Artificial Intelligence-Aided Materials Design - Jha, Rajesh

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Résumé :
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Biographie:
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 ?-Stabilized, ?-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....

Sommaire:
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. It will appeal to readers who are both new to and experienced with the use of AI/ML algorithms in data-driven materials science....