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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. (English) Zbl 0960.74015
Summary: The power of neural networks in identifying material parameters from data obtained by spherical indentation is demonstrated for an academic problem (pure kinematic hardening, given Young’s modulus). To obtain a data basis for the training and validation of the neural network, numerous finite element simulations are carried out for various sets of material parameters. The constitutive model describing finite deformation plasticity is formulated with nonlinear kinematic hardening of Armstrong-Frederick type. The depth-load response of a cyclic indentation process, consisting of loading, unloading and reloading of the indenter, displays a typical hysteresis loop for given material parameters. The inverse problem of leading the depth-load response back to the related parameters in the constitutive equations is solved by using a neutral network.

MSC:
74C15 Large-strain, rate-independent theories of plasticity (including nonlinear plasticity)
74S05 Finite element methods applied to problems in solid mechanics
92B20 Neural networks for/in biological studies, artificial life and related topics
Software:
ABAQUS
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