A Triaxial Testing System to Evaluate Stress-Strain Behavior of Soils for Wide Range of Strain and Strain Rate

1999 ◽  
Vol 22 (1) ◽  
pp. 44 ◽  
Author(s):  
RC Chaney ◽  
KR Demars ◽  
F Santucci de Magistris ◽  
J Koseki ◽  
M Amaya ◽  
...  
Keyword(s):  
Strain Rate ◽  
Triaxial Testing ◽  
Testing System ◽  
Stress Strain ◽  
Strain Behavior ◽  
Strain And Strain Rate ◽  
Wide Range

High Strain-Rate Compressive Behavior and Constitutive Modeling of Selected Polymers Using Modified Ramberg-Osgood Equation

2014 ◽  
Vol 566 ◽  
pp. 80-85
Author(s):  
Kenji Nakai ◽  
Takashi Yokoyama
Keyword(s):  
High Strain Rate ◽  
Strain Rate ◽  
Compressive Stress ◽  
Constitutive Modeling ◽  
High Strain ◽  
Strain Rates ◽  
Stress Strain ◽  
Strain Behavior ◽  
Least Squares Fit ◽  
Wide Range

The present paper is concerned with constitutive modeling of the compressive stress-strain behavior of selected polymers at strain rates from 10-3 to 103/s using a modified Ramberg-Osgood equation. High strain-rate compressive stress-strain curves up to strains of nearly 0.08 for four different commercially available extruded polymers were determined on the standard split Hopkinson pressure bar (SHPB). The low and intermediate strain-rate compressive stress-strain relations were measured in an Instron testing machine. Six parameters in the modified Ramberg-Osgood equation were determined by fitting to the experimental stress-strain data using a least-squares fit. It was shown that the monotonic compressive stress-strain behavior over a wide range of strain rates can successfully be described by the modified Ramberg-Osgood constitutive model. The limitations of the model were discussed.

scholarly journals Estimation of Stress-Strain behavior of polyethylene terephthalate (PET) at different strain rates by Artificial Neural Network under simultaneous stretch scenario

2021 ◽  
Author(s):  
Fei Teng ◽  
Gary Menary ◽  
Savko Malinov ◽  
Shiyong Yan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Strain Rates ◽  
Stress Strain ◽  
Strain Behavior ◽  
Strain And Strain Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
High Degree

In this paper, an Artificial Neural Network (ANN) is used to predict the stress-strain behavior of PET at conditions relevant to Stretch Blow Moulding i.e. Large equibiaxial deformation at elevated temperature and high strain rate. The input vectors considered are temperature, strain, and strain rate with a corresponding output parameter of stress. In the present work, a feed-forward back backpropagation algorithm was used to train the ANN. The ANN is able to approximate the relationship between stress and strain at various strain rates & temperatures to a high degree of accuracy for all conditions tested.

Understanding Dynamic Recrystallization Behavior through a Delay Differential Equation Approach

2007 ◽  
Vol 558-559 ◽  
pp. 441-448 ◽  
Author(s):  
Jong K. Lee
Keyword(s):  
Differential Equation ◽  
Strain Rate ◽  
Critical Strain ◽  
Strain Curve ◽  
Single Peak ◽  
Strain Rates ◽  
Stress Strain Curve ◽  
Stress Strain ◽  
Strain Behavior ◽  
Delay Differential

During hot working, deformation of metals such as copper or austenitic steels involves features of both diffusional flow and dislocation motion. As such, the true stress-true strain relationship depends on the strain rate. At low strain rates (or high temperatures), the stress-strain curve displays an oscillatory behavior with multiple peaks. As the strain rate increases (or as the temperature is reduced), the number of peaks on the stress-strain curve decreases, and at high strain rates, the stress rises to a single peak before settling at a steady-state value. It is understood that dynamic recovery is responsible for the stress-strain behavior with zero or a single peak, whereas dynamic recrystallization causes the oscillatory nature. In the past, most predictive models are based on either modified Johnson-Mehl-Avrami kinetic equations or probabilistic approaches. In this work, a delay differential equation is utilized for modeling such a stress-strain behavior. The approach takes into account for a delay time due to diffusion, which is expressed as the critical strain for nucleation for recrystallization. The solution shows that the oscillatory nature depends on the ratio of the critical strain for nucleation to the critical strain for completion for recrystallization. As the strain ratio increases, the stress-strain curve changes from a monotonic rise to a single peak, then to a multiple peak behavior. The model also predicts transient flow curves resulting from strain rate changes.

scholarly journals Evolution of specimen strain rate in split Hopkinson bar test

2019 ◽  
Vol 233 (13) ◽  
pp. 4667-4687 ◽  
Author(s):  
Hyunho Shin ◽  
Jong-Bong Kim
Keyword(s):  
Strain Rate ◽  
Rate Equation ◽  
Constant Strain Rate ◽  
Strain Curve ◽  
Hopkinson Bar ◽  
Stress Strain ◽  
Split Hopkinson Bar ◽  
Specimen Diameter ◽  
Strain And Strain Rate ◽  
Split Hopkinson

The specimen strain rate in the split Hopkinson bar (SHB) test has been formulated based on a one-dimensional assumption. The strain rate is found to be controlled by the stress and strain of the deforming specimen, geometry (the length and diameter) of specimen, impedance of bar, and impact velocity. The specimen strain rate evolves as a result of the competition between the rate-increasing and rate-decreasing factors. Unless the two factors are balanced, the specimen strain rate generally varies (decreases or increases) with strain (specimen deformation), which is the physical origin of the varying nature of the specimen strain rate in the SHB test. According to the formulated strain rate equation, the curves of stress–strain and strain rate–strain are mutually correlated. Based on the correlation of these curves, the strain rate equation is verified through a numerical simulation and experiment. The formulated equation can be used as a tool for verifying the measured strain rate–strain curve simultaneously with the measured stress–strain curve. A practical method for predicting the specimen strain rate before carrying out the SHB test has also been presented. The method simultaneously solves the formulated strain rate equation and a reasonably estimated constitutive equation of specimen to generate the anticipated curves of strain rate–strain and stress–strain in the SHB test. An Excel® program to solve the two equations is provided. The strain rate equation also indicates that the increase in specimen stress during deformation (e.g., work hardening) plays a role in decreasing the slope of the strain rate–strain curve in the plastic regime. However, according to the strain rate equation, the slope of the strain rate–strain curve in the plastic deformation regime can be tailored by controlling the specimen diameter. Two practical methods for determining the specimen diameter to achieve a nearly constant strain rate are presented.

scholarly journals Investigation of the Mechanical Properties of St. Lawrence River Ice

1971 ◽  
Vol 8 (2) ◽  
pp. 163-169 ◽  
Author(s):  
L. W. Gold ◽  
A. S. Krausz
Keyword(s):  
Mechanical Properties ◽  
Strain Rate ◽  
Yield Stress ◽  
Power Law ◽  
River Ice ◽  
Stress Strain ◽  
Strain Behavior ◽  
Brittle Transition ◽  
Ductile To Brittle Transition ◽  
St Lawrence River

Observations are reported on the stress–strain behavior at −9.5 ± 0.5 °C of four types of ice obtained from the St. Lawrence River. The ice was subject to nominal rates of strain covering the range 2.1 × 10−5 min−1 to 5.8 × 10−2 min−1. A ductile-to-brittle transition was observed for strain rate of about 10−2 min−1. In the ductile range the four types had an upper yield stress that increased with strain rate according to a power law.

Strain-rate controlled Gleeble experiments to determine the stress-strain behavior of HSLA steel S960QL

2018 ◽  
Vol 60 (7-8) ◽  
pp. 733-748
Author(s):  
Sebastian Neubert ◽  
Andreas Pittner ◽  
Michael Rethmeier
Keyword(s):  
Strain Rate ◽  
Hsla Steel ◽  
Stress Strain ◽  
Strain Behavior ◽  
Rate Controlled

scholarly journals Dynamic Mechanical Characteristics and Constitutive Modeling of Rail Steel over a Wide Range of Temperatures and Strain Rates

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Peijie Liu ◽  
Yanming Quan ◽  
Guo Ding
Keyword(s):  
Strain Rate ◽  
Mechanical Behavior ◽  
Split Hopkinson Pressure Bar ◽  
Rail Steel ◽  
Strain Rates ◽  
Dynamic Mechanical ◽  
Flow Response ◽  
Strain And Strain Rate ◽  
Average Absolute Relative Error ◽  
Wide Range

Rail steel plays an indispensable role in the safety and stability of the railway system. Therefore, a suitable constitutive model is quite significant to understand the mechanical behavior of this material. Here, the compressive mechanical behavior of heat-treated U71Mn rail steel over a wide range of strain rates (0.001 s−1–10000 s−1) and temperatures (20°C–800°C) was systematically investigated via uniaxial quasistatic and dynamic tests. The split Hopkinson pressure bar (SHPB) apparatus was utilized to perform dynamic mechanical tests. The effects of temperature, strain, and strain rate on the dynamic compressive characteristics of U71Mn were discussed, respectively. The results indicate that the flow response of U71Mn is both temperature-sensitive and strain rate-sensitive. However, the influence of temperature on the flow response is more remarkable than that of strain rate. On the basis of the experimental data, the original and modified Johnson-Cook (JC) models of the studied material were established, respectively. Using correlation coefficient and average absolute relative error parameters, it is revealed that better agreement between the experimental and predicted stress is reached by the modified JC model, which demonstrates that the modified one can characterize the mechanical behavior of the studied material preferably.

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