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Evaluation of Ti–Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression

Materials, 2020-10, Vol.13 (20), p.4641

2020

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Study of a Bimodal α–β Ti Alloy Microstructure Using Multi-Resolution Spherical Indentation Stress-Strain Protocols

Journal of composites science, 2022-06, Vol.6 (6), p.162

2022

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Probing nanoscale damage gradients in ion-irradiated metals using spherical nanoindentation

Scientific reports, 2017-09, Vol.7 (1), p.11918-12, Article 11918

2017

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Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

Integrating materials and manufacturing innovation, 2014-12, Vol.3 (1), p.90-108

2014

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Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data

Integrating materials and manufacturing innovation, 2013-12, Vol.2 (1), p.54-80

2013

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A Generalized and Modular Framework for Digital Generation of Composite Microstructures

Journal of composites science, 2021-08, Vol.5 (8), p.211

2021

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Critical Comparison of Spherical Microindentation, Small Punch Test, and Uniaxial Tensile Testing for Selective Laser Melted Inconel 718

Applied sciences, 2021-01, Vol.11 (3), p.1061

2021

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Decoding defect statistics from diffractograms via machine learning

npj computational materials, 2021-05, Vol.7 (1), p.1-9, Article 67

2021

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Mechanical characterization of Ti-6Al-4V titanium alloy at multiple length scales using spherical indentation stress-strain measurements

Materials & design, 2016-12, Vol.111 (C), p.463-472

2016

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

Frontiers in materials, 2019-05, Vol.6

2019

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Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Integrating materials and manufacturing innovation, 2016, Vol.5 (1), p.1-15

2016

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Estimating mechanical properties from spherical indentation using Bayesian approaches

Materials & design, 2018-06, Vol.147, p.92-105

2018

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Development of a Robust CNN Model for Capturing Microstructure-Property Linkages and Building Property Closures Supporting Material Design

Frontiers in materials, 2022-03, Vol.9

2022

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A Bayesian Framework for the Estimation of the Single Crystal Elastic Parameters From Spherical Indentation Stress-Strain Measurements

Frontiers in materials, 2019-06, Vol.6

2019

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Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

Integrating materials and manufacturing innovation, 2017-03, Vol.6 (1), p.36-53

2017

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High throughput structure–property relationship for additively manufactured 316L/IN625 alloy mixtures leveraging 2-step Bayesian estimation

Materials & design, 2023-05, Vol.229, p.111892, Article 111892

2023

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A Framework for the Systematic Design of Segmentation Workflows

Integrating materials and manufacturing innovation, 2020-03, Vol.9 (1), p.70-88

2020

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Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

Integrating materials and manufacturing innovation, 2017-03, Vol.6 (1), p.54-68

2017

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Texture-sensitive prediction of micro-spring performance using Gaussian process models calibrated to finite element simulations

Materials & design, 2021-01, Vol.197 (C), p.109198, Article 109198

2021

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Digital Twins for Materials

Frontiers in materials, 2022-03, Vol.9

2022

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New Insights into the Microstructural Changes During the Processing of Dual-Phase Steels from Multiresolution Spherical Indentation Stress–Strain Protocols

Metals (Basel ), 2020-01, Vol.10 (1), p.18

2020

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Mechanical Responses of Primary-α Ti Grains in Polycrystalline Samples: Part II—Bayesian Estimation of Crystal-Level Elastic-Plastic Mechanical Properties from Spherical Indentation Measurements

Integrating materials and manufacturing innovation, 2021-03, Vol.10 (1), p.99-114

2021

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High throughput exploration of process-property linkages in Al-6061 using instrumented spherical microindentation and microstructurally graded samples

Integrating materials and manufacturing innovation, 2016, Vol.5 (1), p.192-211

2016

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Editorial: Machine Learning and Data Mining in Materials Science

Frontiers in materials, 2020-02, Vol.7

2020

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Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition

Integrating materials and manufacturing innovation, 2022-09, Vol.11 (3), p.433-449

2022

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Evaluation of Ti–Mn Alloys for Additive Manufacturing Using High-Throughput Experimental Assays and Gaussian Process Regression

Study of a Bimodal α–β Ti Alloy Microstructure Using Multi-Resolution Spherical Indentation Stress-Strain Protocols

Probing nanoscale damage gradients in ion-irradiated metals using spherical nanoindentation

Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters

Novel microstructure quantification framework for databasing, visualization, and analysis of microstructure data

A Generalized and Modular Framework for Digital Generation of Composite Microstructures

Critical Comparison of Spherical Microindentation, Small Punch Test, and Uniaxial Tensile Testing for Selective Laser Melted Inconel 718

Decoding defect statistics from diffractograms via machine learning

Mechanical characterization of Ti-6Al-4V titanium alloy at multiple length scales using spherical indentation stress-strain measurements

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

Versatile algorithms for the computation of 2-point spatial correlations in quantifying material structure

Estimating mechanical properties from spherical indentation using Bayesian approaches

Development of a Robust CNN Model for Capturing Microstructure-Property Linkages and Building Property Closures Supporting Material Design

A Bayesian Framework for the Estimation of the Single Crystal Elastic Parameters From Spherical Indentation Stress-Strain Measurements

Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials

High throughput structure–property relationship for additively manufactured 316L/IN625 alloy mixtures leveraging 2-step Bayesian estimation

A Framework for the Systematic Design of Segmentation Workflows

Process-Structure Linkages Using a Data Science Approach: Application to Simulated Additive Manufacturing Data

Texture-sensitive prediction of micro-spring performance using Gaussian process models calibrated to finite element simulations

Digital Twins for Materials

New Insights into the Microstructural Changes During the Processing of Dual-Phase Steels from Multiresolution Spherical Indentation Stress–Strain Protocols

Mechanical Responses of Primary-α Ti Grains in Polycrystalline Samples: Part II—Bayesian Estimation of Crystal-Level Elastic-Plastic Mechanical Properties from Spherical Indentation Measurements

High throughput exploration of process-property linkages in Al-6061 using instrumented spherical microindentation and microstructurally graded samples

Editorial: Machine Learning and Data Mining in Materials Science

Application of a Chained-ANN for Learning the Process–Structure Mapping in Mg2SixSn1−x Spinodal Decomposition

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