1
Enhancement of surface finish on oil hardened non shrinkage (OHNS) steel
using Spark EDM –An Taguchi and Genetic algorithm approach
Abstract
Electric Discharge machining (EDM) is a thermo-electric, non-traditional machining process used to machine precise and intricate shapes which are widely used in defense and aerospace industries. Electrical energy is used to generate electrical sparks and material removal mainly occurs due to localized melting and vaporization of material which is carried away by the dielectric fluid flow between the electrodes. The performance of this process is mainly influenced by many electrical parameters like, current, voltage, polarity, and pulse on time, pulse of time, electrode gap and also on non-electrical parameters like work and tool material, dielectric fluid pressure. Roughness is an important parameter when trying to find out whether a surface is suitable for a certain purpose. Rough surfaces often wear out more quickly than smoother surfaces. Optimization is one of the techniques used in manufacturing sectors to arrive for the best manufacturing conditions, which is an essential need for industries towards manufacturing of quality products at lower cost. This paper 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 oil hardened non shrinkage (OHNS) steelto enhance the surface finish by reducing surface roughness using copper electrode. The effect of process parameter on surface of OHNS steel will be investigated and Taguchi analysis 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 surf tester. Genetic algorithm adopted Taguchi approach developed mathematical model used to find out optimal process parameter combination which will enhance the surface finish of the machining.
Key words: ANOVAs, EDM Machine, pulse on time, genetic algorithm, Optimization, Surface roughness
I.INTRODUCTION
[1] Now a day’s innovative changes in the area of non-traditional machining process are not to be considered as replacements for conventional machining methods of metal working. They also do not offer the best alternative solutions for all machining applications. The traditional metal cutting processes utilize shearing action on the work piece for material removal. However, the non-traditional processes depend on other factors such as chemical properties, melting and vaporization of the material, electrolytic displacement of ions and mechanical erosion. The main reasons for using the non-traditional machining processes are to machine high strength alloys, complex surfaces, difficult geometries, high accuracies surface finish.
[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]. 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.
[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 Sharun Sylvester B
Assistant Professor [email protected]
Kiran Kumar.K
[email protected] UG Scholar
Manikandan R
[email protected] UG Scholar
Pradeep kumar M.
[email protected] UG Scholar
Mathivelpandiyan M.
[email protected] UG Scholar
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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 was set at their 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 brass 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 OHNS steel of size 80×50×5 mm3 plate is chosen for conducting the experiment shown in fig.1. Like OHNS steel, alloy is resistant to sea water and steam at high temperatures as well as to salt
and caustic solutions. OHNS steel is a solid
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Figure- 1 Machined Work pieceB.CUTTING TOOL
In this experiment brass rod of 10×30 mm2 has been used. Brass products are famous for their heat resistance, toughness and good machinability. When the work piece or part you are machining is made up of materials such as carbide, tungsten or a mixture of tungsten carbide, brass electrodes will not be as efficient as brass electrodes, due to its chemical composition and disintegration rate.
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.
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 shown in fig.2.
Figure- 2 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 1 and 2 Table 1 Combination of Parameters and their levels
G. EXPERIMENTAL VALUES 1) Machining parameters
x1- Discharge current x2- Pulse on time x3- Voltage 2) Responses
Y -surface roughness Ra (μm) Machining
parameter
Units Levels
-1 0 1
Discharge current x1 A 5 7 9
Pulse on time x2 µs 50 100 150
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Table 2 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=4.926-0.4589X(1)+0.0021X(2)+0.0989X--EQU(1) 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 OHNS steel work piece using the solid Brass 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 58 The Optimization value is
Current =9.2 A Pulse on time=59.23μs Voltage=44.8V
Best fitness for minimization of surface Roughness is 4.3825µm
VII.CONCLUSION
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 OHS steel work piece using the solid Brass 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 (Ip), pulse on time (Ton) and voltage (v) have 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.
The optimum combination of input Parameters for minimization of surface roughness found to be current is 9.2 A, pulse on time is 59.23 µs, voltage is 44.8 V and Best fitness for minimization of surface roughness is 4.3825µm which are obtained using genetic algorithm. The confirmatory test was conducted and regression model has been developed using L9 orthogonal array DoE was verified.
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5 7 150 65 7.1
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7 9 50 65 7.956
8 9 150 45 4.926
55
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