Strain rate history effects for polycrystalline aluminium and theory of intersections

1968 ◽  
Vol 16 (4) ◽  
pp. 255-266 ◽  
Author(s):  
J. Klepaczko
Keyword(s):  
Strain Rate ◽  
History Effects

Viscoplastic Constitutive Equations for Copper With Strain Rate History and Temperature Effects

1978 ◽  
Vol 100 (4) ◽  
pp. 388-394 ◽  
Author(s):  
S. R. Bodner ◽  
A. Merzer
Keyword(s):  
Strain Rate ◽  
Constitutive Equations ◽  
Internal State ◽  
Rate Sensitivity ◽  
Experimental Program ◽  
Internal State Variable ◽  
Plastic Working ◽  
State Variable ◽  
History Effects ◽  
Good Agreement

Elastic-viscoplastic constitutive equations based on a single internal state variable which is a function of plastic work are used to calculate the response of copper to a six decade change of strain rate over a range of temperatures. Calculations were performed for the conditions of an experimental program on copper by Senseny, Duffy, and Hawley, namely, temperatures ranging from 77°K to 523°K and strain rate jumps from 2 × 10−4sec−1 to 3 × 102sec−1 at three strain levels. The computed results are in good agreement with the experiments and show similar strain rate and strain rate history effects. Relations are obtained for the temperature dependence of certain parameters in the equations which indicate correspondence between plastic working and temperature and between strain rate sensitivity and temperature.

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.

Sign in / Sign up

Export Citation Format

Share Document