Neural networks for tip correction of spherical indentation curves from bulk metals and thin metal films

DOI10.1016/J.JMPS.2006.07.003zbMath1162.74417OpenAlexW2113049767MaRDI QIDQ1019406

N. Huber, E. Tyulyukovskiy

Publication date: 2 June 2009

Published in: Journal of the Mechanics and Physics of Solids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmps.2006.07.003

zbMATH Keywords

elastic-viscoplastic materialfinite elementsneural networkscontact mechanicsindentation and hardness

Mathematics Subject Classification ID

Small-strain, rate-dependent theories of plasticity (including theories of viscoplasticity) (74C10) Contact in solid mechanics (74M15) Finite element methods applied to problems in solid mechanics (74S05) Experimental work for problems pertaining to mechanics of deformable solids (74-05) Thin films (74K35)

Related Items (3)

Review on numerical modeling of instrumented indentation tests for elastoplastic material behavior identification ⋮ Modelling and parameter re-identification of nanoindentation of soft polymers taking into account effects of surface roughness ⋮ Nonlinear constitutive models from nanoindentation tests using artificial neural networks

Cites Work

• A numerical approach to spherical indentation techniques for material property evaluation
• New analytical procedure to determine stress-strain curve from spherical indentation data
• Finite deformation plasticity and viscoplasticity laws exhibiting nonlinear hardening rules. I: Constitutive theory and numerical integration
• Determination of constitutive properties of thin metallic films on substrates by spherical indentation using neural networks
• 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
• A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery

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