The text comprehensively focuses on the concepts, implementation, and application of evolutionary algorithms for predicting, modeling, and optimizing the various material removal processes from their origin to the current advancements. This one-of-a-kind book encapsulates all the features related to the application and implementation of evolutionary algorithms for the purpose of predicting and optimizing the process characteristics of different machining methods and their allied processes that will provide comprehensive information. It broadly explains the concepts of employing evolutionary algorithm-based optimization in a broad domain of various material removal processes. Therefore, this book will enable prospective readers to take full advantage of recent findings and advancements in the fields of traditional, advanced, micro, and hybrid machining, among others. Moreover, the simplicity of its writing will keep readers engaged throughout and make it easier for them to understand the advanced topics. The book- • Offers a step-by-step guide to implement evolutionary algorithms for the overall optimization of conventional and contemporary machining processes • Provides in-depth analysis of various material removal processes through evolutionary optimization • Details an overview of different evolutionary optimization techniques • Explores advanced processing of various engineering materials-based case studies It further discusses different nature-inspired algorithms-based modeling, prediction, and modeling of machining responses in attempting advanced machining of the latest materials and related engineering problems along with case studies and practical examples. It will be an ideal reference text for graduate students and academic researchers working in the fields of mechanical engineering, aerospace engineering, industrial engineering, manufacturing engineering, and materials science.

Metal removal processes - cutting and grinding in this book - are an integral part of a large number of manufacturing systems, either as the primary manufacturing process, or as an important part of preparing the tooling for other manufacturing processes. In recent years, industry and educational institutions have concentrated on the metal removal system, perhaps at the expense of the process. This book concentrates on metal removal processes, particularly on the modeling aspects that can either give a direct answer or suggest the general requirements as to how to control, improve or change a metal removal process. This modeling knowledge is more important with automated computer controlled systems than it has ever been before, because quantitative knowledge is needed to design and operate these systems. This senior undergraduate/graduate textbook is aimed at providing the quantitative knowledge, often times at an elementary level, for handling the technological aspects of setting up and operating a metal removal process and interpreting the experience of planning, operating and improving a metal removal process based on rule of thumb approaches.

Optimization is central to any problem involving decision-making in engineering. Optimization theory and methods deal with selecting the best option regarding the given objective function or performance index. New algorithmic and theoretical techniques have been developed for this purpose, and have rapidly diffused into other disciplines. As a result, our knowledge of all aspects of the field has grown even more profound. In Optimization for Engineering Problems, eminent researchers in the field present the latest knowledge and techniques on the subject of optimization in engineering. Whereas the majority of work in this area focuses on other applications, this book applies advanced and algorithm-based optimization techniques specifically to problems in engineering.

This book provides a detailed understanding of optimization methods as they are implemented in a variety of manufacturing, fabrication and machining processes. It covers the implementation of statistical methods, multi-criteria decision making methods and evolutionary techniques for single and multi-objective optimization to improve quality, productivity, and sustainability in manufacturing. It reports on the theoretical aspects, special features, recent research and latest development in the field. Optimization of Manufacturing Processes is a valuable source of information for researchers and practitioners, as it fills the gap where no dedicated book is available on intelligent manufacturing/modeling and optimization in manufacturing. Readers will develop an understanding of the implementation of statistical and evolutionary techniques for modeling and optimization in manufacturing.

Green manufacturing has developed into an essential aspect of contemporary manufacturing practices, calling for environmentally friendly and sustainable techniques. Implementing successful green manufacturing processes not only improves business efficiency and competitiveness but also reduces harmful production in the environment. The Handbook of Research on Green Engineering Techniques for Modern Manufacturing provides emerging perspectives on the theoretical and practical aspects of green industrial concepts, such as green supply chain management and reverse logistics, for the sustainable utilization of resources and applications within manufacturing and engineering. Featuring coverage on a broad range of topics such as additive manufacturing, integrated manufacturing systems, and machine materials, this publication is ideally designed for engineers, environmental professionals, researchers, academicians, managers, policymakers, and graduate-level students seeking current research on recent and sustainable practices in manufacturing processes.

This book comprises select peer-reviewed papers presented at the International Conference on Advanced Engineering Optimization Through Intelligent Techniques (AEOTIT) 2018. The book combines contributions from academics and industry professionals, and covers advanced optimization techniques across all major engineering disciplines like mechanical, manufacturing, civil, automobile, electrical, chemical, computer and electronics engineering. Different optimization techniques and algorithms such as genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), particle swarm optimization (PSO), artificial bee colony (ABC) algorithm, artificial immune algorithm (AIA), teaching-learning-based optimization (TLBO) algorithm and many other latest meta-heuristic techniques and their applications are discussed. This book will serve as a valuable reference for students, researchers and practitioners and help them in solving a wide range of optimization problems.