Optimization for advanced manufacturing processes in fabrication is becoming increasingly important around the world across a range of industries because of its improved productivity, capabilities, efficacy, and sustainability characteristics.

This new book explains how to optimize various industrial systems and processes utilizing new significant statistical and evolutionary methodologies. Statistical, multi-criteria decision-making, and evolutionary strategies for single- and multi-objective optimization are covered here, with relevant theoretical details, salient characteristics, implementation processes, effectiveness studies, and results in manufacturing.

The chapters present a variety of advanced manufacturing processes and optimization techniques. Processes are described with basic explanations, characteristics, and applications in various technological and biomedical domains. The chapters address the processes prediction of mechanical properties, design and development of tools, impact of power supply, increasing machining precision and efficiency, genetic algorithm optimization, additive manufacturing, and much more.

Providing a comprehensive study of advanced manufacturing processes, this volume is a valuable reference for engineers and R&D researchers involved in optimization for advanced manufacturing processes as well as for academics and postgraduate-level students in mechanical engineering, manufacturing engineering, and machining processes.

Sandip Kunar, PhD, is Assistant Professor of Mechanical Engineering at Aditya Engineering College, India. He has carried out his research work at the Saha Institute of Nuclear Physics, Kolkata, and Raja Ramanna Centre for Advanced Technology, Bhabha Atomic Research Centre, India. He has published research papers in journals and conference proceedings, book chapters, and 12 books with international book publishers. He has published one international webinar and has filed two patents. Dr. Kunar has received best innovative paper awards at international conferences and was awarded several scholarships. He is a reviewer for several highly reputed international journals and delivers lectures at faculty development programs, short-term courses, workshops, and seminars.

Prasenjit Chatterjee, PhD, is Professor of Mechanical Engineering and Dean (Research and Consultancy), MCKV Institute of Engineering, West Bengal, India. A prolific author and editor, Dr. Chatterjee is the Editor-in-Chief of the Journal of Decision Analytics and Intelligent Computing and guest edited several journal issues. He is also Series Editor of several book series, including Smart and Intelligent Computing in Engineering; Concise Introductions to AI and Data Science; AAP Research Notes on Optimization and Decision-Making Theories; Frontiers of Mechanical and Industrial Engineering; and River Publishers Series in Industrial Manufacturing and Systems Engineering. Dr. Chatterjee is one of the developers of two multiple-criteria decision-making methods: Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) and Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval (RAFSI).

M. Sreenivas Reddy, PhD, is Director of the Aditya Group of Educational Institutions, Surampalem, Andhra Pradesh, India, where he is also Principal of Aditya Engineering College. He has more than 20 years of teaching experience and five years of industry experience. His research interests include nonconventional machining processes, manufacturing technology, etc. He has published more than 25 research papers in international journals and conference proceedings as well as several books and book chapters. He has filed 11 patents and is a reviewer of more than ten highly reputed international journals.