Automatic parameter identification of a shape memory alloy model using characteristic experimental data points

DOI10.1016/j.euromechsol.2024.105300MaRDI QIDQ6558113

Michael Kaliske, Lucas A. Woodworth

Publication date: 18 June 2024

Published in: European Journal of Mechanics. A. Solids (Search for Journal in Brave)

zbMATH Keywords

constitutive modelshape memory alloysplasticityfunctional fatiguematerial parameter identification

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX)

Cites Work

• Yield or martensitic phase transformation conditions and dissipation functions for isotropic, pressure-insensitive alloys exhibiting SD effect
• Generalized parameter identification for finite viscoelasticity
• Determination of constitutive properties from spherical indentation data using neural networks. I: The case of pure kinematic hardening in plasticity laws. II: Plasticity with nonlinear isotropic and kinematic hardening
• Identification of material properties using indentation test and shape manifold learning approach
• Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences

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