Identification of Dynamic Recrystallization Model Parameters for 40CrMnMoA Alloy Steel Using the Inverse Optimization...

Area of Science:

• Materials Science
• Metallurgy
• Computational Materials Science

Background:

• Microstructure of 40CrMnMoA steel during hot forging dictates its mechanical properties.
• Dynamic recrystallization (DRX) is crucial for refining grain structure and enhancing material properties.
• Accurate prediction of DRX behavior and mechanical properties during hot forging is essential for material processing.

Purpose of the Study:

• To develop and validate an inverse optimization method for accurately determining DRX model parameters in 40CrMnMoA steel.
• To enhance the prediction accuracy of DRX volume fraction and microstructure evolution during hot forging.
• To integrate the optimized DRX model into finite element software for process simulation.

Main Methods:

• Uniaxial isothermal compression experiments on 40CrMnMoA steel (900–1200 °C, 0.005–5 s⁻¹).
• Initial DRX model establishment using true stress-strain data and the traditional averaging method.
• Inverse optimization of DRX model parameters using the adaptive simulated annealing (ASA) algorithm, minimizing mean-square error.

Main Results:

• The optimized DRX model achieved a correlation coefficient (R) of 0.992.
• Average absolute relative error (AARE) and root mean square error (RMSE) for DRX percentage were reduced by 34% and 2%, respectively.
• Finite element simulation using the optimized model showed less than 3% relative error in grain size compared to actual samples.

Conclusions:

• The inverse optimization method accurately identifies DRX model parameters for 40CrMnMoA alloy steel.
• The optimized DRX model significantly enhances the prediction accuracy of microstructure evolution during hot forging.
• This approach provides a reliable tool for simulating and optimizing hot forging processes for improved material properties.