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2022
Conference Paper
Titel
Prediction of microstructure evolution in hot forging and heat treatment using a mean-field material model
Abstract
Reliable simulation tools predicting the material behavior under thermo-mechanical loads are essential for optimizing hot forming and heat treatment processes. By speeding up the development process and reducing the number of trial and error cycles, they can help to improve the resulting material properties, make manufacturing processes faster, and reduce energy consumption. The material behavior and the resulting properties are significantly affected by the microstructure and its evolution including recrystallization, grain coarsening, and precipitate formation. We present a versatile physics-based material model linking a mean-field approach for the microstructure evolution and the thermo-mechanical material behavior using a comprehensive thermodynamic framework. The model is numerically implemented as a stand-alone process simulation tool as well as a post-processor applicable to finite element simulations. We demonstrate the application of both implementations to the hot forging of a microalloyed steel.
Funder
Deutsche Forschungsgemeinschaft -DFG-, Bonn