A preliminary discussion about the application of machine learning in the field of constitutive modeling focusing on alloys

D Li, J Liu, Y Fan, X Yang, W Huang - Journal of Alloys and Compounds, 2024 - Elsevier
With an emphasis on the development of machine learning-based constitutive modeling
approaches, the state of constitutive modeling techniques and applications for metals and …

Simple shear methodology for local structure–property relationships of sheet metals: State-of-the-art and open issues

G Han, J He, S Li, Z Lin - Progress in Materials Science, 2024 - Elsevier
Simple shear presents a local material structure–property relationship and plays an
important role in the development of material design, mechanical modeling, and …

[HTML][HTML] From CP-FFT to CP-RNN: Recurrent neural network surrogate model of crystal plasticity

C Bonatti, B Berisha, D Mohr - International Journal of Plasticity, 2022 - Elsevier
Abstract Recurrent Neural Network (RNN) based surrogate models constitute an emerging
class of reduced order models of history-dependent material behavior. Recently, the authors …

A convolutional neural network based crystal plasticity finite element framework to predict localised deformation in metals

O Ibragimova, A Brahme, W Muhammad… - International Journal of …, 2022 - Elsevier
Convolutional neural networks (CNNs) find vast applications in the field of image
processing. This study utilises the CNNs in conjunction with the crystal plasticity finite …

Lode-dependent anisotropic-asymmetric yield function for isotropic and anisotropic hardening of pressure-insensitive materials. Part I: Quadratic function under non …

Y Lou, JW Yoon - International Journal of Plasticity, 2023 - Elsevier
It is challenging to precisely model the complicated plastic deformation of metals, including
anisotropy in strength and plastic deformation, strength differential effect, anisotropic …

Experimental characterization and crystal plasticity modeling for predicting load reversals in AA6016-T4 and AA7021-T79

S Daroju, T Kuwabara, R Sharma, DT Fullwood… - International Journal of …, 2022 - Elsevier
The detailed contribution of microstructural-level phenomena, such as dislocation structure
development and annihilation, as well as inter-granular and intra-granular backstress fields …

A machine learning model to predict yield surfaces from crystal plasticity simulations

A Nascimento, S Roongta, M Diehl… - International Journal of …, 2023 - Elsevier
We introduce a microstructurally informed machine learning model for predicting the
anisotropic yield surfaces of polycrystalline materials. A full-field, spatially resolved crystal …

Machine learning-based modeling of the coupling effect of strain rate and temperature on strain hardening for 5182-O aluminum alloy

H Shang, P Wu, Y Lou, J Wang, Q Chen - Journal of Materials Processing …, 2022 - Elsevier
This research characterizes the dynamic hardening behavior of an aluminum alloy sheet of
5182-O for the coupling effect of strain rate and temperature. Tests are carried out for …

An evolution of subsequent yield loci under proportional and non-proportional loading path of 'as-received'extruded AZ31 magnesium alloy: Experiments and CPFEM …

CMA Iftikhar, A Brahme, K Inal, AS Khan - International Journal of Plasticity, 2022 - Elsevier
This work presents an experimental investigation of as-received extruded AZ31 magnesium
alloy subjected to proportional and non-proportional loading paths at various finite levels of …

A dislocation-based damage-coupled constitutive model for single crystal superalloy: Unveiling the effect of secondary orientation on creep life of circular hole

Z Guo, Z Song, H Liu, D Hu, D Huang, X Yan… - International Journal of …, 2024 - Elsevier
Circular holes in single crystal (SX) turbine blades are susceptible to creep failure at high
temperatures, due to the stress concentrations near the holes. In this study, a dislocation …