TY - JOUR
T1 - Modeling of the thermal softening of metals under impact loads and their temperature–time correspondence
AU - Zhao, Shixiang
AU - Petrov, Yu V.
AU - Zhang, Yuyi
AU - Volkov, G. A.
AU - Xu, Zejian
AU - Huang, Fenglei
N1 - Publisher Copyright:
© 2023
PY - 2024/1/1
Y1 - 2024/1/1
N2 - The thermal softening related to the stress relaxation, i.e. a reduction in the internal resistance to deformation, due to the increasing bulk temperature is theoretically studied using the incubation time approach. The plastic deformation at high-loading rates is often accompanied by an obvious adiabatic temperature rise. Phenomenological constitutive models describing these thermo-mechanical responses of metals are generally developed by assuming empirical rate-dependent and temperature-dependent components into existing conventional bases originally proposed for quasi-static cases. This work aims to model the thermal softening of metals subjected to high-rate loads using the incubation time approach and examine the temperature–time correspondence, i.e. temperature-dependence of the incubation time, by introducing the relative stress (RS) factor over a wide range of strain, strain rate and temperature. The incubation time approach considers the strain-rate sensitivity as a manifestation of the time sensitivity of materials. Additionally, we conduct a comprehensive analysis of the developed relaxation model of plasticity (RP model) and conclude that this model may be derived by the time-dependent yield surface equation using the incubation time framework. Based on experimental facts for HSLA–65 steel, 93W–4.9Ni–2.1Fe Tungsten-based composite and titanium alloy Ti–6Al–4V, the descriptive abilities of the developed RP model are compared with other constitutive models (phenomenological and micromechanism-based ones) and the artificial neural networks (ANN) model. The advantages and disadvantages of various models are discussed, and the main differences between the ANN model and other constitutive models are examined.
AB - The thermal softening related to the stress relaxation, i.e. a reduction in the internal resistance to deformation, due to the increasing bulk temperature is theoretically studied using the incubation time approach. The plastic deformation at high-loading rates is often accompanied by an obvious adiabatic temperature rise. Phenomenological constitutive models describing these thermo-mechanical responses of metals are generally developed by assuming empirical rate-dependent and temperature-dependent components into existing conventional bases originally proposed for quasi-static cases. This work aims to model the thermal softening of metals subjected to high-rate loads using the incubation time approach and examine the temperature–time correspondence, i.e. temperature-dependence of the incubation time, by introducing the relative stress (RS) factor over a wide range of strain, strain rate and temperature. The incubation time approach considers the strain-rate sensitivity as a manifestation of the time sensitivity of materials. Additionally, we conduct a comprehensive analysis of the developed relaxation model of plasticity (RP model) and conclude that this model may be derived by the time-dependent yield surface equation using the incubation time framework. Based on experimental facts for HSLA–65 steel, 93W–4.9Ni–2.1Fe Tungsten-based composite and titanium alloy Ti–6Al–4V, the descriptive abilities of the developed RP model are compared with other constitutive models (phenomenological and micromechanism-based ones) and the artificial neural networks (ANN) model. The advantages and disadvantages of various models are discussed, and the main differences between the ANN model and other constitutive models are examined.
KW - Artificial neural networks algorithm
KW - Incubation time
KW - Relaxation model of plasticity
KW - Strain rate effect
KW - The temperature–time correspondence
KW - Thermal softening
UR - http://www.scopus.com/inward/record.url?scp=85175841001&partnerID=8YFLogxK
U2 - 10.1016/j.ijengsci.2023.103969
DO - 10.1016/j.ijengsci.2023.103969
M3 - Article
AN - SCOPUS:85175841001
SN - 0020-7225
VL - 194
JO - International Journal of Engineering Science
JF - International Journal of Engineering Science
M1 - 103969
ER -