"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. Readers new to AI/ML algorithms can use the book as a starting point and use the included 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. 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"-- Provided by publisher.

1. Introduction. 2. Metallurgical/Materials Concepts. 3. Artificial Intelligence Algorithms. 4. Case Study 1: Nanomechanics and Nanotribology: Combined Machine Learning-Experimental Approach. 5. Case Study 2: Design of Hard Magnetic Alnico Alloys: Combined Machine Learning-Experimental Approach. 6. Case Study 3: Design of Soft Magnetic Finemet Type Alloys: Combined Machine Learning-CALPHAD Approach. 7. Case Study 4: Design of Nickel-Base Superalloys: Combined Machine Learning-CALPHAD Approach. 8. Case Study 5: Design of Aluminum Alloys: Combined Machine Learning-CALPHAD Approach. 9. Case Study 6: Design of Titanium Alloys for High-Temperature Application: Combined Machine Learning-CALPHAD Approach. 10. Case Study 7: Design of Titanium Based Biomaterials: Combined Machine Learning-CALPHAD Approach. 11. Case Study 8: Industrial Furnaces I: Application of Machine Learning on an Industrial Iron Making Blast Furnace Data. 12. Case Study 9: Industrial Furnaces II: Application of Machine Learning Algorithms on an Industrial LD Steel Making Furnace Data. 13. Software/Codes Included with this Book. 14. Conclusion.

OCLC-licensed vendor bibliographic record.