[HTML][HTML] Counterexample-trained neural network model of rate and temperature dependent hardening with dynamic strain aging
Constitutive models dealing with the thermal and visco-plasticity of metals have seen wide
applications in the automotive industry. A basic plasticity and fracture characterization of a …
applications in the automotive industry. A basic plasticity and fracture characterization of a …
Machine-learning based temperature-and rate-dependent plasticity model: application to analysis of fracture experiments on DP steel
Slow, intermediate and high strain rate experiments are carried out on flat smooth and
notched tensile specimens extracted from dual phase steel sheets. A split Hopkinson …
notched tensile specimens extracted from dual phase steel sheets. A split Hopkinson …
[HTML][HTML] Neural network based rate-and temperature-dependent Hosford–Coulomb fracture initiation model
The accurate description of the strain rate and temperature dependent response of metals is
a perpetual quest in crashworthiness and forming applications. In the present study …
a perpetual quest in crashworthiness and forming applications. In the present study …
Component-based machine learning paradigm for discovering rate-dependent and pressure-sensitive level-set plasticity models
NN Vlassis, WC Sun - Journal of Applied Mechanics, 2022 - asmedigitalcollection.asme.org
Conventionally, neural network constitutive laws for path-dependent elastoplastic solids are
trained via supervised learning performed on recurrent neural networks, with the time history …
trained via supervised learning performed on recurrent neural networks, with the time history …
Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling
Complex structural components made from 7xxx series alloys are usually manufactured
through hot stamping due to their low ductility at room temperature. With the help of a custom …
through hot stamping due to their low ductility at room temperature. With the help of a custom …
Deep learning predicts path-dependent plasticity
Plasticity theory aims at describing the yield loci and work hardening of a material under
general deformation states. Most of its complexity arises from the nontrivial dependence of …
general deformation states. Most of its complexity arises from the nontrivial dependence of …
An evolving plasticity model considering anisotropy, thermal softening and dynamic strain aging
Comprehensive experimental characterization and numerical prediction have been
performed to investigate the thermal effects on the anisotropic flow behavior of two high …
performed to investigate the thermal effects on the anisotropic flow behavior of two high …
Thermodynamically consistent neural network plasticity modeling and discovery of evolution laws
Over the past decade, advancements in computational frameworks and processing power
have made deep neural networks increasingly viable for material modeling. However, purely …
have made deep neural networks increasingly viable for material modeling. However, purely …
[HTML][HTML] Mechanistically informed artificial neural network model for discovering anisotropic path-dependent plasticity of metals
X Liu, J He, S Huang - Materials & Design, 2023 - Elsevier
The plasticity of metals involves various complicated phenomena that have not been fully
discovered or explained by existing theories. The data-driven method provides a new …
discovered or explained by existing theories. The data-driven method provides a new …
Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints
L Borkowski, C Sorini, A Chattopadhyay - Computers & Structures, 2022 - Elsevier
A recurrent neural network (RNN) based model is developed as a surrogate to predict
nonlinear plastic response under multiaxial loading. The RNN-based model is trained and …
nonlinear plastic response under multiaxial loading. The RNN-based model is trained and …