Modeling structure-property relationships with convolutional neural networks: Yield surface prediction based on microstructure images
The use of micromechanics in conjunction with homogenization theory allows for the
prediction of the effective mechanical properties of materials based on microstructural …
prediction of the effective mechanical properties of materials based on microstructural …
[HTML][HTML] From CP-FFT to CP-RNN: Recurrent neural network surrogate model of crystal plasticity
Abstract Recurrent Neural Network (RNN) based surrogate models constitute an emerging
class of reduced order models of history-dependent material behavior. Recently, the authors …
class of reduced order models of history-dependent material behavior. Recently, the authors …
[PDF][PDF] 塑性成形快速数值仿真方法的研究进展
詹梅， 董赟达， 翟卓蕾， 樊晓光， 石志鹏， 安强 - 机械工程学报, 2022 - qikan.cmes.org
精确高效的数值仿真预测模型是塑性成形技术向数字化, 智能化发展的关键技术之一.
为实现大型复杂构件先进塑性成形工艺研究的实时化, 形成了大规模离散模型精简/降维 …
为实现大型复杂构件先进塑性成形工艺研究的实时化, 形成了大规模离散模型精简/降维 …
Determination of ductile fracture properties of 16MND5 steels under varying constraint levels using machine learning methods
The current paper presents a machine learning method based on artificial neural network
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …
(ANN) model for the determination of ductile fracture properties of 16MND5 bainitic forging …
Quantification of α phase strengthening in titanium alloys: crystal plasticity model incorporating α/β heterointerfaces
A strategy to quantify the second phase strengthening of α precipitates in titanium alloys is
proposed to predict the mechanical performance through crystal plasticity finite element …
proposed to predict the mechanical performance through crystal plasticity finite element …
Electro-thermal-mechanical coupled crystal plasticity modeling of Ni-based superalloy during electrically assisted deformation
J Gao, H Li, X Sun, X Zhang, M Zhan - International Journal of Plasticity, 2022 - Elsevier
Electrically assisted (EA) formation has attracted much attention in recent years. However,
the multiscale deformation mechanism of materials under multifield (electrical, thermal, and …
the multiscale deformation mechanism of materials under multifield (electrical, thermal, and …
Texture evolution prediction of 2219 aluminum alloy sheet under hydro-bulging using cross-scale numerical modeling
Y Pei, Y Hao, J Zhao, J Yang, B Teng - Journal of Materials Science & …, 2023 - Elsevier
A simultaneous prediction of macroscopic deformation and microstructure evolution is
critical for understanding the deformation mechanism of components. In this work, the hydro …
critical for understanding the deformation mechanism of components. In this work, the hydro …
Hybrid identification method of coupled viscoplastic-damage constitutive parameters based on BP neural network and genetic algorithm
D Yao, Y Duan, M Li, Y Guan - Engineering Fracture Mechanics, 2021 - Elsevier
The constitutive model based on the theoretical framework of coupled viscoplastic-damage
involves calibration of multiple and high coupling parameters. The inverse calibration by …
involves calibration of multiple and high coupling parameters. The inverse calibration by …
An artificial neural network-based model for roping prediction in aluminum alloy sheet
Roping is a common macroscopic surface defect in AA6XXX aluminum alloy sheets
resulting from the three-dimensional (3-D) spatial distribution of specific textures. The crystal …
resulting from the three-dimensional (3-D) spatial distribution of specific textures. The crystal …
A temporal graph neural network for cross-scale modelling of polycrystals considering microstructure interaction
Abstract Machine learning (ML) based methods have achieved preliminary success in the
constitutive modeling for single crystals or homogenized polycrystals with remarkable …
constitutive modeling for single crystals or homogenized polycrystals with remarkable …