scholarly journals A model of incompressible isotropic hyperelastic material behavior using spline interpolations of tension-compression test data

2009 ◽  
Vol 25 (1) ◽  
pp. 53-63 ◽  
Author(s):  
Theodore Sussman ◽  
Klaus-Jürgen Bathe
Keyword(s):  
Test Data ◽  
Compression Test ◽  
Material Behavior ◽  
Hyperelastic Material ◽  
Isotropic Hyperelastic Material

Determination of the Biaxial Anisotropy Coefficient Using a Single Layer Sheet Metal Compression Test

2021 ◽  
Vol 883 ◽  
pp. 303-308
Author(s):  
Peter Hetz ◽  
Matthias Lenzen ◽  
Martin Kraus ◽  
Marion Merklein
Keyword(s):  
Stress State ◽  
Tensile Stress ◽  
Sheet Metal ◽  
Compression Test ◽  
Test Procedure ◽  
Rolling Direction ◽  
Single Layer ◽  
Material Behavior ◽  
Biaxial Tensile ◽  
Biaxial Tensile Stress

Numerical process design leads to cost and time savings in sheet metal forming processes. Therefore, a modeling of the material behavior is required to map the flow properties of sheet metal. For the identification of current yield criteria, the yield strength and the hardening behavior as well as the Lankford coefficients are taken into account. By considering the anisotropy as a function of rolling direction and stress state, the prediction quality of anisotropic materials is improved by a more accurate modeling of the yield locus curve. According to the current state of the art, the layer compression test is used to determine the corresponding Lankford coefficient for the biaxial tensile stress state. However, the test setup and the test procedure is quite challenging compared to other tests for the material characterization. Due to this, the test is only of limited suitability if only the Lankford coefficient has to be determined. In this contribution, a simplified test is presented. It is a reduction of the layer compression test to one single sheet layer. So the Lankford coefficient for the biaxial tensile stress state can be analyzed with a significantly lower test effort. The results prove the applicability of the proposed test for an easy and time efficient characterization of the biaxial Lankford coefficient.

Very High Cycle Fatigue Testing under Spectrum Loading for Endurance Limit Structural Design

2020 ◽  
Vol 65 (1) ◽  
pp. 1-7
Author(s):  
David T. Rusk ◽  
Robert E. Taylor ◽  
Bruce A. Pregger ◽  
Luis J. Sanchez
Keyword(s):  
Fatigue Test ◽  
Test Data ◽  
High Cycle Fatigue ◽  
Fatigue Testing ◽  
Crack Nucleation ◽  
Peak Stress ◽  
Material Behavior ◽  
Cycle Fatigue ◽  
Very High Cycle Fatigue ◽  
Very High

A program has recently concluded that generated fatigue test data for the influence of a rotorcraft main rotor blade root bending spectrum (Helix) on the crack nucleation mechanisms in 7075-T651 aluminum. High-frequency tests were performed that generated spectrum fatigue failures out to nearly 109 cycles. Fractographic examination showed a distinct change in crack nucleation from slip initiated to inclusion-initiated cracking as the spectrum peak stress level was increased. Spectrum life predictions were made using three different baseline constant-amplitude S-N curves, one using a traditional rotorcraft original equipment manufacturer fitting methodology, one using the high-cycle fatigue (HCF) portion of a strainlife curve, and one that was fitted to S-N data with test lives out to 3×108 cycles. The spectrum life prediction using the S-N curve that properly modeled material behavior in the very high cycle fatigue regime provided a good correlation to the spectrum fatigue test data. Predictions using the other S-N curves were highly conservative.

scholarly journals Concrete Compression Test Data Estimation Based on a Wavelet Neural Network Model

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Haiying Wang ◽  
Xinping Wang ◽  
Chao Wang ◽  
Jian Xu
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Compression Test ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Wavelet Neural Network ◽  
Accurate Estimation ◽  
Short Term ◽  
Data Estimation ◽  
Concrete Cube

Firstly, a genetic algorithm (GA) and simulated annealing (SA) optimized fuzzy c-means clustering algorithm (FCM) was proposed in this paper, which was developed to allow for a clustering analysis of the massive concrete cube specimen compression test data. Then, using an optimized error correction time series estimation method based on the wavelet neural network (WNN), a concrete cube specimen compressive strength test data estimation model was constructed. Taking the results of cluster analysis as data samples, the short-term accurate estimation of concrete quality was carried out. It was found that the mean absolute percentage error, e1, and the root mean square error, e2, for the samples were 6.03385% and 3.3682KN, indicating that the proposed method had higher estimation accuracy and was suitable for concrete compressive test data short-term quality estimations.

Application of Crystal Plasticity Model for Simulation of Polycrystalline Aluminum Sample Behavior During Plain Strain Compression Test

2013 ◽  
Vol 58 (2) ◽  
pp. 493-496 ◽  
Author(s):  
W. Wajda ◽  
Ł. Madej ◽  
H. Paul
Keyword(s):  
Experimental Data ◽  
Compression Test ◽  
Crystal Plasticity ◽  
Material Behavior ◽  
Polycrystalline Aluminum ◽  
Aluminum Specimen ◽  
Aluminum Sample ◽  
Digital Material ◽  
Modeling Material ◽  
Plain Strain

Capabilities of crystal plasticity finite element (CPFE) model in application to modeling polycrystalline aluminum sample behavior during plain strain compression test are discussed within the present work. To simplify analysis of material behavior during plain strain compression the aluminum specimen is composed of only three grains, both in experiment and numerical simulation. To reconstruct appropriate grains morphology a digital material representation (DMR) technique is used. The predicted/calculated values of loads and pole figures are compared with the experimental data. Calculated results remain in good agreement with experimental data what highlight predictive capabilities of the proposed approach in modeling material behavior under loading conditions. The conclusions regarding model capabilities and possible improvements during further work are also drawn in the paper.

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