Inverse Identification of a Constitutive Model for High-Speed Forming Simulation: An Application to Electromagnetic...

Area of Science:

• Materials Science
• Mechanical Engineering
• Computational Mechanics

Background:

• Static tensile tests are insufficient for high-speed forming simulations due to transient effects at high strain rates.
• Accurate constitutive models are crucial for predicting material behavior during high-speed forming processes.

Purpose of the Study:

• To develop an effective inverse modeling strategy for identifying constitutive parameters under high strain rates.
• To enhance the efficiency and accuracy of high-speed forming simulations.

Main Methods:

• Formulated constitutive modeling as an inverse parameter estimation problem using regularized nonlinear least squares.
• Employed the L-curve method for regularization parameter selection and model order reduction for computational efficiency.
• Validated the identified model using electromagnetic metal forming simulation and dynamic material tests (split Hopkinson pressure bar).

Main Results:

• Successfully identified parameters for the modified Johnson-Cook model using a free bulge test.
• Demonstrated significant computational savings through model order reduction.
• Verified and validated both reduced and original simulation models against experimental data.

Conclusions:

• The proposed inverse constitutive modeling approach effectively addresses challenges in high-speed forming simulation.
• Regularized nonlinear least squares, combined with the L-curve method and reduced-order models, provides an efficient and accurate solution.
• This methodology enables reliable material characterization for dynamic forming processes.