Genetic-Algorithm-Based Inverse Optimization Identification Method for Hot-Temperature Constitutive Model Parameters...

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Modeling

Background:

• Accurate constitutive models are crucial for finite element simulation in material volume forming and hot working process optimization.
• Genetic algorithms (GA) offer a method for inverse optimization to identify precise constitutive model parameters.

Purpose of the Study:

• To develop and compare two constitutive models for high-temperature deformation of Ti6Al4V alloy.
• To utilize a genetic algorithm (GA) for precise parameter identification in constitutive models.
• To evaluate the predictive accuracy of the Arrhenius-type (A-T) and Johnson-Cook (JC) models.

Main Methods:

• Hot compression experiments were conducted using a Gleeble-1500D thermal simulator.
• Temperatures ranged from 800 °C to 1000 °C, with strain rates from 0.01 s⁻¹ to 1 s⁻¹.
• The Arrhenius-type (A-T) and Johnson-Cook (JC) models were constructed and optimized using regression and GA-based inverse optimization.

Main Results:

• Both optimized A-T and JC models exhibited high prediction accuracy.
• The optimized A-T model showed a higher correlation coefficient (R) and lower average absolute relative error (AARE) compared to the JC model.
• The A-T model demonstrated a more concentrated relative error distribution, indicating better reliability.

Conclusions:

• The optimized Arrhenius-type (A-T) model is more suitable than the Johnson-Cook (JC) model for characterizing the high-temperature deformation behavior of Ti6Al4V alloy.
• Genetic algorithm-based inverse optimization enhances the precision of constitutive model parameters.
• Accurate constitutive models are vital for advancing material forming simulations and process optimization.