Modeling and Optimization of Process Parameters in Micro Wire EDM by Genetic Algorithm

2009 ◽  
Vol 76-78 ◽  
pp. 566-570 ◽  
Author(s):  
K.P. Somashekhar ◽  
N. Ramachandran ◽  
Jose Mathew
Keyword(s):  
Genetic Algorithm ◽  
Process Parameters ◽  
Electric Discharge ◽  
Regression Models ◽  
Electric Discharge Machining ◽  
Optimization Of Process Parameters ◽  
Wire Electric Discharge Machining ◽  
Factor Regression ◽  
Wedm Process ◽  
Machining Characteristics

The present work is aimed at optimizing the parameters of micro Wire Electric Discharge Machining (µ-WEDM) process by considering the simultaneous effects of input parameters viz: gap voltage, capacitance and feed rate. Experiments were planned and conducted using DoE techniques. ANOVA was performed to find out the significance of each factor. Regression models were developed for the experimental results of surface roughness and overcut of the micro slots produced on aluminium. Then Genetic Algorithm (GA) was employed to determine the values of optimal process parameters for the desired output value of micro wire electric discharge machining characteristics.

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.

Multi-objective optimization of micro wire electric discharge machining parameters using grey relational analysis with Taguchi method

2011 ◽  
Vol 225 (7) ◽  
pp. 1742-1753 ◽  
Author(s):  
K P Somashekhar ◽  
J Mathew ◽  
N Ramachandran
Keyword(s):  
Analysis Of Variance ◽  
Process Parameters ◽  
Electric Discharge ◽  
Grey Relational Analysis ◽  
Performance Characteristics ◽  
Electric Discharge Machining ◽  
Relational Analysis ◽  
Wire Electric Discharge Machining ◽  
Grey Relational ◽  
Wedm Process

Micro wire electric discharge machining (µ-WEDM) is an evolution of conventional wire EDM used for fabricating three-dimensional complex microcomponents, microstructures, and intricate profiles effectively with high-precision capabilities. Being a complex process, it is very difficult to determine optimal parameters for obtaining higher material removal rate (MRR) with minimum overcut (OC), and surface roughness (SR) is a challenging task in µ-WEDM for improving performance characteristics. In this study, a new approach for the optimization of the µ-WEDM process with multiple performance characteristics based on the statistical-based analysis of variance (ANOVA) and grey relational analysis (GRA) is attempted. Analysis of variance was used to study the significance of process parameters on grey relational grade (GRG) which showed capacitance to be the most significant factor. A GRG obtained from the GRA is used to optimize the µ-WEDM process. Optimum process parameters are determined by the GRG as the overall performance index. The process parameters, namely gap voltage, capacitance, and feed rate are optimized by considering multiple performance characteristics including MRR, OC, and SR. To validate the study, confirmation experiment has been carried out at optimal set of parameters, and predicted results have been found to be in good agreement with experimental findings. This approach showed improved machining performance in the µ-WEDM process.

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.

scholarly journals Optimization of process parameters in machining of nimonic super-alloy on EDM using genetic algorithm

2020 ◽  
Vol 2 (1) ◽  
pp. 35-44
Author(s):  
Madderla Sandhya ◽  
D. Ramasamy ◽  
Irshad ahamad Khilji ◽  
Anil Kumar ◽  
S. Chandramouli ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Process Parameters ◽  
Significant Influence ◽  
Optimal Choice ◽  
Copper Electrode ◽  
Taguchi Design Of Experiments ◽  
Optimization Of Process Parameters ◽  
Pulse On Time ◽  
Machining Characteristics

This project aims to investigate and predict the optimal choice for each EDM parameter using Taguchi Method by conducting a limited number of experiments on “Nimonic” Material. These parameters have a significant influence on the machining characteristics like MRR and TWR. Taguchi design of experiments (DOE) are implemented, particularly L9 orthogonal array is chosen and the effect of dominating process parameters is evaluated using analysis of variance. Nimonic refers to a family of Nickel-based high-temperature low creep superalloys. Due to its ability to withstand very high temperatures, Nimonic is ideal for typical applications such as aircraft parts, gas turbine components and blades, exhaust nozzles etc., for instance, where the pressure and heat are extreme. However, the conventional methods are not suitable to machine the hardest material such as Nimonic superalloy. The EDM, one of the popular unconventional machining methods, is used to the machine with a copper electrode, which in turn uses Taguchi methodology to analyze the effect of each parameter on the machining characteristics. The optimal choice for each EDM parameter such as peak current, gap voltage, duty cycle and pulse on time using the Taguchi method and Genetic Algorithm are identified. These parameters have a significant influence on machining characteristics such as MRR, EWR and surface roughness.

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