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Steel Informatics aims to review the application of data-driven computing techniques related to the design of steel, including phase transformation, composition-process-property correlation, and different processing techniques, particularly deformation and joining. This book initiates with fundamentals of informatics followed by a description of applications of statistical analyses in defining the different attributes of steel. The proceeding chapters of this book cover recent applications of statistical, machine learning, expert systems, and optimization algorithms in the domains of iron and steel making, casting, deformation, phase transformation and heat treatment, microstructure analysis, and design of steel.
Features:
Exclusive title focussing on informatics in steel design.
Covers related statistics as well as artificial intelligence and machine learning aspects.
Explains metallurgical aspects lucidly for the data scientists, steel researchers, and industries.
Discusses all aspects of steel technology.
Describes pertinent tools used for related computations.
This book is useful for researchers, professionals, and graduate students in metallurgy, materials science, steel and welding, and computational materials science.
Dedication
About the Authors
Preface
Acknowledgments
Introduction to Informatics and Data Analytics
Materials Informatics and Steel Data
Ironmaking and Steel making
Prediction of Phase Transformation in Steel
Steel Welding: Data-Driven Approaches
Data-Driven Modeling of Mechanical Properties of Steel
Microstructure and Machine Learning
Optimization for Design
Possibilities and Opportunities
Index