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A NON-SCHMID CRYSTAL PLASTICITY FINITE ELEMENT APPROACH TO MULTI-SCALE MODELING OF NICKEL-BASED SUPERALLOYS
Shahriyar Keshavarz1,2, Somnath Ghosh3, Andrew C E Reid2
1Theiss Research, La Jolla, CA, United States.
Summary
This study introduces a multi-scale crystal plasticity model for nickel-based superalloys, capturing thermomechanical behavior across wide temperature ranges. The model accurately predicts orientation-dependent behavior and accelerates simulations by parameterizing microstructural features.
Area of Science:
- Materials Science
- Mechanical Engineering
- Computational Mechanics
Background:
- Nickel-based superalloys are critical for high-temperature applications.
- Accurate constitutive models are needed to predict their thermomechanical behavior.
- Existing models often lack multi-scale capabilities and detailed microstructural representation.
Purpose of the Study:
- To develop and integrate non-Schmid crystal plasticity models across two length scales (sub-grain and homogenized grain).
- To capture the thermomechanical behavior of nickel-based superalloys from 300K to 1223K.
- To incorporate orientation dependencies and tension-compression asymmetry.
Main Methods:
- Developed a size-dependent, dislocation density-based FEM model for the sub-grain Representative Volume Element (RVE).
- Created an Activation Energy based Crystal Plasticity (AE-CP) model for single crystals.
- Bridged sub-grain and homogenized grain scales within a multi-scale framework.
Main Results:
- The models accurately represent orientation dependencies and tension-compression asymmetry.
- The homogenized AE-CP model's parameters are linked to microstructural features (γ' shape, volume fraction, channel-width).
- Simulations show distinct trends for yield stress and hardening in stronger tension/compression directions.
Conclusions:
- The multi-scale framework provides an accurate and efficient approach for simulating nickel-based superalloys.
- The homogenized model significantly speeds up crystal plasticity FE simulations.
- The approach retains accuracy while enabling faster computational predictions.

