Experimental Investigation on Surface morphology and Parametric

Optimization of Ti- 6Al- 2Sn- 4Zr- 2Mo alpha-beta alloy through AWJM

A.Perumal1*. A.Azhagurajan2 S. Suresh Kumar3 C.Kailasanathan1.R. PrithiviRajan4 A.John

Rajan5 G.Venkatesan1 P.R.Rajkumar1

1*Department of Mechanical Engineering, Faculty in Sethu Institute of Technology, Kariyapatti,

Tamilnadu-626115 India.

2 Department of Mechanical Engineering, Faculty in Mepco Schlenk Engineering College,

Sivakasi, Tamilnadu- 626005, India.

3 Department of Mechanical Engineering, Faculty in Kalasalingam Academy of Research and

Education, KrishnanKoil, Tamilnadu, India.

4 Department of Mechanical Engineering, Faculty in Madanapalle Institute of Technology and

Science, Madanapalle, Andhra Pradesh, India.

5School of Mechanical Engineering, VIT University, Vellore, Tamil Nadu, India

*Corresponding Author Email: perumalmech08@gmail.com

1*perumalmech08@gmail.com, 2aazhagu@mepcoeng.ac.in, 3sureshme48@gmail.com, 4uthrakailash@yahoo.co.in, 5prithivi72@gmail.com, 6ajohnrajan@gmail.com

7venkatesan.g@sethu.ac.in, 8puttarajkumar@yahoo.co.in

ABSTRACT:

Abrasive Water Jet Machining (AWJM) is an advanced machining procedure used to cut

a variety of materials that are difficult to machine with the economic production rate. This

investigation is an attempt to generate an optimized parametric space by employing the Taguchi

algorithm in AWJM of 5mm thickness Titanium Alloy 6242(Ti- 6Al- 2Sn- 4Zr- 2Mo). The

parametric studies have been carried out and the optimization factors such as water jet pressure

(WJP), traverse speed (TS and standoff distance (SOD) are identified. WJP of 220-260 bars, TS

of 20 to 40 mm/min and SOD of 1 to 3mm ranges are varied. The various output factors similar

to Material Removal Rate (MRR) and Surface Roughness (Ra) are obtained for the identified

input parameters. The best output has been selected for the identified input parameters by

conducting various experiments. A backpropagation training function is done using nine neurons

in the hidden layer and the network coefficient of the determination (R2) is 99.89 %. The

experimental trials have been done by the design of experiment methods using Taguchi,

Backpropagation neural network and Genetic algorithm techniques to expect the MRR and Ra.

Keyword: Titanium, AWJM, Ra, MRR, BPNN, SEM

1. INTRODUCTION

AWJM innovation is one of the quickest developing non-conventional machining forms.

It has been utilized to machine the greater part of the engineering materials, regardless of

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material properties. In contrast with conventional and most non-customary machining

advancements, AWJM shows well execution in the machining of hard to machine materials, for

example, pottery, titanium, glass, marble and rocks. As the material thickness expands rough

water fly machining turns into the favored cutting strategy, particularly where precision must be

kept up (Boyer, 1996). Hascalik et al. (2007) Titanium and its compounds have fantastic

consumption protections, a great solidarity to-weight proportion and great in higher temperature

properties. Among the few alloying kinds of titanium, Ti–6Al–4V is normally utilized in space

and airplane applications, just as superior car and nautical applications that need materials with

high erosion opposition and quality. Ginting and Nouari, 2007 Ti-6Al-2Sn-4Zr-2Mo (Ti-6242) is

one of the fundamental amalgams utilized in gas turbine air motors as a result of its uncommon

wet blanket opposition up to 540 °C and Ti-6242 has great elastic, creep, weariness and

durability properties. Khan and Haque, 2007. The AWJM process is particularly fit for

machining tough materials to a most extraordinary width of 70–75 mm. The perfect water stream

weight and explore the speed of a cutting plane can make better surface completing while at the

same time machining thick specimens. By observing the input parameters, the MRR after the

workpiece can be accomplished in bearing to required size and shape. This machining procedure

has a higher material removal rate by the disintegration of fine-grained grating components,

outstanding the surface at a high speed. Kumaran et al. (2017) completed analyses utilizing

AWJM parameters, in particular WJP, lower TS and SOD and detailed the impact of parameters

on surface finish. ŽarkoĆojbašić et al. (2011) has examined the impact of surface roughness

framed on the machined surface as for three distinctive fluctuating parameters. The uniform

AWJM is normal that the parameters bothered to the surface waviness and roughness. The

principle focus of bubbly stream rate could be a key to water drop disintegration. Hocheng and

Chang, 1994 has done go after the kerf plan on the creative plate by a rough water jet. There is

another arrangement of WJP, abrasive flow rate and TS for all through the censored underneath

which it can't be practiced for an assured width. Agreeable water-driven vitality, well work

abrasives at sensible speed gives the even kerf surface. No impact on an expansion inflow rate

was observed due to the taper ratio. Sreenivasa rao et al.(2014) have considered the impact of

factors, viz WJP, TS, and SOD of AWJM for MS plate on Ra. Additionally, Taguchi's technique,

investigation of fluctuation and S/N Ratios are utilized to advance the considered variables of

AWJM. In Taguchi's plan of investigations, the L9 orthogonal array is planned and it tends to be

presumed that WP and TS are the most critical parameters and SOD is represent an important

factor. Hascalik A et al.(2007) have evaluated the right estimations of as far as possible and it is

a vital phase towards a powerful procedure execution factor that incorporates WP, TS and SOD.

These are the in charge of the yield reactions, for example, MRR, Ra and kerf angle.Vavhal

Prashant, et al.(2018) Have changed the procedure parameters during the test to meet the best

procedure factor. The MRR can be achieved by using the best process parameter combinations of

high WJP, high TS and high abrasive flow rate which are useful for cutting method. The use of

corundum as abrasive material can increase the cutting productivity twice over, matched to

garnet for the materials. Akkurt et al. (2004) Aluminum-6061 aluminum combination has

preferred surface quality over the unadulterated aluminum in AWJM cutting uses. This

development in surface value can be showed by a combination component in Al-6061.

Combination of parameters is a significant factor in AWJM cutting use. Advanced decrease in

the feed rate for a similar width sample of aluminum-related material results in narrow

development in the surface value. Cutting wear mechanism brings about preferred surface value

over the distortion wear mechanism. It is not encouraged to cut the materials that are more

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slender than a partial width as a result of the adverse impact of extraordinary pressure on more

slender material. Uthayakumar et al.(2015) Machining of titanium compound through abrasive

water stream machining has more noticeable depth and more wider kerf width of entry section 80

meshwork of garnet abrasive elements which leads to surface roughnessleast. Natarajan et al.

(2020) Works announced much of the time have escaped the examination, which presentations

process parametric consequences for the Ra and the MRR for AWJM. The relating

proportionality about the stage changes communicated by minuscule pictures isn't enough

communicated. Subsequently, through this exploration, an endeavor has been set aside a few

minutes to talk about and connect the examination extension incited through the literature

reports. Aditya H Pandya and Hitesh R Raiyani, 2018 the estimation of fitting estimations of the

procedure parameters is a fundamental advance toward a successful procedure execution

parameter which incorporates WJP, TP, SOD this is the answerable for the yield reactions, for

example, MRR, Ra, kerf edge. Perumal et al. (2019) the procedure parameters are very affected

by the machining execution and should be considered for ideal outcomes. In the current

investigation, the hard Ti-6242 alloy was machined by EDM method and machining qualities,

for example, MRR, TWR and Ra were investigated measurably to get the ideal execution. The

investigations were completed in view of the Taguchi symmetrical exhibit technique. From the

factual examination of exploration outcomes, the greatest critical factors were distinguished as

pine peak current, pulse on time and voltage. In addition to that SEM investigation was utilized

to portray the machined surface. McReynolds and Tamirisakandala 2011 the administration

conditions that influence the oxidation energy are condition, temperature, stress, and presentation

time. For long-term raised temperature applications, for example, disks, impellers, and blades in

gas turbine air motors, a-case profundity is fundamentally significant notwithstanding creep

opposition and quality. Alpha-beta Ti combination comprehensively used for flight applications

on account of their high express quality, incredible utilization check, and high-temperature

properties. Chen hee park et al. (2010) For instance, Ti6242Si has been used for stream engine

blower hovers considering its prevalent downer obstruction and higher assistance temperature

(∼550 ◦C) contrasted with the more typical Ti–6Al–4V composite.

In the current investigation, experimental research is approved out on (Ti- 6Al- 2Sn- 4Zr-

2Mo) by variable the WJP, TS and SOD sequentially based on the design of investigates in each

trial. Ra, MRR and the essential metallographic investigation are done with the accessible

instrumentation offices. ANOVA method is utilized to estimate the associated parametric

relations and proportions lastly, backpropagation neural network and genetic algorithm

calculations are locked in to upgrade and produce a parametric space. Furthermore, the improved

development factors are used for the research of establishing the ability of machining of titanium

alloy using conventional and unconventional techniques like EDM, ECM. To conquer the

previously mentioned issue, AWJM is utilized for the machining of Ti-6242 alloy which is

principally utilized in biomedical embeds aeronautic trade and vehicle industries. This paper

presents clearly about machining of titanium alloy through AWJM by using the Taguchi method

with the reaction factor.

2. MATERIALS AND EXPERIMENTAL DETAILS

2.1 Work Materials

In the present work, Ti- (6242) alpha-beta alloy of 5mm thickness has been used as the

workpiece material. The photos of the tasters after machining are shown in Figure 4.Titanium

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alloy metal contains a combination of Ti and other chemical composition and it is shown in

Table.1. Grade Ti- (6242) alpha-beta alloy is a near alpha alloy and it has been industrialized to

handle processes of high-temperature uses up to 5380 C. The material has outstanding strength

and corrosion resistance with moderately good weldability and fabricability. It is mostly used in

automotive parts, medical instruments, aerospace components and engine valves. This material

also observes high temperature and possesses good thermal conductivity. The EDAX of the

material shows the presence of various elements in the workpiece and it is shown in Figure.1.

The chemical composition and the mechanical, as well as physical properties of Ti (6242) alloy

are presented in Tables 1 & 2, respectively. This alloy consists of Ti as major element and Al, Sn

and Zr as a major alloying element.

Figure 1. EDX analysis results of Ti- (6242) alpha-beta alloy

The chemical composition and the properties of the Ti alloy are shown in Table 1 and Table 2

respectively.

Table 1.Chemical composition of Ti- (6242) alpha-beta alloy

Ti Al Sn Zr Mo Si C N O H

85.88 6.20 1.95 3.80 2.0 0.08 0.021 0.008 0.01 0.0016

Table 2.Material Properties

Ultimate

tensile

strength

(MPa)

Yield

strength

(MPa)

Elongation

(%)

at break

Hardness

(Rockwell)

Hardness

(Vickers)

Modulus

of

elasticity

(GPa)

Shear

strength

(MPa)

Shear

Modulus

(GPa)

1010 990 3 34 333 120 690 45.5

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2.2 Experimental Setup

Figure 2.The experimental setup in Abrasive water-jet Machining

Machining of Ti alloy has been done by DWJ1313-FB AWJ Cutter, which has been

interfaced using DIPS6-2230 ultra-high pressure pump. AWJ Cutter machining has the

maximum pressure uphold of 400 Mpa. The width of the water jet nozzle slope is 0.76 mm,

traverse speed is 0 - 15 m/min and cutting accuracy is ± 0.1 mm. The device is fully controlled

by alphanumerical coding systems and the size of the abrasive particles is 80 meshes which are

sharp and irregular shapes. The machining cut is maintained for all the experiments and the

AWJM facility used for the experimental work is shown in Figure .2.

2.3 Design of Experiments (Doe) Using Taguchi Method

The design of the experimental method has been framed by using Taguchi 27 orthogonal

arrays and three types of response factors like WJP, TS and SOD have been selected in AWJM

to perform optimization studies. So, the perfect MRR and good Ra are the parameters used in

AWJM. In the current work, WJP, SOD and TS are the parameters measured. AWJM has been

applied to the 5mm thickness plate by using the three selected response-parameters for three

levels of operations as tabulated in Table 3. Based on the three input factors, the output factors

like MRR and Ra are measured. The WJP from 220 to 260 bars, TS from 20 to 40 mm/min and

SOD from 1 to 3 mm is varied to form the orthogonal array experimental layout. The design of

the experimental studies is performed found on the input factors and the effects on the output

factors are analyzed to improve the performance and efficiency of the machine.

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Table 3. Factors and their levels

Factor Unit Factor Level 1

1 2 3

Water jet Pressure Bar A 220 240 260

Traverse speed mm/min B 20 30 40

Standoff distance Mm C 1 2 3

Figure 3. Sample before machining Figure Sample after machining

The machinability of titanium alloy has been prepared and the test has been conducted

and workpiece materials after machining are shown in Figure. 3. The results of the responses

are reported here and they include MRR and Ra. Table 4 shows the responses of the testing sets.

3. RESULTS AND DISCUSSION

This research work has been done using an AWJM. This trial work has been done by

using three level parameters as shown in Table 3. The parameter used in this experiment is made

by L27 orthogonal array method.

The MRR is controlled mainly by WJP, TS and SOD. The observations of Tables 3 indicate that

in WJP, TS, and SOD, MRR gets increased and the Ra value decreases.

The regression equation of the following responses is listed below

MRR = -5.73 + 0.0285 *Pressure + 3.6685* Traverse speed + 4.421 *standoff distance.

Surface roughness (Ra) = 3.015 - 0.00276* Pressure + 0.01491 *Traverse speed + 0.0845*

standoff distance.

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Table 4.Cumulative experimental data and the corresponding S/N ratio for output response

Trial

No

WJP

(bar)

TS

(mm/min)

SOD

(mm)

MRR

(mm3/min)

Ra

(µm)

S/N ratio of

MRR in dB

S/N ratio of

Ra in dB

1 220 20 1 81.3333 3.0006 38.2053 -9.5441

2 220 20 2 84.1666 2.8713 38.5027 -9.1615

3 220 20 3 86.8333 2.6366 38.7737 -8.4208

4 220 30 1 116.5 2.862 41.3265 -9.1333

5 220 30 2 119.25 3.0583 41.529 -9.7096

6 220 30 3 121.25 2.8153 41.6736 -8.9904

7 220 40 1 151.667 2.756 43.6177 -9.4404

8 220 40 2 153.333 3.7006 43.7127 -11.365

9 220 40 3 160.213 3.2143 44.0823 -10.408

10 240 20 1 78.333 2.7311 37.8789 -8.7264

11 240 20 2 82.514 2.7893 38.329 -8.9099

12 240 20 3 85.501 2.8156 38.6393 -10.009

13 240 30 1 116.11 2.803 41.2891 -8.9524

14 240 30 2 121.25 3.0353 41.6736 -9.644

15 240 30 3 125.25 2.954 41.9555 -9.4082

16 240 40 1 150.24 3.0516 43.5218 -9.6905

17 240 40 2 159.012 2.9406 44.0279 -9.3687

18 240 40 3 163.333 3.2403 44.2614 -10.211

19 260 20 1 80.123 2.4 38.0617 -8.9431

20 260 20 2 84.45 2.693 38.4855 -8.6047

21 260 20 3 87.3333 3.067 38.8235 -9.7342

22 260 30 1 115.75 2.4681 41.2704 -7.8469

23 260 30 2 119.5 2.9566 41.5473 -9.4158

24 260 30 3 125.12 3.3196 41.9382 -9.5989

25 260 40 1 150.667 3.0363 43.5603 -9.6468

26 260 40 2 157.204 3.1643 43.9179 -10.006

27 260 40 3 165.333 3.5261 44.3672 -9.6173

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3.1 Influence of AWJM parameters on machining characteristics

a) WP and TS Vs MRR at SOD = 2mm

b) SOD and TS Vs MRR at WP = 220bar

Figure 4.Influence of parameters on MRR

The changes in the AWJM process parameters affecting MRR are shown in Figure. 4 a

& b. Increased pressure increases the material cut from the workpiece. The force energy induced

between the SOD has increased by the pressure and it results in the increase of more MRR. On

the other hand, the increased SOD also increases the pressure energy produced on the workpiece.

At last, the greater amount of material is removed from the workpiece. The increased pumping

pressure of the SOD increases the MRR, as it increases the TS across the pressure. The force

energy induced by the increased TS improves the material removal and it is also shown in

Figure. 4 a & b.

0

20

40

60

80

100

120

140

160

220 240 260

MR

R (

mm

^3

/min

)

Water pressure (bar)

TS = 20 mm/min

TS = 30 mm/min

TS = 40 mm/min

0

20

40

60

80

100

120

140

160

180

1 2 3

MR

R (

mm

^3/m

in)

Standoff distance (mm)

TS = 20 mm/min

TS = 30 mm/min

TS = 40 mm/min

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a) WP and TS Vs SR at SOD = 3mm

b) TS and SOD Vs SR at WP = 260 bar

Figure 5. Influence of factors on SR

Figure. 5 a & b show the influence of parameters on MRR. While the pressure increases,

it increases the force energy and it causes the erosion of material. This results in the increased

Ra. When TS increases, it increases the pressure energy and it results in the poor finish on the

machined part. On the other hand, increased SOD creates a wider space between the nozzle tip

and the material surface. It results in the scattering of a jet which creates waviness on the

machined section and forms poor surface finish.

The MRR is controlled for the most part by pressure, TS and SOD. The perception of

Figure. 6 presents that great MRR can be accomplished by building up the WJP, TS, and SOD

material removal rate gets expanded.

0

0.5

1

1.5

2

2.5

3

3.5

4

220 240 260

SR

(µ

m)

Water pressure (bar)

TS = 20mm/min

TS = 30mm/min

TS = 40mm/min

0

0.5

1

1.5

2

2.5

3

3.5

4

20 30 40

SR

(µ

m)

Traverse speed (mm/min)

SOD = 1mm

SOD = 2mm

SOD = 3mm

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Figure 6. Interaction plot for MRR Vs Process parameter

Ra specifies the state of the machined surface. The observations of Figure. 7 present that

good Ra can be achieved by reducing the TS, SOD with increasing WJP.

Figure 7. Interaction plot for Ra Vs Process parameter

A Taguchi test examination is made utilizing the well-known programming explicitly utilized for

the design of a trial application known as MINITAB 17. It is utilized to consider the impact of

machining measure WJP, TS and SOD of the three input factors and two out factors MRR and

Ra.

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Table 5. ANOVA for MRR

CONTROL

PARAMETER SOS DOF

MEAN

SQUARE F TEST

CONTRIBUTION

%

WJP 6.1 2 3.0 0.83 0.02474

TS 24224.5 2 12112.2 3339.23 98.2543

SOD 351.9 2 175.9 48.50 1.4273

Error (E) 2.5 20 3.6 0.2940

Total 24585 26 100

Table 5, demonstrates the assortment and a commitment rate an incentive for the Material

removal rate, and Traverse speed having more commitment than another procedure parameter.

The primary water jet pressure having in case commitment for material removal rate was

increase.

Table 6. ANOVA for Surface roughness

CONTROL

FACTOR SOS DOF

MEAN

SQUARE F TEST

CONTRIBUTION

%

WJP 0.05479 2 0.02739 0.72 33.7193

TS 0.49396 2 0.24698 6.53 33.5320

SOD 0.16813 2 0.08406 2.22 31.4133

Error (E) 0.75622 20 0.03781 1.3352

Total 1.47310 26 100

Table.6 demonstrates the assortment and commitment rate an incentive for Surface

roughness (Ra), Traverse speed having more commitment than another procedure parameter. The

principle commitment of the WJP having minimal impact to surface harshness is generally

excellent conditions.

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Figure 8. Best function, current best point mesh size plots for material removal rate

The Best function, current best point mesh size plots for MRR are shown in Figure. 8. This is

the maximum material removal rate for this factor.

Figure 9. Best function, current best point mesh size plots for surface roughness

The Best function, current best point mesh size plots for surface roughness are shown in

Figure. 9. Which are the proof of the minimizing the response.

Optimization methodologies

The improvement systems, including neural networks and bidirectional molecule swarm

enhancement, which are important to build up the proposed methodology are quickly presented

in the accompanying areas.

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3.2 ARTIFICIAL NEURAL NETWORK (ANN)

ANN is a demonstrating technique designed to imitate the qualities of natural neurons in

the human cerebrum and sensory system. An Artificial Neural Network makes a model of the

handling components called ''neurons'' and the weighted unidirectional associations among them

and trains this model to relate yield neurons with input neurons. ''Learning'' is typically

accomplished by enabling the system to modify its association loads to relate each given

information point with its pertinent yield. During preparing, the info information is more than

once displayed and the loads balanced until the blunder between the yield of the system and the

real yield from the given information is limited. This capacity to learn is maybe one of the most

important properties of an Artificial Neural Network in light of the fact that it empowers a

prepared system to give high-precision yield to a lot of beforehand inconspicuous info

information.

3.3 BACK-PROPAGATION ERROR METHOD

The Backpropagation algorithm is a standard technique for optimization method and

parametric investigation and it has been universal for AWJM in the current study. MATLAB

software is utilized for executing the BPNN error method. Artificial Neural Network (ANN) as

being an effective method used for non-linear regression and preparation. In the AWJM process,

parameters in WJP, TS, and SOD are integrated for the purpose of an extremely interactive

process to expect, estimate and calculate the MRR and Ra. The multi-layered feed-forward

network, the Back-propagation system built on error-back-propagation measured knowledge set

of rules has recognized the skill of revealing nonlinear interpolation. The Back-propagation

network comprises input and output layers. The BPNN perfect accepted for this study is shown

in Figure. 10.

𝒂𝒋𝒍 = 𝝈[∑ 𝓦𝒋𝒌

𝒊 𝒂𝒌𝒍−𝟏

𝒌 − 𝒃𝒋𝒍] (3)

j = 1,2, n, k = 1, 2, …

Figure 10. BPNN model utilized for parameter investigation and improvement.

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In the first place, the documentation, which notices to weight in the system, has been

perceived. Wjk implies the heaviness of the gathering from the kth neuron in the level to the jth

neuron in the hth level.

(4)

v= 1, 2 … n, m= 1, 2 … n

Wvmrepresents the heaviness of the construction from the mth neuron in the level to the

Vth neuron in the first level. The task includes the greatest amount repetitions and the precision

of the series of exercises with exact termination environments. The factors used for developed

BPNN ideal are detailed in Table 7. In the BPNN technique, by preparation of the model data,

the output correctness is considered. The trials are casually a selection of as 2, 5, 8, 11,

15,17,20,25, and 26 to test the BPNN model.

Table 7.BPNN factors and interlayer specifics

Name Number No. Pressure Traverse speed

Factor 50 1st 8 8

Maximum Factor, Epoch 1000

Number of input factor 3 2nd 8 9

Number of output factor 1

The error fraction has been expected by the BPNN ideal for MRR and Ra [{Trial value –

Expected value}/Trial value × 100] and it is shown in Table 8. It is observed that the BPNN ideal

is used to calculate approximately the AWJM factors, i. e. WJP and TS could be efficiently

implemented with a minor fraction of error difference between the trial and expected outcomes.

It is seen that BPNN ideal is recycled for approximating the AWJM machining geometric

parameters; i.e. MRR and Ra should be successfully applied, with a minor fraction of error

difference between the trial and expected outcomes. From the BPNN error Table 8, the error

value was considered for removal rate and surface roughness as exposed as 3 %, 5% & 9% and

the extreme value error is nearly 20 %. The errors in testing experiments may be attributed to

experimental error. Figure. 11(a)–(d) reveal the top ability worth for each output factor predicted

by using BPNN ideal. It is obvious from the figures that the best examination execution is made

for the current issues, for example AWJM of titanium Ti-(6242) alpha-beta alloy, because of its

high level of repeatability, are used for interfacing with GA for streamlining.

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Table 8.AWJM environments working for development factor

SI.NO WJP TS

EOP BPOP ERROR EOP BPOP ERROR

2 84.16 83.1876 0.979 2.87 2.21 0.6613

5 119.25 116.45 2.8 3.05 2.98 0.0783

8 153.33 147.65 5.683 3.70 3.58 0.1206

11 82.5 77.25 5.25 2.78 2.654 0.1353

15 125.25 118.52 6.73 2.95 2.74 0.214

17 159 156 3 2.94 2.78 0.1606

20 84 81.25 2.75 2.69 2.83 -0.137

25 150.66 142.38 8.2866 3.03 3.12 -0.0837

26 157 163 -6 3.16 2.89 0.2743

3.4 GA METHOD

The arrangement of computer-based programming and methodology is applied for

performing genetic algorithms for technique factor optimization as for idea engaged with

mechanics of common assortment and hereditary qualities (K.Deb.19966 and 2001). The

algorithm utilities act finishes a lot of persons; normally spoke to by a twofold string including

1's and 0's. The program helps the calculation to make the inquiry space vectors, arbitrarily with

each standard granting separate arrangement. The hereditary calculation mirrors all the

imaginable arrangement sets in the pipeline equal, in spite of the fact that the reason

methodology is under usage. This procedure parameter is an equal example and it escapes the

combination of one explicit nearby hazardous point. The other conspicuous element of these

calculations is the wellness estimation of each string where the wellness work need not be

differential and persistent. Here, the underlying populace implies the potential arrangements of

the streamlining issue, and every potential arrangement is called a person. In this examination, an

appropriate arrangement is made by the estimations of the WJP (bar) TS (mm/min) and SOD

(mm) explained as paired numbers. Similarly, it is legitimate to transform them into genuine

numbers when exposed to advancement issues, as the examination sets the machining conditions

with genuine qualities, rather than twofold codes. The created codes and the arrangements

acquired are delineated in the accompanying area.

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Figure 11.The greatest capability value expected by BPNN perfect

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3.4 Tensile strength

Under static load, the tension test was performed to assess the yield strength and ultimate

strength of the welded specimen using electrical discharge machining. The tensile properties

were tested at room temperature in Ti-6Al-2Sn-4Zr-2Mo alloy, and the ductility of the during

the-peened and post-peened samples was measured over a 60 mm variable range. From Figure12

as can be seen, the yield and ultimate tensile stress of the shot predrilled specimen were found to

be higher than that of the predrilled specimen.

Figure 12. tensile stress-Strain curves in in Ti-6Al-2Sn-4Zr-2Mo alloy

3.5 Vickers Hardness test

The Vickers hardness test was conducted to assess the hardness of the machining

specimens by measuring the depth of a leverage ratio's absorption into the specimen under

certain specified experimental conditions. The machining process samples' From Figure.13 as

can be seen, vickers hardness tester by adding 500 g of load with a dwell time of 10 s. The

hardness was measured over the machine tools at an interval of 2 mm. The hardness value in

source material and fracture shows decreased average. Due to the sudden decrease in the cooling

rate, hardness is not gradual in the heat affected region. During shot peening and before the-

peening activities the maximum heat-affected zone hardness was measured as high in electrical

discharge machining relative to that. The grain boundaries were reconstructed due to shot

blasting which improved the hardness value in the post blasting process compared with the pre-

blasting method. In the surface layers micro twins were formed because of tensile. Hardness of

area and reaction area affected by heat is 280–300 HV.

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Figure 13 Vickers Hardness test in Ti-6Al-2Sn-4Zr-2Mo alloy

3.6 Confirmation Test

From the essential impact plots, the most appropriate procedure parameters or securing

the most solid yield reactions can be without trouble recognized. When the highest quality level

parameters are recognized, at that point it is essential to approve the yield reactions through the

confirmation test. For getting the most extreme MRR, the highest point of the line method

parameter is perceived as A3B3C3. For getting insignificant MRR and Ra, the top-quality

framework factor is perceived as A3B3C3. By thinking about this parameter blend, the test is

done and impacts show that full-size charm in MRR, and Ra when rather than anticipated

qualities. The affirmation filter brought about the greatest MRR as 165.333mm 3/min and least

Ra as 2.6801 (µm).

3.7 Machined surface

Figure. 14 shows the SEM pictures of machined workpiece examples with lower

sensitivity and higher Ra, separately. It has been seen that higher Ra has been seen in a higher

grating flow rate. The vitality is halfway by the mass stream pace of the grating molecule. Since

higher grating flow rate has produced higher vitality, it has started progressively material

expulsion from the workpiece examples. At the point when higher flow rate has been utilized

with higher standoff distance, the material has been expelled in a more significant level with

irregular in nature. Thus, it has created a higher Ra. It has been likewise been tentatively seen

that the lay characteristics of the preliminary have been seen as a wrecked example wing in the

right manner made for water jet as saw in Figure. 14

175

200

225

250

275

300

325

350

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9

Vic

ker'

s H

ard

ne

ss (

HV

)

Distance from centre (mm)

Ti- 6Al- 2Sn- 4Zr- 2Mo Alloy

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Figure 14. SEM micrograph of the sample machined at an abrasive water jet machine

4. CONCLUSION

In this investigation paper, ideal parameters, for example, MRR and Ra of the titanium alloy

are considered. The trials have been directed with various procedure parameters for the assurance

of ideal conditions.

The increase WJP, TS and SOD lead to build MRR. The ideal qualities are WJP 260(bar),

TS 40(mm/min), and SOD 3(mm).

At lower TS and SOD with increment in WJP, decline Ra. The ideal qualities are: WJP

260 (bar), TS 20 (mm/min), and SOD 1(mm).

Therefore, AWJM is one of the most exceptional machining procedures and it can create

great surface quality at required measurement with the ostensible metal misfortune. Thus,

it tends to be utilized for machining any delicate materials without harming the surface

quality and surface properties.

Pressure is the best huge factor in MRR during AWJM. In the interim, TS and SOD are

sub noteworthy parameters that additionally impact MRR and Ra.

BPNN method is used for finding the pressure and traverse speed and the error has been

calculated. The BPNN presents the error percentages of 2, 3and 6 respectively suggesting

its high degree of consistency.

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The shot measuring material's tensile strength increased 20 % compared to that of pre-

peened material, and a further 5 % improvement in tensile strength was observed during

the electrical discharge machining process.

Acknowledgments: The authors thankfully accept the support of the Kalasalingam

Academy of Research and Education, Tamilnadu, India for permission to utilize the

services.

Conflict of Interest: The authors declare that they have no conflict of interest.

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