scholarly journals Determination of finish-cutting operation number and machining-parameters setting in wire electrical discharge machining

1999 ◽  
Vol 87 (1-3) ◽  
pp. 69-81 ◽  
Author(s):  
J.T. Huang ◽  
Y.S. Liao ◽  
W.J. Hsue
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Finish Cutting ◽  
Cutting Operation

scholarly journals Influence of Energy Parameters of Micro WEDM on Kerf

2012 ◽  
Vol 576 ◽  
pp. 527-530
Author(s):  
Mohammad Yeakub Ali ◽  
W.Y.H. Liew ◽  
S.A. Gure ◽  
B. Asfana
Keyword(s):  
Experimental Data ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Kerf Width ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Wire Electrode ◽  
Wire Vibration ◽  
The Wire ◽  
Micro Wedm

This paper presents the estimation of kerf width in micro wire electrical discharge machining (micro WEDM) in terms of machining parameters of capacitance and gap voltage. An empirical model is developed by the analysis of variance (ANOVA) of experimental data. Using a wire electrode of 70 µm diameter, a minimum kerf width is found to be 92 µm for the micro WEDM parameters of 0.01 µF capacitance and 90.25 V gap voltage. Around 30% increament of the kerf is found to be high. The analysis also revealed that the capacitance is more influential parameter than gap voltage on kerf width produced by micro WEDM. As the gap voltage determines the breakdown distance and affects the wire vibration, the wire vibration factor is to be considered in the analysis and in formulation of model in future study.

Modeling and Optimization of Wire Electrical Discharge Machining of Cold-Work Steal 2601

2011 ◽  
Vol 383-390 ◽  
pp. 6695-6703 ◽  
Author(s):  
Abolfazl Golshan ◽  
Soheil Gohari ◽  
Ayob Amran
Keyword(s):  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Cold Work ◽  
Open Circuit ◽  
Experimental Result ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Input Parameters ◽  
Pulse Off Time

In this study, the appropriate input parameters for achieving minimum surface roughness and high material removal rate are selected for wire electrical discharge machining of cold-work steel 2601. Mathematical modeling acquired by experimental result analysis is used to find the relation between input parameters including electrical current, gap voltage, open-circuit voltage and pulse-off time and output parameters. Subsequently, with exploitation of variance analysis, importance and effective percentages of each parameter are studied. The combination of optimum machining parameters is acquired using the analysis of ratios of signal-to-noise. Finally, according to multiple-objective optimization, outputs acquired from Non-dominated Sorting Genetic Algorithm led in achieving appropriate models. The optimization results showed suggested method has a high performance in problem solving.

scholarly journals Wire electrical discharge machining nickel super alloy

Mechanik ◽  
2018 ◽  
Vol 91 (3) ◽  
pp. 220-222
Author(s):  
Rafał Świercz ◽  
Dorota Oniszczuk-Świercz ◽  
Rafał Nowicki
Keyword(s):  
Electrical Discharge ◽  
Material Removal ◽  
Electrical Discharge Machining ◽  
Removal Rate ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Process Performance ◽  
Influence Of Process Parameters ◽  
Super Alloy ◽  
Quality Features

This article presents the influence of process parameters of wire electrical discharge machining using coated brass on the surface roughness and material removal rate of Inconel 718. Studies were conducted by design of the experiment. Based on the survey developed mathematical models which allow selecting the most favorable machining parameters depending on the desired process performance and quality features of the surface texture.

Analysis of wire erosion and workpiece surface roughness in wire electrical discharge machining

2003 ◽  
Vol 217 (5) ◽  
pp. 633-642 ◽  
Author(s):  
N Tosun ◽  
C Cogun
Keyword(s):  
Surface Roughness ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Open Circuit ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Workpiece Surface ◽  
The Wire ◽  
Dielectric Flushing ◽  
Wire Wear

In this study, the effects of machining parameters on the wire wear, on the size of erosion craters on the wire and on the workpiece surface roughness were investigated experimentally in wire electrical discharge machining (WEDM). An attempt was made to correlate the crater volume and the pulse energy. The experiments were conducted under different settings of pulse duration, open-circuit voltage, wire speed and dielectric flushing pressure. The variations of the wire wear, the size of erosion craters on the wire and the workpiece surface roughness with machining parameters were modelled mathematically by using regression analysis. The relationship between the workpiece surface roughness and the crater size was established. The analysis of variance (ANOVA) and F-test were performed to obtain statistically significant process parameters and the percentage contribution of these parameters to the performance outputs.

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

Materials ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 454 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document