Nonlinear constitutive models from nanoindentation tests using artificial neural networks

Haj-Ali, Rami; Kim, Hoan-Kee; Koh, Sau; Saxena, Ashok; Tummala, Rao

doi:10.1016/j.ijplas.2007.02.001

Cited by 49 publications

(13 citation statements)

References 40 publications

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“…No microcrack deflection can be found in the ZrN 0.881 coating. During the penetrating of the indenter, previous studies have revealed that the compressive stress in the nitride is highest just underneath the contact surface and decreases towards the interior of the coating [39][40][41]. Thus, the penetration produced microcracks preferentially initiate just underneath the contact surface and propagate toward the interior of the ZrN 0.881 coating.…”

Section: Deformation Behaviors Of the Zr-si-n Nanocomposite Coatingsmentioning

confidence: 95%

Understanding hardness evolution of Zr–Si–N nanocomposite coatings via investigating their deformation behaviors

Wei

et al. 2016

Journal of the European Ceramic Society

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Section: Deformation Behaviors Of the Zr-si-n Nanocomposite Coatingsmentioning

confidence: 95%

Understanding hardness evolution of Zr–Si–N nanocomposite coatings via investigating their deformation behaviors

Wei

et al. 2016

Journal of the European Ceramic Society

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“…A study by Haj-Ali (Haj- Ali et al, 2008) reported the use of ANNs models in dealing with nanohardness tests in a wide range of materials with nonlinear behavior. ANNs models were trained with separate FE simulations with nonlinear properties and different geometries.…”

Section: Nanomaterialsmentioning

confidence: 99%

Artificial Neural Networks: Applications in Nanotechnology

Amani¹,

Mohammadyani²

2011

Artificial Neural Networks - Application

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“…Haj-Ali et al [17] developed ANN models trained with FE simulations for reproducing nanoindentation curves. However, only the loading part of the curve was used to generate the ANN models.…”

Section: Introductionmentioning

confidence: 99%

Identification of material properties using nanoindentation and surrogate modeling

Gutierrez²,

Toda

et al. 2016

International Journal of Solids and Structures

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In theory, identification of material properties of microscopic materials, such as thin film or single crystal, could be carried out with physical experimentation followed by simulation and optimization to fit the simulation result to the experimental data. However, the optimization with a number of finite element simulations tends to be computationally expensive. This paper proposes an identification methodology based on nanoindentation that aims at achieving a small number of finite element simulations. The methodology is based on the construction of a surrogate model using artificial neural-networks. A sampling scheme is proposed to improve the quality of the surrogate model. In addition, the differential evolution algorithm is applied to identify the material parameters that match the surrogate model with the experimental data.The proposed methodology is demonstrated with the nanoindentation of an aluminum matrix in a die cast aluminum alloy. The result indicates that the methodology has good computational efficiency and accuracy.

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Nonlinear constitutive models from nanoindentation tests using artificial neural networks

Cited by 49 publications

References 40 publications

Understanding hardness evolution of Zr–Si–N nanocomposite coatings via investigating their deformation behaviors

Understanding hardness evolution of Zr–Si–N nanocomposite coatings via investigating their deformation behaviors

Artificial Neural Networks: Applications in Nanotechnology

Identification of material properties using nanoindentation and surrogate modeling

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