151
Parametric Optimization of Spark EDM on Aluminum 6063 and by Genetic Algorithm
Abstract
This research aims to investigate the optimal set of process parameters such as current, pulse ON time, voltage in Electrical Discharge
Machining (EDM) process on work material Aluminum 6063 to enhance the surface finish by reducing surface roughness using copper
electrode. The effect of process parameter on surface of Aluminum 6063 will be investigated and genetic algorithm will be used for process
parameter optimization. For experimentation, current (A), Pulse on time (sec), Voltage (v) will be used as input parameters. For each
experiment, surface roughness will be measured by using surface profilometer. ANOVA will be calculated to analysis the statistical significant
effect. Optimization with genetic algorithm will be provides the collective optimization of responses for improving the mean of the process.
Key words: ANOVAs, EDM Machine, pulse on time, genetic algorithm, Optimization, Surface roughness
I.INTRODUCTION
[1] A second order mathematical model is developed using regression technique of Box-Behnken of Response Surface Methodology (RSM) in design expert software 8.0 and optimization carried out by using genetic algorithm in matlab8.0. This study attempts the application of genetic algorithm to find the optimal solution of the cutting conditions.
[2] Y.S Liao in 1997 premeditated on the machining-parameters optimization of wire electrical discharge machining. An approach to determine the parameters were performed. The design was based upon the taguchi quality approach with analysis of the variances (ANOVA). In the research the significant factors affects the machining performance factors such as Material removal rate (MRR), gap width, surface roughness (SR), sparking frequency, average gap voltage and normal ratio. In the investigation Results demonstrate that the machining models were appropriate and the machining parameters mollify the real requirements.
[3]. Kuo-Ming Tsai , Pei-Jen Wang studied the dimensional analysis for surface finish in electrical discharge machine process .The process parameter for the model were peak current ,pulse duration, electrical polarity and property of the materials .They analysed and verified the result by taguchi method. For the experimentation Cu, Gr (ISEM-8), Ag-w were used for electrode and AISI EK 2, AISI D2, AISI H13 were used as the work piece material. In the investigation the methodically analyse and verification was performed by taguchi method. In the result it was determined that the predictions for the semi empirical model for the best fitting
parameters were obtained by nonlinear optimization methods detected as the good verification experiment .
[4]. In optimization, the Plot functions best fitness preferred so it enables to display various plots of the results of the genetic algorithm
[5]. Puertas and Luis in 2003 studied on the machining parameters of electrical discharge machining. The modelling of the Ra and Rq parameters in function of current, pulse time and pause time were arranged. Factorial design of experiments was united with techniques of regression for the modelling behaviour of the functions which was also reliant on the different types of the variables. It was observed that a strong collaborations between the current and the pulse time due to increase in feed rate of current it was observed that surface roughness be contingent for the better arc constancy causing a uniform production of sparks. Author resolved that in the range of 0.5-6A there was a great deal of difference in the process duration. So, therefore it make essential to perform a satisfactory selection of the process with a lower process time .
[6]. In 2004 George, Raghunath, Manocha and Ashish warrier deliberate on carbon–carbon composites over
electrical discharge machine (E.D.M). The control
parameters used in research were pulse current, gap voltage and pulse-on-time. Where Electrode wear rate (EWR), material removal rate (MRR) were perceived as the response parameters .Experimental design was formed and analysis was accomplished by taguchi approach. It was studied that if the parameters were at lower value, low electrode wear rate was attained and on the other hand material removal rate was achieved at higher rate. In this experimentation L8 orthogonal array was used for Taguchi method. Taguchi method was used for finding the optimum situation for control parameter. Machining parameter were set at their Karthick B
Assistant Professor/Mech Department of Mechanical
Engineering
Ranganathan Engineering College [email protected]
Sakthivel M
Department of Mechanical Engineering
Ranganathan Engineering College [email protected]
Venkatesan S
Department of Mechanical Engineering Ranganathan Engineering
College [email protected]
Antony Noel A
Department of Mechanical Engineering Ranganathan Engineering
College [email protected]
Ram Suri P
Department of Mechanical Engineering Ranganathan Engineering
152 optimum level to mend the process parameters. For this
conformation supplementary experiment work was
performed on the machine .In this observation they also linked the actual and predicted value of material removal rate (M.M.R), and electrode wear rate (EWR).
[7]. In 2005 Kun Ling worked on the electrical discharge machining for the surface finish as the machining process parameter. Aluminium powder was mixed in the dielectric fluid for improving the surface finish. Taguchi L18 method was used for experimentation design. They deliberated control parameters as polarity, peak current, pulse duration, open voltage, gap voltage, and surfactant concentration. For the response parameter surface roughness was measured. In the investigation the authors spotted that when the pure aluminium powder added in the dielectric fluid it does not give better results for the response variables. On the other hand aluminium powder and surfactant both are added it gives better surface finish. By the addition of this mixture lower the insulation property of fluid and also increase the surface status. The major effect of powder is uniform distribution discharge energy in the procedure .
[8]. Lin Y in 2006 investigated on the Machining characteristics and optimization of SKH57 high speed steel by Taguchi method. With the process parameters as polarity, peak current, auxiliary current with high voltage, pulse duration, servo reference. Material removal rate (MRR), tool wear rate (TWR) and surface roughness was as response mutable. Material removal rate and surface roughness were considered as main parameters. As in the result it was determined that material removal rate (MRR), Tool wear rate (TWR) and surface roughness get increased with appreciated to the peak current.
In this framework, the present study is focused on study of the effect of process Parameters on the surface roughness for a different value has been investigated. Moreover, this work treats the effect of current, voltage, pulse on time on surface roughness in EDM operation with copper electrode tool has been discussed.
II. METHODOLOGY
A. GENETIC ALGORITHM
An algorithm based on mechanics of natural selection and natural genetics, which are more robust and more likely to locate global optimum. It is because of this feature that GA
goes through solution space starting from a group of points and not from a single point. The cutting conditions are encoded as genes by binary
Encoding to apply GA in optimization of machining parameters, A set of genes is combined together to form chromosomes, used to perform the basic mechanisms in GA, such as crossover and mutation.
Crossover is the operation to exchange some part of two chromosomes to generate new offspring, which is important when exploring the whole search space rapidly. Mutation is applied after crossover to provide a small randomness to the new chromosomes.
To evaluate each individual or chromosome, the encoded cutting conditions are decoded from the chromosomes and are used to predict machining performance measures. Fitness or objective function is a function needed in the optimization process and selection of next generation in genetic algorithm. Optimum results of cutting conditions are obtained by comparison of values of objective functions among all individuals after a number of iterations. Besides weighting factors and constraints, suitable parameters of GA are
required to operate efficiently. GA optimization
methodology is based on machining performance predictions models developed from comprehensive system of theoretical analysis, experimental (Milon D. Selvam, 2012)
III.EXPERIMENTAL DETAILS A. WORK PIECE MATERIAL
Selection of work piece
In this experiment AL 6063 of size 80×50×5 mm3 plate is chosen for conducting the experiment shown in fig.1. Like Al 6063, alloy is resistant to sea water and steam at high temperatures as well as to salt and caustic solutions. Al 6063 is a solid solution alloy that can only be hardened by cold working. This nickel alloy exhibits characteristics like good corrosion-resistance, good weldability and high strength. Chemical composition shown in table.1
Figure- 1 Machined Workpiece
Table 1 Structure varieties for AL 6063
B.CUTTING TOOL
In this experiment copper rod of 10×30 mm2 has been used. Copper products are famous for their heat resistance, toughness and good machinability. When the workpiece or part you are machining is made up of materials such as AL 6063
Al si Fe Cr Mg Cu Zn
BA L
0.40-0.80
0.70 MAX
0.04 -0.35
00.8 -0.12
0.0 4-0.3
5
153 carbide, tungsten or a mixture of tungsten carbide, copper electrodes will not be as efficient as copper electrodes, due to its chemical composition and disintegration rate. Shown in fig. 2
Figure- 2 Copper Electrodes
C.EXPERIMENTAL SET UP AND CUTTING
CONDITIONS
The experimental work was divided into two series:
The main aim of the first experiments series was the determination of the turning domain and the quantification of surface roughness evolution. This was carried out through facing operation with continuously varying cutting parameters.
The purpose behind the second experiments series was to investigate the effects of cutting parameters on surface roughness, then to establish a correlation between them using the ANOVA. Machining process was carried out in EDM machine. The measurements of average surface roughness (Ra) were taken on surface roughness Tester SJ-210P.Three measurements of surface roughness were obtained at different surface of machined work piece and average value is used in the further analysis
D. SURFACE ROUGHNESS TESTER SJ-210
Roughness plays an important role in determining how a real object will interact with its environment. Rough surfaces usually wear more quickly and have higher friction
coefficients than smooth surfaces Roughness is often a good predictor of the performance of a quality of the mechanical component, since irregularities in the surface may form nucleation sites for cracks or corrosion. On the other hand,
roughness may promote adhesion. The Surface roughness tester used for measuring surface roughness (Ra) in this experimental analysis shown in fig.3
Figure- 3 Surface roughness Tester SJ-210P
E.EDM Machine
The experimentations be there performed by operating on Electric Discharge Machine classified as (die-sinking type) ELECTRONICA -ELECTRAPLUS PS 50ZNC whose polarization on the electrode be located as negative whereas that of work piece be located as positive.
Figure- 4 EDM Dielectric Machine F.COMBINATION OF PARAMETERS AND THEIR LEVELS
In order to investigate the influence of machining parameters on surface roughness, three principal machining parameters, including the current (A), Pulse on time (sec), Voltage (v) were specified as machining parameters. In this study, these machining parameters were chosen as the independent input variables.
The desired responses were the surface roughness and the surface finish which are assumed to be affected by the above three principal machining
In this experimental analysis the parameters has been conducted in three levels -1, 0, 1 shown in table 2
Table 2 Combination of Parameters and their levels
G. EXPERIMENTAL VALUES 1) Machining parameters
α- Discharge current β- Pulse on time γ- Voltage
2) Responses
Y -surface roughness Ra (μm) Machining
parameter
Units Levels
-1 0 1
Discharge current α A 5 7 9
Pulse on time β µs 50 100 150
154 Table 3 Experimental machining parameters
I. REGRESSION EQUATIONS
Computer-generated experimental designs, such as the D-optimal design, have some advantages over traditional response surface designs such as the central composite design .One major advantage is much greater flexibility in selecting response surface model types and the number of experimental runs. For example, for a three-factor and one response surface experiment, the following linear-order model is the standard model for L9 orthogonal array
Y=- 2.30 - 0.087 α +0.00226 β +0.162 γ
V.RESULTS AND DISCUSSIONS
In this investigational experiment on EDM to know the effect of machining outputs taken for consideration are material removal rate and surface roughness of the AL 6063 work piece using the solid Copper tool with side flushing method have been investigated. Both these outputs are important in industrial applications. The conduction of experiment depends upon various parameters settings such as discharge current, pulse on time and voltage has been selected. Based on L9 orthogonal array by L9 orthogonal array design was conducted. MINITAB and MATLAB software package was used for analysis of the experiment. The results on outputs are to some extent be authenticated. The following points conclude the experiment is: In case of surface roughness the voltage is the effective parameter after that current and voltage are less effective on machined work piece analyzed from ANOVA
MINIMIZATION OF SURFACE ROUGHNESS USING GA TOOL
Considering Population 100, Current generation 52. The Optimization value are
Current =9 A Pulse on time=50μs Voltage=45V
Best fitness for minimization of surface Roughness is 4.32µm
VII.CONCLUSION
The optimum combination of input Parameters for minimization of surface roughness found to be current is 9 A, pulse on time is 50 µs, voltage is 45 V and Best fitness for minimization of surface roughness is 4.32µm which are obtained using genetic algorithm. The confirmatory test was conducted and regression model has been developed using L9 orthogonal array was verified.
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Exp.no Machining Parameters
responses
α β γ Y
1
5
50
45
4.7
2
5
150 55
6.434
3
5
200 65
8.589
4
7
50
55
4.102
5
7
150 65
8.328
6
7
200 45
5.98
7
9
50
65
9.123
8
9
150 45
5.623
155
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