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Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes

1, 2022

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Application of machine learning methods for the prediction of roll force and torque during plate rolling of micro-alloyed steel

Journal of Alloys and Metallurgical Systems, 2023-12, Vol.4, p.100044, Article 100044

2023

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Hot deformation behavior and processing map of Nb-V-Ti micro-alloyed steel

Materials today : proceedings, 2020, Vol.28, p.1973-1979

2020

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Development of an Integrated Flow Stress and Roll Force Models for Plate Rolling of Microalloyed Steel

Steel research international, 2022-02, Vol.93 (2), p.n/a

2022

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Synergistic Effect of Calcium and Barium on Non-metallic Inclusions Modification and Improvement in Steel Cleanliness

Transactions of the Indian Institute of Metals, 2024, Vol.77 (9), p.2601-2612

2024

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An Improved Inclusions Modification Strategy Through Barium-Alloyed Cored Wire Injection in Steel Refining

Journal of failure analysis and prevention, 2024, Vol.24 (4), p.1828-1842

2024

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Effect of Warm Rolling Process Parameters on Microstructure and Mechanical Properties of Structural Steels

Transactions of the Indian Institute of Metals, 2022-06, Vol.75 (6), p.1509-1524

2022

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Microstructure–Property Correlation in High-Strength Formable Steel with Varying Nb-Si Content

Journal of materials engineering and performance, 2020-03, Vol.29 (3), p.1901-1908

2020

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Development of Fine-Grained, Low-Carbon Bainitic Steels with High Strength and Toughness Produced Through the Conventional Hot-Rolling and Air-Cooling

Metallurgical and materials transactions. A, Physical metallurgy and materials science, 2016-12, Vol.47 (12), p.6224-6236

2016

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Assessment of Formability of Hot-Rolled Steel through Determination of Hole-Expansion Ratio

Materials and manufacturing processes, 2011-02, Vol.26 (1), p.37-42

2011

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Computational Platforms Used in This Work

Artificial Intelligence-Aided Materials Design, 2022, p.311-320

1, 2022

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Introduction: Artificial Intelligence and Materials Design

Artificial Intelligence-Aided Materials Design, 2022, p.1-16

1, 2022

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Case Study #4: Computational Platform for Developing Predictive Models for Predicting Load-Displacement Curve and AFM Image: Combined Experimental-Machine Learning Approach

Artificial Intelligence-Aided Materials Design, 2022, p.95-106

1, 2022

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Case Study #13: Selection of a Supervised Machine Learning (Response Surface) Algorithm for a Given Problem

Artificial Intelligence-Aided Materials Design, 2022, p.245-269

1, 2022

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Case Study #15: Developing Predictive Models for Flow Stress by Utilizing Experimental Data Generated from Gleeble Testing Machine: Combined Experimental-Supervised Machine Learning Approach

Artificial Intelligence-Aided Materials Design, 2022, p.299-310

1, 2022

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Case Study #9: Titanium Alloys for High-Temperature Applications: Combined Machine Learning-CALPHAD Approach

Artificial Intelligence-Aided Materials Design, 2022, p.179-189

1, 2022

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Authors Biographies

Artificial Intelligence-Aided Materials Design, 2022

2022

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Case Study #10: Design of β-Stabilized, ω-Free, Titanium-Based Biomaterials: Combined Machine Learning-CALPHAD Approach

Artificial Intelligence-Aided Materials Design, 2022, p.191-203

1, 2022

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Case Study #14: Effect of Operating Parameters on Roll Force and Torque in an Industrial Rolling Mill: Supervised and Unsupervised Machine Learning Approach

Artificial Intelligence-Aided Materials Design, 2022, p.271-297

1, 2022

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Case Study #12: Industrial Furnaces II: Development of GUI/APP to Determine Additions in LD Steelmaking Furnace

Artificial Intelligence-Aided Materials Design, 2022, p.231-244

1, 2022

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Case Study #6: Design and Discovery of Soft Magnetic Alloys: Combined Experiment-Machine Learning-CALPHAD Approach

Artificial Intelligence-Aided Materials Design, 2022, p.123-148

1, 2022

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Intro

Artificial Intelligence-Aided Materials Design, 2022

2022

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Case Study #8: Design of Aluminum Alloys: Combined Machine Learning-CALPHAD Approach

Artificial Intelligence-Aided Materials Design, 2022, p.165-177

1, 2022

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Artificial Intelligence Algorithms

Artificial Intelligence-Aided Materials Design, 2022, p.55-94

1, 2022

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Case Study #7: Nickel-Based Superalloys: Combined Machine Learning-CALPHAD Approach

Artificial Intelligence-Aided Materials Design, 2022, p.151-163

1, 2022

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Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes

Application of machine learning methods for the prediction of roll force and torque during plate rolling of micro-alloyed steel

Hot deformation behavior and processing map of Nb-V-Ti micro-alloyed steel

Development of an Integrated Flow Stress and Roll Force Models for Plate Rolling of Microalloyed Steel

Synergistic Effect of Calcium and Barium on Non-metallic Inclusions Modification and Improvement in Steel Cleanliness

An Improved Inclusions Modification Strategy Through Barium-Alloyed Cored Wire Injection in Steel Refining

Effect of Warm Rolling Process Parameters on Microstructure and Mechanical Properties of Structural Steels

Microstructure–Property Correlation in High-Strength Formable Steel with Varying Nb-Si Content

Development of Fine-Grained, Low-Carbon Bainitic Steels with High Strength and Toughness Produced Through the Conventional Hot-Rolling and Air-Cooling

Assessment of Formability of Hot-Rolled Steel through Determination of Hole-Expansion Ratio

Computational Platforms Used in This Work

Introduction: Artificial Intelligence and Materials Design

Case Study #4: Computational Platform for Developing Predictive Models for Predicting Load-Displacement Curve and AFM Image: Combined Experimental-Machine Learning Approach

Case Study #13: Selection of a Supervised Machine Learning (Response Surface) Algorithm for a Given Problem

Case Study #15: Developing Predictive Models for Flow Stress by Utilizing Experimental Data Generated from Gleeble Testing Machine: Combined Experimental-Supervised Machine Learning Approach

Case Study #9: Titanium Alloys for High-Temperature Applications: Combined Machine Learning-CALPHAD Approach

Authors Biographies

Case Study #10: Design of β-Stabilized, ω-Free, Titanium-Based Biomaterials: Combined Machine Learning-CALPHAD Approach

Case Study #14: Effect of Operating Parameters on Roll Force and Torque in an Industrial Rolling Mill: Supervised and Unsupervised Machine Learning Approach

Case Study #12: Industrial Furnaces II: Development of GUI/APP to Determine Additions in LD Steelmaking Furnace

Case Study #6: Design and Discovery of Soft Magnetic Alloys: Combined Experiment-Machine Learning-CALPHAD Approach

Intro

Case Study #8: Design of Aluminum Alloys: Combined Machine Learning-CALPHAD Approach

Artificial Intelligence Algorithms

Case Study #7: Nickel-Based Superalloys: Combined Machine Learning-CALPHAD Approach

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