AFM Indentation and Material Property Identification of Soft Hydrogels

Author(s):  
Sakya Tripathy ◽  
Edward Berger ◽  
Kumar Vemaganti

There is growing evidence of the importance of mechanical deformations on various facets of cell functioning. This asks for a proper understanding of the cell’s characteristics as a mechanical system in different physiological and mechanical loading conditions. Many researchers use atomic force microscopy (AFM) indentation and the Hertz contact model for elastic material property identification under shallow indentation. For larger indentations, many of the Hertz assumptions are not inherently satisfied and the Hertz model is not directly useful for characterizing nonlinear elastic or inelastic material properties. We have used exponential hyperelastic material in FE simulations of the AFM indentation tests. A parameter identification approach is developed for hyperelastic material property determination from the simulated data. We collected AFM indentation data on agarose gel and developed a simple algorithm for contact point detection. The contact point correction improves the prediction of elastic modulus over the case of visual contact point identification. The modulus of 1% agarose gel was found to be about 15 kPa using the proposed correction, with mild but non-trival hardening with deeper indentation. The experimental data is compared with the results from the FE simulations and shows that over the hardening portion of the indentation response, our proposed parameter identification approach successfully captures the experimental data.

Author(s):  
Di Yao ◽  
Philipp Ulbricht ◽  
Stefan Tonutti ◽  
Kay Büttner ◽  
Prokop Günther

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.


Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.


Author(s):  
Qun Chen ◽  
Zong-Xiao Yang ◽  
Zhumu Fu

Purpose The problem of parameter identification for biaxial piezoelectric stages is still a challenging task because of the existing hysteresis, dynamics and cross-axis coupling. This study aims to find an accurate and systematic approach to tackle this problem. Design/methodology/approach First, a dual-input and dual-output (DIDO) model with Duhem-type hysteresis is proposed to depict the dynamic behavior of the biaxial piezoelectric stage. Then, a systematic identification approach based on a modified differential evolution (DE) algorithm is proposed to identify the unknown parameters of the Duhem-type DIDO model for a biaxial piezostage. The randomness and parallelism of the modified DE algorithm guarantee its high efficiency. Findings The experimental results show that the characteristics of the biaxial piezoelectric stage can be identified with adequate accuracy based on the input–output data, and the peak-valley errors account for 2.8% of the full range in the X direction and 1.5% in the Y direction. The attained results validated the correctness and effectiveness of the presented identification method. Originality/value The classical DE algorithm has many adjustment parameters, which increases the inconvenience and difficulty of using in practice. The parameter identification of Duhem-type DIDO piezoelectric model is rarely studied in detail and its successful application based on DE algorithm on a biaxial piezostage is hitherto unexplored. To close this gap, this work proposed a modified DE-based systematic identification approach. It not only can identify this complicated model with more parameters, but also has little tuning parameters and thus is easy to use.


PAMM ◽  
2014 ◽  
Vol 14 (1) ◽  
pp. 341-342 ◽  
Author(s):  
Nils Hendrik Kröger ◽  
Daniel Juhre

2019 ◽  
Vol 23 (Suppl. 2) ◽  
pp. 575-582 ◽  
Author(s):  
Evgenii Kuznetsov ◽  
Sergey Leonov ◽  
Dmitry Tarkhov ◽  
Alexander Vasilyev

The paper deals with a parameter identification problem for creep and fracture model. The system of ordinary differential equations of kinetic creep theory is applied for describing this model. As for solving the parameter identification problem, we proposed to use the technique of neural network modeling, as well as the multilayer approach. The procedures of neural network modeling and multilayer approximation constructing application is demonstrated by the example of finding parameters for uniaxial tension model for isotropic steel 45 specimens at creep conditions. The solution corresponding to the obtained parameters agrees well with theoretical strain-damage characteristics, experimental data, and results of other authors.


Solar Energy ◽  
2019 ◽  
Vol 193 ◽  
pp. 51-64 ◽  
Author(s):  
Danny Jonas ◽  
Manuel Lämmle ◽  
Danjana Theis ◽  
Sebastian Schneider ◽  
Georg Frey

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