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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.
Shubhabrata Datta is presently working as Research Professor in the Department of Mechanical Engineering, and Coordinator of the Centre for Composites and Advanced Materials at SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India. His research interests are in the domain of Materials Informatics, Alloy Design, Composites and Biomaterials. Subhas Ganguly is an Associate Professor in the Department of Metallurgical and Materials Engineering, National Institute of Technology, Raipur, India. His research interests include Artificial Intelligence and Machine learning in Materials Engineering, Computational Optimization and Data Science for Metallurgical problems, Advance Steel and Alloy Design, Phase Transformation and Friction Stir Welding.
1 Introduction to Informatics and Data Analytics; 2 Materials Informatics and Steel Data; 3 Ironmaking and Steelmaking; 4 Prediction of Phase Transformation in Steel; 5 Steel Welding: DatäDriven Approaches; 6 DatäDriven Modeling of Mechanical Properties of Steel; 7 Microstructure and Machine Learning; 8 Optimization for Design; 9 Possibilities and Opportunities
