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Predicting Microstructure-Sensitive Fatigue-Crack Path in 3D Using a Machine Learning Framework

JOM (1989), 2019-08, Vol.71 (8), p.2680-2694

2019

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Mechanisms driving high-cycle fatigue life of as-built Inconel 718 processed by laser powder bed fusion

Materials science & engineering. A, Structural materials : properties, microstructure and processing, 2019-07, Vol.761, p.137993, Article 137993

2019

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Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches

JPhys materials, 2021-07, Vol.4 (3), p.32002

2021

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An adaptive-remeshing framework to predict impact-induced skull fracture in infants

Biomechanics and modeling in mechanobiology, 2020-10, Vol.19 (5), p.1595-1605

2020

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A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data

Composites science and technology, 2021-05, Vol.207, p.108627, Article 108627

2021

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Effects of laser-energy density and build orientation on the structure–property relationships in as-built Inconel 718 manufactured by laser powder bed fusion

Additive manufacturing, 2020-12, Vol.36, p.101425, Article 101425

2020

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Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions

Computational materials science, 2022-05, Vol.207, p.111290, Article 111290

2022

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A recrystallization heat-treatment to reduce deformation anisotropy of additively manufactured Inconel 718

Materials & design, 2021-01, Vol.198, p.109228, Article 109228

2021

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A void descriptor function to uniquely characterize pore networks and predict ductile-metal failure properties

International journal of fracture, 2020-09, Vol.225 (1), p.47-67

2020

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A multi-scale, multi-physics modeling framework to predict spatial variation of properties in additive-manufactured metals

Modelling and simulation in materials science and engineering, 2019-03, Vol.27 (2), p.25009

2019

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Investigating the effect of grain structure on compressive response of open-cell metal foam using high-fidelity crystal-plasticity modeling

Materials science & engineering. A, Structural materials : properties, microstructure and processing, 2021-04, Vol.812, p.140847, Article 140847

2021

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A multiscale comparison of stochastic open-cell aluminum foam produced via conventional and additive-manufacturing routes

Materials science & engineering. A, Structural materials : properties, microstructure and processing, 2017-11, Vol.707, p.181-192

2017

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Quantitative analysis of three‐dimensional fatigue crack path selection in Mg alloy WE43 using high‐energy X‐ray diffraction microscopy

Fatigue & fracture of engineering materials & structures, 2024-04, Vol.47 (4), p.1150-1171

2024

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Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

JOM (1989), 2018-07, Vol.70 (7), p.1143-1146

2018

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Determination of representative volume elements for small cracks in heterogeneous, linear-elastic domains

Engineering fracture mechanics, 2019-10, Vol.220, p.106643, Article 106643

2019

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A voxel-based remeshing framework for the simulation of arbitrary three-dimensional crack growth in heterogeneous materials

Engineering fracture mechanics, 2019-03, Vol.209, p.404-422

2019

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Reconstructed and analyzed X-ray computed tomography data of investment-cast and additive-manufactured aluminum foam for visualizing ligament failure mechanisms and regions of contact during a compression test

Data in brief, 2018-02, Vol.16, p.601-603

2018

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Predicting microstructurally sensitive fatigue‐crack path in WE43 magnesium using high‐fidelity numerical modeling and three‐dimensional experimental characterization

Fatigue & fracture of engineering materials & structures, 2024-03, Vol.47 (3), p.862-883

2024

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High-throughput feature extraction for measuring attributes of deforming open-cell foams

IEEE transactions on visualization and computer graphics, 2020-01, Vol.26 (1), p.140-150

2020

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Three-dimensional characterization of microstructurally small fatigue-crack evolution using quantitative fractography combined with post-mortem X-ray tomography and high-energy X-ray diffraction microscopy

Acta materialia, 2014-09, Vol.76, p.413-424

2014

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Data-Driven Correlation Analysis Between Observed 3D Fatigue-Crack Path and Computed Fields from High-Fidelity, Crystal-Plasticity, Finite-Element Simulations

JOM (1989), 2018-07, Vol.70 (7), p.1159-1167

2018

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Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods

Computational materials science, 2020-04, Vol.175, p.109599, Article 109599

2020

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The AFRL Additive Manufacturing Modeling Challenge: Predicting Micromechanical Fields in AM IN625 Using an FFT-Based Method with Direct Input from a 3D Microstructural Image

Integrating materials and manufacturing innovation, 2021-06, Vol.10 (2), p.157-176

2021

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Effect of chemical milling on low-cycle fatigue behavior of an Al–Mg–Si alloy

Corrosion science, 2013-03, Vol.68, p.144-153

2013

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A Tool to Generate Grain-Resolved Open-Cell Metal Foam Models

Integrating materials and manufacturing innovation, 2019-06, Vol.8 (2), p.247-256

2019

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Predicting Microstructure-Sensitive Fatigue-Crack Path in 3D Using a Machine Learning Framework

Mechanisms driving high-cycle fatigue life of as-built Inconel 718 processed by laser powder bed fusion

Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches

An adaptive-remeshing framework to predict impact-induced skull fracture in infants

A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data

Effects of laser-energy density and build orientation on the structure–property relationships in as-built Inconel 718 manufactured by laser powder bed fusion

Convolutional neural networks for expediting the determination of minimum volume requirements for studies of microstructurally small cracks, Part I: Model implementation and predictions

A recrystallization heat-treatment to reduce deformation anisotropy of additively manufactured Inconel 718

A void descriptor function to uniquely characterize pore networks and predict ductile-metal failure properties

A multi-scale, multi-physics modeling framework to predict spatial variation of properties in additive-manufactured metals

Investigating the effect of grain structure on compressive response of open-cell metal foam using high-fidelity crystal-plasticity modeling

A multiscale comparison of stochastic open-cell aluminum foam produced via conventional and additive-manufacturing routes

Quantitative analysis of three‐dimensional fatigue crack path selection in Mg alloy WE43 using high‐energy X‐ray diffraction microscopy

Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior

Determination of representative volume elements for small cracks in heterogeneous, linear-elastic domains

A voxel-based remeshing framework for the simulation of arbitrary three-dimensional crack growth in heterogeneous materials

Reconstructed and analyzed X-ray computed tomography data of investment-cast and additive-manufactured aluminum foam for visualizing ligament failure mechanisms and regions of contact during a compression test

Predicting microstructurally sensitive fatigue‐crack path in WE43 magnesium using high‐fidelity numerical modeling and three‐dimensional experimental characterization

High-throughput feature extraction for measuring attributes of deforming open-cell foams

Three-dimensional characterization of microstructurally small fatigue-crack evolution using quantitative fractography combined with post-mortem X-ray tomography and high-energy X-ray diffraction microscopy

Data-Driven Correlation Analysis Between Observed 3D Fatigue-Crack Path and Computed Fields from High-Fidelity, Crystal-Plasticity, Finite-Element Simulations

Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine- and deep-learning methods

The AFRL Additive Manufacturing Modeling Challenge: Predicting Micromechanical Fields in AM IN625 Using an FFT-Based Method with Direct Input from a 3D Microstructural Image

Effect of chemical milling on low-cycle fatigue behavior of an Al–Mg–Si alloy

A Tool to Generate Grain-Resolved Open-Cell Metal Foam Models

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