A smart rheologic MSD model pushed/calibrated/evaluated by experimental impulses

Yoshida, Haruyuki; Murata, Yuki; Noborio, Hiroshi

doi:10.1109/iros.2005.1545209

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…By taking its time derivative, we haveu Voigt N (t) + u vis N (t) = 0. Substituted this into (15) and considered the second equation of (14) with λ vis 2 µ vis 1 = λ vis 1 µ vis 2 , we have:…”

Section: A Appendix Amentioning

confidence: 99%

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Physical parameter identification of rheological object based on measurement of deformation and force

Wang

Namima

Hirai

2009

2009 IEEE International Conference on Robotics and Automation

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There are many kinds of deformable objects in our living life. Some of them exhibit rheological behaviors when they are subject to external force, such as human tissues, human organs, and food. If we want to simulate or control such behaviors, we have to know the physical parameters of the object in advance. In this paper, we propose an approach to identify these parameters based on 2D finite element (FE) simulation and measurement of deformation and force. At first, 2D FE model used to simulate rheological deformation was described. Then, identification method was presented according to the analysis of simulation results. Identification results for simulation were also given. Finally, this method was applied to a object made of clay. Deformation and force were measured by camera and tactile sensor respectively. The identification results show the validity and effectiveness of this method.

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Section: A Appendix Amentioning

confidence: 99%

“…Some of them employed MSD model to simulate and analyze the deformation of objects [12], [13], [14], [15]. But none of them took inner deformation into account.…”

Section: Introductionmentioning

confidence: 99%

Physical parameter identification of rheological object based on measurement of deformation and force

Wang

Namima

Hirai

2009

2009 IEEE International Conference on Robotics and Automation

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“…The authors explored the lattice [Noborio et al (2003), Nogami et al (2004a)], the truss [Nogami et al (2004b)], and the hierarchical [Ikawa & Noborio (2007)] structures, with decreased MSD elements connected between nodes to reduce the computation cost. Two optimization methods, modified randomized algorithm [Noborio et al (2003)] and genetic algorithm [Yoshida et al (2005)], were used to estimate the physical parameters. The authors successfully captured the deformation behaviors, but failed to reproduce the force responses at the same time [Yoshida et al (2005)].…”

Section: Introductionmentioning

confidence: 99%

“…Two optimization methods, modified randomized algorithm [Noborio et al (2003)] and genetic algorithm [Yoshida et al (2005)], were used to estimate the physical parameters. The authors successfully captured the deformation behaviors, but failed to reproduce the force responses at the same time [Yoshida et al (2005)]. The MSD modeling method has advantages of simple formulation and relatively low computation cost, but the formulation is not based on continuum mechanics and the geometrical topology significantly affects the simulation accuracy.…”

Section: Introductionmentioning

confidence: 99%

Finite Element Modeling and Physical Property Estimation of Rheological Food Objects

Wang¹,

Hirai²

2012

JFR

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The purpose of this study is to accurately simulate the rheological behaviors of food objects undergoing a loading-unloading operation using finite element (FE) model. Due to the presence of residual deformation, it is difficult to model rheological behaviors. Especially, it is hard to accurately reproduce both rheological force and residual deformation simultaneously. In this study, objects made of food materials were tested. Force and deformation measurements were recorded for parameter estimation. Constitutive models were investigated for describing rheological behaviors. A parallel five-element model including two dual-moduli viscous elements was proposed to accurately predict both rheological force and residual deformation simultaneously. 2D/3D FE model was formulated for simulating rheological behaviors. To estimate the parameters, an effective four-step method was established based on nonlinear optimization which aimed at minimizing the differences of forces and deformation between simulation and experiments. The proposed FE model and parameter estimation method were validated in both 2D and 3D cases and good agreements were achieved in both rheological forces and deformation between numerically simulated and experimentally measured data.

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“…Unfortunately, this model is still a 1D case. Mass-springdamper (MSD) model was employed to model a food dough and different topological structures were investigated in [5], [6]. The MSD model has an advantage of low computational cost [7], but the formulation is not based on continuum mechanics and the simulation accuracy is quite limited.…”

Section: Introductionmentioning

confidence: 99%

Green strain based FE modeling of rheological objects for handling large deformation and rotation

Wang

Hirai

2011

2011 IEEE International Conference on Robotics and Automation

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Rheological object, such as clay, various food products, biological organs and tissues, has both elastic and plastic properties. Modeling of such objects has not been studied intensively comparing with elastic objects. Previous rheological models were mostly based on linear constitutive law of stress and strain, which is limited to small deformation. In this paper, finite element (FE) model of rheological objects based on linear Cauchy strain tensor was summarized at first. Nonlinear FE model formulated with Green strain tensor was then presented for simulating large deformation and deformation with rotation motion. Instead of constant and symmetrical connection matrices in the linear model, nonlinear model yields non-constant and unsymmetrical connection matrices, which vary with displacements of nodal points. Simulation results with both linear and nonlinear FE models were presented to show the differences. We found that the nonlinear model formulated with Green strain tensor demonstrates more natural simulation behaviors.

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A smart rheologic MSD model pushed/calibrated/evaluated by experimental impulses

Cited by 10 publications

References 23 publications

Physical parameter identification of rheological object based on measurement of deformation and force

Physical parameter identification of rheological object based on measurement of deformation and force

Finite Element Modeling and Physical Property Estimation of Rheological Food Objects

Green strain based FE modeling of rheological objects for handling large deformation and rotation

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