This book sheds light on the development of traditional and advanced optimization methods. Their use in various tradition and non-tradition manufacturing and machining processes for an improved manufacturability is reported. This includes key elements of implementing conventional statistical methods, multi-objective and multi-criteria decision-making methods and evolution of single and multi-target optimization techniques using soft computing to enhance production performance, efficiency and sustainability in manufacturing. The latest research in this area as well as possible avenues of future research are also highlighted.

Autorentext

Dr. Sunil Pathak is specialized in gear engineering and advanced/hybrid manufacturing processes. He has been working in the field of gear engineering since last 9 years and advanced manufacturing for over 6 years. He has conducted extensive research on advanced finishing of gears. He possesses specialized skills in gear finishing, gear metrology (micro, and macro-geometry) and measurement of gear accuracy. He is also working in developing cold spray coatings as sustainable process for manufacturing of 3D additive manufacturing components and repair/remanufacturing engineering, where he has especially gained experience in materials and remanufacturing engineering. He has developed analytical models of manufacturing processes and optimizing parameters to achieve overall sustainability in manufacturing.

Inhalt

Part I Optimization using traditional techniques (4 Chapters)

Use of traditional design of experiments techniques such as full factorial design, Taguchi, response surface methodology and mixed model design to optimize the various traditional and non-traditional manufacturing and machining processes.

Part II Multi-criteria decision making through soft computing and evolutionary techniques (4 Chapters)

This part of the book will cover multiobjective optimizations using soft computing techniques such as artificial neural network (ANN) and fuzzy logic, artificial intelligence (AI) based genetic algorithms (GA), PSO (Particle swarm optimization), GRA (Grey relational analyses), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and Hybrid methods.