# Experimental Investigation of Heat-Treated Tool on Wire Electric Discharge Machining of Titanium Alloy (Ti-6Al-4V)

Authors: R. Manoj Samson; Ashvin Sridhar; R. Ranjith; Solomon Jenoris Muthiya; Joshuva Arockia Dhanraj; Murgayya S. Basavankattimath; Agegnehu Shara Shata
Journal: Advances in Materials Science and Engineering
 (2022)

---

## Abstract

Ti-6Al-4V is known for its lightweight, high tensile strength, exceptional corrosion resistance, and low thermal coefficient of expansion due to which it finds its application in precision surgical instruments and aeronautical and marine engine parts. In this study, Ti-6Al-4V has been machined using wire EDM machine with two different wire materials. The wire thickness of 0.25 mm heat-treated brass and zinc-coated brass tool and super cool oil as dielectric fluid were utilized for the machining process. TaguchiL9 orthogonal array was used. Ton (Pulse ON time), Toff (Pulse OFF time), wire feed, and Ip (Current) were the input parameters, and the response parameters such as surface roughness (Ra), material removal rate (MRR), tool wear rate (TWR), and recast layer thickness were optimized by TOPSIS optimization. From the results, higher tool wear rate was observed in brass tool. The titanium percentage after machining was found to be 55.05% for zinc-coated brass wire, whereas it was 51.58% for brass wire. Better productivity and surface integrity were observed in zinc-coated brass tool compared to heat-treated brass. MRR increased to 54.93%, surface roughness decreased to 2.40%, recast layer thickness increased to 3.44%, and tool wear rate was increased to 47.96%.


---

## Body

## 1. Introduction

Titanium alloy is one of the hardest materials that exhibits minimum weight ratio, exceptional corrosion resistance, low density, high strength, low thermal coefficient of expansion, and excellent properties at elevated temperature [1]. Its applications are mainly in manufacturing precision surgical instruments and aeronautical and marine engine components [2]. Ti-6Al-4V is considered to be one of the most difficult-to-be machined materials. To machine harder materials, nonconventional machining techniques are preferred over the conventional machining techniques, like lathe, CNC, drilling, milling, and so on. Nonconventional machining techniques are often preferred for harder materials and to improve the tool life. Ti-6Al-4V finds extensive use in manufacturing aeronautical parts the surface integrity of the machine work piece should be at par excellence to avoid fatigue and cracks which lead to major failures. Considering Ti-6Al-4V’s exceptional physical and chemical properties, a nonconventional method of wire EDM is being carried out. WEDM is a machining process that uses spark-generated electrical current to make the desired shape that helps in maintaining the surface integrity. Material removal from the work piece is done by a series of electrical discharges that are rapidly recurring between the two electrodes [3]. There are many advantages; for example, machining complex shapes easily, tool life is increased, precise cutting leaves very fewer burrs on its surface, and so on follows when WEDM is chosen. The cons include consumption of higher energy levels, very expensive than conventional machining, and the ability to machine only conductive materials. Hence, EDM wire cut machining is used. Jadam et al. [4] elucidated that the selection of tools for the manufacturing process includes many choices like copper (Cu), brass, zinc, molybdenum, and coated wires. The zinc-coated brass wire and brass wire of 0.25 mm thickness are chosen over Cu wires that are predominantly used. The reason for choosing brass over Cu is that, even though the conductivity of Cu is high, it possesses a high melting point, low vapor pressure rating, and low tensile strength, whereas brass, which is an alloy of copper, possesses a low melting point, high vapor pressure rating, and high tensile strength, which makes it the best choice in the selection criteria. The second tool chosen for comparison was a zinc-coated brass wire of the same thickness. When a material is given a zinc coating, its properties are enhanced. Since the brass wire was coated with zinc, it exhibited excellent improvement in the time of machining, good discharge characteristics, and better surface finish when compared with that of brass wire. The zinc coating helps to increase the tool life. To bring clarity to the work done, Pramanik and Basak [5] have identified the optimizing parameters for the fatigue life of Ti-6Al-4V. Using the technique, they have claimed that the propagation of the fatigue cracks does not influence the machined finished surface. Palanikumar et al. [6] have shown that the continuous generation of chips is caused by the cutting zone’s high temperature. There is evidence of shear localization, which is a frequent occurrence when titanium is machined. At the tip of the chips, we saw some undeformed elements. The increased temperature would affect the tool life and finish of machined items. When the temperature is lower, short chips also emerge. Akkuş and Yaka [7] investigated that low values for Ra, tool wear, and energy consumption are sought, the target has been set to minimal after conducting various optimizations. Predictions for Ra, tool wear, and energy consumption were produced with an accuracy of 89.1%, 58.33%, and 96.75%, respectively. The reason for the significant error rate in tool wear prediction due to multiple optimizations is that, although feed rate is the effective parameter for Ra and energy consumption, cutting speed is the effective parameter for tool wear. Aydın [8] investigated that both computational and experimental tests revealed that plastic deformation occurred inside the PSZ around the tooltip. It was discovered that at considerably higher cutting speeds, serration of chips was begun by extensive periodic fracturing in the PSZ and progressed to the chip. Rao and Selvaraj [9] have conducted the optimization of the parameters such as servo voltage, servo feed, feed current, pulse on, wire tension, pulse off and concluded that the pulse off, pulse on, and peak current are widely remarkable. Kumar et al. [10] have analyzed the influence of EDM on wire to enhance the productivity of the surface finish of titanium alloy which develops a tolerable refinement on its surface finish. Pramanik et al. [11] have examined the process parameters on multilayered recast layers, identifying the presence of traces of wire electrode on its surface, holes, and cracks on Ti-6Al-4V. Chalisgaonkar et al. [12] have carried out the two different techniques for material removal rate and surface roughness for the characteristics of the machined surface using the principle of WEDM on the titanium alloy. Amorim et al. [13] concluded that the zinc-coated wire produced a better material removal rate and surface integrity at a higher wire feed rate when compared to the brass tool. Srinivasan et al. [14] have concluded that TOPSIS optimization is one of the best multicriterion decision-making criteria. TOPSIS optimization technique is considered to be one the finest methods of optimization when multiple criteria decision analysis is considered [15]. After the machining process, the response parameters were optimized using the TOPSIS optimization technique. The metallurgical aspects were studied using FESEM and EDS. Ra, MRR, TWR, and recast layer thickness were found to be higher in the zinc-coated brass wire when compared to those of heat-treated brass wire. A proper set of input parameters should be chosen to obtain better surface integrity. An improved situation was found when a heat-treated zinc-coated brass wire was used. Upon the extensive literature survey conducted, a very less number of works were performed on comparing heat-treated brass and zinc-coated brass as a tool in the wire EDM process.


## 2. Materials and Methods

Ti-6Al-4V is chosen because of its extensive physical and chemical properties. This metal exhibits excellent cryogenic properties. This work is a comparative study of the response parameters obtained by machining the workpiece with two different wires of 0.25 mm thickness utilized. The heat-treated brass wire and the zinc-coated brass wire was used as tool in wire EDM machine. The titanium (Ti-6Al-4V) workpiece was machined into rectangular shape of dimensions 10 × 20 mm using wire EDM machine. From the literature survey [16,17], it was finalized that the input parameters were Wire Feed, Ipeak, Pulse OFF time, and Pulse ON time. Similarly, response parameters were recast layer thickness, tool wear rate (TWR), surface roughness (Ra), and material removal rate (MRR). The recast layer thickness is measured using Machine Vision OLM, which uses VMS3.1 software. FESEM is conducted on the wire EDM machined sample of 10 × 20 mm to delineate the surface integrity of the machined surface. The specimen was first cleansed with acetone solution. To remove the impurities found on the surface, later the specimen was mounted onto a vacuumed chamber, which was then monitored under greater magnification. The SEM photograph was taken with two different magnifications of 300x and 600x. Energy Dispersion Spectroscopy (EDS) is used to find the change in the material composition of the workpiece. The EDS results were obtained from the same equipment. The EDS conducted on the machined surface of the workpiece to find the change in the composition of Ti-6Al-4V.


### 2.1. Design of Experiments (DOE)

The DOE was created by using Taguchi Analysis in Minitab 2018. The level was fixed to be three, and four parameters were entered. Tables1 and 2 were formulated by Minitab. L9 was chosen in consideration of the material cost. The machined parts with brass wire as a tool are mentioned in Figure 1.Table 1
Levels of DOE.
LevelTon (μsec)Toff (μsec)Ip (A)Wire feed (m/min)I406280II6010390III80144100Table 2
Design of experiments.
Exp.Ton (μsec)Toff (μsec)Ip (A)Wire feed (m/min)L1406280L24010390L340144100L46063100L56010480L66014290L7806490L880102100L98014380Figure 1
Wire EDM machined samples.


### 2.2. Experimental Work for Brass and Zinc-Coated Brass Wire as a Tool

The wire is wound around the wire drum, and it is aligned in such a position to machine the required surface. After fixing the constants of the input parameters, the start command is activated. In accordance with the feed rate given, the wire drum spins, and the machining starts with the givenIp, Pulse ON, and Pulse OFF. In case if the wire is cut during the machining process, the entire reel on the wire drum should be unwounded and reeled again. After each machining process, the workpiece is weighed, and the material loss is calculated. Similarly, the tool wear is also found by weighing it before and after machining process. The time required for a specimen to be machined is noted down. After the workpiece is machined to the desired shape, its surface roughness is studied using the Surf com apparatus and the recast layer is found using the Machine Vision OLM.


#### 2.2.1. Material Removal Rate

MRR can be delineated by the difference between before weight (Wa) and after weight (Wb) of the machining process by the time taken (t). An increased material removal rate will have an upper hand in the machining process by improving the productivity rate.


#### 2.2.2. Surface Roughness

The irregularities that regulate the surface finish are called surface roughness. To regulate a better surface finish and a good accuracy in the micromilling operation, it is very essential to cut the low chip feed. The feed per tooth should always be kept minimal at 1μm. The machine that is being used to calculate the surface roughness is Surf com. To get the final Ra value, we need to calculate the surface roughness of all of its sides and then find the average Ra. The unit is μm [18].


#### 2.2.3. Recast Layer Thickness

The recast layer is a thin layer formed on the machined surface after the solidification. Machine Vision OLM was utilized to measure the recast layer thickness. Recast Layer thickness is measured to find change in chemical composition of the machined surface.


#### 2.2.4. Tool Wear Rate

The Tool wear rate is the ratio of difference of weight before machining and after weight after machining to time taken. TWR is inversely proportional to the tool life.


## 2.1. Design of Experiments (DOE)

The DOE was created by using Taguchi Analysis in Minitab 2018. The level was fixed to be three, and four parameters were entered. Tables1 and 2 were formulated by Minitab. L9 was chosen in consideration of the material cost. The machined parts with brass wire as a tool are mentioned in Figure 1.Table 1
Levels of DOE.
LevelTon (μsec)Toff (μsec)Ip (A)Wire feed (m/min)I406280II6010390III80144100Table 2
Design of experiments.
Exp.Ton (μsec)Toff (μsec)Ip (A)Wire feed (m/min)L1406280L24010390L340144100L46063100L56010480L66014290L7806490L880102100L98014380Figure 1
Wire EDM machined samples.


## 2.2. Experimental Work for Brass and Zinc-Coated Brass Wire as a Tool

The wire is wound around the wire drum, and it is aligned in such a position to machine the required surface. After fixing the constants of the input parameters, the start command is activated. In accordance with the feed rate given, the wire drum spins, and the machining starts with the givenIp, Pulse ON, and Pulse OFF. In case if the wire is cut during the machining process, the entire reel on the wire drum should be unwounded and reeled again. After each machining process, the workpiece is weighed, and the material loss is calculated. Similarly, the tool wear is also found by weighing it before and after machining process. The time required for a specimen to be machined is noted down. After the workpiece is machined to the desired shape, its surface roughness is studied using the Surf com apparatus and the recast layer is found using the Machine Vision OLM.


### 2.2.1. Material Removal Rate

MRR can be delineated by the difference between before weight (Wa) and after weight (Wb) of the machining process by the time taken (t). An increased material removal rate will have an upper hand in the machining process by improving the productivity rate.


### 2.2.2. Surface Roughness

The irregularities that regulate the surface finish are called surface roughness. To regulate a better surface finish and a good accuracy in the micromilling operation, it is very essential to cut the low chip feed. The feed per tooth should always be kept minimal at 1μm. The machine that is being used to calculate the surface roughness is Surf com. To get the final Ra value, we need to calculate the surface roughness of all of its sides and then find the average Ra. The unit is μm [18].


### 2.2.3. Recast Layer Thickness

The recast layer is a thin layer formed on the machined surface after the solidification. Machine Vision OLM was utilized to measure the recast layer thickness. Recast Layer thickness is measured to find change in chemical composition of the machined surface.


### 2.2.4. Tool Wear Rate

The Tool wear rate is the ratio of difference of weight before machining and after weight after machining to time taken. TWR is inversely proportional to the tool life.


## 2.2.1. Material Removal Rate

MRR can be delineated by the difference between before weight (Wa) and after weight (Wb) of the machining process by the time taken (t). An increased material removal rate will have an upper hand in the machining process by improving the productivity rate.


## 2.2.2. Surface Roughness

The irregularities that regulate the surface finish are called surface roughness. To regulate a better surface finish and a good accuracy in the micromilling operation, it is very essential to cut the low chip feed. The feed per tooth should always be kept minimal at 1μm. The machine that is being used to calculate the surface roughness is Surf com. To get the final Ra value, we need to calculate the surface roughness of all of its sides and then find the average Ra. The unit is μm [18].


## 2.2.3. Recast Layer Thickness

The recast layer is a thin layer formed on the machined surface after the solidification. Machine Vision OLM was utilized to measure the recast layer thickness. Recast Layer thickness is measured to find change in chemical composition of the machined surface.


## 2.2.4. Tool Wear Rate

The Tool wear rate is the ratio of difference of weight before machining and after weight after machining to time taken. TWR is inversely proportional to the tool life.


## 3. Results and Discussion

### 3.1. WEDM Influence on MRR,Ra, Recast Layer, and Tool Wear Rate

From the results obtained, MRR was found to be higher. While using zinc-coated brass wire, the best MRR was found inL6 experiment with the process parameters such as 60 μs of Ton, 14 μs of Toff, 2 A of Ip, and 90 m/min of wire feed. Similarly, for the surface finish, the least value is to be chosen to ensure the best surface integrity. For the zinc-coated brass wire, the least value of 3.99 μm was found for L1experiment. Likewise for the recast layer thickness and the tool wear rate, the least results are chosen to be the best results. For recast layer thickness, the best one was from L2 experiment, which gave a satisfying value of 0.18029 mm and for the tool wear rate, it was from L6, giving a convincing value of 1.0353 g/min. Apparently, L6 gave the best MRR value too. Similar kind of trend was observed in results of MRR, Ra, recast layer thickness, and tool wear rate in the previously conducted research on wire EDM process [18–23].


### 3.2. TOPSIS Optimization

The technique of order of preference by similarity to the ideal solution is the common technique that is being used to comprehend the problems in decision making. This technique enables us to form a comparison between all the alternatives that are included in the comparison. This method is highly useful in large-scale industries, like automobile and aeronautical industries [24–26].


#### 3.2.1. TOPSIS Optimization for Brass Wire

Stage 1.
Determination of a Performance Matrix
To calculate the performance matrix, first, we need to calculate the sum of squares forRa, TWR, MRR, and thickness of the recast layer as shown in Table 3. The performance matrix is shown in Table 4.Table 3
Design of experiments using brass wire as a tool.
Exp.Ton (μs)Toff (μs)Ip (A)Wire feed (m/min)Material removal rate (g/min)Ra (μm)Recast layer thickness (mm)Tool wear rate (g/min)L14062800.554 × 10−15.04440.22144.1250L240103902.297 × 10−16.37800.29233.0982L3401441001.575 × 10−15.30100.32772.1325L460631002.307 × 10−15.13500.17423.9872L560104801.758 × 10−15.44370.29823.5280L660142901.060 × 10−14.09370.46692.9682L78064902.666 × 10−16.83720.23375.6324L8801021001.083 × 10−14.63980.50742.5554L980143801.097 × 10−15.22160.52801.9895Table 4
Determination of performance matrix.
Exp.MRRTWRRaRecast layer thicknessL10.05544.1255.04440.2214L20.22973.09826.3780.2923L30.15752.13255.3010.3277L40.23073.98725.1350.1742L50.17583.52805.44370.2982L60.10602.96824.09370.4669L70.26665.63246.83720.2337L80.10832.55544.63980.5074L90.10971.98955.22160.5280SOS0.27084017110.5288927262.52608521.16996348Stage 2.
Calculation of the Normalized Decision Matrix
The formula for calculating normalized decision matrix is denoted in equation (1). Table 5 depicts the normalized decision matrix table.(1)zij=xij∑xij2.Table 5
Normalized decision matrix.
Exp.MRRTWRRaRecast layer thicknessL10.1064518530.392361190.3113316590.204687567L20.4413716720.2946941670.3936391490.270235663L30.3026383910.2028388460.3271685680.302963486L40.4432931860.3792539480.3169233350.16105047L50.337802090.3355758250.3359757660.2756903L60.2036804410.2823288450.2526560960.43165594L70.5122755240.5357418590.421980180.216059098L80.2080999220.243064190.2863604450.469098787L90.2107900410.1892369910.3222681370.488143791Stage 3.
Calculation of Weighted Normalized Decision Matrix
Weights are being allocated based the importance of the attributes. All response parameters are considered as beneficial attributes. Hence, the weights of equal percentage of 0.25 are allotted for all the response parameters. Based on the importance of the attributes, any value can be chosen by the decision maker, but the total weights must be equal to one [14,15]. Weights of attributes are shown in Table 6. The weighted normalized matrix is shown in Table 7.
The weighted normalized matrix can be calculated by using(2)Aij=Wij×Zij.Table 6
Weights of the response parameters.
MRRTWRRaRecast layer thickness0.250.250.250.25Table 7
Weighted normalized matrix.
Exp.MRRTWRRaRecast layer thicknessL10.0266129630.0980902980.0778329150.051171892L20.1103429180.0736735420.0984097870.067558916L30.0756595980.0507097110.0817921420.075740871L40.1108232960.0948134870.0792308340.040262618L50.0844505220.0838939560.0839939410.068922575L60.050920110.0705822110.0631640240.107913985L70.1280688810.1339354650.1054950450.054014775L80.0520249810.0607660480.0715901110.117274697L90.052697510.0473092480.0805670340.122035948Stage 4.
Determination of the Ideal (Bi+) and (Bi−)
The ideal best and worst are given in Table8:(1)
Ideal positive solu. =Vj+ (min or max)(2)
Ideal negative solu. = Vj− (min or max)Table 8
Determination of ideal (Bi+) and ideal (Bi−).
MRRTWRRaRecast layer thicknessBi+0.1280688810.0473092480.0631640240.040262618Bi−0.0266129630.1339354650.1054950450.122035948Stage 5.
Calculate the Euclidian distance between ideal (Bi+) and ideal (Bi-) using equations (3) and (4). The calculated Euclidian distance is shown in Table 9. (3)Ki+=∑Bi+−Aij20.5,(4)Ki−=∑Bi−−Ajj20.5.Table 9
Euclidian distance between ideal (Bi+) and ideal (Bi−).
Exp.Ki+Ki−L10.1149182620.084093899L20.0547415520.116876655L30.0660606490.109713992L40.0530302300.126485555L50.0670543120.095564971L60.1052153560.081214637L70.0973916760.122148202L80.1093874740.158989338L90.1125636970.162477728Stage 6.
Determine the relative performance closeness to the ideal solu. using equation (5).
Table10 shows the relative performance closeness.(5)Pi=Ki−ki++ki−.Table 10
Relative performance closeness.
Exp.PiL10.422556586L20.681027133L30.624174177L40.704592941L50.587660758L60.435630747L70.556382753L80.592410860L90.590739116Stage 7.
Ranking of the Performance Matrix
0 <Pi < 1, closest to 1 is preferred. Table 11 shows the rank of the performance matrix.
Figure2 shows the graph of Pi versus rank for heat-treated brass as tool.Table 11
Ranking of the performance matrix.
Exp.PiRankL10.4225565869L20.6810271332L30.6241741773L40.7045929411L50.5876607584L60.4356307478L70.5563827537L80.5924108605L90.5907391166Figure 2
Pi versus rank for heat-treated brass wire as tool.


#### 3.2.2. TOPSIS Optimization for Zinc-Coated Brass Wire

Stage 8.
Determination of Performance Matrix
To calculate the performance matrix, first, we need to calculate the sum of squares forRa, TWR, MRR, and the thickness of the recast layer shown in Table 12. The performance matrix is shown in Table 13.Table 12
Design of experiments using zinc-coated brass wire as a tool.
Exp.Ton (μs)Toff (μs)Ip (A)Wire feed (m/min)Material removal rate (g/min)Ra (μm)Recast layer thickness (mm)Tool wear rate (g/min)L14062802.222 × 10−13.99520.33273.0555L240103901.946 × 10−14.43270.18022.8224L3401441001.509 × 10−16.09510.19631.8868L460631004.441 × 10−14.67470.32353.8490L560104803.303 × 10−14.67960.36273.0238L660142905.915 × 10−14.99920.28801.0353L78064905.333 × 10−14.79210.40324.9777L8801021001.439 × 10−14.10950.29541.3087L980143801.363 × 10−16.61280.34521.4366Table 13
Determination of performance matrix.
Exp.MRRTWRRaRecast layer thicknessL10.2223.05553.99520.3327L20.19462.82244.43270.1802L30.15091.88686.09510.1963L40.44413.8494.67470.3235L50.33033.02384.67960.3627L60.59151.03534.99920.288L70.53334.97774.79210.4032L80.14391.30874.10950.2954L90.13631.43666.61280.3452SOS1.0898129174.44606232225.08550970.869837Stage 9.
Calculation of Normalized Decision Matrix
The formula for calculating normalized decision matrix is denoted as follows. Table14 depicts the normalized decision matrix table.(6)zij=xij∑xij2.Table 14
Normalized decision matrix.
Exp.MRRTWRRaRecast layer thicknessL10.2126556860.3541289420.2662960690.356725259L20.1864089940.3271129200.2954571950.193212779L30.1445483920.2186779540.4062628090.210475408L40.4254071640.4460946810.3115874640.346860900L50.3163971770.3504549490.3119140690.388891649L60.5666028760.1199900810.3332166880.308797339L70.5108526020.5769097160.3194126440.432316275L80.1378430330.1516768280.2739146220.316731715L90.1305629280.1665002910.4407695860.370127922Stage 10.
Calculation of Weighted Normalized Decision Matrix
Weights are being allocated based the importance of the attributes. All response parameters are considered as beneficial attributes. So, the weights of equal percentage of 0.25 are allotted for all the response parameters [14,15]. The weights of attributes are presented in Table 15. The weighted normalized matrix is shown in Table 16.
We can calculate the weighted normalized matrix using(7)Aij=Wij×Zij.Table 15
Weights of the response parameters.
MRRTWRRaRecast layer thickness0.250.250.250.25Table 16
Weighted normalized matrix.
Exp.MRRTWRRaRecast layer thicknessL10.0531639220.0885322360.0665740170.089181315L20.0466022480.081778230.0738642990.048303195L30.0361370980.0546694880.1015657020.052618852L40.1063517910.111523670.0778968660.086715225L50.0790992940.0876137370.0779785170.097222912L60.1416507190.029997520.0833041720.077199335L70.1277131500.1442274290.0798531610.108079069L80.0344607580.0379192070.0684786550.079182929L90.0326407320.0416250730.1101923960.092531980Stage 11.
Determination of the Ideal (Bi+) and (Bi−)
The ideal best and worst are given in Table17:(1
)Ideal positive solu. =Vj+ (min or max).(2)
Ideal negative solu. = Vj− (min or max).Table 17
Determination of the ideal (Bi+) and ideal (Bi−).
MRRTWRRaRecast layer thicknessBi+0.1416507190.029997520.0665740170.048303195Bi−0.0326407320.1442274290.1101923960.108079069Stage 12.
Calculate the Euclidian distance between ideal (Bi+) and ideal (Bi−) using equations (8) and (9). The calculated Euclidian distance is shown in Table 18. (8)Ki+=∑Bi+−Aij20.5,(9)Ki−=∑Bi−−Ajj20.5.Table 18
Euclidian distance between the ideal (Bi+) and ideal (Bi−).
Exp.Ki+Ki−L10.1136980510.076045013L20.1084831870.094803545L30.1139511870.105750200L40.0974485420.089455414L50.0987699880.080740933L60.0333898930.163120152L70.1303542040.099795962L80.1118464410.117812451L90.1260044670.103773578Stage 13.
Determine the relative performance closeness to the ideal solu. using equation (10).
Table19 shows the relative performance closeness.(10)Pi=Ki−ki++ki−.Table 19
Relative performance closeness to the ideal solu.
Exp.PiL10.400778880L20.466353825L30.481336058L40.478617017L50.449782846L60.830085565L70.433612382L80.512988850L90.451625297Stage 14.
Rank the performance matrix.
0 <Pi < 1, closest to 1 is preferred. Table 20 shows the rank of the performance matrix.
Figure3 shows the graph of Pi versus rank for zinc-coated wire as a tool.Table 20
Rank of the performance matrix.
Exp.PiRankL10.400778889L20.4663538255L30.4813360583L40.4786170174L50.4497828467L60.8300855651L70.4336123828L80.5129888502L90.4516252976Figure 3
Pi versus rank for zinc-coated wire as a tool.


### 3.3. Field Emission Scanning Electron Microscopy (FESEM) Analysis

The machined surfaces of Ti-6Al-4V machined using heat-treated brass and zinc-coated brass tool were undergone with SEM analysis. The surface cracks were not noticed on the machined surfaces. The SEM photographs of machined surface using heat-treated brass tool are shown in Figures4–7. The machined surface was with overlapping craters, cavities, pile of debris, and rippled surface. This is due to the applied stress, melting, and solidification of the machined surface. Zinc-coated brass tool machined surface had lesser craters, cavities, and rippled surface. The absence of dimples and river marking on the machined surface confirms the absence of surface cracks. The formation of surface cracks was due to high fluctuating stresses applied during the process of machining. Similar kind of observation was reported on wire EDM machine surfaces [27–31].Figure 4
Machined surface using heat-treated brass tool with 300x magnification.Figure 5
Machined surface using heat-treated brass tool with 600x magnification.Figure 6
Machined surface using zinc-coated tool with 300x magnification.Figure 7
Machined surface using zinc-coated tool with 600x magnification.


### 3.4. Energy Dispersion Spectroscopy (EDS)

Energy Dispersion Spectroscopy is a methodology used to find changes in the material composition of the workpiece. The EDS results were obtained from the same equipment that was used for getting the SEM results. The EDS conducted at the machined edge of the workpiece revealed certain changes in the composition of the material. Carbon and oxygen deposits were found at the edges. Traces of metals like sodium (Na), barium (Ba), and zinc (Zn) were found. Based on these, certain assumptions can be made about these depositions. They are assumed to be deposited on the workpiece from the electrolyte used.The EDS analysis is taken based on TOPSIS optimization of the performance closeness. For brass wire,L4 (Figure 8) is being ranked first based on performance closeness and hence the EDS analysis is conducted on that particular specimen and the same for zinc-coated brass wire, L6 (Figure 9) is being ranked first based on performance closeness and hence EDS analysis is conducted on that particular specimen. The titanium percentage after machining was found to be 55.05% for zinc-coated brass wire whereas it was 51.58% for brass wire, from which it can be inferred that there has been no change in the property when the zinc-coated brass wire was used, whereas the aluminum composition dropped from 5.992% to 3.33% for zinc-coated brass wire. Due to WEDM process, some amount of oxidation has taken place, which results in the presence of oxygen and carbon deposits. The change in composition of the machined surface was due to the melting and solidification of the machined surface. Similar kind of change in composition of the workpiece was observed in previously undergone research work in wire EDM process [29,31].Figure 8
EDS analysis forL4 workpiece using brass wire as a tool.Figure 9
EDS analysis forL6 workpiece using zinc-coated brass wire as a tool.


### 3.5. Response Value Table Using Closeness Value

To narrow down to the best possible combinations of the input parameters, the response table has been formulated using the Minitab 17 software. The process parameters such asTon, Toff, Ip, and Wire-feed are represented as A, B, C, and D, respectively.From Table21 and Figure 10, it is found that the optimized combination of the input parameters is A3B2C2D3. It denotes that the results obtained from the above combination get the best in class output. The output parameters having the following values as Ton = 40 μ sec, Toff = 10 μ sec, Ip = 3 A, and wire feed = 100 m/min deliver the best outcome.Table 21
Response table for brass wire.
LevelTonToffIpWire feed10.57590.56120.48350.533720.5760.62040.65880.557730.57980.55020.58940.6404Rank3223Figure 10
Main effects plot for means for brass wire.Similarly, from Table22 and Figure 11 above, it is found that the optimized combination of the input parameters is A2B3C1D2. It denotes that the results obtained from the above combination get the best in class output. The output parameters having the following values as Ton = 40 μs, Toff = 14 μs, Ip = 2 A, and wire feed = 90 m/min deliver the best outcome. Thus, from the above two response tables and graphs of the main effects plot for means, we can conclude that zinc-coated brass wire had a better response to the closeness value.Table 22
Response table for zinc-coated brass wire.
LevelTonToffIpWire feed10.44950.43770.58130.434120.58620.47640.46550.576730.46610.58770.45490.491Rank2312Figure 11
Main effects plot for means for zinc-coated brass wire.


### 3.6. Confirmation Test

The confirmation has been carried out to check the repeatability of the machine used. The highly ranked specimens have been considered, such asL4 and L6 for brass and zinc-coated brass wire, respectively. The value obtained for specimen L4 for MRR, TWR, Ra, and recast layer thickness were 0.2237, 3.821, 4.9210, and 0.1675, respectively. Similarly, for specimen L6, the values obtained were 0.5723, 0.9891, 4.8442, and 0.2791, respectively. From this, it can be concluded that the values produced by the confirmation test are having an error percentage below 5%, which proves that the repeatability factor of the machine is good.


## 3.1. WEDM Influence on MRR,Ra, Recast Layer, and Tool Wear Rate

From the results obtained, MRR was found to be higher. While using zinc-coated brass wire, the best MRR was found inL6 experiment with the process parameters such as 60 μs of Ton, 14 μs of Toff, 2 A of Ip, and 90 m/min of wire feed. Similarly, for the surface finish, the least value is to be chosen to ensure the best surface integrity. For the zinc-coated brass wire, the least value of 3.99 μm was found for L1experiment. Likewise for the recast layer thickness and the tool wear rate, the least results are chosen to be the best results. For recast layer thickness, the best one was from L2 experiment, which gave a satisfying value of 0.18029 mm and for the tool wear rate, it was from L6, giving a convincing value of 1.0353 g/min. Apparently, L6 gave the best MRR value too. Similar kind of trend was observed in results of MRR, Ra, recast layer thickness, and tool wear rate in the previously conducted research on wire EDM process [18–23].


## 3.2. TOPSIS Optimization

The technique of order of preference by similarity to the ideal solution is the common technique that is being used to comprehend the problems in decision making. This technique enables us to form a comparison between all the alternatives that are included in the comparison. This method is highly useful in large-scale industries, like automobile and aeronautical industries [24–26].


### 3.2.1. TOPSIS Optimization for Brass Wire

Stage 1.
Determination of a Performance Matrix
To calculate the performance matrix, first, we need to calculate the sum of squares forRa, TWR, MRR, and thickness of the recast layer as shown in Table 3. The performance matrix is shown in Table 4.Table 3
Design of experiments using brass wire as a tool.
Exp.Ton (μs)Toff (μs)Ip (A)Wire feed (m/min)Material removal rate (g/min)Ra (μm)Recast layer thickness (mm)Tool wear rate (g/min)L14062800.554 × 10−15.04440.22144.1250L240103902.297 × 10−16.37800.29233.0982L3401441001.575 × 10−15.30100.32772.1325L460631002.307 × 10−15.13500.17423.9872L560104801.758 × 10−15.44370.29823.5280L660142901.060 × 10−14.09370.46692.9682L78064902.666 × 10−16.83720.23375.6324L8801021001.083 × 10−14.63980.50742.5554L980143801.097 × 10−15.22160.52801.9895Table 4
Determination of performance matrix.
Exp.MRRTWRRaRecast layer thicknessL10.05544.1255.04440.2214L20.22973.09826.3780.2923L30.15752.13255.3010.3277L40.23073.98725.1350.1742L50.17583.52805.44370.2982L60.10602.96824.09370.4669L70.26665.63246.83720.2337L80.10832.55544.63980.5074L90.10971.98955.22160.5280SOS0.27084017110.5288927262.52608521.16996348Stage 2.
Calculation of the Normalized Decision Matrix
The formula for calculating normalized decision matrix is denoted in equation (1). Table 5 depicts the normalized decision matrix table.(1)zij=xij∑xij2.Table 5
Normalized decision matrix.
Exp.MRRTWRRaRecast layer thicknessL10.1064518530.392361190.3113316590.204687567L20.4413716720.2946941670.3936391490.270235663L30.3026383910.2028388460.3271685680.302963486L40.4432931860.3792539480.3169233350.16105047L50.337802090.3355758250.3359757660.2756903L60.2036804410.2823288450.2526560960.43165594L70.5122755240.5357418590.421980180.216059098L80.2080999220.243064190.2863604450.469098787L90.2107900410.1892369910.3222681370.488143791Stage 3.
Calculation of Weighted Normalized Decision Matrix
Weights are being allocated based the importance of the attributes. All response parameters are considered as beneficial attributes. Hence, the weights of equal percentage of 0.25 are allotted for all the response parameters. Based on the importance of the attributes, any value can be chosen by the decision maker, but the total weights must be equal to one [14,15]. Weights of attributes are shown in Table 6. The weighted normalized matrix is shown in Table 7.
The weighted normalized matrix can be calculated by using(2)Aij=Wij×Zij.Table 6
Weights of the response parameters.
MRRTWRRaRecast layer thickness0.250.250.250.25Table 7
Weighted normalized matrix.
Exp.MRRTWRRaRecast layer thicknessL10.0266129630.0980902980.0778329150.051171892L20.1103429180.0736735420.0984097870.067558916L30.0756595980.0507097110.0817921420.075740871L40.1108232960.0948134870.0792308340.040262618L50.0844505220.0838939560.0839939410.068922575L60.050920110.0705822110.0631640240.107913985L70.1280688810.1339354650.1054950450.054014775L80.0520249810.0607660480.0715901110.117274697L90.052697510.0473092480.0805670340.122035948Stage 4.
Determination of the Ideal (Bi+) and (Bi−)
The ideal best and worst are given in Table8:(1)
Ideal positive solu. =Vj+ (min or max)(2)
Ideal negative solu. = Vj− (min or max)Table 8
Determination of ideal (Bi+) and ideal (Bi−).
MRRTWRRaRecast layer thicknessBi+0.1280688810.0473092480.0631640240.040262618Bi−0.0266129630.1339354650.1054950450.122035948Stage 5.
Calculate the Euclidian distance between ideal (Bi+) and ideal (Bi-) using equations (3) and (4). The calculated Euclidian distance is shown in Table 9. (3)Ki+=∑Bi+−Aij20.5,(4)Ki−=∑Bi−−Ajj20.5.Table 9
Euclidian distance between ideal (Bi+) and ideal (Bi−).
Exp.Ki+Ki−L10.1149182620.084093899L20.0547415520.116876655L30.0660606490.109713992L40.0530302300.126485555L50.0670543120.095564971L60.1052153560.081214637L70.0973916760.122148202L80.1093874740.158989338L90.1125636970.162477728Stage 6.
Determine the relative performance closeness to the ideal solu. using equation (5).
Table10 shows the relative performance closeness.(5)Pi=Ki−ki++ki−.Table 10
Relative performance closeness.
Exp.PiL10.422556586L20.681027133L30.624174177L40.704592941L50.587660758L60.435630747L70.556382753L80.592410860L90.590739116Stage 7.
Ranking of the Performance Matrix
0 <Pi < 1, closest to 1 is preferred. Table 11 shows the rank of the performance matrix.
Figure2 shows the graph of Pi versus rank for heat-treated brass as tool.Table 11
Ranking of the performance matrix.
Exp.PiRankL10.4225565869L20.6810271332L30.6241741773L40.7045929411L50.5876607584L60.4356307478L70.5563827537L80.5924108605L90.5907391166Figure 2
Pi versus rank for heat-treated brass wire as tool.


### 3.2.2. TOPSIS Optimization for Zinc-Coated Brass Wire

Stage 8.
Determination of Performance Matrix
To calculate the performance matrix, first, we need to calculate the sum of squares forRa, TWR, MRR, and the thickness of the recast layer shown in Table 12. The performance matrix is shown in Table 13.Table 12
Design of experiments using zinc-coated brass wire as a tool.
Exp.Ton (μs)Toff (μs)Ip (A)Wire feed (m/min)Material removal rate (g/min)Ra (μm)Recast layer thickness (mm)Tool wear rate (g/min)L14062802.222 × 10−13.99520.33273.0555L240103901.946 × 10−14.43270.18022.8224L3401441001.509 × 10−16.09510.19631.8868L460631004.441 × 10−14.67470.32353.8490L560104803.303 × 10−14.67960.36273.0238L660142905.915 × 10−14.99920.28801.0353L78064905.333 × 10−14.79210.40324.9777L8801021001.439 × 10−14.10950.29541.3087L980143801.363 × 10−16.61280.34521.4366Table 13
Determination of performance matrix.
Exp.MRRTWRRaRecast layer thicknessL10.2223.05553.99520.3327L20.19462.82244.43270.1802L30.15091.88686.09510.1963L40.44413.8494.67470.3235L50.33033.02384.67960.3627L60.59151.03534.99920.288L70.53334.97774.79210.4032L80.14391.30874.10950.2954L90.13631.43666.61280.3452SOS1.0898129174.44606232225.08550970.869837Stage 9.
Calculation of Normalized Decision Matrix
The formula for calculating normalized decision matrix is denoted as follows. Table14 depicts the normalized decision matrix table.(6)zij=xij∑xij2.Table 14
Normalized decision matrix.
Exp.MRRTWRRaRecast layer thicknessL10.2126556860.3541289420.2662960690.356725259L20.1864089940.3271129200.2954571950.193212779L30.1445483920.2186779540.4062628090.210475408L40.4254071640.4460946810.3115874640.346860900L50.3163971770.3504549490.3119140690.388891649L60.5666028760.1199900810.3332166880.308797339L70.5108526020.5769097160.3194126440.432316275L80.1378430330.1516768280.2739146220.316731715L90.1305629280.1665002910.4407695860.370127922Stage 10.
Calculation of Weighted Normalized Decision Matrix
Weights are being allocated based the importance of the attributes. All response parameters are considered as beneficial attributes. So, the weights of equal percentage of 0.25 are allotted for all the response parameters [14,15]. The weights of attributes are presented in Table 15. The weighted normalized matrix is shown in Table 16.
We can calculate the weighted normalized matrix using(7)Aij=Wij×Zij.Table 15
Weights of the response parameters.
MRRTWRRaRecast layer thickness0.250.250.250.25Table 16
Weighted normalized matrix.
Exp.MRRTWRRaRecast layer thicknessL10.0531639220.0885322360.0665740170.089181315L20.0466022480.081778230.0738642990.048303195L30.0361370980.0546694880.1015657020.052618852L40.1063517910.111523670.0778968660.086715225L50.0790992940.0876137370.0779785170.097222912L60.1416507190.029997520.0833041720.077199335L70.1277131500.1442274290.0798531610.108079069L80.0344607580.0379192070.0684786550.079182929L90.0326407320.0416250730.1101923960.092531980Stage 11.
Determination of the Ideal (Bi+) and (Bi−)
The ideal best and worst are given in Table17:(1
)Ideal positive solu. =Vj+ (min or max).(2)
Ideal negative solu. = Vj− (min or max).Table 17
Determination of the ideal (Bi+) and ideal (Bi−).
MRRTWRRaRecast layer thicknessBi+0.1416507190.029997520.0665740170.048303195Bi−0.0326407320.1442274290.1101923960.108079069Stage 12.
Calculate the Euclidian distance between ideal (Bi+) and ideal (Bi−) using equations (8) and (9). The calculated Euclidian distance is shown in Table 18. (8)Ki+=∑Bi+−Aij20.5,(9)Ki−=∑Bi−−Ajj20.5.Table 18
Euclidian distance between the ideal (Bi+) and ideal (Bi−).
Exp.Ki+Ki−L10.1136980510.076045013L20.1084831870.094803545L30.1139511870.105750200L40.0974485420.089455414L50.0987699880.080740933L60.0333898930.163120152L70.1303542040.099795962L80.1118464410.117812451L90.1260044670.103773578Stage 13.
Determine the relative performance closeness to the ideal solu. using equation (10).
Table19 shows the relative performance closeness.(10)Pi=Ki−ki++ki−.Table 19
Relative performance closeness to the ideal solu.
Exp.PiL10.400778880L20.466353825L30.481336058L40.478617017L50.449782846L60.830085565L70.433612382L80.512988850L90.451625297Stage 14.
Rank the performance matrix.
0 <Pi < 1, closest to 1 is preferred. Table 20 shows the rank of the performance matrix.
Figure3 shows the graph of Pi versus rank for zinc-coated wire as a tool.Table 20
Rank of the performance matrix.
Exp.PiRankL10.400778889L20.4663538255L30.4813360583L40.4786170174L50.4497828467L60.8300855651L70.4336123828L80.5129888502L90.4516252976Figure 3
Pi versus rank for zinc-coated wire as a tool.


## 3.2.1. TOPSIS Optimization for Brass Wire

Stage 1.
Determination of a Performance Matrix
To calculate the performance matrix, first, we need to calculate the sum of squares forRa, TWR, MRR, and thickness of the recast layer as shown in Table 3. The performance matrix is shown in Table 4.Table 3
Design of experiments using brass wire as a tool.
Exp.Ton (μs)Toff (μs)Ip (A)Wire feed (m/min)Material removal rate (g/min)Ra (μm)Recast layer thickness (mm)Tool wear rate (g/min)L14062800.554 × 10−15.04440.22144.1250L240103902.297 × 10−16.37800.29233.0982L3401441001.575 × 10−15.30100.32772.1325L460631002.307 × 10−15.13500.17423.9872L560104801.758 × 10−15.44370.29823.5280L660142901.060 × 10−14.09370.46692.9682L78064902.666 × 10−16.83720.23375.6324L8801021001.083 × 10−14.63980.50742.5554L980143801.097 × 10−15.22160.52801.9895Table 4
Determination of performance matrix.
Exp.MRRTWRRaRecast layer thicknessL10.05544.1255.04440.2214L20.22973.09826.3780.2923L30.15752.13255.3010.3277L40.23073.98725.1350.1742L50.17583.52805.44370.2982L60.10602.96824.09370.4669L70.26665.63246.83720.2337L80.10832.55544.63980.5074L90.10971.98955.22160.5280SOS0.27084017110.5288927262.52608521.16996348Stage 2.
Calculation of the Normalized Decision Matrix
The formula for calculating normalized decision matrix is denoted in equation (1). Table 5 depicts the normalized decision matrix table.(1)zij=xij∑xij2.Table 5
Normalized decision matrix.
Exp.MRRTWRRaRecast layer thicknessL10.1064518530.392361190.3113316590.204687567L20.4413716720.2946941670.3936391490.270235663L30.3026383910.2028388460.3271685680.302963486L40.4432931860.3792539480.3169233350.16105047L50.337802090.3355758250.3359757660.2756903L60.2036804410.2823288450.2526560960.43165594L70.5122755240.5357418590.421980180.216059098L80.2080999220.243064190.2863604450.469098787L90.2107900410.1892369910.3222681370.488143791Stage 3.
Calculation of Weighted Normalized Decision Matrix
Weights are being allocated based the importance of the attributes. All response parameters are considered as beneficial attributes. Hence, the weights of equal percentage of 0.25 are allotted for all the response parameters. Based on the importance of the attributes, any value can be chosen by the decision maker, but the total weights must be equal to one [14,15]. Weights of attributes are shown in Table 6. The weighted normalized matrix is shown in Table 7.
The weighted normalized matrix can be calculated by using(2)Aij=Wij×Zij.Table 6
Weights of the response parameters.
MRRTWRRaRecast layer thickness0.250.250.250.25Table 7
Weighted normalized matrix.
Exp.MRRTWRRaRecast layer thicknessL10.0266129630.0980902980.0778329150.051171892L20.1103429180.0736735420.0984097870.067558916L30.0756595980.0507097110.0817921420.075740871L40.1108232960.0948134870.0792308340.040262618L50.0844505220.0838939560.0839939410.068922575L60.050920110.0705822110.0631640240.107913985L70.1280688810.1339354650.1054950450.054014775L80.0520249810.0607660480.0715901110.117274697L90.052697510.0473092480.0805670340.122035948Stage 4.
Determination of the Ideal (Bi+) and (Bi−)
The ideal best and worst are given in Table8:(1)
Ideal positive solu. =Vj+ (min or max)(2)
Ideal negative solu. = Vj− (min or max)Table 8
Determination of ideal (Bi+) and ideal (Bi−).
MRRTWRRaRecast layer thicknessBi+0.1280688810.0473092480.0631640240.040262618Bi−0.0266129630.1339354650.1054950450.122035948Stage 5.
Calculate the Euclidian distance between ideal (Bi+) and ideal (Bi-) using equations (3) and (4). The calculated Euclidian distance is shown in Table 9. (3)Ki+=∑Bi+−Aij20.5,(4)Ki−=∑Bi−−Ajj20.5.Table 9
Euclidian distance between ideal (Bi+) and ideal (Bi−).
Exp.Ki+Ki−L10.1149182620.084093899L20.0547415520.116876655L30.0660606490.109713992L40.0530302300.126485555L50.0670543120.095564971L60.1052153560.081214637L70.0973916760.122148202L80.1093874740.158989338L90.1125636970.162477728Stage 6.
Determine the relative performance closeness to the ideal solu. using equation (5).
Table10 shows the relative performance closeness.(5)Pi=Ki−ki++ki−.Table 10
Relative performance closeness.
Exp.PiL10.422556586L20.681027133L30.624174177L40.704592941L50.587660758L60.435630747L70.556382753L80.592410860L90.590739116Stage 7.
Ranking of the Performance Matrix
0 <Pi < 1, closest to 1 is preferred. Table 11 shows the rank of the performance matrix.
Figure2 shows the graph of Pi versus rank for heat-treated brass as tool.Table 11
Ranking of the performance matrix.
Exp.PiRankL10.4225565869L20.6810271332L30.6241741773L40.7045929411L50.5876607584L60.4356307478L70.5563827537L80.5924108605L90.5907391166Figure 2
Pi versus rank for heat-treated brass wire as tool.


## 3.2.2. TOPSIS Optimization for Zinc-Coated Brass Wire

Stage 8.
Determination of Performance Matrix
To calculate the performance matrix, first, we need to calculate the sum of squares forRa, TWR, MRR, and the thickness of the recast layer shown in Table 12. The performance matrix is shown in Table 13.Table 12
Design of experiments using zinc-coated brass wire as a tool.
Exp.Ton (μs)Toff (μs)Ip (A)Wire feed (m/min)Material removal rate (g/min)Ra (μm)Recast layer thickness (mm)Tool wear rate (g/min)L14062802.222 × 10−13.99520.33273.0555L240103901.946 × 10−14.43270.18022.8224L3401441001.509 × 10−16.09510.19631.8868L460631004.441 × 10−14.67470.32353.8490L560104803.303 × 10−14.67960.36273.0238L660142905.915 × 10−14.99920.28801.0353L78064905.333 × 10−14.79210.40324.9777L8801021001.439 × 10−14.10950.29541.3087L980143801.363 × 10−16.61280.34521.4366Table 13
Determination of performance matrix.
Exp.MRRTWRRaRecast layer thicknessL10.2223.05553.99520.3327L20.19462.82244.43270.1802L30.15091.88686.09510.1963L40.44413.8494.67470.3235L50.33033.02384.67960.3627L60.59151.03534.99920.288L70.53334.97774.79210.4032L80.14391.30874.10950.2954L90.13631.43666.61280.3452SOS1.0898129174.44606232225.08550970.869837Stage 9.
Calculation of Normalized Decision Matrix
The formula for calculating normalized decision matrix is denoted as follows. Table14 depicts the normalized decision matrix table.(6)zij=xij∑xij2.Table 14
Normalized decision matrix.
Exp.MRRTWRRaRecast layer thicknessL10.2126556860.3541289420.2662960690.356725259L20.1864089940.3271129200.2954571950.193212779L30.1445483920.2186779540.4062628090.210475408L40.4254071640.4460946810.3115874640.346860900L50.3163971770.3504549490.3119140690.388891649L60.5666028760.1199900810.3332166880.308797339L70.5108526020.5769097160.3194126440.432316275L80.1378430330.1516768280.2739146220.316731715L90.1305629280.1665002910.4407695860.370127922Stage 10.
Calculation of Weighted Normalized Decision Matrix
Weights are being allocated based the importance of the attributes. All response parameters are considered as beneficial attributes. So, the weights of equal percentage of 0.25 are allotted for all the response parameters [14,15]. The weights of attributes are presented in Table 15. The weighted normalized matrix is shown in Table 16.
We can calculate the weighted normalized matrix using(7)Aij=Wij×Zij.Table 15
Weights of the response parameters.
MRRTWRRaRecast layer thickness0.250.250.250.25Table 16
Weighted normalized matrix.
Exp.MRRTWRRaRecast layer thicknessL10.0531639220.0885322360.0665740170.089181315L20.0466022480.081778230.0738642990.048303195L30.0361370980.0546694880.1015657020.052618852L40.1063517910.111523670.0778968660.086715225L50.0790992940.0876137370.0779785170.097222912L60.1416507190.029997520.0833041720.077199335L70.1277131500.1442274290.0798531610.108079069L80.0344607580.0379192070.0684786550.079182929L90.0326407320.0416250730.1101923960.092531980Stage 11.
Determination of the Ideal (Bi+) and (Bi−)
The ideal best and worst are given in Table17:(1
)Ideal positive solu. =Vj+ (min or max).(2)
Ideal negative solu. = Vj− (min or max).Table 17
Determination of the ideal (Bi+) and ideal (Bi−).
MRRTWRRaRecast layer thicknessBi+0.1416507190.029997520.0665740170.048303195Bi−0.0326407320.1442274290.1101923960.108079069Stage 12.
Calculate the Euclidian distance between ideal (Bi+) and ideal (Bi−) using equations (8) and (9). The calculated Euclidian distance is shown in Table 18. (8)Ki+=∑Bi+−Aij20.5,(9)Ki−=∑Bi−−Ajj20.5.Table 18
Euclidian distance between the ideal (Bi+) and ideal (Bi−).
Exp.Ki+Ki−L10.1136980510.076045013L20.1084831870.094803545L30.1139511870.105750200L40.0974485420.089455414L50.0987699880.080740933L60.0333898930.163120152L70.1303542040.099795962L80.1118464410.117812451L90.1260044670.103773578Stage 13.
Determine the relative performance closeness to the ideal solu. using equation (10).
Table19 shows the relative performance closeness.(10)Pi=Ki−ki++ki−.Table 19
Relative performance closeness to the ideal solu.
Exp.PiL10.400778880L20.466353825L30.481336058L40.478617017L50.449782846L60.830085565L70.433612382L80.512988850L90.451625297Stage 14.
Rank the performance matrix.
0 <Pi < 1, closest to 1 is preferred. Table 20 shows the rank of the performance matrix.
Figure3 shows the graph of Pi versus rank for zinc-coated wire as a tool.Table 20
Rank of the performance matrix.
Exp.PiRankL10.400778889L20.4663538255L30.4813360583L40.4786170174L50.4497828467L60.8300855651L70.4336123828L80.5129888502L90.4516252976Figure 3
Pi versus rank for zinc-coated wire as a tool.


## 3.3. Field Emission Scanning Electron Microscopy (FESEM) Analysis

The machined surfaces of Ti-6Al-4V machined using heat-treated brass and zinc-coated brass tool were undergone with SEM analysis. The surface cracks were not noticed on the machined surfaces. The SEM photographs of machined surface using heat-treated brass tool are shown in Figures4–7. The machined surface was with overlapping craters, cavities, pile of debris, and rippled surface. This is due to the applied stress, melting, and solidification of the machined surface. Zinc-coated brass tool machined surface had lesser craters, cavities, and rippled surface. The absence of dimples and river marking on the machined surface confirms the absence of surface cracks. The formation of surface cracks was due to high fluctuating stresses applied during the process of machining. Similar kind of observation was reported on wire EDM machine surfaces [27–31].Figure 4
Machined surface using heat-treated brass tool with 300x magnification.Figure 5
Machined surface using heat-treated brass tool with 600x magnification.Figure 6
Machined surface using zinc-coated tool with 300x magnification.Figure 7
Machined surface using zinc-coated tool with 600x magnification.


## 3.4. Energy Dispersion Spectroscopy (EDS)

Energy Dispersion Spectroscopy is a methodology used to find changes in the material composition of the workpiece. The EDS results were obtained from the same equipment that was used for getting the SEM results. The EDS conducted at the machined edge of the workpiece revealed certain changes in the composition of the material. Carbon and oxygen deposits were found at the edges. Traces of metals like sodium (Na), barium (Ba), and zinc (Zn) were found. Based on these, certain assumptions can be made about these depositions. They are assumed to be deposited on the workpiece from the electrolyte used.The EDS analysis is taken based on TOPSIS optimization of the performance closeness. For brass wire,L4 (Figure 8) is being ranked first based on performance closeness and hence the EDS analysis is conducted on that particular specimen and the same for zinc-coated brass wire, L6 (Figure 9) is being ranked first based on performance closeness and hence EDS analysis is conducted on that particular specimen. The titanium percentage after machining was found to be 55.05% for zinc-coated brass wire whereas it was 51.58% for brass wire, from which it can be inferred that there has been no change in the property when the zinc-coated brass wire was used, whereas the aluminum composition dropped from 5.992% to 3.33% for zinc-coated brass wire. Due to WEDM process, some amount of oxidation has taken place, which results in the presence of oxygen and carbon deposits. The change in composition of the machined surface was due to the melting and solidification of the machined surface. Similar kind of change in composition of the workpiece was observed in previously undergone research work in wire EDM process [29,31].Figure 8
EDS analysis forL4 workpiece using brass wire as a tool.Figure 9
EDS analysis forL6 workpiece using zinc-coated brass wire as a tool.


## 3.5. Response Value Table Using Closeness Value

To narrow down to the best possible combinations of the input parameters, the response table has been formulated using the Minitab 17 software. The process parameters such asTon, Toff, Ip, and Wire-feed are represented as A, B, C, and D, respectively.From Table21 and Figure 10, it is found that the optimized combination of the input parameters is A3B2C2D3. It denotes that the results obtained from the above combination get the best in class output. The output parameters having the following values as Ton = 40 μ sec, Toff = 10 μ sec, Ip = 3 A, and wire feed = 100 m/min deliver the best outcome.Table 21
Response table for brass wire.
LevelTonToffIpWire feed10.57590.56120.48350.533720.5760.62040.65880.557730.57980.55020.58940.6404Rank3223Figure 10
Main effects plot for means for brass wire.Similarly, from Table22 and Figure 11 above, it is found that the optimized combination of the input parameters is A2B3C1D2. It denotes that the results obtained from the above combination get the best in class output. The output parameters having the following values as Ton = 40 μs, Toff = 14 μs, Ip = 2 A, and wire feed = 90 m/min deliver the best outcome. Thus, from the above two response tables and graphs of the main effects plot for means, we can conclude that zinc-coated brass wire had a better response to the closeness value.Table 22
Response table for zinc-coated brass wire.
LevelTonToffIpWire feed10.44950.43770.58130.434120.58620.47640.46550.576730.46610.58770.45490.491Rank2312Figure 11
Main effects plot for means for zinc-coated brass wire.


## 3.6. Confirmation Test

The confirmation has been carried out to check the repeatability of the machine used. The highly ranked specimens have been considered, such asL4 and L6 for brass and zinc-coated brass wire, respectively. The value obtained for specimen L4 for MRR, TWR, Ra, and recast layer thickness were 0.2237, 3.821, 4.9210, and 0.1675, respectively. Similarly, for specimen L6, the values obtained were 0.5723, 0.9891, 4.8442, and 0.2791, respectively. From this, it can be concluded that the values produced by the confirmation test are having an error percentage below 5%, which proves that the repeatability factor of the machine is good.


## 4. Conclusion

Ti-6Al-4V is successfully machined using heat-treated brass and zinc-coated brass tool. The following results were obtained:(i)
Zinc-coated brass tool produced better productivity and surface quality compared to the heat-treated brass tool.(ii)
Zinc-coated brass tool produced lesser recast layer compared to heat-treated brass tool.(iii)
Lower tool wear rate was observed in zinc-coated tool compared to heat-treated brass.(iv)
From TOPSIS optimization,L4 is ranked first based on the performance score for brass wire and L6 is ranked first based on the performance score for zinc-coated brass wire.(v)
The SEM of machined surface showed the presence of overlapping craters, cavities, pile of debris, and rippled surface.(vi)
The 6.30% decrease in titanium content in Ti-6Al-4V was observed in heat-treated brass tool when compared to zinc-coated brass tool.(vii)
From this experimental investigation, there were 54.93% increase in MRR, 2.40% decrease in surface roughness, and 3.44% increase in recast layer and 47.96% decrease in tool wear rate was observed in zinc-coated tool compared to heat-treated brass tool. The zinc coating has improved the productivity, surface quality with minimal change in the composition of Ti-6Al-4V.



---

*Source: 4160276-2022-06-27.xml*