Authors: Sumantra Mandal PV Sivaprasad RK Dube
Publish Date: 2007/06/12
Volume: 16, Issue: 6, Pages: 672-
Abstract
An artificial neural network ANN model was developed to predict the microstructural evolution of a 15Cr15Ni22MoTi 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 metalforming operations like forge hammer hydraulic press and rolling carried out in the temperature range 11731473 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 phenomenaThe authors would like to express their sincere thanks to Dr S Venugopal Head Metal Forming Tribology Section and Dr SK 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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