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Neural network modeling for the prediction of texture evolution of hot deformed aluminum alloys

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Abstract

Commercial aluminum rolling mills operate under very restricted thermomechanical conditions determined from experience and plant trials. In this paper we report results for four-stand tandem mill rolling simulations within and beyond the thermomechanical conditions typical of a rolling mill by plane strain compression (PSC) testing to assess the effect of deformed conditions on the texture of the hot deformed aluminum strip after annealing. A neural network modeling study was then initiated to find a predictive relationship between the observed texture and the thermomechanical parameters of strain, strain rate, and temperature. The model suggested that temperature is the prime variable that influences texture. Such models can be used to evaluate optimal strategies for the control of process parameters of a four-stand tandem mill.

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References

  1. R.A. Ricks: Philos. Trans. R. Soc. London, 1999, Ser. A, 357, pp. 1513–29.

    Article  CAS  ADS  Google Scholar 

  2. I. Gutierrez and M. Fuentes: “Influence of the Microstructural Changes Occurring During Steady State Hot Deformation on Static Recrystallization Kinetics and Recrystallized Grain Size of Commercial Aluminum,” Recrystallization 90., Ed., T Chandra TMS, 1990 Warrendale, PA pp. 807–12.

  3. N. Raghunathan, M.A. Zaidi, and T. Sheppard: “Recrystallization Kinetics of Al—Mg Alloys AA 5056 and AA 5083 After Hot Deformation,” Mater. Sci. Technol., 1986, 2, pp. 938–45.

    CAS  Google Scholar 

  4. C.M. Sellers, A.M. Irisarri, and E.S. Puchi: “Recrystallization Characteristics of Aluminum—1% Magnesium Under Hot Working Conditions” in Microstructural Control in Aluminum Alloys: Deformation, Recovery, and Recrystallization, E. Henry Chia and H.J. McQeen, ed., The Metallurgical Society/AIME, Warrendale, PA, 1986, pp. 179–96.

    Google Scholar 

  5. H.E. Vatne, T. Furu, R. Orsund, and E. Nes: “Modeling Recrystallization After Hot Deformation of Aluminum,” Acta. Mater., 1996, 44, pp. 4463–73.

    Article  CAS  Google Scholar 

  6. E.S. Puchi, J. Beynon, and C.M. Sellars: “Simulation of Hot Rolling Operations on Commercial Aluminum Alloys,” Proc. Int. Conf. On Physical Metallurgy of Thermomechanical Processing of Steels and Other Metals: THERMC ’88,. 1988, I. Tamura, ed., The Iron and Steel Institute of Japan, Tokyo, Japan, 1988, pp. 572–79.

    Google Scholar 

  7. P.D. Hodgson and R.K. Gibbs: “A Mathematical Model to Predict the Mechanical Properties of Hot Rolled C-Mn and Microalloyed Steels,” ISIJ Int., 1992, 32, pp. 1329–38.

    CAS  Google Scholar 

  8. R. Sandstrom and R. Lagneborg: “A Model for Static Recrystallization After Hot Deformation,” Acta Metall., 1975, 23, pp. 481–88.

    Article  CAS  Google Scholar 

  9. T. Furu, H.R. Shercliff, G.J. Baxter, and C.M. Sellars: “The Influence of Transient Deformation Conditions on Recrystallization During Thermomechanical Processing of an Al-1% Mg Alloy,” Acta Mater., 1999, 47, pp. 2377–89.

    Article  CAS  Google Scholar 

  10. H.K.D.H. Bhadeshia, D.J.C. Mackay, and L.E. Svensson: “Impact Toughness of C-Mn Steel Arc Welds—Bayesian Neural Network Analysis,” Mater. Sci. Technol., 1995, 11(10), pp. 1046–51.

    CAS  Google Scholar 

  11. S.M. Roberts, J. Kusiak, Y.L. Liu, A. Forcellese, and P.J. Withers: “Prediction of Damage Evolution in Forged Aluminum Metal Matrix Composites Using a Neural Network Approach,” J. Mater. Proc. Technol., 1998, 80–81, pp. 507–12.

    Article  Google Scholar 

  12. G.J. Marshall: “Simulation of Commercial Hot Rolling by Laboratory Plane Strain Compression and its Application to Aluminum Industry Challenges.” Proc. of the Second Symposium of Hot Deformation of Aluminum Alloys II, 1998, ed., T.R. Bieler, L.A. Lalli, and S.R. MacEwen, ed., The Minerals, Metals and Materials Society, Warrendale, PA, 1998, pp. 367–82.

    Google Scholar 

  13. D.J.C. Mackay: “Bayesian Interpolation,” Neural Comput., 1992, 4, pp. 415–47.

    Google Scholar 

  14. D.J.C. Mackay: “Probable Networks and Plausible Predictions-a Review of Practical Bayesian Methods for Supervised Neural Networks,” Network: Computat. Neural Syst., 1995, 6, pp. 469–505.

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Variable Energy Cyclotron Centre 1/AF, 700 064, Bidhan Nagar Kolkata, India

    P. Barat

  2. Materials Science Centre University of Manchester and UMIST, Grosvenor St., M17 HS, Manchester, UK

    P. J. Withers

Authors
  1. P. Barat
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  2. P. J. Withers
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Barat, P., Withers, P.J. Neural network modeling for the prediction of texture evolution of hot deformed aluminum alloys. J. of Materi Eng and Perform 12, 623–628 (2003). https://doi.org/10.1361/105994903322692402

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  • DOI: https://doi.org/10.1361/105994903322692402

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