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Mehdi Shaban Ghazani

Mehdi Shaban Ghazani

Academic rank: Associate Professor
ORCID: 0000-0003-4942-4157
Education: PhD.
ScopusId: 57194052303
HIndex: 0/00
Faculty: Faculty of Interdisciplinary Sciences and Technologies
Address:
Phone: 04137745000

Research

Title
Application of Artificial Neural Network to Predict the Hot Flow Behavior of Ti-Nb Microalloyed Steel During Hot Torsion Deformation
Type
JournalPaper
Keywords
Ti-Nb microalloyed steel; Hot compression; Flow stress prediction; Artificial neural network; Dynamic recrystallization;
Year
2022
Journal TRANSACTIONS OF THE INDIAN INSTITUTE OF METALS
DOI
Researchers Mehdi Shaban Ghazani

Abstract

In the present study, the hot compressive deformation behavior of Ti-Nb bearing microalloyed steel was investigated in the temperature range of 850–1100 C and strain rates of 0.01–1 s-1. Flow curves obtained from hot compression tests were analyzed, and it was demonstrated that dynamic recrystallization is the main softening mechanism during deformation at 900, 1000, and 1100 C at all strain rates. It was also demonstrated that dynamic recovery occurs during deformation at 800 C. Based on the obtained results, an artificial neural network was constructed to predict the hot flow softening behavior of Ti-Nb microalloyed steel which is due to the occurrence of dynamic recrystallization and recovery. Results obtained from artificial neural network were compared with the experimental values of flow stress, and a very good correlation was observed between them. This indicates the excellent capability of the developed artificial neural network to predict the flow softening due to complex microstructural evolutions accompanying dynamic recrystallization and dynamic recovery.