Identification of hyper-viscoelastic material parameters of a soft member connected to another unidentified member by applying a dynamic load

Hajhashemkhani, Maedeh; Hematiyan, M. R.; Goenezen, Sevan

doi:10.1016/j.ijsolstr.2019.01.032

Cited by 17 publications

(4 citation statements)

References 36 publications

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“…Herein, we employ the damped Gauss-Newton (DGN) method [36] for solving the optimization problem. The authors have found this gradient-based optimization method very efficient for analysis of various inverse problems, e.g., [37][38][39][40]. The vector of unknowns, i.e.…”

Section: -Inverse Analysismentioning

confidence: 99%

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

Hematiyan,

Khosravifard,

Mohammadi

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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There are six elastic constants for an anisotropic body in plane strain/stress conditions. In the inverse problem of this study, it is assumed that the elastic constants of an anisotropic body are unknown, while the displacements or strains at several sampling points of the body under static loading are provided. For the first time, a boundary-type meshfree method, i.e. the method of fundamental solutions, is used for solving the problem, where the sensitivity analysis is performed by direct differentiation of the discretized equations. For that, the closed-form relations for sensitivity of the displacements/strains with respect to the elastic constants are analytically derived.Using a numerical study, it is shown that the proposed sensitivity analysis is much more advantageous compared to traditional finite difference approximation. A simple method for proper selection of initial guesses is also proposed. Two different example problems under plane strain and plane stress conditions are provided to investigate the accuracy of the proposed inverse method.Moreover, the effects of the number of measurement data, the measurement error, and the configuration of sampling points on the solution of the inverse problem are studied. It is observed that the solutions are more accurate in the cases where the sampling points are located at different parts of the body.

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Section: -Inverse Analysismentioning

confidence: 99%

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

Hematiyan,

Khosravifard,

Mohammadi

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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“…In Figure 1 a schematic of the inverse algorithm is shown. Inverse analysis based on the damped Gauss-Newton method can results in stable and accurate solutions [27][28][29] .…”

Section: Inverse Analysismentioning

confidence: 99%

An inverse method for elastic constants identification of two-layer hyperelastic bodies with suction loading

Hajhashemkhani,

Hematiyan

2023

Proceedings of the Institution of Mechanical Engineers, Part L:

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Some soft tissues of the human body and biomaterials are considered as multi-layer structures with layer-specific material properties. Identification of the material parameters of these distinct layers is challenging and has attracted considerable attention in recent decades. In this research, an inverse finite element method, which uses the data obtained from simulated suction experiments, is developed. The method employs a limited number of surface-measured data to recover the layer-specific hyperelastic material parameters of a two-layer structure. The Gauss-Newton method is employed to minimize a cost function, which is defined in terms of the difference between measured and calculated displacements of the surface points. The sensitivity analysis during the optimization process is carried out by the finite difference method. The effects of pipette diameter, noise in experimental data, the initial guess and the hyperelastic material model, on the robustness and efficiency of the inverse method are investigated.

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“…However, one must take into account both time-dependent and time-independent properties to obtain an accurate response of elastomeric materials. Several researchers 18,[49][50][51][52][53][54] have combined the hyperelastic and viscoelastic models to create a hyper-viscoelastic model in order to understand the mechanical response of elastomeric materials. Maedeh Hajhashemkhani et al 50 developed an inverse algorithm to obtain material parameters of a hyper-viscoelastic model of a soft member.…”

Section: Introductionmentioning

confidence: 99%

“…Several researchers 18,[49][50][51][52][53][54] have combined the hyperelastic and viscoelastic models to create a hyper-viscoelastic model in order to understand the mechanical response of elastomeric materials. Maedeh Hajhashemkhani et al 50 developed an inverse algorithm to obtain material parameters of a hyper-viscoelastic model of a soft member. The model was built using a Neo-hookean spring and Maxwell elements connected in parallel.…”

Section: Introductionmentioning

confidence: 99%

A hyper-viscoelastic constitutive model for elastomers: A case study of hydrogenated nitrile butadiene rubber and polychloroprene rubber

Dhakad,

Hipparkar,

Kumar

et al. 2023

Journal of Elastomers & Plastics

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Elastomer materials are widely used as shock absorbers in a variety of practical applications including the automotive, aerospace, marine industries and structures. The straightforward Hooke’s law is insufficient to adequately describe the behavior of elastomeric materials due to their nonlinear elastic and viscous properties. This study proposes a hyper-viscoelastic model to describe the mechanical behavior of Hydrogenated Nitrile Butadiene Rubber (HNBR) 62 Duro shore A and Polychloroprene Rubber (PCR) 55 Duro shore A. Six distinct hyperelastic models—Yeoh, Rivlin, Arruda-Boyce, Mooney-Rivlin, Neo-Hookean, and Ogden— are compared herein to explain the nonlinear elastic behavior of elastomers. While the time-dependent characteristics of the considered materials have been described using the four parameters Generalised Maxwell (GM) model. The material parameters of models are determined using the least square fit (LSF) optimization algorithm from the uniaxial, planar, and stress relaxation test data. The stability and suitability of each hyperelastic model is assessed using the Drucker stability. The accuracy of each model is represented by Root Mean Square Error (RMSE) of curve fitting between theoretical and experimental data. On this basis, the Ogden order three ( N = 3) model and the four-parameter GM models are selected which well agreed with the experimental test data and they are integrated to generate the hyper-viscoelastic constitutive model. In addition, the hyper-viscoelastic model is used to simulate the HNBR and PCR dampers under shock load. The numerically generated response is finally compared with the experimental test result of natural rubber (NR) 60 IRH from the existing literature to confirm accuracy of the proposed model.

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Identification of hyper-viscoelastic material parameters of a soft member connected to another unidentified member by applying a dynamic load

Cited by 17 publications

References 36 publications

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

An inverse method for elastic constants identification of two-layer hyperelastic bodies with suction loading

A hyper-viscoelastic constitutive model for elastomers: A case study of hydrogenated nitrile butadiene rubber and polychloroprene rubber

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