Optimization of Material Removal Rate and Surface Roughness for Wire Electric Discharge Machining of AA7075 Composites using Grey Relational Analysis

2018 ◽  
Vol 9 (5) ◽  
Author(s):  
G. Ramanan ◽  
J. Edwin Raja Dhas ◽  
M. Ramachandran
Keyword(s):  
Surface Roughness ◽  
Electric Discharge ◽  
Grey Relational Analysis ◽  
Removal Rate ◽  
Electric Discharge Machining ◽  
Relational Analysis ◽  
Wire Electric Discharge Machining ◽  
Grey Relational ◽  
Pulse On Time ◽  
The Impact

In automobile industries, usage of unconventional machining is increased due to their precision and accuracy. This research work is planned to upgrade the Wire Electric Discharge Machining (WEDM) process parameters by considering the impact of discharge current, pulse on time, pulse off time and servo speed rate. Tests have been led with these parameters for the measurement of metal removal rate and surface roughness for each of the trial run. This information has been used to fit a quadratic numerical model. Predicted information has been used as a graphical representation for demonstrating the impact of the parameters on chose reactions. Predicted information given by the models has been utilized as a part of an ideal parametric mix to accomplish the unrealistic yield of the procedure. Response surface method with grey relational analysis has been utilized for enhancement. The ideal value has been checked to the predicted value from the confirmation tests.

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.

Characteristics optimization of powder mixed electric discharge machining using titanium powder for die steel materials

2017 ◽  
Vol 232 (3) ◽  
pp. 281-298 ◽  
Author(s):  
Tien-Long Banh ◽  
Huu-Phan Nguyen ◽  
Cuong Ngo ◽  
Duc-Toan Nguyen
Keyword(s):  
Electric Discharge ◽  
Grey Relational Analysis ◽  
Material Removal ◽  
Titanium Powder ◽  
Removal Rate ◽  
Quality Characteristics ◽  
Tool Wear Rate ◽  
Electric Discharge Machining ◽  
Relational Analysis ◽  
Grey Relational

In the present study, four quality characteristics of the electrical discharge are simultaneously presented and optimized using titanium powder mixed electric discharge machining. The Taguchi method and the grey relational analysis are applied to the processing parameters to investigate the following: workpiece material, tool material, polarity, pulse-on time, current, pulse-off time, and powder concentration. The combination of the Taguchi method and grey relational analysis is applied to optimize simultaneously four quality characteristics of powder mixed electric discharge machining, including material removal rate, tool wear rate, surface roughness, and microhardness surface. Optimal results by the Taguchi–grey relational analysis show that both surface roughness and tool wear rate decrease, while both material removal rate and microhardness surface increase, respectively. This approach proves effective in terms of improving the processing efficiency of the study parameters. The results from both optimization calculations and experimentation demonstrate high accuracy and efficiency. Furthermore, powder mixed electric discharge machining has improved significantly. The concentration of titanium powder is the processing parameter with the strongest influence on the efficiency of powder mixed electric discharge machining.

Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

2021 ◽  
Vol 1026 ◽  
pp. 28-38
Author(s):  
I. Vishal Manoj ◽  
S. Narendranath ◽  
Alokesh Pramanik
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Electric Discharge ◽  
Cutting Speed ◽  
Electric Discharge Machining ◽  
Surface Method ◽  
Wire Electric Discharge Machining ◽  
Pulse On Time

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.

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