Process Maps for Predicting Austenite Fraction (vol.%) in Medium-Mn Third-Generation Advanced High-Strength Steels

Area of Science:

• Materials Science
• Metallurgy
• Computational Materials Science

Background:

• Medium-manganese (Mn) steels are crucial for automotive applications due to their tunable mechanical properties.
• Controlling the austenite phase fraction during heat treatment is key to achieving desired steel performance.
• Predictive models are needed to optimize the complex intercritical annealing process.

Purpose of the Study:

• To develop predictive process maps for austenite volume fraction in medium-Mn steels.
• To understand the influence of intercritical annealing parameters and starting microstructure on austenite formation.
• To provide insights for optimizing steel properties for automotive applications.

Main Methods:

• Utilized a combination of microstructural analysis and DICTarray-based (DICTRA) modeling.
• Developed process maps correlating austenite volume percentage with intercritical annealing parameters.
• Employed a modified Koistinen and Marburger equation to calculate retained austenite at room temperature.

Main Results:

• Austenite fraction showed a strong dependence on manganese content (4-12 wt.%) and intercritical annealing temperatures (600-740 °C).
• Calculations were performed for specific carbon (0.2 wt.%), aluminum (1.5 wt.%), and silicon (1.0 wt.%) contents.
• The study quantified the relationship between annealing temperature, austenite composition, and retained austenite volume percentage.

Conclusions:

• The developed process maps offer valuable guidance for designing intercritical treatments in medium-Mn steels.
• These maps facilitate the optimization of steel microstructures for enhanced properties in automotive components.
• Predictive modeling aids in efficient material design and property tailoring for specific applications.