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Simulation of PLC Effect Using Regularized Large-Strain Elasto-Plasticity.
Marzena Mucha1, Balbina Wcisło1, Jerzy Pamin1
1Chair for Computational Engineering, Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland.
Materials (Basel, Switzerland)
|June 24, 2022
Summary
This study models the Portevin-Le Chatelier (PLC) effect in metallic materials using advanced computational techniques. Numerical simulations accurately replicate the observed serrated stress-strain behavior and material instabilities.
Area of Science:
- Materials Science
- Computational Mechanics
- Solid Mechanics
Background:
- The Portevin-Le Chatelier (PLC) effect is a material instability observed in metallic alloys.
- It is characterized by localized plastic shear bands and serrations in stress-strain curves.
- Understanding and predicting the PLC effect is crucial for material design and performance.
Purpose of the Study:
- To develop a constitutive model for simulating the Portevin-Le Chatelier (PLC) effect.
- To numerically investigate the phenomenon of propagating instabilities in metallic materials.
- To validate the model against experimental data for tensioned specimens.
Main Methods:
- Development of a geometrically non-linear thermo-visco-plasticity model with an Estrin-McCormick hardening function.
- Incorporation of viscosity and heat conductivity to regularize softening effects.
- Numerical simulations using AceGen/FEM on rod and dog-bone specimens.
Main Results:
- The developed model successfully reproduces the serrated stress-strain response characteristic of the PLC effect.
- Simulations show good agreement with experimental results for a dog-bone specimen.
- Parameter sensitivity studies align with expectations for the pre-peak regime.
Conclusions:
- The proposed constitutive model provides a viable framework for simulating the Portevin-Le Chatelier (PLC) effect.
- Numerical simulations are effective in capturing the complex behavior of this material instability.
- The study confirms the model's predictive capability and its consistency with experimental observations.
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