Related references
Note: Only part of the references are listed.Zoning additive manufacturing process histories using unsupervised machine learning
Sean P. Donegan et al.
MATERIALS CHARACTERIZATION (2020)
Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods
Carl Herriott et al.
COMPUTATIONAL MATERIALS SCIENCE (2020)
Feature engineering of material structure for AI-based materials knowledge systems
Surya R. Kalidindi
JOURNAL OF APPLIED PHYSICS (2020)
Applied machine learning to predict stress hotspots II: Hexagonal close packed materials
Ankita Mangal et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2019)
Smart finite elements: A novel machine learning application
German Capuano et al.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING (2019)
Application of artificial neural networks in micromechanics for polycrystalline metals
Usman Ali et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2019)
Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations
Denise Reimann et al.
FRONTIERS IN MATERIALS (2019)
Extracting dislocation microstructures by deep learning
Yuqi Zhang et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2019)
Convolutional neural network-based method for real-time orientation indexing of measured electron backscatter diffraction patterns
Yu-Feng Shen et al.
ACTA MATERIALIA (2019)
Material structure-property linkages using three-dimensional convolutional neural networks
Ahmet Cecen et al.
ACTA MATERIALIA (2018)
Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials
Andrea Rovinelli et al.
NPJ COMPUTATIONAL MATERIALS (2018)
Machine learning for molecular and materials science
Keith T. Butler et al.
NATURE (2018)
Real-time coherent diffraction inversion using deep generative networks
Mathew J. Cherukara et al.
SCIENTIFIC REPORTS (2018)
Instantiation of crystal plasticity simulations for micromechanical modelling with direct input from microstructural data collected at light sources
Reeju Pokharel et al.
SCRIPTA MATERIALIA (2017)
Adaptive Strategies for Materials Design using Uncertainties
Prasanna V. Balachandran et al.
SCIENTIFIC REPORTS (2016)
In-situ observation of bulk 3D grain evolution during plastic deformation in polycrystalline Cu
Reeju Pokharel et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2015)
DREAM. 3D: A Digital Representation Environment for the Analysis of Microstructure in 3D
Michael A. Groeber et al.
INTEGRATING MATERIALS AND MANUFACTURING INNOVATION (2014)
An elasto-viscoplastic formulation based on fast Fourier transforms for the prediction of micromechanical fields in polycrystalline materials
Ricardo A. Lebensohn et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2012)
Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications
F. Roters et al.
ACTA MATERIALIA (2010)
Using texture components in crystal plasticity finite element simulations
D Raabe et al.
INTERNATIONAL JOURNAL OF PLASTICITY (2004)