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OPTIMIZATIONS OF MACHINING
PARAMETER IN WIRE EDM FOR 316L
STAINLESS STEEL BY USING TAGUCHI
METHOD, ANOVA, AND GREY ANALYSIS
Manish Saini, Rahul Sharma, Abhinav, Gurupreet Singh, Prabhat Mangla
Student, Mechanical Department GNI-Mullana, Kurukshetra University, Kurukshetra, Haryana, India
Er. Amit Sethi
Asist. Professor, Mechanical Deptt, GNI–Mullana Kurukshetra University, Kurukshetra, Haryana, India
ABSTRACT
WEDM is one of the non-traditional method used for the machining complex shape structure and components made up of hard material like composites and HSS. This is an experimental investigation of wire electro-discharge machining (WEDM) of 316L SS. The outstanding characteristics of stainless steel 316L such as their compatibility and noticeable physical, mechanical and biological performance has led to increased application of them in various industries especially in biomedical industries over the last 50 years.316L SS is used extensively for weldments where its immunity to carbide precipitation due to welding assures optimal corrosion resistance. There are some difficulties in machining of stainless steel by conventional machining. On the other hand, unconventional machining process especially Wire electrical discharge machining (WEDM) are more appropriate techniques for machining difficult to machine materials such as stainless steel. Electrical conductive materials are cut by wire EDM that uses a wire as electrode in an electro-thermal mechanism. The machines also specialize in cutting complex contours or fragile geometries that would be difficult to be produced using conventional cutting methods. The focus of this paper is on machining of stainless steel with WEDM because of the above mentioned features of WEDM and its suitability for machining stainless steel. In this study the effect of nine parameters including five controlled such as servo voltage(SV), peak current(Ip), pulse-on time(Ton), pulse-off time (Toff), wire feed (WF) and four remains fixed such as water pressure (WP), wire tension(WT), servo feed(SF),voltage potential (VP) on process performance parameters such as cutting speed and surface roughness are investigated. A Taguchi L16 design of
experiment (DOE) is applied to determine the effect of significant parameters on WEDM performance. The optimal parameters were obtained as (A1 B4 C4 D1 E2) for cutting rate and (A4 B1 C1 D4 E2) for surface roughness. Optimum predicted values for cutting rate and surface roughness are 2.2009mm/min and = 0.5598 µm respectively. By using ANOVA three parameters namely pulse-on time, pulse-off time and servo voltage were found the most significant affecting the cutting rate and surface roughness under 99% confidence level. By grey analysis, the optimum machining parameters setting can be obtained for considering maximum cutting speed and minimum surface roughness simultaneously. Thus the optimal set of process parameters is (A2 B2 C4 D4 E2).
Key words: 316l Ss, Biomaterials, Cutting Speed, Surface Roughness,
Taguchi, Anova, Grey Analysis
Cite this Article Manish Saini, Rahul sharma, Abhinav, Gurupreet Singh,
Prabhat Mangla and Er. Amit Sethi. Optimizations of Machining Parameter In Wire EDM For 316l Stainless Steel by Using Taguchi Method, Anova, and Grey Analysis. International Journal of Mechanical Engineering and Technology, 7(2), 2016, pp. 307–320.
http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=2
1. INTRODUCTION
Wire electrical discharge machining (WEDM) is an important technology, which demands high-speed cutting and high-precision machining to realize productivity and improved accuracy. Wire electrical discharge machining (WEDM) is an indispensable machining technique for producing complicated cut outs through difficult to machine metals without using high cost grinding or expensive formed tools [1]. WEDM is an extremely potential electro thermal process for hard metal alloy to get the high precision. When the servo voltage is increased, there will be spark produced and temperature will be high of 10,000 of degrees. Due to this high temperature metal will be removed from the work piece. Material is eroded from the work piece (Anode) and wire tool electrode (Cathode) separated by deionised water such as dielectric fluid and continuously flushes away the machining debris. The movement of the wire is controlled by CNC technology. WEDM has greatly altered the tooling and manufacturing industry, resulting in dramatic improvements in accuracy, quality productivity and profit. Over the years, WEDM process has remained as a competitive economical machining option fulfilling the demanding matching requirements imposed by the short product development cycle and the growing cost pressure. Stainless steels have been widely used in various industries because of their good corrosion resistance and mechanical properties. Among of them, austenitic stainless steel 304 is the commonest type of stainless steels. However, due to the low hardness, poor wear resistance of stainless steel, sensitive to pitting corrosion and stress corrosion cracking in chloride solution, [2−4] the strength of the stainless steel can be reduced, which limits its application in industrial production. Therefore, how to improve the corrosion resistance of stainless steel in chloride ion solution and the wear resistance of stainless steel has been a problem. In this case, it came into being and developed rapidly that the ceramic films were used to coat on the stainless steel surface [5−7]. Stainless steel materials are widely used for multiple applications because of their good mechanical properties and very good corrosion resistance in a number of environments [8]. stainless steel 316L is having a good resistance to creep
and fatigue, excellent corrosion resistance and biocompatibility, excellent weldability and easier to machine therefore 316L stainless steel is having more applications like oil & petroleum , refining equipment, food processing equipment, pharmaceutical processing equipment, architectural, biomedical .stainless steel constitute prominent class of valuable iron alloys. They are used in variety of applications when enhanced properties like corrosion and oxidation resistance. Coupled to good mechanical characteristics are required. The stainless steel grades currently manufactured by sintering correspond generally to the grade manufactured with other technology.316L austenitic stainless steel is now a day’s widely used engineering material due to its excellent oxidation resistance and good formability. On refining grains of 316L stainless steel several technique have been used in which coarse grains are refined via plastic deformation or subsequent re-crystallization mechanical milling, cold rolling, severe plastic deformation. 316L were mechanically milled and sintered at 1173k. 316L tends to work harden if machined too quickly for this reason low speed and constant feed rates are recommended. Additionally, WEDM is able to cut metals as thin as 0.004”.the wire of WEDM unit emits sparks on all sides, which means the cut must be thicker than the wire itself. Wire electrode is generally made of copper, brass or tungsten of diameter 0.05mm to 0.3mm, which is capable to achieve very small corner radii. Thus WEDM has evolved from a simple means of making tools and dies to the best alternatives of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality [9-11].
Figure 1 Schematic diagram of working of Wire-EDM
2. MATERIAL SELECTION
Selection of material depends upon the desire weld ability qualities which must rely on basic properties of the material, such as strength, corrosion or erosion resistance, ductility, and toughness. The properties of the various metallurgical characteristics associated with the thermal cycles encountered in the welding operation must also be included in the design process. The specimens of 20mm x 10mm x10mm are prepared. The Stainless steel 316 L alloy was been used in this study. Chemical composition of work piece is very much essential for selecting the type of process and
their controllable variables. Table 1 indicates the chemical composition of Stainless steel 316L.
Table 1 Chemical composition of Stainless steel 316L. (Wt.%)
C % Mn % P % S % Si % Cu % Ni % Cr % V % Mo % Fe % 0.0306 1.191 0.0305 0.0026 0.2062 0.1041 10.34 16.73 0.0433 2.015 Balance
3. METHOD OF EXPERIMENT
Taguchi methodTaguchi method is a powerful tool for the design of high quality systems. It provides simple, efficient and systematic approach to optimize designs for performance, quality and cost. Optimization of process parameters is the key step in Taguchi method to achieving high quality without increasing cost. This is because optimization of process parameters can improve quality characteristics and optimal process parameters obtained from Taguchi method are insensitive to the variation of environmental conditions and other noise factors. Classical process parameters design is complex and not an easy task. To solve this task the Taguchi method uses a special design of orthogonal arrays to study the entire process parameter space with a small number of experiments only.
Anova
The analysis of variance (ANOVA) of raw data and S/N data were performed to determine the significant and insignificant variables and to show their effects on the response characteristic. Then, the response curves (main effect) were plotted for raw data and S/N data in order to examine the parametric effects on the response characteristics. Finally, the optimal values of significant process parameters in terms of mean response characteristics are defined based on analyzing the ANOVA table and response curves.
Grey relational analysis (GRA)
The Grey Theory was introduced by Dr. Deng J.L. (1982) which includes Grey relational analysis, Grey modeling prediction and decision making of a systems including incomplete information, multi-input and discrete and poor data information where as partial information is to be known and partial information is unknown. A grey relational grade is obtained to evaluate the multiple performance characteristics. In GRA optimization of complicated multiple performance characteristics can be converted into optimization of a single grey relational grade.
Experiment setup
The mechanism of metal removal in wire electrical discharge machining mainly involves the removal of material due to melting and vaporization caused by the electric spark discharge generated by a pulsating direct current power supply between the electrodes. In this mechanism, negative electrode is a continuously moving wire and the positive electrode is the work piece. The sparks will generate between two closely spaced electrodes. Different numbers of experiments were performed to study
the effects of the various machining parameters of wire electric discharge machining. The studies have been undertaken to investigate the effect of peak current (Ip),wire feed (Wf), servo voltage (SV), pulse on time (Ton) and pulse off time (Toff).The material of the wire is zinc coated brass wire having a diameter of 0.25mm .For the calculation of the cutting speed (CS) and surface roughness(SR) we cut the small pieces of the material 316L stainless steel of dimension 20mm x 10mm x10mm to measure and surface roughness with surface roughness tester SRT-8210 and wire EDM machine calculates the cutting speed, and the value is displayed on the output screen of the CNC interface.
Table 2 Process parameters and their levels
Factor Process Parameters Level 1 Level 2 Level 3 Level 4
A Servo voltage (SV) 20 30 40 50
B Peak current (Ip) 90 110 130 150
C Pulse-on time (Ton) 106 110 114 118
D Pulse-off time (Toff) 30 35 40 45
E Wire feed (Wf) 3 5 - -
The WEDM experiments were performed in order to study the effect of process parameters on the output response characteristics such as cutting speed and surface roughness with the help of TAGUCHI method.
4. RESULT AND DISCUSSIONS
Results by Taguchi
The WEDM experiments and using the parametric approach of the Taguchi’s method were conducted in this study. In this section, the influence of the various process parameters on the cutting speed and surface roughness for different experimental conditions is discussed.
Cutting Speed (CS)
The wire electric discharge machining calculates the cutting speed, and the value is displayed on the output screen of the CNC interface. For wire electric discharge machining, cutting speed is a desirable characteristic and it should be as high as possible to give least machine cycle time leading to increased productivity. In the present study cutting rate is a measure of job cutting which is digitally displayed on the screen of the machine and is given quantitatively in mm/min.
Table 3 Response table for cutting rate (Mean data)
Level SV Ip Ton Toff WF
1 1.7913 1.4015 0.8268 1.8111 1.5340 2 1.6009 1.6194 1.4017 1.6887 1.6171 3 1.5109 1.5266 1.8727 1.5313 4 1.3991 1.7546 2.2009 1.2711 Delta 0.3922 0.3531 1.3741 0.5400 0.0831 Rank 3 4 1 2 5
For example, the average effect on cutting speed (Mean data) for parameters Wf and Ip at level 1 can be calculated as follows:
Wf = (1.1368+1.6586+2.297+2.223+1.2137+2.1686+0.9046+0.6694)/8= 1.53387 Ip= (1.1368+1.2495+1.2137+2.0061)/4= 1.40075
Table 4 Response table for cutting rate (S/N Data)
Level SV Ip Ton Toff Wf
1 4.762 2.694 -1.892 4.876 2.965 2 3.035 3.313 2.634 4.036 3.436 3 3.061 2.824 5.224 2.839 4 1.945 3.971 6.837 1.052 Delta 2.816 1.276 8.729 3.824 0.471 Rank 3 4 1 2 5
For example, the average effect on cutting speed (S/N data) for parameters Ip and Ton at level 1 can be calculated as follows:
Ip= (1.11368+1.93473+1.68223+6.04705)/4= 2.6944 Ton = (1.11368-3.9582-1.23862-3.48629)/4= -1.8923 50 40 30 20 2.4 2.0 1.6 1.2 0.8 150 130 110 90 106 110 114 118 45 40 35 30 2.4 2.0 1.6 1.2 0.8 5 3 SV M ea n of M ea ns IP TON TOFF WF
Main Effects Plot for Means Data Means
50 40 30 20 7.5 5.0 2.5 0.0 150 130 110 90 106 110 114 118 45 40 35 30 7.5 5.0 2.5 0.0 5 3 SV M ea n of S N ra ti os IP TON TOFF WF
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
Figure 3 Effects of response of process parameters on cutting rate (S/N data)
From figure 2 and 3, it is clear that the cutting rate increases with the increase of pulse on time, peak current and wire feed, and decreases with increase in pulse off time and servo voltage. This is because the discharge energy increases with the pulse on time and peak current which result in faster cutting rate. As the pulse off time decreases, the number of discharges within a given period becomes more which leads to a higher cutting rate. With increase in servo voltage the average discharge gap gets widened resulting into a lower cutting rate. The effect of wire feed on cutting rate is not very significant.
Selection of optimum process parameters have been made from the response table. Here response table is used to calculate the effect of each level of process parameter on performance measure. The response tables 3 and 4 show the average of each response characteristic (Raw data, S/N data) for each level of each factor. As cutting rate is the “higher the better” type quality characteristic, it can be seen from Figure 2 that the first level of wire servo voltage (A1), fourth level of peak current (B4), fourth level of pulse on time (C4), first level of pulse off time (D1) and second level of wire feed (E2) provide maximum value of cutting rate. The S/N data analysis (Figure 3) also suggests the same levels of the variables (A1 B4 C4 D1 E2) as the best levels for maximum CS in WEDM process.
Larger the better:
10
log
(MSD
)
N
S
HB HB
(1) where R 1 j ) 2 j (1/y R 1 MSDHBSurface roughness (SR)
One of a good predictor of Wire EDM performance is surface roughness because nucleation sites can be formed for cracks or corrosion by irregularity in the surface. It is quantified by the vertical deviations of a real surface from its ideal form. If these deviations are large, the surface is rough; if small, the surface is smooth. In this work the surface roughness was measured by Mitutoyo Surftest SRT-8210. In order to see the effect of process parameters on surface roughness, the average values of surface roughness for each parameter at levels 1, 2, 3 and 4 for raw data and S/N data are tabulated in table 5 and 6 respectively.
Table 5 Response table for mean surface roughness (Mean data)
Level SV Ip Ton Toff WF
1 3.304 2.187 1.770 2.841 2.701 2 2.761 2.429 2.353 2.842 2.543 3 2.248 2.785 2.921 2.514 4 2.177 3.090 3.446 2.293 Delta 1.127 0.903 1.675 0.548 0.158 Rank 2 3 1 4 5
Table 6 Response table for surface roughness (S/N data)
Level SV Ip Ton Toff WF
1 -10.168 -6.692 -4.806 -8.810 -8.204 2 -7.892 -7.266 -7.164 -8.706 -7.554 3 -6.848 -8.199 -8.979 -7.645 4 -6.608 -9.359 -10.567 -6.355 Delta 3.560 2.668 5.761 2.455` 0.650 Rank 2 3 1 4 5
Method for calculating the response for surface roughness is same as that of the response for cutting rate. And the same average values for raw data and S/N data are plotted in figure 4 and 5 respectively. It is clear that the surface roughness increases with the increase of pulse on time, peak current and servo voltage, and decreases with increase in pulse off time. There is no significant change in the surface roughness with the increase of wire feed. The discharge energy increases with the pulse on time and peak current and larger discharge energy produces a larger crater, causing a larger surface roughness value on the work piece.
50 40 30 20 3.6 3.2 2.8 2.4 2.0 150 130 110 90 106 110 114 118 45 40 35 30 3.6 3.2 2.8 2.4 2.0 5 3 SV Me an o f M ea ns IP TON TOFF WF
Main Effects Plot for Means Data Means
Figure 4 Effects of response of process parameters on surface roughness (Raw data)
50 40 30 20 -4 -6 -8 -10 150 130 110 90 106 110 114 118 45 40 35 30 -4 -6 -8 -10 5 3 SV Me an o f S N ra tio s IP TON TOFF WF
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Figure 5 Effects of response of process parameters on surface roughness (S/N data) As the pulse off time decreases, the number of discharges increases which causes poor surface accuracy. The response tables 5 and 6 show the average of each response characteristic (raw data, S/N data) for each level of each factor. As cutting rate is the “lower the better” type quality characteristic, it can be seen from Figure 4 that the fourth level of servo voltage (A4),first level of peak current (B1), first level of pulse on time (C1), fourth level of pulse off time (D4) and second level of wire feed (E2) provide maximum value of surface roughness. The S/N data analysis Figure 5 also suggests the same levels of the variables (A4, B1, C1, D4 and E2) as the best levels for minimum SR in wire electric discharge machining process.
Lower the better:
10log(MSD ) N S LB LB (2) Where, R 1 j 2 j LB (y ) R 1 MSDResult by Anova
In order to predict the optimal values of the machining characteristics, only significant parameters are considered, and those effect is great on the machining characteristics. These significant parameters were found using Analysis of Variance (ANOVA) on S/N data of machining characteristics. Analysis of variance (ANOVA) is a common statistical technique to determine the percent contribution of each factor for results of experiments. It calculates parameters known as sum of square (SS), pure SS, variance, degree of freedom (DOF) and F-ratio. Since the procedure of ANOVA is a very complicated and employs a considerable of statistical formulae. Results of the ANOVA are given in the tables 7 and 8 for cutting rate and surface roughness respectively.
Table 7 Analysis of variance for cutting rate (S/N data)
Analysis of Variance for CS, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
SV 3 0.33001 0.33001 0.011000 4.98 0.172 Ip 3 0.2664 0.2664 0.08888 4.03 0.205 Ton 3 4.28091 4.28091 1.42697 64.63 0.015 Toff 3 0.65170 0.65170 0.21723 9.84 0.094 Wf 1 0.02766 0.02766 0.02766 1.25 0.379 Error 2 0.04416 0.04416 0.02208 Total 15 5.60107 S = 0.148585 R-Sq = 99.21% R-Sq (adj.) = 94.09%
Table 8 Analysis of variance for surface roughness (S/N data)
Source DF Seq SS Adj SS Adj MS F P
SV 3 3.29201 3.29201 1.09734 69.50 0.014 Ip 3 1.88929 1.88929 0.62976 39.89 0.025 Ton 3 6.26404 6.26404 2.08801 132.24 0.008 Toff 3 0.86200 0.86200 0.28733 18.20 0.053 Wf 1 0.09994 0.09994 0.09994 6.33 0.128 Error 2 0.03158 0.03158 0.01579 Total 15 12.43885 S = 0.125656 R-Sq = 99.75% R-Sq (adj) = 98.10%
Optimal value for CS
Form the tables 7, it is clear that three process parameter namely pulse on time (C) and pulse off time (D) are the most significant process parameters affecting the cutting rate. Wire feed and peak current shows the least contribution. The optimal value is predicted using the Eq. (3); the optimum value is calculated as follow,
= T i (3) Here, significant parameters are three in number. So above equation becomes
= T (C )
= 1.575538+ (2.2009-1.575538) = 2.2009 mm/min
Prediction of optimal value for SR
Form the tables 8, it is clear that three process parameter namely peak current (B), pulse on time (C), pulse off time (D) are the most significant process parameters affecting the surface roughness. Wire feed shows the least contribution. The optimum value is calculated as the similar way as in case of CR. So the optimum value is,
= T i (3)
= T (A4 T) (B1 ) (C1 ) (D4 )
=2.6224 (2.177 2.6224) (2.187 2.6224) (1.770 2.6224) (2.293 2.6224) = 0.5598 µm
Results by Grey relational analysis (GRA)
In GRA, optimization of complicated multiple performance characteristics can be converted into optimization of a single gray relational grade.
Steps in GRA
GRA consists of three steps:
1. Data Pre-processing (Normalization). 2. Calculating the grey relational coefficients. 3. Calculating the grey relational grade
Step 1:
First step is associated with the normalization of results. When the range of the series is too large or the optimal value of a quality characteristic is too enormous, it will causes the influence of some factors to be ignored. The original experimental data must be normalized to avert such effect. It is the process of transforming the original sequence to a comparable sequence. Normalization is done in the range of zero and one, the process is known as grey relational generating. Three types of data normalization are there in the GRA, lower the better (LB), the higher the better (HB) and nominal the best (NB).
Lower is better (LB) (4) Higher is Better (HB) (5) Nominal is best (NB) (6)
Let the original reference sequence is X0(k). is normalized value of the kth
element in the ith sequence, is desired value of the kth quality characteristic, max is the largest value of , and min is the smallest value of , Where i = 1,2,………,n; k = 1,2,……,p; n (=32) is the number of experiments and p (=2) is the number of quality characteristics.
Step 2:
Second step is to display the relationship between optimal and actual normalized value. Grey relational coefficient shows such kind of relationship. For this we have to calculate deviation sequences of the normalized data. The grey relational coefficient can be expressed as
0,i(k)
(7)
i = 1,… ,n; k = 1,…,p
where 0,i(k) is the relative difference of kth element between comparative
sequence Xi and the reference sequence X0 (also called GRC), is the absolute
value of difference between X0(k) and Xi(k). [ = X0(k) - Xi (k) ]
is a distinguishing or identification coefficient, and its value lie between zero and one. In general it is set to 0.5.
Step 3:
Gray relational grade is the weighting sum of grey relational coefficient. Highest Grey Relational Grade gives the best multiple machining characteristics. In this research, it had been taken the average of the grey relational co-efficient as the grey relational grade. The grey relational grade is determined by Eq. 8.
GRG = k 0,i(k), i = 1,2,…….,32 (8)
Selection of optimum level
Basically, the larger the grey relational grade, the better is the multiple performance characteristics. It is clear from table 9 and figure 6 if the process parameter setting on
(A2 B2 C4 D4 E2), then it has the highest grey relational grade. Therefore, A (30V),
B (110 ampere), C (118µs), D (45μs), and E (5mm/min) is the optimal parameter combination for multi-machining characteristics. The main effects of each process parameter on grey relational grade are given in table 9.
Table 9 Response table for mean GRG
Level SV Ip Ton Toff Wf
1 0.5698 0.5607 0.5509 0.5969 0.5701 2 0.6333 0.6271 0.5536 0.5854 0.6137 2 0.5880 0.5844 0.6100 0.5804 4 0.5765 0.5955 0.6530 0.6050 Delta 0.0634 0.0664 0.1021 0.0246 0.0436 Rank 3 2 1 5 4
50 40 30 20 0.650 0.625 0.600 0.575 0.550 150 130 110 90 106 110 114 118 45 40 35 30 0.650 0.625 0.600 0.575 0.550 5 3 SV M ea n of M ea ns IP TON TOFF WF
Main Effects Plot for Means Data Means
Figure 6 Shows the graphical representation of values which are tabulated in table 9
5. CONCLUSION
In present work, wire electrical discharge machining (WEDM) for 316L has been studied. Grey relational analysis (GRA), Anova along with Taguchi method was used to optimize the Cutting Speed (CS) and surface roughness (SR), simultaneously. Based on the results and discussions, the following conclusions are made:
Using Taguchi method, CS and SR were optimized individually. The cutting speed is mostly affected by pulse-on time (Ton) , Pulse off time (Toff) and Servo Voltage (SV) and surface roughness are mostly affected by the peak current (Ip), pulse-on time (Ton) , Pulse off time (Toff) and Servo Voltage (SV). Anova has been applied to find the significant process parameter. Basically, the larger the grey relational grade, the better is the multiple performance characteristics. The process parameter setting of (A2 B2 C4 D4 E2) has the highest grey relational grade. Therefore, A (30V), B (110 ampere), C (118µs), D (45μs), and E (5 mm/min) is the optimal parameter combination for multi-machining characteristics.
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