Nature-Inspired Optimization in Advanced Manufacturing Processes and Systems

Ganesh M. Kakandikar, Dinesh G. Thakur

• Publisher: CRC Press
• Year: 2020
• ISBN:
  9781000258462
• Edition: 1
• Topic: Design
• Subtopic: Industrial Design

The manufacturing system is going through substantial changes and developments in light of Industry 4.0. Newer manufacturing technologies are being developed and applied. There is a need to optimize these techniques when applied in different circumstances with respect to materials, tools, product configurations, and process parameters.

This book covers computational intelligence applied to manufacturing. It discusses nature-inspired optimization of processes and their design and development in manufacturing systems. It explores all manufacturing processes, at both macro and micro levels, and offers manufacturing philosophies. Nonconventional manufacturing, real industry problems and case studies, research on generative processes, and relevance of all this to Industry 4.0 is also included.

Researchers, students, academicians, and industry professionals will find this reference title very useful.

1 Investigation on Process Parameters of EN-08 Steel by Using DoE and Multi-Objective Genetic Algorithm Approach

Syed Anjum Alam

Rajasthan Technical University

Ashish Goyal

Manipal University Jaipur

Manish Dadhich

Rajasthan Technical University

CONTENTS

1.1 Introduction

1.2 Materials and Methodology

1.3 Results and Discussion

1.3.1 Rank Identification for Cutting Time (CT)

1.3.2 Optimal Solution for CT

1.3.3 Rank Identification for Surface Roughness (Ra)

1.3.4 Optimal Solution for RA

1.3.5 Contour Plot Analysis for Cutting Time and Surface Roughness

1.3.6 Interaction Plot for Cutting Time and Surface Roughness

1.3.7 Adequacy Check Analysis

1.3.8 Regression Modeling Equation

1.3.9 MOGA Optimization Technique

1.4 Conclusion

References

1.1 Introduction

The milling machining process is the most widely used process to fabricate complex profiles on difficult-to-cut materials. Oktem et al. [1] improved the slicing condition of the milling machining by using the response surface methodology approach. Prete et al. [2] built up a forecast model for uneven surface in flat end mill utilizing RSM approach. The ANN methodology was utilized to anticipate surface roughness, and GA was utilized to improve the surface roughness. Alam et al. [3] selected speed, feed rate, and depth of cut parameters of milling machine. The quadratic expectation was combined with GA to enhance the machining procedure parameters for surface roughness. Chandrasekaran et al. [4] surveyed the use of soft computing tools, i.e., neural networks, fuzzy sets, genetic algorithms, simulated annealing, ant colony optimization, and PSO, in various machining processes such as turning, milling, drilling, and grinding. Turkes et al. [5] proposed a model of surface unpleasantness to explore the impacts of geometrical parameters of the cutting device. It was concluded that the quadratic model is suitable for the ideal estimation of geometrical parameters.

Kadirgama et al. [6] built up a mode...

Table of contents

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Table of Contents
• Foreword
• Preface
• Editors
• Contributors
• Chapter 1 Investigation on Process Parameters of EN-08 Steel by Using DoE and Multi-Objective Genetic Algorithm Approach
• Chapter 2 Multi-Objective Optimization for Improving Performance Characteristics of Novel Curved EDM Process Using Jaya Algorithm
• Chapter 3 Artificial Neural Networks (ANNs) for Prediction and Optimization in Friction Stir Welding Process: An Overview and Future Trends
• Chapter 4 Energy-Efficient Cluster Head Selection for Manufacturing Processes Using Modified Honeybee Mating Optimization in Wireless Sensor Networks
• Chapter 5 Multiobjective Design Optimization of Power Take-Off (PTO) Gear Box Through NSGA II
• Chapter 6 Improving the Performance of Machining Processes Using Opposition-Based Learning Civilized Swarm Optimization
• Chapter 7 Application of Particle Swarm Optimization Method to Availability Optimization of Thermal Power Plants
• Chapter 8 Optimization of Incremental Sheet Forming Process Using Artificial Intelligence-Based Techniques
• Chapter 9 Development of Non-dominated Genetic Algorithm Interface for Parameter Optimization of Selected Electrochemical-Based Machining Processes
• Chapter 10 ANN Modeling of Surface Roughness and Thrust Force During Drilling of SiC Filler-Incorporated Glass/Epoxy Composites
• Chapter 11 Multi-objective Optimization of Laser-Assisted Micro-hole Drilling with Evolutionary Algorithms
• Chapter 12 Modeling and Pareto Optimization of Burnishing Process for Surface Roughness and Microhardness
• Chapter 13 Selection of Components and Their Optimum Manufacturing Tolerance for Selective Assembly Technique Using Intelligent Water Drops Algorithm to Minimize Manufacturing Cost
• Chapter 14 Enhancing the Surface Roughness Characteristics of Selective Inhibition Sintered HDPE Parts: An Integrated Approach of RSM and Krill Herd Algorithm
• Chapter 15 Optimization of Abrasive Water Jet Machining Parameters of Al/Tic Using Response Surface Methodology and Modified Artificial Bee Colony Algorithm
• Index