.

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Barrios, J.A., Villanueva, C., Cavazos, A. et al. Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill. J. Iron Steel Res. Int. 23, 116–123 (2016). https://doi.org/10.1016/S1006-706X(16)30022-X

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  • DOI: https://doi.org/10.1016/S1006-706X(16)30022-X

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