About this Abstract |
| Meeting |
2027 TMS Annual Meeting & Exhibition
|
| Symposium
|
Physical Thermo-Mechanical Simulation: Laboratory Advances and In-Situ Diffraction Methods
|
| Presentation Title |
An Integrated Framework for Constitutive Modelling and Hot Workability Mapping for Optimization of Processing Window in Steels |
| Author(s) |
Deepayan Roy, Lambodar Sahoo, Atul Kumar, Gopinath K., Balamuralikrishnan R. |
| On-Site Speaker (Planned) |
Deepayan Roy |
| Abstract Scope |
Optimizing industrial processing of advanced steels requires precise recrystallization control. This study presents a framework to characterize complex flow behaviour during thermomechanical processing of a low-carbon HSLA steel.
This work bridges laboratory simulations with industrial practices by developing advanced Activation Energy & Processing (AEP) and hot rolling maps. To accurately predict highly non-linear flow behaviour, an advanced machine learning-based Arrhenius-type (ML-AT) constitutive model was used on the experimental flow data acquired from hot deformation tests conducted in the austenitic regime, overcoming limitations of traditional linear regression methods. AEP maps systematically evaluate intrinsic thermo-mechanical responses to delineate safe microstructural domains from flow instability. Furthermore, hot rolling maps translate these fundamental limits into applied multi-pass industrial schedules to control inter-pass recrystallization.
By establishing this predictive framework first, subsequent industrial hot rolling was successfully executed. This validates the approach, determining optimum processing windows and offering deeper insights into hot workability of steels. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Iron and Steel, Machine Learning, Characterization |