Optimization of the WEDM Parameters on Machining Incoloy800 Super alloy with Multiple Quality Characteristics

Muthu Kumar.V et al. / International Journal of Engineering Science and Technology Vol. 2(6), 2010, 1538-1547

Optimization of the WEDM Parameters on Machining Incoloy800 Super alloy with

Multiple Quality Characteristics

Muthu Kumar Va*, Suresh Babu Ab, Venkatasamy Rc and Raajenthiren Md

aAssistant Professor, Department of Mechanical Engineering, Saveetha Engineering College, Chennai, India- 602 105.

bLecturer, Department of Manufacturing Engineering, Anna University, Chennai, India – 600 025.

cProfessor, Department of Mechanical Engineering, Saveetha Engineering College, Chennai, India- 602 105.

d Professor, Department of Chemical Engineering, Anna University, Chennai, India – 600 025.

Abstract

The present work demonstrates optimization of Wire Electrical Discharge Machining process parameters of Incoloy800 super alloy with multiple performance characteristics such as Material Removal Rate (MRR), surface roughness and Kerf based on the Grey–Taguchi Method. The process parameters considered in this research work are Gap Voltage, Pulse On-time, Pulse Off-time and Wire Feed. Taguchi’s L9 Orthogonal Array was used to conduct experiments. Optimal levels of process parameters were identified using Grey Relational Analysis and the relatively significant parameters were determined by Analysis of Variance. The variation of output responses with process parameters were mathematically modelled by using non-linear regression analysis method and the models were checked for their adequacy. Result of confirmation experiments shows that the established mathematical models can predict the output responses with reasonable accuracy.

Keywords: Grey-Taguchi method, WEDM, MRR, Surface roughness, kerf.

1. INTRODUCTION

Due to their high temperature mechanical strength and high corrosion resistance properties, super alloys are nowadays used in Marine, Space and other applications. Their ability to maintain their mechanical properties at high temperatures severely hinders the machinability of these alloys [1, 2]. Its poor thermal diffusivity generates high temperature at the tool tip as well as high thermal gradients in the cutting tool, affecting the tool life adversely. Incoloy800 is very chemically reactive and therefore, has a tendency to weld to the cutting tool during machining thus, leading to premature tool failure. Owing to all these problems, it is very difficult to machine Incoloy800 by conventional machining processes and moreover, by conventionally used tool materials. Of late, modern machining techniques such as Wire Electrical Discharge Machining (WEDM) are increasingly being used for machining such hard materials. Hence, this study focussed on machining of Incoloy800 using WEDM, in order to satisfy production and quality requirement.

The selection of optimum machining parameters in WEDM is an important step. Improperly selected parameters may result in serious problems like short-circuiting of wire, wire breakage and work surface damage which is imposing certain limits on the production schedule and also reducing productivity. As Material Removal Rate (MRR), Surface Roughness (Ra) and kerf width (k) are most important responses in WEDM; various investigations have been carried out by several researchers for improving the MRR, Surface Finish and kerf width [3–7]. However, the problem of selection of machining parameters is not fully depending on machine controls rather material dependent.

For the optimal selection of process parameters, the Taguchi method has been extensively adopted in

manufacturing to improve processes with single performance characteristic. However, traditional Taguchi method cannot solve multi-objective optimization problem. To overcome this, the Taguchi method coupled with Grey relational analysis has a wide area of application in manufacturing processes [8– 13]. This approach can

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solve multi-response optimization problem simultaneously. Deng (1982) [14] proposed grey relational analysis to fulfil the crucial mathematical criteria for dealing with a poor, incomplete, and uncertain system. Through the grey relational analysis, a grey relational grade is obtained to evaluate the multiple performance characteristics. As a result, optimization of the complicated multiple performance characteristics can be converted into the optimization of a single grey relational grade. The grey–Taguchi method was established for combining both grey relational analysis and the Taguchi method.

This study investigated the multi-response optimization of WEDM process for machining of Incoloy 800 using combination of Grey Relational analysis and Taguchi method to achieve higher Material Removal Rate (MRR) , lower surface roughness(Ra) and Kerf width(k). Finally, the analysis of variance (ANOVA) and necessary confirmation tests were conducted to validate the experimental results.

2. MATERIALS AND METHODS

2.1. Work Material

Incoloy800, High strength temperature resistant (HSTR) alloy, was used for the present investigation. The table 1 shows the chemical composition of Incoloy800.

Table 1 Chemical composition of Incoloy 800

Chemical composition

Wt%

C Cr Mn Al Mo Ni Fe Ti W V Co

0.096 20.096 0.501 0.302 0.335 34.991 42.821 0.304 0.066 0.027 0.07

2.2. Schematic of machining

The experiments were carried out on a Four-axes Electronica Ecocut CNC WEDM machine.The electrode material used was a 0.25 mm diameter brass wire. A small gap of 0.025 mm to 0.05 mm is maintained in between the wire and work-piece . The high energy density erodes material from both the wire and work piece by local melting and vaporizing. The di-electric fluid (de-ionized water) is continuously flashed through the gap along the wire, to the sparking area to remove the debris produced during the erosion. A collection tank is located at the bottom to collect the used wire erosions and then is discarded. The wires once used cannot be reused again, due to the variation in dimensional accuracy. 2.3. Process parameters and design

Input process parameters such as Gap Voltage (A), Pulse-on time (B), Pulse-off-time (C) and Wire Feed (D) used in this study are shown in Table 2. Each factor is investigated at three levels to determine the optimum settings for the WEDM process. These parameters and their levels were chosen based on the review of literature, experience, significance and their relevance as per the few preliminary pilot investigations. The smallest standard 3-level OA L9 (34) is chosen for this case.

Table 2 Control Factors and their Levels

Table 3 shows the nine cutting experimental runs with the assigned levels of the process parameters according to the selected L9 orthogonal layout.

Symbol Control Factors Unit Level 1 Level 2 Level 3

A Gap Voltage Volts 50 60 70

B Pulse-on Time μs 6 8 10

C Pulse-off Time μs 4 6 8

D Wire Feed mm/min 6 8 10

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Table 3 Orthogonal Array and Experimental Results

Exp. No. Control Factors Responses

A B C D MRR(g/min) Ra(μm) k (mm)

1 1 1 1 1 0.04833 3.11 0.317

2 1 2 2 2 0.05351 3.31 0.324

3 1 3 3 3 0.05128 3.6 0.299

4 2 1 2 3 0.04192 3.67 0.33

5 2 2 3 1 0.04295 3.97 0.322

6 2 3 1 2 0.05011 4.04 0.343

7 3 1 3 2 0.03844 4.11 0.356

8 3 2 1 3 0.03974 4.26 0.368

9 3 3 2 1 0.04538 4.4 0.376

In this study most important output performances in WEDM such as Material Removal Rate (MRR), Surface Roughness (Ra) and kerf width (k) were considered for optimizing machining parameters. The surface finish value (in μm) was obtained by measuring the mean absolute deviation, Ra (surface roughness) from the average surface level using a Computer controlled surface roughness tester. The Kerf width was measured using the Video measuring system (VMS 2010F). The Material Removal Rate (MRR) is calculated [15] as,

cktvMRR (1)

Where, k is the Kerf width (mm), t is the thickness of work piece (mm), cv is the Cutting speed (mm/min) and

ρ is the Density of the work piece material (g/mm3).

3. OPTIMIZATION STEPS USING GREY-TAGUCHI METHOD

Step 1: In this step, the original response values are transformed into S/N ratio values. Further analysis is carried out based on these S/N ratio values. The material removal rate is a higher-the-better performance characteristics, since the maximization of the quality characteristic of interest is sought and can be expressed as

n

10 2i = 1 ij

1 1S/N ratio = - log

n y

(2)

Where n = number of replications and yij = observed response value

Where i=1, 2... ....n; j = 1, 2...k.

The surface roughness and kerf width are the lower-the-better performance characteristic and the loss function for the same can be expressed as

n2

10 iji = 1

1S/N ratio = - log y

n

(3)

The S/N ratio values for the experimental results were calculated and presented in the Table 4.

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Table 4. S/N Ratio values

Exp. No. Control Factors S/N ratios

A B C D MRR Ra k

1 1 1 1 1 -26.3157 -9.85521 9.978815

2 1 2 2 2 -25.4313 -10.3966 9.7891

3 1 3 3 3 -25.801 -11.1261 10.48658

4 2 1 2 3 -27.5516 -11.2933 9.629721

5 2 2 3 1 -27.3407 -11.9758 9.842883

6 2 3 1 2 -26.0015 -12.1276 9.294118

7 3 1 3 2 -28.3043 -12.2768 8.971

8 3 2 1 3 -28.0154 -12.5882 8.683044

9 3 3 2 1 -26.8627 -12.8691 8.496243

Step 2: In the grey relational analysis, a data pre-processing is first performed in order to normalize the raw data for analysis. Normalization is a transformation performed on a single data input to distribute the data evenly and scale it into an acceptable range for further analysis. In this study, a linear normalization of the S/N ratio is performed in the range between zero and unity, which is also called the grey relational generating [16]. yij is normalized as Zij (0≤Zij≤1) by the following formula to avoid the effect of adopting different units and to reduce the variability. The normalized material removal rate corresponding to the larger-the-better criterion can be expressed as

ij ij,

ij

ij, ij,

y - min y i =1,2,.....nZ =

max y i =1,2,.....n - min y i = 1,2,.....n (4)

The surface roughness and kerf width should follow the lower-the-better criterion and can be

expressed as

ij, ij

ij

ij, ij,

max y i = 1,2,.....n - yZ

max y i = 1,2,.....n - min y i = 1,2,.....n (5)

The normalized S/N ratio values are given in Table 5.

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Table 5. Normalized S/N Ratio values

Exp. No Control Factors Normalized S/N ratio

A B C D MRR Ra k

1 1 1 1 1 0.692185 0 0.255114

2 1 2 2 2 1 0.179622 0.350432

3 1 3 3 3 0.871307 0.421668 0

4 2 1 2 3 0.262008 0.477169 0.430508

5 2 2 3 1 0.335393 0.70362 0.32341

6 2 3 1 2 0.80153 0.753993 0.599125

7 3 1 3 2 0 0.803501 0.761469

8 3 2 1 3 0.100552 0.90681 0.906146

9 3 3 2 1 0.501777 1 1

Step 3: The grey relational coefficient is calculated to express the relationship between the ideal (best) and actual normalized experimental results. The grey relational coefficient can be expressed as:

o ioj

Δmin + ξΔmaxγ y k ,y k =

Δ k + ξΔmax (6)

Where; j = 1,2...n; k = 1,2...m, n is the number of experimental data items and m is the number of responses. yo(k) is the reference sequence (yo(k) = 1, k = 1,2...m); yj(k) is the specific comparison sequence.

oj o jΔ = y k - y k = The absolute value of the difference between yo (k) and yj (k).

o jmin min min y k -y kj i k

is the smallest value of yj(k).

o jmax max max y k - y kj i k

is the largest value of yj(k).

Where ξ is the distinguishing coefficient, which is defined in the range 0≤ ξ ≤1. The WEDM process parameters are equally weighted in this study, and therefore ζ is 0.5[14]. Step 4: The grey relational grade is determined by averaging the grey relational coefficient corresponding to each performance characteristic. The overall performance characteristic of the multiple response process depends on the calculated grey relational grade. The grey relational coefficient can be expressed as:

m

j iji = 1

1γ = γ

k (7)

Where j is the grey relational grade for the jth experiment and k is the number of performance characteristics. This approach converts a multiple response process optimization problem into a single response optimization situation with the objective function of an overall grey relational grade. Table 6 shows the grey relation coefficient and grey relational grade for each experiment using the L9 orthogonal array. The higher grey relational grade reveals that the corresponding experimental result is closer to the ideally normalized value. Experiment 2 has the best multiple performance characteristic among 9 experiments, because it has the highest grey relational grade shown in Table 6 and Figure 1. The higher the value of the grey relational grade, the closer the corresponding factor combination is, to the optimal. A higher grey relational grade implies better product quality; therefore, on the basis of the grey relational grade, the factor effect can be estimated and the optimal level for each controllable factor can also be determined.

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Table 6 – Grey Relational Co-efficient and Grey Relational Grade

Exp. No Control Factors Grey Relational Co-efficient

Grey Grade A B C D MRR Ra k

1 1 1 1 1 0.618953 0.333333 0.401643 0.56866

2 1 2 2 2 1 0.378679 0.434946 0.881363

3 1 3 3 3 0.795301 0.463679 0.333333 0.715942

4 2 1 2 3 0.40388 0.488839 0.467512 0.418739

5 2 2 3 1 0.42933 0.627841 0.424957 0.448743

6 2 3 1 2 0.71585 0.670235 0.555016 0.695205

7 3 1 3 2 0.333333 0.717876 0.677019 0.406156

8 3 2 1 3 0.357284 0.8429 0.841958 0.454313

9 3 3 2 1 0.50089 1 1 0.600712

Since the experimental design is orthogonal, it is possible to separate out the effect of each machining parameter on the grey relational grade at different levels. For example, the mean of the grey relational grade for the gap voltage at levels 1, 2 and 3 can be calculated by averaging the grey relational grade for the experiments 1 to 3, 4 to 6, and 7 to 9, respectively.

Figure 1. Grey relational grades for maximum MRR , Minimum Ra and minimum kerf width

The mean of the grey relational grade for each level of the other machining parameters can be computed in a similar manner. The mean of the grey relational grade for each level of the machining parameters is summarized and shown in Table 7.

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Table 7. The main effects of the Factors on the Grey Relational Grade

*Optimum Levels

Step 5: Determine the optimal factor and its level combination:

Fig. 2 shows the grey relational grade graph. Basically, the larger the grey relational grade, the better is the multiple performance characteristic. However, the relative importance among the machining parameters for the multiple performance characteristics still needs to be known, so that the optimal combinations of the machining parameter levels can be determined more accurately. With the help of Figure 2 and Table 7, the optimal parametric combination(The Optimal Selected Levels are bolded in Table 7) has been determined. The optimal factor setting becomes A1 (gap voltage, 50 V), B3 (pulse on time, 10 μs), C2 (pulse off time, 6 μs) and D2 (wire feed rate, 8 mm/min) ).

Figure 2. Response graph of average grey relational grade

4. ANALYSIS OF VARIANCE (ANOVA)

The results obtained from the experiments were analyzed using Analysis of Variance to find the significance of each input factor on the measures of process performances, Material Removal Rate, surface roughness and Kerf width. Using the grey grade value, ANOVA is formulated for identifying the significant factors. The results of ANOVA are presented in Table 8.

Symbol Control Factors Level 1 Level 2 Level 3 Max-min

A Gap Voltage 0.721988* 0.520896 0.48706 0.234928

B Pulse ON Time 0.464519 0.594806 0.67062* 0.206101

C Pulse OFF Time 0.572726 0.633605* 0.523614 0.109991

D Wire Feed 0.539372 0.660908* 0.529665 0.131243

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Table 8 Results of ANOVA

Symbol Cutting Factors Sum of Squares DOF Mean

Squares %P*

A Gap Voltage 0.0968 2 0.0484 45.59

B Pulse-on Time 0.0652 2 0.0326 30.71

C Pulse-off Time 0.0182 2 0.0091 8.58

D Wire Feed 0.0321 2 0.016 15.12

Total 0.2123 8 0.1419 100 *Percentage Contribution

The results of the ANOVA are represented in graphical form and from the graphical representation it is clear that gap voltage is the major influencing factor contributing 45.59% to performance measures, followed by pulse-on time contributing 30.71%, pulse-off time contributing 8.58% and Wire Feed contributing 15.12%.

5. CONFIRMATION EXPERIMENT

The confirmation test for the optimal parameter setting with its selected levels was conducted to evaluate the quality characteristics for WEDM of Incoloy 800. Experiment 2 (Table 6) shows the highest grey relational grade, indicating the optimal process parameter set of A1B2C2D2 has the best multiple performance characteristics among the nine experiments [17], which can be compared with results of confirmation experiment for validation of results. Table 9 shows the comparison of the experimental results using the initial (A1B2C2D2) and optimal (A1 B3C2D2) WEDM parameters on Incoloy 800. The response values obtained from the confirmation experiment are MRR = 0.05765 g/min, Ra = 3.10 μm and Kerf = 0.296 mm. The Material Removal Rate shows an increased value of 0.05351 g/min to 0.05765 g/min, the Surface Roughness shows a reduced value of 3.31μm to 3.10 μm and the Kerf width shows a reduced value of 0.324to 0.296 mm respectively. The corresponding improvement in Material Removal Rate, Surface Roughness and Kerf width were 7.74%, 6.34% and 8.64% respectively.

Table 9 Optimization results of OA(L9) Vs Grey theory design

Optimal process parameters

Orthogonal Array Grey theory Design

Level A1B2C2D2 A1 B3C2D2

MRR (g/min) 0.05351 0.05765

Ra (μm) 3.31 3.10

Kerf (mm) 0.324 0.296

6. DEVELOPMENT OF MATHEMATICAL MODELS

The experimental results are used to obtain the mathematical relationship between process parameters and machining outputs. The coefficients of mathematical models are computed using method of multiple regressions. In this study, SPSS (Software Package for Statistical Solutions) was used for the regression analysis. This software is used to test several models, viz., linear, exponential, power series (user-defined).

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Out of all models tested, the model that has high coefficient of multiple determination (r2) value is chosen. The adequacy of the models and the significance of coefficients are tested by applying analysis of variance.

The relationship between response variable(s) and process parameters can be expressed as:

Y = c A x By Cz Ds (8)

where Y is the output response(s)

c,x,y,z, and s - regression variables.

A,B,C and D – Process parameters

The surface roughness Ra is expressed as:

Ra = 0.120A0.716 B0.200 C0.041 D0.025 (r2 = 0.983) (9)

and the material removal rate MRR is expressed as:

MRR = 0.469 A-0.651 B0.268 C-0.065 D-0.055 (r2 = 0.910) (10)

Also the kerf width is expressed as

k = 0.056 A0.471 B0.025 C-0.067 D-0.036 (r2 = 0.907) (11) The high correlation coefficients (r2) indicate the suitability of the function (model) and the correctness

of the calculated constants. Equations 9, 10 and 11 were used successfully to estimate the machining outputs without experimentation.

8. CONCLUSIONS

In this paper, an application of combined Taguchi Method and Grey Relational Analysis, to improve the multi-response characteristics of MRR (Material Removal Rate), Surface roughness in the Wire-Cut EDM (Electrical Discharge Machining) of Incoloy 800 has been reported. As a result, this method greatly simplifies the optimization of complicated multiple performance characteristics and since it does not involve complicated mathematical computations, this can be easily utilized by the stakeholders of the Manufacturing world.

(i) The optimal ‘process parameters’ based on Grey Relational Analysis for the Wire-Cut EDM of Incoloy 800 include a 50 V Gap Voltage, 10 μs pulse on-time, 6 μs pulse off-time and 8 mm/minute Wire Feed rate.

(ii) While applying the Grey-Taguchi method, The Material Removal Rate shows an increased value of 0.05351 g/min to 0.05765 g/min, the Surface Roughness shows a reduced value of 3.31μm to 3.10 μm and the Kerf width shows an reduced value of 0.324to 0.296 mm respectively, which are positive indicators of efficiency in the machining process. Thus, it can be concluded that the Grey-Taguchi Method, is most ideal and suitable for the parametric optimization of the Wire-Cut EDM process, when using the multiple performance characteristics such as MRR (Material Removal Rate), Surface Roughness and kerf width, for machining the Incoloy 800.

(iii) Mathematical relations between the machining parameters, namely Gap Voltage, Pulse On-time, Pulse Off-time and Wire Feed and performance characteristics such as MRR, Ra and kerf are established by the regression analysis method. The established mathematical models can be used in estimating the material removal rate, surface roughness and kerf width without conducting experiments.

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Optimization of the WEDM Parameters on Machining Incoloy800 Super alloy with Multiple Quality Characteristics

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