Artificial Intelligence-Aided Materials Design
portes grátis
Artificial Intelligence-Aided Materials Design
AI-Algorithms and Case Studies on Alloys and Metallurgical Processes
Jha, Rajesh; Jha, Bimal Kumar
Taylor & Francis Ltd
03/2022
334
Dura
Inglês
9780367765279
15 a 20 dias
635
Descrição não disponível.
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Supervise Ml;AI algorithms;CALPHAD Approach;machine learning in materials science;Unsupervised Ml;data-driven materials development;Data Set;CALPHAD;Som Analysis;titanium alloys;Multi-objective Optimization;aluminum alloys;Case Study;finemet alloys;Supervised Machine Learning Approaches;alnico alloys;Scatter Plot;ironmaking;Som;steelmaking;HMT;nickel-based superalloys;Supervise Ml Algorithm;Titanium Aluminides;MOPSO;Unsupervised Machine Learning Algorithms;Strain Rate;Hot Metal;Phase Field Model;Multi-objective Design Optimization;AFM Imaging;Sc Addition;Inverse Design;HCA Analysis;FACTSAGE Software;RAAE
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.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Supervise Ml;AI algorithms;CALPHAD Approach;machine learning in materials science;Unsupervised Ml;data-driven materials development;Data Set;CALPHAD;Som Analysis;titanium alloys;Multi-objective Optimization;aluminum alloys;Case Study;finemet alloys;Supervised Machine Learning Approaches;alnico alloys;Scatter Plot;ironmaking;Som;steelmaking;HMT;nickel-based superalloys;Supervise Ml Algorithm;Titanium Aluminides;MOPSO;Unsupervised Machine Learning Algorithms;Strain Rate;Hot Metal;Phase Field Model;Multi-objective Design Optimization;AFM Imaging;Sc Addition;Inverse Design;HCA Analysis;FACTSAGE Software;RAAE
