Experimental identification and mathematical modeling of viscoplastic material behavior

1995 ◽  
Vol 7 (1) ◽  
pp. 73-96 ◽  
Author(s):  
P. Haupt ◽  
A. Lion
Keyword(s):  
Mathematical Modeling ◽  
Material Behavior ◽  
Viscoplastic Material ◽  
Experimental Identification

Computer Aided Design of an Effective Fixture for FSW Processes of Titanium Alloys

2011 ◽  
Vol 473 ◽  
pp. 304-309
Author(s):  
Gianluca Buffa ◽  
Livan Fratini
Keyword(s):  
Titanium Alloys ◽  
Computer Aided Design ◽  
Welding Process ◽  
Optimal Solution ◽  
Fusion Welding ◽  
Material Behavior ◽  
Viscoplastic Material ◽  
3D Fem ◽  
Friction Stir ◽  
Aluminum And Magnesium Alloys

During the last years welded titanium components have been extensively applied in aeronautical and aerospace industries because of their high specific strength and corrosion resistance properties. Friction Stir Welding (FSW) is a solid state welding process, currently industrially utilized for difficult to be welded or “unweldable” aluminum and magnesium alloys, able to overcome the drawbacks of traditional fusion welding techniques. When titanium alloys are concerned, additional problems arise as the need for very high strength and high temperature resistant tools, gas shield protection and high stiffness machines. Additionally, the process is characterized by an elevated sensitivity to temperature variations, which, in turn, depends on the main operative parameters. Numerical simulation represents the optimal solution in order to perform an effective process optimization with affordable costs. In this paper, a fully 3D FEM model for the FSW process is proposed, that is thermo-mechanically coupled and with rigid-viscoplastic material behavior. Experimental clamping parts are modeled and the thermal loads are calculated at the varying of the cooling strategy. Finally, the effectiveness of the cooling systems is evaluated through experimental tests.

MATHEMATICAL MODELING AND EXPERIMENTAL IDENTIFICATION OF BACKLASH NONLINEARITY IN PROTOTYPE OF A ROBOT GANTRY WITH ELECTRIC DRIVE AND BALL SCREW TRANSMISSION

2015 ◽  
Author(s):  
Angelo Fernando Fiori ◽  
Ismael Barbieri Garlet ◽  
Odmartan Ribas Maciel ◽  
Andrei Fiegenbaum ◽  
Antonio Carlos Valdiero ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Electric Drive ◽  
Ball Screw ◽  
Experimental Identification ◽  
Backlash Nonlinearity

Computational Aspects in the Elasto/Viscoplastic Material Behavior of Solids

2012 ◽  
Vol 567 ◽  
pp. 192-199 ◽  
Author(s):  
Fabio de Angelis
Keyword(s):  
Constitutive Model ◽  
Material Behavior ◽  
Inelastic Behavior ◽  
Viscoplastic Material ◽  
Solid Materials ◽  
Numerical Examples ◽  
Computational Aspects ◽  
Computational Procedures ◽  
Consistent Tangent Operator ◽  
Algorithmic Procedure

In the present paper a computational algorithmic procedure is presented for modeling the elasto/viscoplastic behavior of solid materials. The effects of different loading programs on the inelastic behavior of rate-sensitive materials are analyzed with specific numerical examples. An appropriate solution scheme and a consistent tangent operator are applied which are capable to be adopted for general computational procedures. Numerical computations and results are reported which illustrate the rate-dependence of the constitutive model in use.

3D-FE-Analysis of CT-specimens including viscoplastic material behavior and nonlocal damage

2009 ◽  
Vol 46 (2) ◽  
pp. 352-357 ◽  
Author(s):  
Jana Velde ◽  
Ursula Kowalsky ◽  
Tim Zümendorf ◽  
Dieter Dinkler
Keyword(s):  
Fe Analysis ◽  
Material Behavior ◽  
Viscoplastic Material ◽  
Nonlocal Damage

Development of LSTM networks for predicting viscoplasticity with effects of deformation, strain rate and temperature history

2021 ◽  
pp. 1-30
Author(s):  
Lahouari Benabou
Keyword(s):  
Strain Rate ◽  
Short Term Memory ◽  
Internal State ◽  
Material Behavior ◽  
Temperature History ◽  
Loading Conditions ◽  
Viscoplastic Material ◽  
Temperature Changes ◽  
History Effects ◽  
Deformation Strain Rate

Abstract In this paper, long short-term memory (LSTM) networks are used in an original way to model the behavior of a viscoplastic material solicited under changing loading conditions. The material behavior is dependent on history effects of plasticity which can be visible during strain rate jumps or temperature changes. Due to their architecture and internal state (memory), the LSTM networks have the ability to remember past data to update their current state, unlike the traditional artificial neural networks (ANNs) which fail to capture history effects. Specific LSTM networks are designed and trained to reproduce the complex behavior of a viscoplastic solder alloy subjected to strain rate jumps, temperature changes or loading-unloading cycles. The training datasets are numerically generated using the constitutive viscoplastic law of Anand which is very popular for describing solder alloys. The Anand model serves also as a reference to evaluate the performances of the LSTM networks on new data. It is demonstrated that this class of networks is remarkably well suited for replicating the history plastic effects under all the tested loading conditions.

A new loading history for identification of viscoplastic properties by spherical indentation

2004 ◽  
Vol 19 (1) ◽  
pp. 101-113 ◽  
Author(s):  
N. Huber ◽  
E. Tyulyukovskiy
Keyword(s):  
Strain Curve ◽  
Material Behavior ◽  
Creep Process ◽  
Spherical Indentation ◽  
Loading History ◽  
Viscoplastic Material ◽  
Stress Strain Curve ◽  
Material Parameters ◽  
Depth Data ◽  
Variable Combination

In this paper a new loading history for extracting the stress–strain curve as well as the viscosity and creep behavior from indentation experiments is developed. It is based on a simple model describing the viscoplastic spherical indentation with a power-law hardening rule and a velocity-dependent overstress. Using this model, patterns were generated consisting of load-depth data and corresponding material parameters. The loading history for the simulation of the patterns was considered as a variable combination of loading and creep processes. To compare the identification potential of different loading histories, the inverse problem of determining the viscoplastic material parameters was solved by using neural networks. The emerging loading history uses a multiple-creep process with equidistant load steps and allows an identification of material parameters with much higher accuracy than with single creep. It will be used for further work, where the identification method is generalized using more realistic finite element simulations for a finite deformation elastic–viscoplastic material behavior.

Experimental Characterization and Viscoplastic Modeling of the Temperature Dependent Material Behavior of Underfill Encapsulants

2011 ◽  
Author(s):  
Nusrat J. Chhanda ◽  
Jeffrey C. Suhling ◽  
Pradeep Lall
Keyword(s):  
Flip Chip ◽  
Mechanical Response ◽  
Testing Machine ◽  
Production Equipment ◽  
Material Behavior ◽  
Specimen Preparation ◽  
Viscoplastic Material ◽  
Stress Strain ◽  
Release Agent ◽  
Stress Levels

In this work, the viscoplastic mechanical response of a typical underfill encapsulant has been characterized via rate dependent stress-strain testing over a wide temperature range, and creep testing for a large range of applied stress levels and temperatures. A specimen preparation procedure has been developed to manufacture 80 × 5 mm uniaxial tension test samples with a specified thickness of .5 mm. The test specimens are dispensed and cured with production equipment using the same conditions as those used in actual flip chip assembly, and no release agent is required to extract them from the mold. Using the manufactured test specimens, a microscale tension-torsion testing machine has been used to evaluate stress-strain and creep behavior of the underfill material as a function of temperature. Stress-strain curves have been measured at 5 temperatures (25, 50, 75, 100 and 125 C), and strain rates spanning over 5 orders of magnitude. In addition, creep curves have been evaluated for the same 5 temperatures and several stress levels. With the obtained mechanical property data, several viscoelastic and viscoplastic material models have been fit to the data, and optimum constitutive models for subsequent use in finite element simulations have been determined.

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