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.

The development of new and superior materials is beneficial within industrial settings, as well as a topic of academic interest. By using computational modeling techniques, the probable application and performance of these materials can be easily evaluated. Computational Approaches to Materials Design: Theoretical and Practical Aspects brings together empirical research, theoretical concepts, and the various approaches in the design and discovery of new materials. Highlighting optimization tools and soft computing methods, this publication is a comprehensive collection for researchers, both in academia and in industrial settings, and practitioners who are interested in the application of computational techniques in the field of materials engineering.

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.

Due to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of artificial neural networks in industrial and control engineering applications. The book begins with a review of applications of artificial neural networks in textile industries. Particular applications in textile industries follow. Parts continue with applications in materials science and industry such as material identification, and estimation of material property and state, food industry such as meat, electric and power industry such as batteries and power systems, mechanical engineering such as engines and machines, and control and robotic engineering such as system control and identification, fault diagnosis systems, and robot manipulation. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks in industrial and control engineering areas. The target audience includes professors and students in engineering schools, and researchers and engineers in industries.

Focuses entirely on demystifying the field and subject of ICME and provides step-by-step guidance on its industrial application via case studies This highly-anticipated follow-up to Mark F. Horstemeyer’s pedagogical book on Integrated Computational Materials Engineering (ICME) concepts includes engineering practice case studies related to the analysis, design, and use of structural metal alloys. A welcome supplement to the first book—which includes the theory and methods required for teaching the subject in the classroom—Integrated Computational Materials Engineering (ICME) For Metals: Concepts and Case Studies focuses on engineering applications that have occurred in industries demonstrating the ICME methodologies, and aims to catalyze industrial diffusion of ICME technologies throughout the world. The recent confluence of smaller desktop computers with enhanced computing power coupled with the emergence of physically-based material models has created the clear trend for modeling and simulation in product design, which helped create a need to integrate more knowledge into materials processing and product performance. Integrated Computational Materials Engineering (ICME) For Metals: Case Studies educates those seeking that knowledge with chapters covering: Body Centered Cubic Materials; Designing An Interatomic Potential For Fe-C Alloys; Phase-Field Crystal Modeling; Simulating Dislocation Plasticity in BCC Metals by Integrating Fundamental Concepts with Macroscale Models; Steel Powder Metal Modeling; Hexagonal Close Packed Materials; Multiscale Modeling of Pure Nickel; Predicting Constitutive Equations for Materials Design; and more. Presents case studies that connect modeling and simulation for different materials' processing methods for metal alloys Demonstrates several practical engineering problems to encourage industry to employ ICME ideas Introduces a new simulation-based design paradigm Provides web access to microstructure-sensitive models and experimental database Integrated Computational Materials Engineering (ICME) For Metals: Case Studies is a must-have book for researchers and industry professionals aiming to comprehend and employ ICME in the design and development of new materials.

In the digital landscape, the Metaverse emerges as a frontier of boundless possibilities. Yet, its potential remains largely untapped. The pressing challenge lies in harnessing the power of machine learning to navigate this uncharted territory, where virtual reality, augmented reality, and immersive technologies converge to redefine human interaction and experience. Impact and Potential of Machine Learning in the Metaverse offers a comprehensive examination of how machine learning techniques can shape the future of the Metaverse. This advanced work addresses key domains such as healthcare, education, gaming, and beyond. By delving into topics like digital twins in healthcare and blockchain-enabled security, the book not only sheds light on advancements but also confronts challenges head-on, inspiring scholars to explore new research directions and interdisciplinary collaborations. Through real-world case studies and practical applications, readers gain actionable insights into leveraging machine learning for transformative impact in the Metaverse.