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Full Description
This book describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including hard and soft magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the MATLAB® and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference.
Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats
Helps readers to develop predictive models through AI/ML algorithms by writing their own computer code or using resources where they do not have to write code
Covers downloadable resources such as MATLAB GUI/APP and Python implementation that can be used on common mobile devices
Discusses the CALPHAD approach and ways to use data generated from it
Features a chapter on metallurgical/materials concepts to help readers understand the case studies and thus proper implementation of AI/ML algorithms under the framework of data-driven materials science
Uses case studies to examine the importance of using unsupervised machine learning algorithms in determining patterns in datasets
This book is written for materials scientists and metallurgists interested in the application of AI, ML, and data science in the development of new materials.
Contents
1. Introduction. 2. Metallurgical/Materials Concepts. 3. Artificial Intelligence Algorithms. 4. Case Study #4: Computational Platform for Developing Predictive Models for Predicting Load-Displacement Curve and AFM Image: Combined Experimental-Machine Learning Approach. 5. Case Study #5: Design of Hard Magnetic Alnico Alloys: Combined Machine Learning-Experimental Approach. 6. Case Study #6: Design and Discovery of Soft Magnetic Alloys: Combined Machine Learning-CALPHAD Approach. 7. Case Study #7: Nickel-Base Superalloys: Combined Machine Learning-CALPHAD Approach. 8. Case Study #8: Design of Aluminum Alloys: Combined Machine Learning-CALPHAD Approach. 9. Case Study #9: Titanium Alloys for High-Temperature Application: Combined Machine Learning-CALPHAD Approach. 10. Case Study #10: Design of β-Stabilized, ω-Free Titanium Based Biomaterials: Combined Machine Learning-CALPHAD Approach. 11. Case Study #11: Industrial Furnaces I: Application of Machine Learning on an Industrial Iron-Making Blast Furnace Data. 12. Case Study #12: Development of GUI/APP to Determine Additions in LD Steel Making Furnace. 13. Case Study #13: Selection of a Supervised Machine Learning(Response Surface) Algorithm for a Given Problem. 14. Case Study #14: Effect of Operating Parameters on Roll Force and Torque in an Industrial Rolling Mill: Supervised and Unsupervised Machine Learning Approach. 15. Case Study #15: Developing Predictive Models for Flow Stress by Utilizing Experimental Data Generated From Gleeble Testing Machine: Combined Experimental-Supervised Machine Learning Approach. 16. Computational Platforms Used in This Work.
