product quantity-quality optimization in cutting...

International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS Vol:19 No:06 111

191706-2525-IJMME-IJENS © December 2019 IJENS I J E N S

Abstract— The applications of titanium alloys are widely used

for aerospace industry as this material possess light weight, high

strength, and excellent corrosion resistance. However, this

material has shown poor machinability with traditional cutting

operations. This is one of the reasons why alternatives operation

was investigated. The trading-off between attaining high

productivity and meeting the demanding requirements on high

surface finish are the biggest challenges for wire electrical

discharge machining (WEDM) to become alternative operation for

cutting. Both productivity and surface quality are key

performance evaluation in the WEDM operation, however, they

are always conflicting with each other. Therefore, the present

study highlights the application of a generic algorithm (GA) to

obtain optimal cutting parameter for high productivity and fine

surface finish simultaneously. In this study, the productivity is

identified through the term material removal rate (MRR) and the

quality surface finish is identified by arithmetic average surface

roughness value. The results revealed that parameter voltage has

strong influence on the surface roughness with a contribution

73.06% and table feedrate dominated other parameters with

86.16% contribution to the MRR. The optimized parameters yield

0.957 mm³/min for MRR and 0.167 µm for surface roughness as

the value predicted and recommended by GA.

Index Term— WEDM, Titanium Alloy, Multi-objective

optimization, Genetic Algorithm.

I. INTRODUCTION

Titanium alloys’ applications has been mainly found in

aerospace industries especially for aircraft airframe and engine

parts due to their combination of good mechanical and chemical

properties [1]. One of the common titanium alloys for these

applications is Ti-6Al-4V alloy. This material offers great

combination properties such as outstanding in strength-to-

weight ratio and excellent in opposition to the corrosion and

fatigue [2]. Despite those benefits, the rapid tool wear is among

the drawbacks possessed in term of machining operation with

traditional operations because of low thermal conductivity and

high chemical reactivity of titanium alloy properties that led to

the rise of cutting temperature and stimulate strong adhesion

between cutting tool and the workpiece [3], [4].

Therefore, the advanced machining process is favored over

the traditional operations for cutting of the Ti-6Al-4V alloy.

Wire electrical discharge machining (WEDM) is one of the

spark erosion-based advanced cutting process that utilized the

fundamental of electro-thermal conversion energy to cut the

conductive material by a series of discrete sparks. In WEDM,

there is no physical contact between the tool (electrode wire)

and the workpiece material, hence it can cut any type of

electrically conductive materials regardless of their mechanical

properties especially hardness [5], [6].

Generally, the performance of WEDM operation is evaluated

in terms of the productivity and the surface finish quality [7].

The productivity of the WEDM specifies as economics of the

operation that is usually identified by the term material removal

rate (MRR). The high value of MRR represent excellent

productivity. In term of the surface finish quality, the arithmetic

average surface roughness is usually used to represent the

cutting conditions of the surface. Additionally, low value of

arithmetic average surface roughness indicated excellent

quality of cutting surface. Both of this performance responses

strongly depend on the proper selection of the cutting parameter

[8], [9].

In most cases, the selection of WEDM cutting parameters is

done by machinist, and this method greatly depends on their

own experiences. Due to the variation in skill and experiences,

it is difficult to identify the ideal parameters in order to obtain

high productivity with good surface finish and also maintaining

the same quality for another batch cutting operations [10]. If the

optimum or near optimum parameter selected was not suitable,

the production cycle will become time-consuming and the

production efficiency will be low. In addition, obtaining

optimum parameter for more than one responses characteristics

requires one at a time trials that make numerous number of

experiment [11]. Even the ideal solution for one responses

characteristics could not be extend towards the other response

characteristics for example, in WEDM, as the surface quality

increases, the MRR that represent the productivity tends to

decrease, since the influence of parameters on surface quality

and the MRR possess are conflicting in nature, there is no

unique combination of cutting parameters that offer better

surface finish as well as high MRR simultaneously. Other than

that, identifying the best cutting parameter for both responses is

Product quantity-quality optimization in cutting

operations of aerospace grade titanium alloys by

wire electrical discharge machining M.A.M Zakaria1*, R. Izamshah1,2, M.S. Kasim1,2 and M.S.A Aziz1,2

1Cluster for Advanced Materials and Precision Engineering, Advanced Manufacturing Centre

(AMC), Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal, Melaka,

Malaysia. 2Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100

Durian Tunggal, Melaka, Malaysia.

*Corresponding author: [email protected]



very challenging because there are too many controllable

cutting parameters and several of the machine have

supplementary modes and controllers to regulate the stability of

cutting operations like reverse polarity, arcing controller,

rough-cut mode and finishing mode [12], [13],[14].

Therefore, it is essential to optimize quality and productivity

simultaneously. In this paper, an advanced multi-objective

optimization method which is genetic algorithm (GA) based on

evolutionary computation has been used to obtain optimal

parameters in the cutting of Ti-6Al-4V by WEDM. GA method

is inspired by the process of biological evolution since this

approach is capable to identify the optimal solutions without

assigning relative importance weightage to the response. Thus,

this made it a robust adaptive optimization method that offers

multiple solutions with just a single run without requiring any

gradient data and inherent parallelism in searching the design

space [15], [16].

A series of experiments was performed in this study, and the

effects of the cutting parameters on the MRR and surface

roughness has been identified through analysis of variance

(ANOVA) and regression analysis. Afterwards, the linear

regression equation has been generated to represent as fitness

function to be optimized by GA.

II. METHODOLOGY

The experiments were performed on Mitsubishi RA90

machine as shown in Fig. 1. This type of machine has special

features in which it is capable to control the degree of arc pulses

and short pulses and it is known as stabilizers by machine

manufacturers [6]. In this research study, to evaluate the

performance of each cutting parameters, all 10 controllable

cutting parameters (factors) were investigated. Table 1 and Fig.

2 show the details of the parameters and the descriptions.

Furthermore, the performance measures selected were MRR

and arithmetic average surface roughness (Ra) as target

functions (responses, outputs).

Fig. 1. WEDM experimental setup

Fig. 2. Fundamental of pulse characteristic for Mitsubishi RA90

TABLE I

WEDM RA90 PARAMETERS, LEVEL AND ITS FUNCTIONS

Parameters Level Functions

Open Circuit

Voltage (Vo) 4-16 Notch

Purposely for controlling the gap voltage

level during no-load. High value represents

high voltage.

Intensity of

Pulse (IP) 3-12 Notch

Purposely for controlling the peak current

concentration for flowing in the discharge

gap specific to normal pulse.

Off Time

(OFF)

1-10

Notch

Known as pulse-off time. Controls the time

interval in the middle of end and new of the

applied discharge.

Stabilizer A

(SA) 2-5 Notch

Purposely used to fine tuning the amperage

of current focus on controlling the arc pulse.

Stabilizer B

(SB) 3-15 Notch

Purposely used to fine tuning the pulse off-

time.

Stabilizer E

(SE) 1-5 Notch

Purposely used to controls the short pulse

and machining stability. Using high value

will decelerate the machining, but then the

breakage of wire will be difficult to occur.

Voltage Gap

(VG) 42-70 Volts

Controls the machining voltage at stable

conditions. Used regularly as a target value

during machining with optimum speed.

Electrode Wire

Speed (WS) 12-14 Notch

Controls the speed of wire. High value

represents the rapid wire feeding into

machining zone.

Wire Tension,

WT 11-14 Notch

Regulates the tension of the electrode wire.

High value represents high tension of wire

applied.

Table Feedrate 0.15-0.25

mm/min

Regulates the machine table feedrate. High

value represents faster motion of the

machine table.

An experiment with modified Taguchi L12 orthogonal array

methodology as design scheme was established as indicated in

Table 2, because it is noticeable that the influence of parameters

on the determined target function are nonlinear and

manufacturer’s suggestion parameter are restricted to certain

materials. Moreover, the wire ruptured during machining was

able to be eradicated and provide good combinations among the

parameters in ensuring the successful of cutting process by

using this new design scheme.

The design scheme has been analyzed with Minitab software

for analysis of variance (ANOVA) and the regression equation

has been developed. The Matlab software has been used to

perform the multi-optimization of cutting parameter in the

study.

https://en.wikipedia.org/wiki/Biological_evolution



Table II

Experimental design and results

Run

Open Circuit Voltage (Notch)

Pulse Intensity (Notch)

Off Time

(Notch)

Stabilizer A

(Notch)

Stabilizer B

(Notch)

Stabilizer E

(Notch)

Voltage Gap

(Volts)

Electrode Wire Speed

(Notch)

Wire Tension (Notch)

Table FeedRate (mm/min)

1 16 6 1 3 11 5 47 12 11 0.25

2 16 7 1 5 9 5 52 12 11 0.25

3 16 6 1 4 9 5 42 12 11 0.25

4 12 4 1 2 12 1 44 12 13 0.25

5 9 4 1 2 15 1 57 12 13 0.25

6 9 4 1 2 15 1 57 12 13 0.25

7 10 12 1 2 15 1 55 14 13 0.25

8 8 4 1 2 12 1 48 12 13 0.25

9 5 3 10 2 3 1 70 12 13 0.15

10 6 3 10 2 5 1 70 12 13 0.15

11 8 3 10 2 3 1 50 12 14 0.15

12 4 3 8 2 3 1 70 12 13 0.25

Ti-6Al-4V with a thickness 10 mm has been used in this

study as workpiece material. The chemical compositions of the

workpiece material are; Al. 6.9; V. 4.1; C. 0.10; Fe. 0.30; Si.

0.15; O. 0.20; N. 0.05; H. 0.015. Hardness of the workpiece

material is at 36HRC [17].

The cutting process were performed using commercial non-

submersible type WEDM machine Mitsubishi RA90 and

additionally, brass electrode wire with diameter 0.25 mm has

also been used.

In order to ensure stability of the cutting process, deionized

water with 0.2 MPa pressure jet has been used to flush away the

debris in the cutting zone.

The cutting machined surface has been evaluated according

to arithmetic average surface roughness (Ra) by using portable

surface roughness tester (SJ-301, Mitutoyo) specifically with

regards to ISO 4287:1997. In order to obtain the value of MRR,

theoretical of equation 1 has been used. K was denoted as the

kerf width (mm), h was signified as thickness (mm) of the

workpiece materials and FR was signified as machine table

feedrate (mm/min). This kerf width was examined by using a

stereomicroscope (Meiji Techno EMZ-13TR).

𝑀𝑅𝑅 = 𝐾ℎ𝐹𝑅 (𝑚𝑚3/𝑚𝑖𝑛) (1)

III. RESULTS AND DISCUSSION

A. Effect of process parameters on MRR and Surface Roughness

ANOVA for MRR and surface roughness has been

performed in order to obtain information about the levels of

variability and quantify the influence of parameters to the

quality characteristics. Fig. 3, Table 3 and Table 4 summarized

the influence of parameters and its contribution to both

responses. Table III

ANOVA of MRR factors

Source

Degree

of

freedom

Adjusted

sum of

square

Adjusted

mean

square

F-Value %

Contribution

Vo 1 0.000165 0.000165 277.35 5.54

IP 1 0.000058 0.000058 98.12 1.95

OFF 1 0.000027 0.000027 45.76 0.91

SA 1 0.000022 0.000022 36.45 0.74

SB 1 0.000024 0.000024 39.59 0.81

SE 1 0.000030 0.000030 51.17 1.01

VG 1 0.000011 0.000011 19.34 0.37

WS 1 0.000058 0.000058 97.65 1.95

WT 1 0.000016 0.000016 26.79 0.54

Feedrate 1 0.002565 0.002565 4317.42 86.16

Total 11 0.328058

Table IV

ANOVA of surface roughness factors

Source Degree of freedom

Adjusted sum of square

Adjusted mean square

F-value % Contribution

Vo 1 0.12373 0.123734 68.74 73.06

IP 1 0.00026 0.000258 0.14 0.15

OFF 1 0.01164 0.011641 6.47 6.87

SA 1 0.00001 0.000014 0.01 0.01

SB 1 0.01283 0.012835 7.13 7.58

SE 1 0.00755 0.007553 4.2 4.46

VG 1 0.00579 0.005793 3.22 3.42

WS 1 0.00038 0.000381 0.21 0.22

WT 1 0.00269 0.002688 1.49 1.59

Feedrate 1 0.00266 0.002658 1.48 1.57

Total 11 7.33406



Fig. 3. Percentage contribution of input cutting parameters for MRR and surface roughness

Fundamentally in WEDM, the rapidity of production and the

quality of the cutting surface textures depend on the voltage-

current pulse waveform. The pulse characteristics in WEDM

play important duty in energy conversion to create discharge

channel and simultaneously affect the size of formed crater on

the cutting surface [18]. The higher energy will remove high

volume of material and leave wide and deep crater size while,

low discharge energy will remove small amount of material

volume and leave tiny and shallow crater size.

In this study, all the parameters contributed to the voltage-

current pulse waveform especially the electrical parameters as

indicated in Fig. 2. Among the electrical parameters in this

research study, voltage open dominated other electrical

parameters for the MRR and surface roughness. By referring to

the F-value and percentage contribution, voltage open shows

strong influence with a contribution of 73.06% to the surface

roughness but not to MRR with only 5.54% which is behind the

parameter feedrate. As shown in Fig. 4 and Fig. 6, by increasing

voltage open from 4 to 16 notch, the MRR and surface

roughness increased. When high value of voltage is used, the

large energy is ionized during pre-breakdown which

subsequently eroded high volume of material by producing

wide and deep crater that indirectly deteriorated the surface

quality [19]. This phenomenon happens to both voltage

parameters which are the voltage open and voltage gap.

Another interesting parameter that is noteworthy for

discussion in this research study is table feedrate. By increasing

the feeding speed of workpiece to the machining zone, the high

MRR with low surface roughness can be achieved as indicated

in Fig. 5 and Fig. 7. The plausible explanation for this result is

that when feedrate increases, the time taken for the cutting

process is shorter which directly increased the MRR because to

obtain the MRR requires the machining time as input value that

represent the volume of material removed divided by

machining time [20]. Thus, machining time is shorter and high

MRR can also be achieved [21]. For surface roughness, the

occurrences of normal pulse have been taken over with short

and arc pulses when increasing the feedrate. Therefore, this

condition reduces the occurrence of effective discharge channel

carried by normal pulses [22].

Other than that, the surface finish of the cutting part improves

by applying high frequency off-time. In this research study,

there are two type of the off-time parameter which are the

parameter OFF and stabilizer-B. The only difference of

stabilizer-B from the parameter OFF is, stabilizer-B is used for

fine-tuning the pulse off-time, but both of these parameters

intentionally used for regulating the time interval in the middle

of end and new voltage applied. Moreover, these parameters are

dominated by other parameter on surface roughness responses

with percentage contribution 6.87% for OFF and 7.58% for

stabilizer-B but less influenced to the MRR with percentage

contribution 0.91% for OFF and 0.81% for stabilizer-B as

indicated in Table 4 and Fig. 3. These results are likely to be

related to the numerous numbers of sparks fell off during the

cutting process that causes less amounts of discharge occurring

for particular period resulting in small number of craters and

less damage on the surface [8]. This condition led to the

reduction in MRR and surface roughness value and at the same

time allowing the debris to be flushed away from the machining

zone which subsequently flattened the craters. Basically, the

surface roughness value depends on the size of the crater and

the shallow crater with a larger diameter leads to a better

workpiece surface finish.



Fig. 4. Mean effect plot of MRR for voltage open, pulse intensity, off-time, stabilizer-A and stabilizer-B

Fig. 5. Mean effect plot of MRR for stabilizer-E, voltage gap, wire speed, wire tension and federate

Another important parameter related to the productivity and

quality in WEDM cutting operation is the amperage of current.

Basically, the discharge current has direct effects to the amount

of eroded material. Among all the amperage of current

parameters used in this study, occurrences of short pulses are

believed to have overtaken the normal pulses. In this research

works, the short pulse has been controlled by stabilizer-E and

when the notch value increased from 1 to 5 notch, the MRR and

surface roughness values had dropped due to the large amounts

of short pulse. This is because the short pulses do not have

adequate energy in generating sufficient plasma channel to

erode the materials compared to normal pulses [23]. As can be



seen from the Table 3 and Table 4, stabilizer-E almost

dominated other current parameter in terms of surface

roughness and MRR.

Fig. 6. Mean effect plot of surface roughness for voltage open, pulse intensity, off-time, stabilizer-A and stabilizer-B

Fig. 7. Mean effect plot of surface roughness for stabilizer-E, voltage gap, wire speed, wire tension and federate

In the aspect of electrode wire performance, it can be seen

that MRR is less impacted when wire tension is changed from

low to high as compared to the wire speed. Wire tension possess

0.54% of the percentage contribution which is less than the wire

speed at 1.95% as indicated in Table 3. In term of surface

roughness, wire tension possesses high influence rather than

wire speed with percentage contribution of 1.59% for wire

tension and 0.22% for wire speed.



The increase in wire tension is expected to reduce the wire

vibration and hence the reduction in MRR and surface

roughness [5]. Meanwhile, employing fast wire speed will

restrict the ignition delay time in attaining particular period of

time to sufficiently remove the materials with the formation of

ideal plasma channel. Therefore, the arc pulses will take place

to erode the materials with less volume at the same time

deteriorate the surface finish [24]. Ignition delay time is an

important time in the event of plasma channel before allowing

the electrons and ions to flow through it during the discharge

phase [25]. As recognized, the pulses without ignition delay are

considered as harmful arc that will damage the surface quality

of the cutting parts.

In summary, each parameter contributes to the MRR and

surface quality with different manners and performance in term

of whether it has a strong influence or weak influence on the

respective responses. Fundamentally, the parameters in

achieving high MRR are unable to obtain excellent surface

finish as discussed previously because the performance of both

responses are conflicting in nature. The higher of the volumetric

material removed by extreme discharge energy, the wider and

deeper the crater size that spoiled the surface finish topography.

Therefore, there is a need for a multiple objective optimization

technique in solving this problem.

B. Linear Regression Model

In order to employ the multi-optimization by genetic

algorithm, a regression analysis was performed to establish the

relationship among the independent and dependent variables for

prediction purpose. In this study, a first-order regression model

(equation 2) has been used to form linear regression equation to

develop relationship just for the main effects and it is depending

on the number of independent variables (x), levels and the

experimental trials. On equation 2, β is denoted as regression

coefficients, x is denoted as independent variables, y is denoted

as dependent variable and ε is denoted as random error while,

index k and i are denoted as the total number of variables and a

specific variable between 1 and k respectively.

𝑦 = 𝛽0 + ∑ 𝛽𝑖𝑘𝑖=1 𝑥𝑖 + 𝜀 (2)

Equation 3 and equation 4 represent the first-order regression

equation generated by Minitab software according to the

experimental data. Other than that, Table 5 illustrated the

comparison between prediction and experimental data for both

responses to evaluate the accuracy of the proposed regression

equation. The percentage relative error has been calculated to

identify the prediction accuracy of the regression equation and

it is indicated that average relative error among the trials were

0.036% for MRR and 0.892% for Ra which reflects the

proposed regression mathematical equation in providing

excellent prediction accuracy for both responses in this research

and suitable to be used for multi-optimization by GA.

MRR = 1.62 + 0.00686(Vo) + 0.0288(Ip) - 0.0112(OFF) - 0.00721(SA) - 0.00419(SB) - 0.0165(SE) + 0.000999(VG) -

0.115(WS) - 0.0172(WT) + 2.47(Feedrate) (3)

Ra = 1.00 + 0.188(Vo) - 0.061(Ip) - 0.233(OFF) - 0.0057(SA) -

0.0980(SB) -0.260(SE) + 0.0224(VG) + 0.294(WS) -

0.223(WT) - 2.51(Feedrate) (4)

Table V

Predicted results by regression model and its prediction accuracy for MRR and Ra

Trials

Material Removal Rate

(mm³/min) Error

(%)

Arithmetic Average

Roughness, Ra (µm) Error

(%) Prediction Experiment Prediction Experiment

1 0.8364 0.8366 0.025 2.514 2.520 0.23

2 0.8641 0.8644 0.029 2.750 2.756 0.22

3 0.8326 0.8328 0.028 2.593 2.598 0.21

4 0.7830 0.7832 0.029 2.319 2.324 0.23

5 0.4205 0.4205 0.005 0.674 0.684 1.45

6 0.7628 0.7635 0.090 1.752 1.788 2.02

7 0.7681 0.769 0.120 1.995 2.004 0.44

8 0.7595 0.7597 0.022 1.656 1.662 0.34

9 0.4220 0.4221 0.014 0.682 0.692 1.43

10 0.7628 0.7625 0.041 1.752 1.728 1.38

11 0.4054 0.4055 0.015 0.575 0.584 1.52

12 0.6846 0.6845 0.012 0.709 0.718 1.24

C. Multi-optimization by genetic algorithm

In this study, genetic algorithm (GA) has been used to

perform the multi-optimization with the aid of optimtool using

gamultiobj solver of MATLAB features. The target of the

optimization process is to regulate the optimal cutting

parameters in order to maximize MRR as high as possible and

simultaneously minimize surface roughness value the lowest



possible. The objective functions formulated are given below:

Objective I: Max [MRR]

Objective II: Min [Surface Roughness]

The multi-optimization according to the fitness function is

subjected to the lower and upper boundaries of cutting

parameters. The range values of the cutting parameters are as

allowed by the machine. Therefore, the boundaries of the multi-

optimization solution are given as follows:

{

4 ≤ 𝑉𝑜 ≤ 16 3 ≤ 𝐼𝑝 ≤ 121 ≤ 𝑂𝐹𝐹 ≤ 102 ≤ 𝑆𝐴 ≤ 53 ≤ 𝑆𝐵 ≤ 151 ≤ 𝑆𝐸 ≤ 542 ≤ 𝑉𝐺 ≤ 7012 ≤ 𝑊𝑆 ≤ 1411 ≤ 𝑊𝑇 ≤ 140.15 ≤ 𝐹𝑅 ≤ 0.25

In order to solve the multiple optimization of this study, the

following GA parameters and options have been employed:

Population type: Double vector; Population size: 50; Selection

function: Tournament with size 2; Mutation function:

Constrained dependent; Crossover function: Scattered;

Distance measure function: Crowding; Migration direction:

Forward with 0.2 fraction and 20 interval; Pareto-front

population fraction: 0.1.

Fig. 8. Pareto-optimal set of solutions for MRR and surface roughness

The Pareto-front of optimized objective is shown in Fig. 8.

According to Pareto-front plot, both of the response variables

which are MRR and Ra are located along the y-axis and x-axis

respectively. The individual non-dominated solution points

were represented by the star marks. Fig. 8 shows that MRR

increased gradually from 0.93 mm³/min to 0.96 mm³/min with

less deviation of Ra value. Therefore, the optimum solution can

be found in this region as maximum MRR can be obtained with

minimum value of Ra. After that, the margin amount of MRR

increased equally with the increasing amount of Ra after the

value of 0.5 µm. This circumstance reflects the nature of

WEDM cutting operation in which the high volume of material

removed is caused by the large and deep crater dimension that

subsequently deteriorate the surface texture of the cutting

surfaced.

20 non-dominated solutions are gained at the end of 108

iterations. Several results of the objective function for MRR, Ra

and the decision parameters of these non-dominated solution

sets are tabulated in Table 6. Based on Table 6, it can be

observed that the minimum surface roughness value that can be



achieved is 0.167 µm and the maximum value for MRR that can

be achieved is 0.957 mm³/min. It is also indicated that the

optimal solution is obtained at the 14th iterations from 20th

iteration of the GA algorithm. The confirmation experiment

(Table 7) for optimized parameter recommended by GA found

value of 0.171 µm for the surface roughness and 0.944 mm³/min

for the MRR which reflected the excellent accuracy of the

prediction in multiple responses parameter optimization. The

relative error for both results is found acceptable which is less

than 3%.

Table VI

Several sets of pareto optimal solution point

Solution

Number

Iteration

Ordinal Vo IP OFF SA SB SE VG WS WT

Table

FeedRate

Material

Removal Rate

(mm³/min)

Arithmetic

Average

Roughness, Ra

(µm)

1 16 5 12 4 2 15 1 69 12 11 0.25 0.973 0.524

2 9 7 12 1 2 13 1 69 12 11 0.25 1.034 1.868

3 4 5 12 1 2 15 1 50 12 11 0.25 0.988 0.764

4 14 6 12 5 2 15 1 62 12 11 0.25 0.957 0.167

5 20 5 12 1 2 15 1 50 12 11 0.25 0.986 0.744

6 5 6 12 4 2 15 2 62 12 12 0.25 0.934 0.143

7 12 5 12 1 2 15 1 62 12 11 0.25 0.999 1.029

8 17 7 12 1 2 15 1 62 12 11 0.25 1.017 1.487

9 10 6 12 1 2 3 1 69 12 11 0.25 1.057 2.481

10 9 7 12 1 2 13 1 69 12 11 0.25 1.034 1.868

Table VII

Confirmation of experiment for optimized parameters

Vo IP OFF SA SB SE VG WS WT Table

Feedrate

MRR

(mm³/min) Error

(%)

Ra (µm) Error

(%) Pred. Exp. Pred. Exp.

6 12 5 2 15 1 62 12 11 0.25 0.957 0.944 1.358 0.167 0.171 2.34

IV. CONCLUSION

In this study, an efficient multi-objective optimization

methodology using the combination of two technique which are

linear regression and the genetic algorithm (GA) is introduced.

The benefit is that there is an increase for flexibility in optimal

cutting parameters selection and it simultaneously enhance the

productivity and surface quality. The following conclusions

were drawn from this study:

Feedrate table is the dominant parameter affecting MRR as

its percentage contribution for this response variables

obtained through ANOVA is 86.16%. The high MRR can

be achieved by using fast feeding speed of workpiece in

entering the machining zone.

Voltage open is the dominant parameter affecting surface

roughness as its percentage contribution for this response

variables obtained through ANOVA is 73.06%. By

applying high voltage will produce wide and deep crater

that roughen the surface finish.

The maximum value of MRR up to 0.957 mm³/min with

low surface roughness of 0.167 µm is achieved under

optimum cutting parameters proposed by GA approach and

the confirmation experiment is found to be highly

acceptable with error percentage of less 3%.

It is clear this optimization method has the ability to solve

the trade-off for two responses that are contradictory in

nature resulted from WEDM cutting parameters and also

able to enhance the production efficiency.

ACKNOWLEDGEMENTS

The authors would like to express their gratitude to UTeM

Zamalah Scheme for the financial support. We also want to give

our heartfelt thanks to Fakulti Kejuruteraan Pembuatan (FKP)

and Advanced Manufacturing Centre (AMC) of Universiti

Teknikal Malaysia Melaka (UTeM) for the experimental

facilities. This research is supported by the Ministry of

Education Malaysia, grants scheme no:

PRGS/1/2019/TK03/UTEM/02/1.

REFERENCES [1] T. M. Pollock, “Alloy design for aircraft engines,” Nat. Publ. Gr., vol.

15, no. 8, pp. 809–815, 2016.

[2] S. Baragetti and E. V Arcieri, “Corrosion fatigue behavior of Ti-6Al-

4 V: Chemical and mechanical driving forces,” Int. J. Fatigue, vol. 112,

pp. 301–307, 2018.

[3] S. Koseki, K. Inoue, K. Sekiya, S. Morito, T. Ohba, and H. Usuki,

“Wear mechanisms of PVD-coated cutting tools during continuous

turning of Ti-6Al-4V alloy,” Precis. Eng., vol. 47, pp. 434–444, 2017.

[4] M. S. Asiyah et al., “Performance of CVD coated carbide tool by

optimizing machining parameters during turning titanium alloy TI-



6AL-4V ELI in Flooded Condition,” J. Adv. Manuf. Technol., vol. 12,

no. 2, pp. 401–412, 2017.

[5] F. Nourbakhsh, K. P. Rajurkar, A. P. Malshe, and J. Cao, “Wire electro-

discharge machining of titanium alloy,” Procedia CIRP, vol. 5, no. 0,

pp. 13–18, 2013.

[6] R. Izamshah, M. Akmal, M. S. Kasim, S. A. Sundi, and M. Hadzley,

“A statistical comparison in selection of wire-EDM process parameters

for machining titanium alloy,” J. Adv. Manuf. Technol., vol. 10, no. 2,

pp. 45–55, 2016.

[7] M. Manjaiah, S. Narendranath, and S. Basavarajappa, “A review on

machining of titanium alloys,” Rev. Adv. Mater. Sci., vol. 36, no. 1, pp.

89–111, 2014.

[8] M. Manjaiah, R. F. Laubscher, A. Kumar, and S. Basavarajappa,

“Parametric optimization of MRR and surface roughness in wire electro

discharge machining (WEDM) of D2 steel using Taguchi-based utility

approach,” Int. J. Mech. Mater. Eng., vol. 11, no. 1, 2016.

[9] M. A. M. Ali et al., “Performance of wire-edm parameters on

machining characteristics of titanium alloy (Ti6Al4V) using Taguchi

method,” in Malaysia University Conference Engineering Technology,

2014.

[10] I. Vojtko, V. Simkulet, P. Baron, and I. Orlovský, “Microstructural

Characteristics Investigation of the Chip-Making Process after

Machining,” Appl. Mech. Mater., vol. 616, no. 1, pp. 344–350, 2014.

[11] 11. R. V. Rao and V. D. Kalyankar, “Optimization of modern

machining processes using advanced optimization techniques: A

review,” Int. J. Adv. Manuf. Technol., vol. 73, no. 5–8, pp. 1159–1188,

2014.

[12] Y. Fan, J. Bai, C. Li, and W. Xu, “Research on precision pulse power

technology of WEDM,” Procedia CIRP, vol. 6, no. 1, pp. 267–273,

2013.

[13] F. Han, J. Jiang, and D. Yu, “Influence of machining parameters on

surface roughness in finish cut of WEDM,” Int. J. Adv. Manuf.

Technol., vol. 34, no. 5–6, pp. 538–546, 2007.

[14] Y. Sun, Y. Gong, Y. Liu, Q. Li, and Y. Zhou, “Experimental study on

surface characteristics and improvement of microelectrode machined

by low speed wire electrical discharge turning,” Arch. Civ. Mech. Eng.,

vol. 17, no. 4, pp. 964–977, 2017.

[15] G. H. Gowd, U. Nagaraju, P. Rajesh, and T.V. Vardhan, “Modeling &

optimization of laser beam drilling process using genetic algorithm,”

Int. J. Mech. Mechatronics Eng., vol. 17, no. 3, pp. 39–47, 2017.

[16] B. O. P. Soepangkat, R. Norcahyo, M. K. Effendi, A. Sateria, and B.

Pramujati, “Application of BPNN-GA to optimize drilling process of

stainless steel-GFRP stacks,” Int. J. Mech. Mechatronics Eng., vol. 19,

no. 4, pp. 30-39, 2019.

[17] R. Izamshah, M. Akmal, M. S. Kasim, M. K. Sued, S. A. Sundi, and M.

Amran, “Parametric study on parameter effects in hybrid micro wire

electrical discharge turning,” J. Adv. Manuf. Technol., vol. 12, no. 1,

pp. 1–12, 2018.

[18] Y. S. Liao, J. T. Huang, and Y. H. Chen, “A study to achieve a fine

surface finish in Wire-EDM,” J. Mater. Process. Technol., vol. 149, no.

1, pp. 165–171, 2004.

[19] Y. Gong, Y. Su, J. Cheng, C. Wang, Y. Liu, and Z. Zhu, “Modeling and

experimental study on breakdown voltage (BV) in low speed wire

electrical discharge machining (LS-WEDM) of Ti-6Al-4V,” Int. J. Adv.

Manuf. Technol., vol. 90, no. 5–8, pp. 1–16, 2016.

[20] R. Izamshah, M. Akmal, M. A. Ali, and M. S. Kasim, “Performance

evaluation of rotary mechanism characteristics by response surface

methodology in cylindrical wire electrical discharge turning,” Adv.

Mater. Process. Technol., vol. 4, no. 2, pp. 281–295, 2017.

[21] S. R. N. Aravind, S. Sowmya, and K. P. Yuvaraj, “Optimization of

metal removal rate and surface roughness on wire edm using taguchi

method,” in IEEE-International Conference On Advances In

Engineering, Science And Management, 2012, pp. 155–159.

[22] W. M. Lee and Y. S. Liao, “Adaptive control of the WEDM process

using a self-tuning fuzzy logic algorithm with grey prediction,” Int. J.

Adv. Manuf. Technol., vol. 34, no. 5, pp. 527–537, 2007.

[23] A. Caggiano, R. Perez, T. Segreto, R. Teti, and P. Xirouchakis,

“Advanced sensor signal feature extraction and pattern recognition for

wire EDM process monitoring,” vol. 42, no. Isem Xviii, pp. 34–39,

2016.

[24] K. Ter Pey Tee, R. Hosseinnezhad, M. Brandt, and J. Mo, “Pulse

discrimination for electrical discharge machining with rotating

electrode,” Mach. Sci. Technol., vol. 17, no. 2, pp. 292–311, Apr. 2013.

[25] A. Caggiano, R. Teti, R. Perez, and P. Xirouchakis, “Wire EDM

monitoring for zero-defect manufacturing based on advanced sensor

signal processing,” Procedia CIRP, vol. 33, pp. 315–320, 2015.

product quantity-quality optimization in cutting...

Documents