Process Modeling and Optimization in Modern Manufacturing

Ravi Pratap Singh, Narendra Kumar, Vishal Francis, Ankur Jaiswal

• Publisher: CRC Press
• Year: 2025
• eBook ISBN: 9781040438404
• Topic: Technology & Engineering
• Index: Technology & Engineering

This book covers modeling and optimization of various modern manufacturing processes such as advanced machining, hybrid manufacturing, and additive manufacturing including related case studies in these domains. Various areas like smart manufacturing, hybrid manufacturing, 3D printing, process modeling and characterization, optimization, and so forth are covered in detail. The focus of this book is on artificial neural network, finite element analysis, firefly/genetic algorithm, particle swarm, and fuzzy-based techniques, which are the main optimization and modeling techniques.

Features of the book:

• Provides in-depth investigations on prospects of modeling and optimization of modern manufacturing processes.
• Detailed overview on different evolutionary and bio-inspired optimization techniques and their implementation.
• Provides step-by-step guidance on how to use machine learning for the enhancement of productivity and quality in modern manufacturing processes.
• Discusses sustainability and Industry 4.0-based content.
• Includes case studies and practical examples.

This book is aimed at researchers and graduate students in mechanical, manufacturing, production, and industrial engineering.

Table of contents

• Cover
• Half Title
• Series
• Title
• Copyright
• Contents
• Preface
• Editor Biographies
• List of Contributors
• List of Figures
• List of Tables
• Chapter 1 Introduction to Process Modeling and Optimization in Modern Manufacturing: An Overview
• Chapter 2 Recent Advances in the Applications of Machine Learning Optimization Techniques in Modern and Hybrid Manufacturing for Quality, Sustainability, and Productivity: Some Case Studies
• Chapter 3 Modeling and Optimization of Abrasive Water Jet Machining Process on Surface Quality of Green Composite Using Nature-Inspired Techniques Methods: Comparative Study of Teaching Learning-Based Optimization (TLBO), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO)
• Chapter 4 Machine Learning–Enabled Gesture Recognition in 3D Printed Robotic Prostheses: Advances in Electromyography Control
• Chapter 5 Investigation of the AISI 1040 Steel Machining Characteristics with the Application of Cutting Fluid (Corn oil+ Al2O3) Using the Taguchi-TOPSIS (T-T) Approach
• Chapter 6 Optimization of Electro Discharge Machining Parameters for Additively Manufactured Composite [AlSi10Mg + Niobium Carbide (NbC)] Using Random Forest Algorithm
• Chapter 7 Investigation, Modeling, and Advanced Optimization of Additive Manufacturing Characteristics: A Study on Evolutionary Methods
• Chapter 8 Analysis for Development of High-Performance Polymer Nanocomposites for FDM-Based Additive Manufacturing
• Chapter 9 Machine Learning–Based Optimization for FDM-Printed Poly Lactic Acid Parts
• Chapter 10 Experimental Investigation on Cutting Rate in μECDM of Si-Based Pyrex Glass: Evolutionary Parametric Optimization and Surface Morphology
• Chapter 11 Micro Drilling in Cu-Based Shape Memory Alloy via μ-ECM: Influence of Input Variables on GWO- and PSO-Based Advanced Optimization
• Index