Optimization of electric discharge machining process using the response surface methodology and genetic algorithm approach

2013 ◽  
Vol 14 (5) ◽  
pp. 709-717 ◽  
Author(s):  
Chorng-Jyh Tzeng ◽  
Rui-Yang Chen
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Electric Discharge ◽  
Machining Process ◽  
Electric Discharge Machining ◽  
Genetic Algorithm Approach

Parametric Optimization of EDM Process of Aluminium Boron Carbide Composite Using Desirability Function Approach and Genetic Algorithm

2014 ◽  
Vol 592-594 ◽  
pp. 684-688 ◽  
Author(s):  
K.R. Thangadurai ◽  
A. Asha
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Boron Carbide ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Machining Process ◽  
Electric Discharge Machining ◽  
Carbide Composite ◽  
Edm Process

Electric discharge machining process is an unconventional machining process primarily used for machining the materials such as difficult to machine in conventional machining process, hardest material and composite materials. In the present work, a study is made to find out the optimum EDM process parameters during machining of AA6061-15% boron carbide composite fabricated through stir casting technique. Three process parameters such as Current, pulse on time and pulse of time are opted as machining parameter variables. Response surface methodology is used to formulate the mathematical model for material removal rate, tool wear rate and surface roughness. Response surface methodology and genetic algorithm are applied to optimize the machining parameters individually by taking combined objective function and compared. Genetic algorithm optimization techniques yields better results than desirability approach. Key words: Electric discharge machining, MRR, TWR, Ra, RSM, Genetic algorithm

Study on Wire Electric Discharge Machining Based on Response Surface Methodology and Genetic Algorithm

2012 ◽  
Vol 622-623 ◽  
pp. 1280-1284 ◽  
Author(s):  
Pragya Shandilya ◽  
P.K. Jain ◽  
N.K. Jain
Keyword(s):  
Genetic Algorithm ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Metal Matrix ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Response Parameter ◽  
Wire Electric Discharge Machining

Wire electric discharge machining (WEDM) is one of the most popular non-conventional machining processes for machining metal matrix composites (MMCs). The present research work deals the parametric optimization of the input process parameters for response parameter during WEDM of SiCp/6061 Al metal matrix composite (MMC). Response surface methodology (RSM) and genetic algorithm (GA) integrated with each other to optimize the process parameters. RSM has been used to plan and analyze the experiments. Four WEDM parameters namely servo voltage, pulse-on time, pulse-off time and wire feed rate were varied to study their effect on the quality of cut in SiCp/6061 Al MMC using cutting width (kerf) as response parameter. The relationship between kerf and machining parameters has been developed by using RSM. The mathematical model thus than developed was then employed on GA to optimized the process parameters.

Process Optimization of Electric Discharge Machining Using Response Surface Methodology

2015 ◽  
Vol 813-814 ◽  
pp. 393-397
Author(s):  
Rajinder Kumar ◽  
Neel Kanth Grover ◽  
Amandeep Singh
Keyword(s):  
Response Surface Methodology ◽  
Wear Rate ◽  
Tool Wear ◽  
Response Surface ◽  
Electric Discharge ◽  
Material Removal ◽  
Machining Process ◽  
Work Piece ◽  
Tool Wear Rate ◽  
Electric Discharge Machining

Electric Discharge Machining (EDM) is one of the most commonly used non-traditional machining processes. Complex geometries can be easily manufactured using EDM. Material removal is achieved by producing continuous spark occurring between well shaped tool electrode and work piece. EDM does not involve direct contact of tool and work piece. Machining process involves a number of input variables like, current, voltage, pulse on/off which in turn affect the machining efficiency of EDM. These process parameters must be optimized to attain high material removal rate and low tool wear rate. The present paper presents theoptimization of tool wear rate of copper and brass electrode on machining of EN-47 using Response Surface Methodology (RSM).

Multi-Objective Optimization in Wire-Cut Electric Discharge Machining of Al 6063/Al2O3 Metal Matrix Composite through Response Surface Methodology

2014 ◽  
Vol 592-594 ◽  
pp. 534-539 ◽  
Author(s):  
K. Hemalatha ◽  
V.S.K. Venkatachalapathy ◽  
N. Alagumurthi
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Electric Discharge ◽  
Machining Process ◽  
Electric Discharge Machining ◽  
Weight Percentage ◽  
Experimental Values ◽  
Pulse On Time ◽  
Pulse Off Time ◽  
Varying Mass

This paper proposed a response surface methodology technique to optimize the multi-response of wire-cut electric discharge machining process. The machining was done on Al 6063 plate is casted with varying mass of Al2O3(3%, 6%, 9%). Stir casting process is adopted for casting the composite plate. Design Expert is used to identify the effect of key operating factors on output measures such as surface finish and kerf by using Pulse on-time, pulse off-time, servo feed and varying mass of Al2O3(3%, 6%, 9%). Also the distribution of Alumina and Aluminium is examined by microstructure analysis, and the material is tested for its mechanical Properties such as tensile strength and Hardness. We found that with respect to increase in pulse on-time and weight percentage of alumina the Surface roughness and kerf decreased leaving a better finish. Also a comparison has been done between the result obtained through response surface methodology and experimental values which indicates that the experimental values are very much close to the predicted values.

Modelling and multi-response optimization of wire electric discharge machining parameters using response surface methodology and grey–fuzzy algorithm

2015 ◽  
Vol 231 (10) ◽  
pp. 1760-1774 ◽  
Author(s):  
Bikash Choudhuri ◽  
Ruma Sen ◽  
Subrata Kumar Ghosh ◽  
Subhash Chandra Saha
Keyword(s):  
Stainless Steel ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Electric Discharge ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Wire Electric Discharge Machining ◽  
Grey Relational ◽  
Stainless Steel 316

Wire electric discharge machining is a non-conventional machining wherein the quality and cost of machining are influenced by the process parameters. This investigation focuses on finding the optimal level of process parameters, which is for better surface finish, material removal rate and lower wire consumption for machining stainless steel-316 using the grey–fuzzy algorithm. Grey relational technique is applied to find the grey coefficient of each performance, and fuzzy evaluates the multiple performance characteristics index according to the grey relational coefficient of each response. Response surface methodology and the analysis of variance were used for modelling and analysis of responses to predict and find the influence of machining parameters and their proportion of contribution on the individual and overall responses. The measured values from confirmation experiments were compared with the predicted values, which indicate that the proposed models can be effectively used to predict the responses in the wire electrical discharge machining of AISI stainless steel-316. It is found that servo gap set voltage is the most influential factor for this particular steel followed by pulse off time, pulse on time and wire feed rate.

Batch growth of Kluyveromyces lactis cells from deproteinized whey: Response surface methodology versus Artificial neural network—Genetic algorithm approach

2016 ◽  
Vol 109 ◽  
pp. 305-311 ◽  
Author(s):  
Fábio Coelho Sampaio ◽  
Tamara Lorena da Conceição Saraiva ◽  
Gabriel Dumont de Lima e Silva ◽  
Janaína Teles de Faria ◽  
Cristiano Grijó Pitangui ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Kluyveromyces Lactis ◽  
Genetic Algorithm Approach ◽  
Artificial Neural ◽  
Batch Growth

scholarly journals Modeling and Analysis of Machining Parameters and Responses of Wirecut Electric Discharge Machining of Al2124/SiCp using Response Surface Methodology and Soft Computing Techniques

2019 ◽  
Vol 8 (2) ◽  
pp. 5429-5434
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Soft Computing ◽  
Electric Discharge ◽  
Optimization Techniques ◽  
Machining Parameters ◽  
Electric Discharge Machining ◽  
Soft Computing Techniques ◽  
Input Parameters ◽  
Single Objective

In this work, Wirecut Electric Discharge Machining (WEDM) of Al 2124/ SiCp metal matrix composite material is studied to evaluate the influence of input parameters on response characteristics namely, kerf, Material Removal Rate (MMR), Surface Roughness (SR), Recast Layer Thickness (RCT), and Surface Crack Density (SCD). Central composite design, a technique from design of experiments is used to conduct 31 experiments. The input parameters selected for estimation of machinability are pulse on time (Ton), pulse off time (Toff), current (IP), and Servo Voltage (SV). Analysis of variance (ANOVA) is carried out to know the effect of influence parameters on responses. The regression models are developed in Response Surface Methodology (RSM)and are used in soft computing techniques as input equations for optimizing the single and multi-response optimization of response parameters. Desirability approach is used in single and multi-objective optimization of response parameters. Single objective optimization is carried out by RSM, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Firefly Algorithms (FA). Confirmation experiments are conducted on the adequacy of the mathematical models developed in RSM and it shows good agreement between experimental and predicted values. The variation of predicted responses from different optimization techniques for single objective optimization is found to be less than 1%. From the results it is also observed that for single objective optimization all evolutionary algorithms are found to be suitable for WEDM

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