Shubhabrata Datta - Böcker

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.

Several statistical techniques are used for the design of materials through extraction of knowledge from existing data banks. These approaches are getting more attention with the application of computational intelligence techniques. This book illustrates the alternative but effective methods of designing materials, where models are developed through capturing the inherent correlations among the variables on the basis of available imprecise knowledge in the form of rules or database, as well as through the extraction of knowledge from experimental or industrial database, and using optimization tools.

Soft Computing in Smart Manufacturing and Materials explains the role of soft computing in the manufacturing industries. It presents the techniques, concepts and design principles behind smart soft computing, and describes how they can be applied in the development and manufacture of smart materials. It provides perspectives for design and commissioning of intelligent applications, including in health care, agriculture, and production assembly, and reviews the latest intelligent technologies and algorithms related to the methodologies of monitoring and mitigation of sustainable engineering.Introduces soft computing techniques for the creation of sustainable solutions for smart materials and manufacturingOffers perspectives for design, development, and commissioning of intelligent applicationsReviews the latest intelligent technologies and algorithms related to monitoring and mitigation of sustainable engineeringDiscusses the implementation of soft computing in the various areas of engineering materialsLooks at future sustainable and intelligent monitoring techniques that will benefit manufacturing

Several statistical techniques are used for the design of materials through extraction of knowledge from existing data banks. These approaches are getting more attention with the application of computational intelligence techniques. This book illustrates the alternative but effective methods of designing materials, where models are developed through capturing the inherent correlations among the variables on the basis of available imprecise knowledge in the form of rules or database, as well as through the extraction of knowledge from experimental or industrial database, and using optimization tools.

This book describes different approaches for solving industrial problems like product design, process optimization, quality enhancement, productivity improvement and cost minimization. Several optimization techniques are described. The book covers case studies on the applications of classical as well as evolutionary and swarm optimization tools for solving industrial issues. The content is very helpful for industry personnel, particularly engineers from the Operation, R&D and Quality Assurance sectors, and also the academic researchers of different engineering and/or business administration background.

This book covers different machine learning techniques such as artificial neural network, support vector machine, rough set theory and deep learning. The book includes several case studies, helping researchers in academia and industries aspiring to use machine learning for solving practical industrial problems.