Modeling Microstructural Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network


An artificial neural network (ANN) model was developed to predict the microstructural evolution of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel (Alloy D9) during dynamic recrystallization (DRX). The input parameters were strain, strain rate, and temperature whereas microstructural features namely, %DRX and average grain size were the output parameters. The ANN was trained with the database obtained from various industrial scale metal-forming operations like forge hammer, hydraulic press, and rolling carried out in the temperature range 1173-1473 K to various strain levels. The performance of the model was evaluated using a wide variety of statistical indices and the predictability of the model was found to be good. The combined influence of temperature and strain on microstructural features has been simulated employing the developed model. The results were found to be consistent with the relevant fundamental metallurgical phenomena.

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    There were two models, one for %DRX and another for average grain size. However, in the text sometimes the word “model” is used to refer to the two models collectively.


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The authors would like to express their sincere thanks to Dr. S. Venugopal, Head, Metal Forming & Tribology Section and Dr. S.K. Ray, Head, Materials Technology Division for useful discussions. The authors also gratefully acknowledge Dr. Baldev Raj, Director, Indira Gandhi Centre for Atomic Research (IGCAR) for his constant encouragement throughout the course of this work.

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Correspondence to P.V. Sivaprasad.

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Mandal, S., Sivaprasad, P. & Dube, R. Modeling Microstructural Evolution During Dynamic Recrystallization of Alloy D9 Using Artificial Neural Network. J. of Materi Eng and Perform 16, 672–679 (2007).

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  • artificial neural network
  • austenitic stainless steel
  • dynamic recrystallization
  • grain size
  • microstructural evolution