Parameter identification of bimodal, magnetostrictive material model

1999 ◽  
Vol 85 (8) ◽  
pp. 4497-4499 ◽  
Author(s):  
Ann Reimers ◽  
Edward Della Torre
Keyword(s):  
Parameter Identification ◽  
Material Model ◽  
Magnetostrictive Material

A virtual work model for the design and parameter identification of cylindrical pressure-driven soft actuators

2021 ◽  
pp. 1-16
Author(s):  
Frederik Lamping ◽  
Kristin M. de Payrebrune
Keyword(s):  
Parameter Identification ◽  
Virtual Work ◽  
Fiber Reinforcement ◽  
Material Model ◽  
Geometric Constraints ◽  
Dependent Behavior ◽  
Applied Forces ◽  
Work Model ◽  
Soft Actuators ◽  
Pressure Driven

Abstract In this paper, we derive a model based on the principle of virtual work to describe the deformations of cylindrical pressure-driven soft actuators with four types of fiber reinforcement and with externally applied forces. Such cylindrical actuators are often used as the basis for multi-chamber soft robotic systems, for example bending actuators. In the virtual work model, each type of reinforcement leads to particular geometric constraints; the energy of the stretched material is determined by the Yeoh material model. Finally, the stretch of the actuator is solved numerically by a minimization problem. The virtual work model yielded only little deviations of the predicted stretch relative to Finite Element simulations in Abaqus. The key contribution of the virtual work model is improved parameter identification for the modeling of cylindrical soft actuators, as it illustrates the possibility to distinguish between material-dependent behavior and geometry-dependent behavior of these actuators. Also, the virtual work model is applicable in the design process of the investigated actuators. We demonstrate that an optimization of the actuator's inner and outer radii and of its fiber angle, respectively, is possible and we derive design rules including criteria for the choice of fiber reinforcement.

Comparison of Three Methods for Material Hardening Parameter Identification under Cyclic Tension-Compression Loadings: Roll Levelling Case Study

2015 ◽  
Vol 651-653 ◽  
pp. 957-962 ◽  
Author(s):  
Elena Silvestre ◽  
Eneko Sáenz de Argandoña ◽  
Lander Galdos ◽  
Joseba Mendiguren
Keyword(s):  
Parameter Identification ◽  
Kinematic Hardening ◽  
High Strength Steels ◽  
High Strength ◽  
Material Model ◽  
Forming Process ◽  
Element Analysis ◽  
Transient Behaviour ◽  
Cyclic Tension ◽  
Hardening Parameter

The roll levelling is a forming process used to remove the residual stresses and imperfections of metal strips by means of plastic deformations. The process is especially important to avoid final geometrical errors when coils are cold formed or when thick plates are cut by laser. In the last years, and due to the appearance of high strength materials such as Ultra High Strength Steels, machine design engineers are demanding a reliable tool for the dimensioning of the levelling facilities. In response to this demand, Finite Element Analysis is becoming an important technique able to lead engineers towards facilities optimization through a deeper understanding of the process.In this scenario, the accuracy and quality of the simulation results are highly dependent on the accuracy of the implemented material model. During roll levelling process, the sheet metal is subjected to cyclic tensile-compressive deformations, therefore a proper constitutive. model which considers the phenomena that occurs during cyclic loadings, such as the Bauschinger effec, work hardeningt and the transient behaviour, is needed. The prediction of all these phenomena which affect the final shape of the product are linked to the hardening rule.In the present paper, the roll levelling simulation of a DP1000 steel is performed using a combined isotropic-kinematic hardening formulation introduced by Chaboche and Lemaitre since its simplicity and its ability to predict the Bauschinger effect. The model has been fitted to the experimental curves obtained from a cyclic tension-compression test, which has been performed by means of a special tool developed to avoid the buckling of the specimen during compressive loadings. The model has been fitted using three different material hardening parameter identification methodologies which have been compared.

Parameter identification for an advanced material model for intact rock

2014 ◽  
pp. 215-220
Author(s):  
D Unteregger ◽  
G Hofstetter ◽  
M Haltmeier ◽  
A Ostermann
Keyword(s):  
Parameter Identification ◽  
Material Model ◽  
Intact Rock ◽  
Advanced Material

Identification of Anisotropic Material Parameters in Elastic Tissue Using Magnetic Resonance Imaging of Shear Waves

2015 ◽  
Author(s):  
Dennis J. Tweten ◽  
Ruth J. Okamoto ◽  
John L. Schmidt ◽  
Joel R. Garbow ◽  
Philip V. Bayly
Keyword(s):  
Magnetic Resonance ◽  
Parameter Identification ◽  
Shear Wave ◽  
Material Properties ◽  
Anisotropic Material ◽  
Shear Waves ◽  
Material Model ◽  
Elastic Tissue ◽  
Shear Wave Speed ◽  
Material Parameters

This paper describes the application of a material parameter identification method based on elastic shear wave propagation to simulated and experimental data from magnetic resonance elastography (MRE). In MRE, the displacements of traveling transverse and longitudinal waves in elastic, biological tissue are captured as complex 3D images. Typically, the magnitude of these waves is small, and the equations of waves in linear elastic media can be used to estimate the material properties of tissue, such as internal organs, muscle, and the brain. Of particular interest are fibrous tissues which have anisotropic properties. In this paper, an anisotropic material model with three material parameters (shear modulus, shear anisotropy, and tensile anisotropy) is the basis for parameter identification. This model relates shear wave speed, propagation direction, and polarization to the material properties. A directional filtering approach is applied to isolate the speed and polarization of shear waves propagating in multiple directions. The material properties are then estimated from the material model and isolated shear waves using weighted least squares.

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