INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN WIRE ELECTRICAL DISCHARGE MACHINING OF INCONEL...

7/29/2019 INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN WIRE ELECTRICAL DISCHARGE MACHINING OF INCO

1/12

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6340(Print), ISSN 0976 6359(Online) Volume 4, Issue 1, January - February (2013) IAEME

54

INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN

WIRE ELECTRICAL DISCHARGE MACHINING OF INCONEL 625

Rodge M. K1, Sarpate S. S2, Sharma S. B3

1&2Research scholars,

3Professor

Production Engineering Dept., SGGSIE&T, Nanded, India.

([email protected])

ABSTRACT

Continuous research in the field of material science leads to production of very

hard, tough, high temperature and corrosion resistant materials which are difficult-to

machine with conventional methods. Advanced manufacturing processes play an

important role in production of complicated profiles on such difficult-to-machine

components. Inconel 625 is one of the recent materials developed to have high

strength, toughness and corrosion resistant. The high degree of accuracy, fine surfacequality and good productivity made wire electrical discharge machining (WEDM) a

valuable tool in todays manufacturing scenario. The right selection of the machiningconditions is the most important aspect to take into consideration in the processes

related to WEDM. As electrode wire is not reused, its wear is generally ignored.

However, it is interesting to study wire wear as it may have an effect on kerf width

and surface quality of the product. The present study is focused on investigation of the

effect of process parameters on multiple performance measures such as cutting width,electrode wear and hardness during WEDM of Inconel 625. The control factors

considered are: pulse-on time, pulse-off time, upper flush, lower flush, wire feed andwire tension. The relationships between control factors and responses are established

by means of regression analysis. The study demonstrates that, there is a goodagreement between experimental and predicted (theoretical) values of performance

measures.

Keywords: Orthogonal array (OR), Signal-to-noise (S/N) ratio, Taguchis design of

experiment (DOE), Wire Electrical Discharge Machining (WEDM)

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERINGAND TECHNOLOGY (IJMET)

ISSN 0976 6340 (Print)ISSN 0976 6359 (Online)

Volume 4 Issue 1 January- February (2013), pp. 54-65 IAEME: www.iaeme.com/ijmet.aspJournal Impact Factor (2012): 3.8071 (Calculated by GISI)

www.jifactor.com

IJMET I A E M E


2/12



55

1. INTRODUCTIONAs newer and more exotic materials with requirement of complex shapes are

developed, conventional machining operations will continue to reach their limitations. Wireelectrical discharge machining (WEDM) is an extremely potential (thermoelectric) process

having capacity to machine parts made up of conductive materials regardless of their

hardness, toughness and geometry. In WEDM, a series of discrete electrical sparks between

the work and tool electrodes immersed in a liquid dielectric medium melt and vaporize

minute amounts of the work material which is then ejected and flushed away by the dielectric

fluid. Latest WEDMs are assisted by CNC table to produce any complex two and three

dimensional profiles on work. Due to high process capability, this method is widely used in

manufacturing of car wheels, special gears, various press tools, dies and similar complex and

intricate shapes. Hence, the increased use of the WEDM in manufacturing will continue to

grow at an accelerated rate.

Wire electrical discharge machining manufacturers and users emphasize on achievement of

better stability, higher machining productivity along with desired accuracy and surfacequality. However, due to involvement of large number of variables, even a highly skilled

WEDM operator is rarely able to achieve the optimal performance. Proper selection of

process parameters for best process performance is a challenging job. An effective way to

attempt this problem is to establish the relationship between performance measures of the

process and its controllable input parameters. Optimization of process parameters can play a

important role in this regard. In WEDM, the commonly affecting process parameters are

ignition pulse current, time between two pulses, pulse duration, servo voltage, wire speed,

wire tension and dielectric fluid injection pressure. Any slight variation in one of the

parameters can affect the production quality and economics of the process. The parameter

settings given by manufacturers are only applicable for commonly used steel grades and

alloys.

The important performance measures in WEDM are metal removal rate (MRR), cutting width(kerf) and surface quality. In WEDM operations, MRR determines the economics of

machining and rate of production where as kerf denotes degree of precision and dimensional

accuracy. The internal corner radius to be produced is limited by the kerf. The gap between

the electrode wire and work usually ranges from 0.025 to 0.075 mm and it is constantly

maintained by a computer controlled positioning system. In setting the machining parameters,

particularly in rough cutting operation, the goal is twofold: the maximization of MRR and

minimization of kerf.

Konda R. et al. [1999] classified the various potential factors affecting the WEDM

performance measures into five major categories: the different properties of workmaterial,

dielectric fluid, machine characteristics, adjustable machining parameters and component

geometry. They have applied the design of experiments (DOE) technique to study and

optimizethe possible effects of variables and validated the experimentalresults using noise-to-signal (S/N) ratio analysis. Different areas of WEDM research identified by Ho K. H. et al.

[2004] are: such as process optimization, process monitoring and control. The settings for the

various process parameters play a crucial role in producing an optimal machining

performance. The application of adaptive control systems to the WEDM is vital for the

monitoring and control of the process. The authors have investigated the advanced

monitoring and control systems including the fuzzy, the wire breakage and the self-tuning

adaptive control systems used in WEDM process. Cabanes I. et al. [2008] analyzed new


3/12



56

symptoms that allow us to predict wire breakage. Symptoms may be increase in discharge

energy, peak current, increase/decrease in ignition delay time. They have proposed a novel

wire breakage monitoring and diagnostic system with virtual instrumentation system (VIS)

that measures relevant magnitudes and diagnostic system (DS) that detect new quality cuttingregimes and predicts wire breakage. Almost in 80% of total wire breakage cases, the

anticipation time longer than 50 ms has been detected. Efficiency of supervision system has

been quantified to 82%.

Parashar Vishal et al. [2010] analyzed kerf width of wire cut electro discharge machining of

SS304L steel using DOE technique. They have used statistical methods and regression

analysis for finding kerf width. Mixed OR of L32 is used for experimentation. ANOVA is

used to find out the variables affecting kerf width more significantly. Results show that pulse-

on time and dielectric flushing pressure are the most significant factor to the kerf width.

Theoretical and experimental results appeared to be in good agreement. Mohammadreza

Shabgard et al. [2011] used 3D finite element for prediction of the white layer thickness, heat

affected zone (HAZ) and surface roughness (SR) of electro discharge machined AISI H13

tool steel. They carried out experimental investigations to validate the numerical results. Bothnumerical and experimental results show that increasing the pulse-on time leads to a higher

white layer thickness, depth of HAZ and surface roughness and increase in the pulse current

slightly decrease the white layer thickness and depth of HAZ with increase in SR.

Experimental and numerical results are closer to each other. Manoj Malik et al. [2012] have

carried out optimization of process parameters of WEDM using Zinc-coated brass wire for

MRR, electrode wear rate (EWR) and SR. They observed that, for minimum EWR, pulse-on

time and pulse peak current should be high. For EWR, pulse peak current is the most critical

factor and duty cycle time is the least significant parameter.

From the literature it is found that, most of the authors have studied the effect process

parameters on different response variables while WEDM of commonly used materials like,

die steel, EN31, AISI H13 steel, etc. Studies on WEDM of Inconel 625 are scantly available.

Inconel 625 is recently coming up as one of best candidate materials which is extensivelyused in various applications including: marine, aerospace, chemical processing, nuclear

reactors and pollution control equipments. It is used in any environment that requires

resistance to heat and corrosion retaining mechanical properties. It is an alloy having

excellent corrosion resistance in a wide range of corrosive media being especially resistant to

pitting and crevice. It is a favorable choice for sea water applications. Therefore, it is

interesting to study the effect of control parameters on work as well as electrode materials.

The studies about effect of control factors on hardness of work material is important as it is

one of the most critical parameter in relation to wear and tear of components made out of

WEDM.

2. METHODOLOGY

2.1 Taguchi MethodGenerally, the machine tool builder provides information in the form of tables to be

used for setting machining parameters. The selection of process parameter relies heavily on

the experience of the operators. With a view to alleviate this difficulty, a simple but reliable

method based on statistically designed experiments is suggested for investigating the effects

of various process parameters and to get optimal process settings. This new experimental

strategy proposed by Genichi Taguchi is called Taguchi method. It is a powerful


4/12



57

experimental design tool which uses simple, effective and systematic approach for deriving

the optimal levels of machining parameters. This approach efficiently reduces the effect of

the sources of variation. It requires minimum experimental cost due to reduction in number of

experiments required to meet the specific requirements in terms of quality and reliability. Ituses specially constructed tables known as orthogonal arrays (OA). Each row represents a set

of parameters for a particular experiment. This is a best way to study the effect of large

number of variables on desired quality characteristics with small number of experiments.

2.2 Design of Experiment

To evaluate the effects of machining parameters on performance characteristics (kerf,

wire wear and hardness of machined surface) a specially designed experimental procedure is

required. In this study, the Taguchi method, a powerful tool for parameter design of the

performance characteristics is used to determine optimal machining parameters for minimum

of kerf, minimum wire wear and higher hardness of the machined surface. Six control factors

with five levels each and three response variables are used to get qualitative results. The

control factors represent stability in design of manufacturing process whereas the noisefactors denote all factors that cause variation. Table 1 shows six parameters with five levels

each. This may increase number of experiments to be carried out. However, it may help in

getting a good relationship between input and output parameters. Based on Taguchi method

we use L25 orthogonal array (obtained using Minitab 16 software) as shown in Table 2. The

experiments are performed as per orthogonal array which has 25 rows indicating 25 of

experiments. The results are shown in Table 2. The kerf is measured with the help of a

microscope. The loss in weight of electrode wire per meter length is determined by

subtracting final weight from initial weight of the wire. The hardness of machined surface is

measured with the help of hardness tester.

Table 1: Parameters and their levels

Factors level 1 level 2 level 3 level 4 level 5 Unit

Pulse-on time (Ton) 3 4 5 6 7 s

Pulse-off time (Toff) 3 4 5 6 7 s

Wire Feed (WF) 6 7 8 9 10 mm/s

Upper Flush (UF) 6 7 8 9 10 kg/cm2

Lower Flush (LF) 6 7 8 9 10 kg/cm2

Wire Tension (WT) 60 70 800 900 1000 kg

2.3 Experimental set-upThe inputs used in the present study are chosen through review of literature,

experience and some preliminary investigations. Each time an experiment was performed, a

particular set of input parameters was chosen. The work piece is a block of Inconel 625 with

length 100 mm width 30 mm thickness 10 mm. The workpiece material composition is

tested at ICS, Pune. Its percentage composition is: C - 0.035, Mn - 0.130, Si - 0.247, S -

0.010, P - 0.0064, Cr - 22.86, Ni 58.1, Mo - 8.55, W -


5/12



58

depth along the length of the work are taken. The experiments are performed on Maxicute

WEDM (Figure 1). The machine allows operator to choose input parameters according to

geometry and material of electrodes from a manual provided by the WEDM manufacturer.

Figure 1: Maxicut-e WEDM

2.4 Signal-To-Noise RatioIn signal-to-noise (S/N) ratio signal represents the desirable value (mean for output

parameters) and noise represents undesirable value (the square deviation of output

parameters). Thus, it is the ratio of mean to square deviation. It is designated by symbol

with unit of dB. The characteristic for which the lower value represents better performance,

the S/N ratio should be smaller the better (SB) and the characteristic for which the large value

represents better performance, the S/N ratio should be larger the better (LB). In this study the

parameters kerf and wire wear should have lower values and hardness of the machined

surface should have larger values.

The loss function (L) for kerf width, wire wear and hardness is defined as:LSB =

kerf

LSB =

ww

LLB =

1/ hv

where Ykerf, Yww and Yhv are the responses for kerf width, wire wear and hardness

respectively and n denotes the number of experiments. The S/N ratios can be calculated as a

logarithmic transformation of the loss function as shown below.

S/N ratio for kerf width = -10 log10 (LSB) i)

S/N ratio for wire wear = -10 log10 (LSB) ii)

S/N ratio for hardness = -10 log10 (LLB) iii)

The analysis is done using the popular software specifically used for design of experimentapplications known as MINITAB 16. The S/N ratio for kerf width, wire wear and hardness is

computed using Eqs. i), ii) and iii) respectively for each treatment as shown in Table 2. Then,

overall mean for S/N ratios of kerf width, wire wear and hardness are calculated as average of

all treatment responses for each level (Table 3, 4 and 5). The graphical representation of the

effect of the six control factors on kerf width, wire wear and hardness is shown in Figure 2, 3

and 4 respectively.


6/12



59

Table 2: Orthogonal array, experimental results for kerf width (KW), wire wear (WW) and

hardness (HV) of WEDMed surface along with S/N ratios

S.N Ton Tof U L W WT KW S/N WW S/N HV S/N1 3 3 6 6 6 600 0.33 9.6297 0.01 40.000 310 49.827

2 3 4 7 7 7 700 0.32 9.8970 0.01 40.000 312 49.883

3 3 5 8 8 8 800 0.33 9.6297 0.03 30.457 286 49.127

4 3 6 9 9 9 900 0.34 9.3704 0.01 40.000 301 49.571

5 3 7 10 10 10 100 0.35 9.1186 0.03 30.457 331 50.396

6 4 3 7 8 9 100 0 31 10.172 0.01 40.000 325 50.237

7 4 4 8 9 10 600 0.30 10.457 0.01 40.000 325 50.237

8 4 5 9 10 6 700 0.35 9.1186 0.03 30.457 295 49.396

9 4 6 10 6 7 800 0.30 10.457 0.02 33.979 276 48.818

10 4 7 6 7 8 900 0.31 10.172 0.02 33.979 311 49.855

11 5 3 8 10 7 900 0.30 10.457 0.03 30.457 325 50.237

12 5 4 9 6 8 100 0.29 10.752 0.02 33.979 341 50.65513 5 5 10 7 9 600 0.28 11.056 0.03 30.457 296 49.425

14 5 6 6 8 10 700 0.34 9.3704 0.04 27.958 290 49.248

15 5 7 7 9 6 800 0.30 10.457 0.02 33.979 325 50.237

16 6 3 9 7 10 800 0.29 10.752 0.03 30.457 301 49.571

17 6 4 10 8 6 900 0.28 11.056 0.01 40.000 309 49.799

18 6 5 6 9 7 100 0.28 11.056 0.02 33.979 299 49.513

19 6 6 7 10 8 600 0.33 9.6297 0.03 30.457 311 49.855

20 6 7 8 6 9 700 0.33 9.6297 0.02 33.979 309 49.799

21 7 3 10 9 8 700 0.30 10.457 0.05 26.020 297 49.455

22 7 4 6 10 9 800 0.29 10.752 0.01 40.000 310 49.827

23 7 5 7 6 10 900 0.29 10.752 0.01 40.000 298 49.48424 7 6 8 7 6 100 0.32 9.8970 0.01 40.000 295 49.396

25 7 7 9 8 7 600 0.31 10.172 0.02 26.020 290 49.248

Table 3: Response Table forS/N ratios (smaller the better) for kerf width

Level Ton Toff UF LF WF WT

1 9.529 10.294 10.196 10.244 10.032 10.189

2 10.076 10.583 10.182 10.355 10.408 9.6950

3 10.419 10.323 10.014 10.081 10.128 10.410

4 10.425 9.7450 10.033 10.360 10.196 10.362

5 10.406 9.9100 10.429 9.8150 10.090 10.199

Delta 0.8960 0.8380 0.4150 0.5450 0.3760 0.7150

Rank 1 2 5 4 6 3


7/12



60

Table 4: Response Table forS/N ratios (smaller the better) for wire wear


1 36.18 33.39 35.18 36.39 36.89 34.98

2 35.68 38.80 36.89 34.98 34.48 31.68

3 31.37 33.07 34.98 34.48 30.98 33.77

4 33.77 34.48 33.77 34.80 36.89 36.89

5 36.00 33.28 32.18 32.37 33.77 35.68

Delta 4.82 5.73 4.70 4.02 5.91 5.20

Rank 4 2 5 6 1 3

Table 5: Response Table forS/N ratios (larger the better) for hardness


1 49.76 49.67 49.65 49.72 49.73 49.72

2 49.71 50.08 49.94 49.63 49.54 49.563 49.96 49.39 49.76 49.53 49.78 49.52

4 49.71 49.38 49.69 49.80 49.77 49.79

5 49.48 49.91 49.58 49.94 49.79 50.04

Delta 0.48 0.70 0.36 0.41 0.25 0.52

Rank 3 1 5 4 6 2

Figure 2: Graphs for kerf width

Figure 3: Graphs for wire wear


8/12



61

Figure 4: Graphs for hardness of WEDMed surface

Table 6: Experimental and Predicted values of kerf width (KW), wire wear (WW) andhardness (HV)

where E = Experimental, P = Predicted values from regression analysis

S. N. EKW PKW EW PW EH PHV

1 0.33 0.3138 0.01 0.016

310 304.00

2 0.32 0.3166 0.01 0.020

312 308.00

3 0.33 0.3258 0.03 0.024

286 311.10

4 0.34 0.3300 0.01 0.028

301 314.20

5 0.35 0.3378 0.03 0.032

331 317.30

6 0.31 0.3000 0.01 0.017

325 322.24

7 0.30 0.3200 0.01 0.036

325 309.44

8 0.35 0.3200 0.03 0.026

295 306.44

9 0.30 0.3100 0.02 0.027

276 298.7410 0.31 0.3200 0.02 0.020

311 308.00

11 0.30 0.2800 0.03 0.020

325 317.00

12 0.29 0.2900 0.02 0.021

341 309.88

13 0.28 0.3100 0.03 0.040

296 297.00

14 0.34 0.3200 0.04 0.033

290 306.68

15 0.30 0.3200 0.02 0.023

325 303.00

16 0.29 0.2880 0.03 0.034

301 308.00

17 0.28 0.2900 0.01 0.024

309 305.00

18 0.28 0.3000 0.02 0.017

299 314.00

19 0.33 0.3210 0.03 0.036

311 302.00

20 0.33 0.3100 0.02 0.037 309 294.0021 0.30 0.2800 0.05 0.037

297 303.00

22 0.29 0.2900 0.01 0.030

310 313.16

23 0.29 0.2800 0.01 0.031

298 305.00

24 0.32 0.2900 0.01 0.021

295 302.00

25 0.31 0.3100 0.02 0.040

290 289.66


9/12



62

The regression analysis is used for modeling the responses in terms of process variables. The

regression equations are obtained by using Minitab software.

Regression equation for kerf width

KW = 0.318 - 0.00760Ton + 0.00580Toff - 0.00100UF +0 .00320LF + 0.00040WF-0.000024WT

Regression equation for wire wear

WW = - 0.0098 + 0.00200Ton + 0.00140Toff + 0.00220UF + 0.00060LF + 0.00280WF

-0.000030 WT

Regression equation for hardness of machined surface

HV = 286 -2.06Ton - 2.16Toff - 1.30UF + 2.16LF + 1.22WF + 0.0318WT

From these equations, the predicted (theoretical) values of kerf width, wire wear and

machined surface hardness are determined manually for all set of parameters. Table 6 shows

experimental and predicted values for above said process responses.

3. RESULTS AND DISCUSSION

The purpose of the experimentation is to identify the factors which have strong effects

on the machining performance. From mean of S/N ratios (Table 3) for kerf width, it is found

that pulse-on time has highest rank 1. Therefore, it has most significant effect on kerf width.

As pulse-on time increases the kerf width increases significantly. The wire feed has least

effect on kerf width. The order of other influencing parameters for kerf width is: pulse-off

time, wire tension, lower flush and upper flush. Also, from mean of S/N ratios (Table 4) for

wire wear, it is observed that, the wire feed has highest rank 1 and therefore, it affects wire

wear significantly. The wire wear initially decreases significantly with increase in wire feed;

however it increases with further rise in wire feed. This may be due the combined effect of

other factors. The lower flush has least effect on wire wear. The order of other influencing

parameters for wire wear is: pulse-off time, wire tension, pulse-on time and upper flush.

Table 5 shows that, for hardness of the machined surface, the pulse-off time has highest rank1 and hence, it affects hardness of the machined surface most significantly. However, the

effect of increase in pulse-off time on hardness of the machined surface has no fixed nature.

The hardness first increases and then decreases significantly. Again it increases. The wire

feed has least effect on hardness. The order of other influencing parameters of hardness is:

wire tension, pulse-on time, lower flush, upper flush and upper flush.

From Table 3, the optimal combination of process parameters for minimum kerf width is

found to be: A1B4C3D5E1F2. The symbols A, B, C, D, E and F represents process

parameters: Ton, Toff, UF, LF, WF and WT respectively and numbers represents the levels.

This means, to have minimum kerf width, Ton should be set on level 1, Toff on 4, UF on 3,

LF on 5, WF on 1 and WT on 2. Similarly from Table 4, it is observed that, the optimal

combination of process parameters for minimum wire wear is: A3B3C5D5E3F2. This means,

to have minimum wire wear, Ton should be set on level 3, Toff on 3, UF on 5, LF on 5, WFon 3 and WT on 2. It is to be noted that the optimal levels of factors differ widely for both the

objectives (for minimum kerf width and minimum wire wear). From Table 5, the optimal

combination of process parameters for hardness of wire electrical discharge machined surface

is:A3B2C2D5E5F5. The hardness of the machined surface should be more for better working

performance. This means to have high hardness Ton should be set on level 3, Toff on 2, UF

on 2, LF on 5, WF on 5 and WT on 5.From Figure 5, it is observed that, the predicted values for kerf width determined from regressionanalysis are in agreement to experimental values. However, wire wear during the WEDM operations


10/12



63

is sensitive to many operational parameters other than current characteristics of the system such aswire tension, wire feed, flushing conditions which must have attributed toward the non uniformity in

experimental wire wear reading with respect to predicted one (Figure 6). Lower wire wear generally

results in higher kerf width and the present experimental study also depicts the similar results. Figure

7, shows good agreement between experimental and predicted hardness values reasonably. Thehardness of the machined surface is first decreased and then improved when lower flush, wire feedand wire tension is increased. Whereas it first increases and then decreases when pulse-on, pulse-off

and upper flush is increased. Thus, the recommended values are of the combined effect of the processparameters.

Figure 5: Comparison of experimental and predicted values of kerf width

Figure 6: Comparison of Experimental and predicted values of wire wear

Figure 7: Comparison of Experimental and predicted values of hardness

0.25

0.27

0.29

0.31

0.33

0.35

0.37

1 3 5 7 9 11 13 15 17 19 21 23 25

Kerfwidth

EKW

PKW

0

0.01

0.02

0.03

0.04

0.05

0.06

1 3 5 7 9 11 13 15 17 19 21 23 25

Wirewear

EWW

PWW

240

260

280

300

320

340

360

1 3 5 7 9 11 13 15 17 19 21 23 25

HARDNESSHV

EHV

PHV


11/12



64

4. CONCLUSIONS

WEDM process parameters optimization is responsive not only to the process

variables but also the work materials. Therefore, for quality machining performance of amaterial, parameter optimization is essential to result cost effective usages of the material for

the given application. The present investigation revealed that pulse-on ranks high in terms of

machining performance of Inconel 625 and it has a predominant effect on kerf width. During

machining of Inconel, as pulse-on increases, the kerf width increases which resulted

relatively lower wire wear. The wire wear initially decreases significantly with increase in

wire feed; however it increases with further rise in wire feed. This may be due the combined

effect of other factors. With regard to hardness of machined components, pulse-off causes

significant variation. Optimized process parameters could be used as guideline for WEDM of

Inconel 625.

5. REFERENCES

1. Atul Kumar and D. K. Singh, Strategic optimization and investigation effect of process

parameters on performance of Wire Electrical Discharge Machine (WEDM), Int. J. of

Engineering Science and Technology, Vol. 4, No. 6, 2012, 2766-2772..

2. Atul Kumar and D. K. Singh, Performance analysis of Wire Electrical Discharge

Machining (WEDM), Int. J. of Engineering Science and Technology, Vol. 1, No. 4, 2012,

1-9.

3. Cabanes I., Portillo E, Marcos M. and Sanchez J A, On-line prevention of wire breakage

in wire electro-discharge machining, Robotics and Computer-Integrated Manufacturing,

Volume 24, Issue 2, April 2008, Pages 287-298

4. Choudhary Rajesh, H. Kumar and R. K. Garg, Analysis and evaluation of heat affected

zone in electrical discharge machining of EN-31 die steel, Indian Journal of Engineering

and Material Science, Vol. 17, 2010, 91-98.5. Debabrata Mandal, Surjya K. Pal and Partha Saha, Modeling of electrical discharge

machining process using back propagation neural network and multi-objective

optimization using non-dominating sorting genetic algorithm-II, Journal of Materials

processing Technology 186 (2007) 154-162.

6. Hsien-Ching Chen, Jen-Chang Lin, Yung-Kuang Yang, and Chih-Hung Tsai,

Optimization of wire electrical discharge machining for pure tungsten using a neural

network integrated simulated annealing approach, Expert Systems with Applications 37

(2010) 7147-7153.

7. Konda R., Rajurkar K. P., Bishu R. R. , Guha A. and Parson M., Design of experiments

to study and optimize process performance, Int. J. Quality, Reliability and Management,

16 (1) (1999) 5671.

8. Mahapatra S. S. and Amar Patnaik, Parametric optimization of WEDM using Taguchitechnique, Journal of Braz. Soc. of Mech. Sci. and Engg. (2006).

9. Manoj Malik, Rakesh Kumar Yadav, Nitesh Kumar, Deepak Sharma and Manoj,

Optimization of process parameters of wire EDM using Zinc-coated brass wire,

International Journal of Advanced Technology & Engineering Research (IJATER), Vol. 2,

No. 4, 2012, 127-130.


12/12



65

10.Manna A and Bhattacharyya, Study for optimization of CNC-Wire cut EDM parameters

during of Al/Sic-MMC using design of experiment, 20th

AIMTDR, BIT, Ranchi 13-14

December (2002) 294-299.

11.Mohammadreza Shabgard, Samad Nadimi Bavil Oliaei, Mirsadegh Seyedzavvar andAhmad Najadebrahimi, Experimental investigation and 3D finite element prediction of

the white layer thickness, heat affected zone, and surface roughness in EDM process,

Journal of Mechanical Science and Technology 25 (12) (2011) 3173-3183.

12.Parashar Vishal, A. Rehaman, J. L. Bhagoria, Y.M. Puri, Kerf width analysis for wire cut

electro discharge machining of SS304L using design of experiments, Indian Journal of

Science and Technolgy, vol. 3 No. 4, 2010, 369-373.

13.Pujari Srinivasa Rao, Koona Ramji and Beela Satyanarayana, Prediction of material

removal rate for Aluminum BIS-24345 alloy in Wire-Cut EDM, Int. J. of Engineering

Science and Technology, Vol. 2, No. 12, 2010, 7729-7739.

14.Sarkar S., Mitra S. and Bhattacharyya B., Parametric analysis and optimization of wire

electrical discharge machining of -titanium aluminide alloy, Journal of Materials

Processing Technology 159 (2005) 286-294.15.Scott D., Boyina S. and Rajurkar K. P., Analysis and optimization of parameter

combination in wire electrical discharge machining, Int. J. Prod. Res. 29 (11) (1991)

21892207.

16.Shajan Kuriakose and M. S. Shunmugam, Characteristics of wire-electro discharge

machined Ti6A14V surface, Materials Letters, 58 (2004) 2231-2237.

17.Sivakiran S., Bhaskar Reddy and Eswara Reddy, Effect of process parameters on MRR in

wire electrical discharge machining of En31 steel, Int. J. of Engineering Research and

applications (IJERA), Vol. 2, No. 6, 2012, 1221-1226.

18.Tarng Y. S., Ma S. C. and Chung L. K., Determination of optimal cutting parameters in

wire electrical discharge machining, Int. J. Mach. Tools & Manuf. 35 (12) (1995) 1693

1701.

19. Satyanarayana.B, Ranga Janardhana.G, Kalyan.R.R and Hanumantha Rao.D, Predictionof Optimal Cutting Parameters For High Speed Dry Turning of Inconel 718 Using

Gonns, International Journal of Mechanical Engineering & Technology (IJMET),

Volume 3, Issue 3, 2012, pp. 294 - 305, Published by IAEME.

20 U. D. Gulhane, A. B. Dixit, P. V. Bane and G. S. Salvi, Optimization Of Process

Parameters For 316L Stainless Steel Using Taguchi Method And Anova, International

Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp.

67 - 72, Published by IAEME.

21. U. D. Gulhane, S. B. Mishra and P. K. Mishra, Enhancement of Surface Roughness of

316L Stainless Steel and Ti-6al-4v Using Low Plasticity Burnishing: Doe Approach,

International Journal of Mechanical Engineering & Technology (IJMET), Volume 3,

Issue 1, 2012, pp. 150 - 160, Published by IAEME.

INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN WIRE ELECTRICAL DISCHARGE MACHINING OF INCONEL...

Documents

Transcript of INVESTIGATION ON PROCESS RESPONSE AND PARAMETERS IN WIRE ELECTRICAL DISCHARGE MACHINING OF INCONEL...