Modelling and multi-objective optimization of process parameters of wire electrical discharge machining using non-dominated sorting genetic algorithm-II

The growing demand for the use of high strength to weight alloys in industries for manufacturing complex structures challenges the machinability of such advanced materials. In the present investigation, the machinability of SiC particle reinforced Al 2124 composite was studied on Wire electrical discharge machining (WEDM). The process parameters namely pulse on-time (Ton), pulse off time (Toff), peak current (IP), and servo voltage (SV) were optimized by utilizing the central composite design layout. The output responses such as kerf and material removal rate (MRR) were studied in detail. The single and multi-objective optimization was studied for a combination effect using Derringer’s desirability approach and Genetic Algorithm (GA). The experimental and predicted values for each response were validated at the optimized condition. The experimental results were found in line with the predicted values. Multi objective optimization of kerf and MRR by GA showing better result compared to RSM.

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Experimental investigation and multi-objective optimization of micro-wire electrical discharge machining of a titanium alloy using Jaya algorithm

Advances in Production Engineering & Management ◽

10.14743/apem2019.2.326 ◽

2019 ◽

Vol 14 (2) ◽

pp. 251-263 ◽

Cited By ~ 1

Author(s):

M. Singh ◽

J. Ramkumar ◽

R.V. Rao ◽

J. Balic

Keyword(s):

Experimental Investigation ◽

Titanium Alloy ◽

Electrical Discharge ◽

Electrical Discharge Machining ◽

Wire Electrical Discharge Machining ◽

Jaya Algorithm ◽

Multi Objective Optimization ◽

Multi Objective

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Modeling and Optimization of Fractal Dimension in Wire Electrical Discharge Machining of EN 31 Steel Using the ANN-GA Approach

Materials ◽

10.3390/ma12030454 ◽

2019 ◽

Vol 12 (3) ◽

pp. 454 ◽

Cited By ~ 6

Author(s):

Arkadeb Mukhopadhyay ◽

Tapan Barman ◽

Prasanta Sahoo ◽

J. Davim

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fractal Dimension ◽

Process Parameters ◽

Electrical Discharge ◽

Electrical Discharge Machining ◽

Wire Electrical Discharge Machining ◽

Machining Parameters ◽

Machined Surface ◽

Wedm Process

To achieve enhanced surface characteristics in wire electrical discharge machining (WEDM), the present work reports the use of an artificial neural network (ANN) combined with a genetic algorithm (GA) for the correlation and optimization of WEDM process parameters. The parameters considered are the discharge current, voltage, pulse-on time, and pulse-off time, while the response is fractal dimension. The usefulness of fractal dimension to characterize a machined surface lies in the fact that it is independent of the resolution of the instrument or length scales. Experiments were carried out based on a rotatable central composite design. A feed-forward ANN architecture trained using the Levenberg-Marquardt (L-M) back-propagation algorithm has been used to model the complex relationship between WEDM process parameters and fractal dimension. After several trials, 4-3-3-1 neural network architecture has been found to predict the fractal dimension with reasonable accuracy, having an overall R-value of 0.97. Furthermore, the genetic algorithm (GA) has been used to predict the optimal combination of machining parameters to achieve a higher fractal dimension. The predicted optimal condition is seen to be in close agreement with experimental results. Scanning electron micrography of the machined surface reveals that the combined ANN-GA method can significantly improve the surface texture produced from WEDM by reducing the formation of re-solidified globules.

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Multi-Objective Optimization of Wire Electrical Discharge Machining (WEDM) Process Parameters Using Weighted Sum Genetic Algorithm Approach

Journal of Advanced Manufacturing Systems ◽

10.1142/s0219686716500086 ◽

2016 ◽

Vol 15 (02) ◽

pp. 85-100 ◽

Cited By ~ 11

Author(s):

P. C. Padhi ◽

S. S. Mahapatra ◽

S. N. Yadav ◽

D. K. Tripathy

Keyword(s):

Genetic Algorithm ◽

Electrical Discharge ◽

Electrical Discharge Machining ◽

Wire Electrical Discharge Machining ◽

Weighted Sum ◽

Multi Objective ◽

Pulse On Time ◽

Pulse Off Time ◽

Machining Parameter ◽

Wedm Process

The present work is aimed at optimizing the cutting rate (CR), surface roughness (Ra) and dimensional deviation (DD) in wire electrical discharge machining (WEDM) of EN-31 steel considering various input parameters such as pulse-on-time, pulse-off-time, wire tension, spark gap set voltage and servo feed. A face centered central composite design of response surface methodology (RSM) has been adopted to develop the empirical model for the responses. It is often desired to obtain a single parameter setting that can decrease Ra and DD and increase CR simultaneously. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying all the objectives in any one solution. The optimum search of the machining parameter values for maximization of CR and minimization of Ra and DD are formulated as a multi-objective, multi-variable, nonlinear optimization problem using genetic algorithm weighted sum method to evaluate the performance.

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