Optimization of Multiple Performances for EDM in Gas Media Using Grey Relational Analysis

2014 ◽  
Vol 620 ◽  
pp. 173-178
Author(s):  
Fang Pin Chuang ◽  
Yan Cherng Lin ◽  
Han Ming Chow ◽  
A. Cheng Wang
Keyword(s):  
Grey Relational Analysis ◽  
Electrical Discharge Machining ◽  
Design Method ◽  
Removal Rate ◽  
Performance Characteristics ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational ◽  
Machining Parameter

The aim of this investigation is to optimize the multiple performance characteristics of electrical discharge machining (EDM) for SKD 61 tool steel in gas media using grey relational analysis. The three most important machining characteristics namely material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR) were considered as the measures of the performance characteristics. A series of experiments were conducted according to an L18 orthogonal array based on the Taguchi experimental design method. The observed data obtained from the experiments were evaluated to determine the optimization of machining parameters correlated with multiple performance characteristics through grey relational analysis. Moreover, analysis of variance (ANOVA) was conducted to explore the significant machining parameters crucially affecting the multiple performance characteristics. In addition, the optimal combination levels of machining parameters were also determined from the response graph of grey relational grades for each level of machining parameter.

Optimization of Multi-Performance Characteristics for a Hybrid Process of EDM and AJM Using Grey Relational Analysis

2015 ◽  
Vol 789-790 ◽  
pp. 100-104
Author(s):  
Yan Cherng Lin ◽  
Jung Chou Hung ◽  
A. Cheng Wang ◽  
Han Ming Chow
Keyword(s):  
Grey Relational Analysis ◽  
Electrical Discharge Machining ◽  
Design Method ◽  
Removal Rate ◽  
Performance Characteristics ◽  
Hybrid Process ◽  
Electrode Wear ◽  
Process Variables ◽  
Relational Analysis ◽  
Grey Relational

The aim of this study is to optimize the process variables of a novel hybrid process of electrical discharge machining (EDM) and abrasive jet machining (AJM) using grey relational analysis. The multi-performance characteristics associated with the developed hybrid process such as the material removal rate (MRR), electrode wear rate (EWR), and surface roughness (SR) were explored through an experimental study according to an L18 orthogonal array based on the Taguchi experimental design method. The experimental results confirmed the multi-performance characteristics of the developed hybrid process would clearly be improved through optimizing the process variables using grey relational analysis.

Optimization of Multiple Response Characteristics of EDM Process Using Taguchi-Based Grey Relational Analysis and Modified PSO

2015 ◽  
Vol 14 (03) ◽  
pp. 123-148 ◽  
Author(s):  
S. Panda ◽  
D. Mishra ◽  
B. B. Biswal ◽  
P. Nanda
Keyword(s):  
Grey Relational Analysis ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Work Piece ◽  
Optimum Level ◽  
Machining Parameters ◽  
Multi Objective ◽  
Relational Analysis ◽  
Grey Relational

Electrical discharge machining is an alternative process for machining complex and intricate shapes. In this paper, an inter-relationship of various electrical discharge machining parameters, namely discharge current, pulse on and off time and dielectric flow rate on material removal rate (MRR), tool wear rate (TWR), surface finish ( SR a) and dimensional tolerance using a Taguchi–Grey relational analysis. The response surface methodology is used to develop a second order model for MRR, TWR and SR a in terms of process parameters. Finally, a multi-objective optimization problem is formulated by using MRR, TWR and SR a. The multi-objective problem is then optimized through a modified particle swarm optimization (PSO) algorithm to find the optimum level of parameters. In this research, the results of the proposed method are validated through confirmation experiment. The work piece material used for experimentation is stainless steel of S304 grade.

Multi-Objective Optimization of EDM Parameters Using Grey Relational Analysis for Titanium Alloy (Ti–6Al–4V)

2014 ◽  
Vol 592-594 ◽  
pp. 540-544 ◽  
Author(s):  
A. Palanisamy ◽  
R. Rekha ◽  
S. Sivasankaran ◽  
C. Sathiya Narayanan
Keyword(s):  
Grey Relational Analysis ◽  
Removal Rate ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Edm Process

In this paper optimization of the electrical discharge machining (EDM) process with multiple performance characteristics based on the orthogonal array with the grey relational analysis was studied and investigated. A grey relational grade obtained from the grey relational analysis is used to solve the EDM process. Optimal machining parameters are determined by considering the grey relational grade as the performance index. The input independent parameters of peak current, pulse on time and pulse off time were examined and optimized on multiple response characters (material removal rate, electrode wear ratio and surface roughness). Experimental results have shown that machining performance in the EDM process can be improved effectively through this approach.

Multiple Performance Optimization in WEDM Parameters Using Grey Relational Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 357-361
Author(s):  
T. Rajmohan ◽  
Gopi Krishna ◽  
Ankit Kumar Singh ◽  
A.P.V. Swamy Naidu
Keyword(s):  
Grey Relational Analysis ◽  
Performance Characteristics ◽  
304L Stainless Steel ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Wedm Process ◽  
Off Time

In this investigation, a new approach is based on Grey Relational Analysis and Taguchi method to optimize the machining parameters with multi performance characteristics in WEDM of 304L SS. Experiments are conducted using Taguchi Quality Concept, L9,3-level orthogonal array was chosen for experiments .The WEDM parameters namely pulse-on time (TON), pulse-off time (TOFF), and wire feed (WF) on material removal rate (MRR) .The Grey Relational Analysis with multiple performance characteristics indicates that the pulse-on time (TON), pulse-off time (TOFF) are the most significant factors . The optimum machining parameters have been identified by Grey relational analysis and significant contribution of parameters can be determined by analysis of variance (ANOVA). The confirmation test is also conducted to validate the test result. The results from this study will be useful for manufacturing engineers to select appropriate WEDM process parameters to machine 304L Stainless Steel.

scholarly journals Multi-Performance Optimization in Electrical Discharge Machining of Al2O3 Ceramics Using Taguchi Base AHP Weighted TOPSIS Method

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1647
Author(s):  
Yue-Peng Zeng ◽  
Chiang-Lung Lin ◽  
Hong-Mei Dai ◽  
Yan-Cherng Lin ◽  
Jung-Chou Hung
Keyword(s):  
Process Parameters ◽  
Performance Optimization ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Statistical Technique ◽  
Electrode Wear ◽  
Machining Parameters ◽  
Relative Closeness ◽  
Machining Parameter

The main application of electrical discharge machining in ceramic processing is limited to conductive ceramics. However, the most commonly used non-conductive potteries in modern industry, such as aluminum oxide (Al2O3), also reveal the limitations of choosing a suitable process. In this study, Taguchi based TOPSIS coupled with AHP weight method to optimize the machining parameters of EDM on Al2O3 leads to better multi-performance. The results showed that the technique is suitable for tackling multi-performance machining parameter optimization. The adhesive foil had a significant impact on material removal rate, electrode wear rate, and surface roughness, according to the findings. In addition, the response graph of relative closeness is used to determine the optimal combination levels of machining parameters. A confirmation test revealed a good agreement between predicted and experimental preference values at an optimum combination of the input parameters. The suggested experimental and statistical technique is a simple, practical, and reliable methodology for optimizing EDM process parameters on Al2O3 ceramics. This approach might be utilized to optimize and improve additional process parameters in the future.

Modelling and Optimization of Machining parameters for Turning Automotive Shafts using RSM and Grey Relational Analysis

2020 ◽  
Vol 12 (4) ◽  
Author(s):  
S. Dinesh ◽  
K. Rajaguru ◽  
K. Saravanan ◽  
R. Yokeswaran ◽  
V. Vijayan
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Input Parameters ◽  
Grey Relational ◽  
Central Composite ◽  
Modelling And Optimization

Automotive shafts require maximum strength with regard to axial, bending and torsional loading to transmit power to various parts of a vehicle. Hence, it is very critical to analyse the manufacturing process and its governing parameters to exercise control over the surface properties of the shaft as it needs to be precisely manufactured in terms of dimensions and the surface roughness. The effect of three input parameters over two responses are considered as two major criteria's for production of shaft. The input parameters are speed, feed and depth of cut whereas the responses are material removal rate and surface roughness. Central Composite design was used and experimental results were analysed with Response Surface Methodology. ANOVA analysis was carried out to identify the most contributing parameter for MRR and SR. Grey Relational Analysis was adopted to identify the most feasible combination of machining parameters for turning process. The optimized parameter is identified as speed of 1000 rpm, 0.15 mm of feed and 0.35 mm of depth of cut using Grey Relational Analysis.

Multi-Response Optimization of EDM for Ti-6Al-4V Using Taguchi - Grey Relational Analysis

2017 ◽  
Vol 266 ◽  
pp. 43-50 ◽  
Author(s):  
Ritesh Joshi ◽  
Gopal Zinzala ◽  
Nishit Nirmal ◽  
Kishan Fuse
Keyword(s):  
Grey Relational Analysis ◽  
Removal Rate ◽  
Optimization Technique ◽  
High Strength ◽  
Operating Conditions ◽  
Electrode Wear ◽  
Relational Analysis ◽  
Grey Relational ◽  
Pulse On Time ◽  
Pulse Off Time

Ti-6Al-4V is a titanium alloy having high strength, low weight and corrosion resistant which is a required characteristic for a material to be used in aerospace industry. Titanium, being a hard alloy is difficult to the machine via conventional methods so it is a call to use non-conventional processes. In present work, the effects on Ti-6Al-4V by drilling a hole of Ø 6 mm using copper (99%) electrode in electric discharge machining (EDM) process is analyzed. Experiments were performed under different operating conditions of peak current, pulse-on time and pulse-off time. Multi-objective optimization technique Grey relational analysis is used for process optimization of material removal rate (MRR) and electrode wear rate (EWR). Experiments are designed using an L9 orthogonal array. ANOVA is used for finding most contributing parameter followed by confirmation tests for validating the results.

Determination of Optimum Parameters for Multi-Performance Characteristic in Turning of Al 6061-6% ZrB2 in-situ Metal Matrix Composite Using Grey Relational Analysis

2012 ◽  
Vol 2 (1) ◽  
pp. 11-29 ◽  
Author(s):  
A. Mahamani ◽  
N. Muthukrishnan ◽  
V. Anandakrishnan
Keyword(s):  
Metal Matrix Composite ◽  
Matrix Composite ◽  
Grey Relational Analysis ◽  
Feed Rate ◽  
Metal Matrix ◽  
Performance Characteristics ◽  
Machining Parameters ◽  
Relational Analysis ◽  
Grey Relational

In-situ aluminum matrix composite is the innovation of high performance material technology and it has superior interfacial integrity and thermodynamic stability between the matrix and reinforcement. During synthesis, the ZrB2 particle is formed by exothermic reaction within the aluminum melt. As a result, small, fine and oxide free reinforcements are formed. Excessive temperature released from in-situ chemical reaction will facilitate the homogeneous distribution of particles in entire shape of the composites. Making the engineering components from this composite material require machining operations. Therefore, addressing the machinability issues of the composite is very important. This paper proposes an approach to optimize the machining parameters in turning of Al 6061-6% ZrB2 in-situ Metal Matrix Composite (MMC) with multiple performance characteristics by using grey relational analysis. The effect of in-situ ZrB2 reinforcement particles on machinability behavior need to be studied. The machining parameters, namely cutting speed, feed rate and depth of cut are optimized with considerations of multiple performance characteristics including surface roughness, tool wear and cutting force. It is concluded that the feed rate has the strongest effect. The confirmation experiment indicates that there is a good agreement between the estimated value and experimental value of the Grey relational grade.

Optimization by Grey Relational Analysis of EDM Parameters on Machining Ti-6Al-4V

2010 ◽  
Vol 139-141 ◽  
pp. 540-544 ◽  
Author(s):  
Zhi Ping Xie ◽  
Ji Ming Zheng ◽  
Bian Li Quan
Keyword(s):  
Taguchi Method ◽  
Process Parameters ◽  
Material Removal Rate ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Removal Rate ◽  
Electrode Wear ◽  
Relational Analysis ◽  
Wear Ratio ◽  
Grey Relational

In this paper, parameter optimization of the electrical discharge machining process to Ti–6Al–4V alloy considering the multiple responses using the Taguchi method and grey relational analysis is reported. The multi-response optimization of the process parameters are material removal rate (MRR) and electrode wear rate (EWR). The machining parameters including discharge current, voltage, pulse on time and duty factor. Experiment based on the orthogonal array, The optimized process parameters simultaneously leading to a lower electrode wear ratio and higher material removal rate are then verified through a confirmation experiment. The experimental result for the optimal setting shows that there is considerable improvement in the process. The validation experiments show an improved electrode wear ratio of 2.8%, material removal rate of 45.8% when the Taguchi method and grey relational analysis are used.

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