Optimization of Process Parameters of Powder Mixed Electric Discharge machining for D2 Steel

Inhaltsverzeichnis (Table of Contents)

• CHAPTER 1: INTRODUCTION
• 1.1 Introduction
• 1.2 History of EDM
• 1.3 Working Principle of EDM
• 1.4 Process variables
• 1.5 Performance Measures
• 1.6 Categories of EDM
• 1.6.1 Die-sinking EDM
• 1.6.2 Wire EDM
• 1.7 Powder mixed EDM
• 1.8 Advantages of EDM
• 1.9 Dis-advantages of EDM
• 1.10 Applications of EDM
• CHAPTER 2: LITERATURE REVIEW
• 2.1 Influence of powder characteristics
• 2.2 Gap in Literature Survey
• 2.3 Problem Formulation
• 2.4 Objectives for Research Work
• 2.5 Methodology Flow Chart
• CHAPTER 3: EXPERIMENTAL DETAILS
• 3.1 Experimental set-up of EDM
• 3.2 Selection of materials
• 3.2.1 Work-piece material
• 3.2.2 Tool material
• 3.2.3 Powder material
• 3.3 Process parameters
• 3.4 Design of experiments using Taguchi
• 3.5 Performance Measures
• 3.5.1 Material removal rate (MRR)
• 3.5.2 Tool wear rate (TWR)
• 3.5.3 Surface roughness (SR)
• 3.5.4 Hole Diameter Variation(HDV)
• 3.5.5 Scanning electron microscope (SEM)
• 3.6 Work-piece after machining and testing
• CHAPTER 4: OPTIMIZATION OF OUTPUT PARAMETERS
• 4.1 Introduction
• 4.2 Experimental results of MRR, TWR, SR and HDV
• 4.3 S/N Ratios of MRR, TWR, SR and HDV
• 4.4 Optimization of material removal rate
• 4.4.1 Results for S/N Ratios of MRR
• 4.4.2 Results for mean of MRR
• 4.5 Optimization of tool wear rate
• 4.5.1 Results for S/N Ratios of TWR
• 4.5.2 Results for mean of TWR
• 4.6 Optimization of surface roughness
• 4.6.1 Results for S/N Ratios of SR
• 4.6.2 Results for mean of SR
• 4.7 Optimization of hole diameter variation
• 4.7.1 Results for S/N ratios of HDV
• 4.7.2 Results for mean of HDV
• 4.8 Calculation of optimal design for output responses
• 4.8.1 Optimal design for MRR
• 4.8.2 Optimal design for TWR
• 4.8.3 Optimal design for SR
• 4.8.4 Optimal design for HDV
• CHAPTER 5: GREY RELATIONAL ANALYSIS
• 5.1 Introduction
• 5.2 Experimental Data for MRR, TWR, SR and HDV
• 5.3 Grey Relational Analysis
• 5.3.1 Normalize the Original Sequence and Deviation Sequence
• 5.3.2 Grey Relational Coefficient and Grey Relational Grade
• CHAPTER 6: SCANNING ELECTRON MICROSCOPE (SEM)
• 6.1 Introduction

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This research aims to optimize the process parameters of powder mixed electric discharge machining (EDM) for D2 steel, focusing on achieving improved material removal rate, reduced tool wear, and enhanced surface quality. The study utilizes a robust design of experiments approach based on Taguchi's methodology to identify the optimal parameter settings.

• Optimization of process parameters in powder mixed EDM
• Influence of powder characteristics on machining performance
• Application of Taguchi's methodology for parameter optimization
• Analysis of material removal rate, tool wear rate, surface roughness, and hole diameter variation
• Grey relational analysis for multi-response optimization

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: This chapter provides an introduction to EDM, its history, working principle, process variables, performance measures, and various categories including powder mixed EDM. It also highlights the advantages, disadvantages, and applications of EDM.
• Chapter 2: This chapter reviews existing literature on the influence of powder characteristics on EDM performance, identifies gaps in the research, and formulates the research problem. It outlines the objectives of the study and presents a methodology flow chart.
• Chapter 3: This chapter details the experimental setup for EDM, including the selection of work-piece, tool, and powder materials. It explains the process parameters used, the design of experiments approach using Taguchi's method, and the performance measures assessed: material removal rate, tool wear rate, surface roughness, hole diameter variation, and scanning electron microscope (SEM) analysis.
• Chapter 4: This chapter presents the experimental results for the performance measures and analyzes the signal-to-noise (S/N) ratios. It then focuses on optimizing each performance measure individually by determining the optimal parameter settings based on the S/N ratios and mean values.
• Chapter 5: This chapter introduces grey relational analysis (GRA) as a method for multi-response optimization. It outlines the process of normalizing the original sequence and deviation sequence and calculating the grey relational coefficient and grey relational grade.

Schlüsselwörter (Keywords)

The primary focus areas of this research are powder mixed electric discharge machining, process parameter optimization, D2 steel, Taguchi's methodology, material removal rate, tool wear rate, surface roughness, hole diameter variation, grey relational analysis, and scanning electron microscopy.