☆ 4.8 Article

An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials

NPJ COMPUTATIONAL MATERIALS (2023)

Related references

Note: Only part of the references are listed.

Article Chemistry, Physical

Lossless multi-scale constitutive elastic relations with artificial intelligence

Jaber Rezaei Mianroodi et al.

Summary: This research demonstrates the seamless transition of the elastic response of materials between atomic and continuum scales using artificial intelligence. By training a convolutional neural network model, the researchers were able to accurately predict the elastic properties of nanoporous materials and achieve higher efficiency compared to traditional methods.

NPJ COMPUTATIONAL MATERIALS (2022)

Add to Collection

Article Multidisciplinary Sciences

Learning the stress-strain fields in digital composites using Fourier neural operator

Meer Mehran Rashid et al.

Summary: This study proposes a neural operator-based framework (FNO) for learning the mechanical response of composite materials. The model shows high accuracy in predicting stress and strain fields for complex geometric structures, even with minimal training data and purely based on microstructure information. It also exhibits zero-shot generalization and zero-shot super-resolution capabilities for unseen geometries.

ISCIENCE (2022)

Add to Collection

Article Engineering, Mechanical

Dislocation nucleation in Al single crystal at shear parallel to (111) plane: Molecular dynamics simulations and nucleation theory with artificial neural networks

Alexander E. Mayer et al.

Summary: In this study, a theory on homogeneous nucleation of dislocations was developed to predict plasticity incipience in FCC single crystals. The energy barrier of nucleation was found to only include the line defect energy of the critical loop, emphasizing the importance of balancing various energetic contributions. Through molecular dynamics simulations and artificial neural networks, the theory was validated and shown to be applicable in predicting nucleation thresholds under different conditions.

INTERNATIONAL JOURNAL OF PLASTICITY (2021)

Add to Collection

Article Multidisciplinary Sciences

Deep learning model to predict complex stress and strain fields in hierarchical composites

Zhenze Yang et al.

Summary: An AI-based method is developed to predict physical properties of materials directly from their microstructure geometry, bridging the gap between design space and physical performance. The approach shows remarkable accuracy in predicting physical fields and derivative material properties, and offers extensibility in predicting complex materials behavior, improving the efficiency of evaluating physical properties of hierarchical materials.

SCIENCE ADVANCES (2021)

Add to Collection

Article Chemistry, Physical

Teaching solid mechanics to artificial intelligence-a fast solver for heterogeneous materials

Jaber Rezaei Mianroodi et al.

Summary: A deep neural network (DNN) is proposed as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials, showing efficient performance and high accuracy in predicting stress distribution. The DNN model is capable of simulating micromechanics in elastic-plastic materials with significant acceleration compared to typical solvers.

NPJ COMPUTATIONAL MATERIALS (2021)

Add to Collection

Article Engineering, Mechanical

A hybrid approach to simulate the homogenized irreversible elastic-plastic deformations and damage of foams by neural networks

Christoph Settgast et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2020)

Add to Collection

Review Materials Science, Multidisciplinary

Spectral methods for full-field micromechanical modelling of polycrystalline materials

Ricardo A. Lebensohn et al.

COMPUTATIONAL MATERIALS SCIENCE (2020)

Add to Collection

Article Engineering, Mechanical

Strain rate and temperature dependent fracture of aluminum alloy 7075: Experiments and neural network modeling

Kedar S. Pandya et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2020)

Add to Collection

Article Materials Science, Multidisciplinary

DAMASK - The Dusseldorf Advanced Material Simulation Kit for modeling multi-physics crystal plasticity, thermal, and damage phenomena from the single crystal up to the component scale

F. Roters et al.

COMPUTATIONAL MATERIALS SCIENCE (2019)

Add to Collection

Article Computer Science, Interdisciplinary Applications

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

M. Raissi et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2019)

Add to Collection

Article Engineering, Mechanical

Application of artificial neural networks in micromechanics for polycrystalline metals

Usman Ali et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2019)

Add to Collection

Review Materials Science, Multidisciplinary

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Frederic E. Bock et al.

FRONTIERS IN MATERIALS (2019)

Add to Collection

Article Engineering, Multidisciplinary

DBFFT: A displacement based FFT approach for non-linear homogenization of the mechanical behavior

S. Lucarini et al.

INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE (2019)

Add to Collection

Article Engineering, Multidisciplinary

FFT-based homogenization for microstructures discretized by linear hexahedral elements

Matti Schneider et al.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING (2017)

Add to Collection

Article Materials Science, Multidisciplinary

Identifying Structure-Property Relationships Through DREAM.3D Representative Volume Elements and DAMASK Crystal Plasticity Simulations: An Integrated Computational Materials Engineering Approach

Martin Diehl et al.

JOM (2017)

Add to Collection

Article Mechanics

Fourier-based schemes for computing the mechanical response of composites with accurate local fields

Francois Willot

COMPTES RENDUS MECANIQUE (2015)

Add to Collection

Article Engineering, Mechanical

Numerically robust spectral methods for crystal plasticity simulations of heterogeneous materials

P. Shanthraj et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2015)

Add to Collection

Review Materials Science, Multidisciplinary

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)

Add to Collection

Article Engineering, Mechanical

Nonlinear constitutive models from nanoindentation tests using artificial neural networks

Rami Haj-Ali et al.

INTERNATIONAL JOURNAL OF PLASTICITY (2008)

Add to Collection

© Peeref 2019-2024. All rights reserved.