Study on surface integrity of high speed turning of Inconel 718 using Taguchi DoE approach

International Journal of Applied Engineering Research

ISSN 0973-4562 Volume 10, Number 2 (2015) pp. 4191-4200

© Research India Publications

http://www.ripublication.com

Study on Surface Integrity of High Speed Turning of Inconel

718 Using Taguchi DOE Approach

Dr.M.Nataraj1, M.Ramamoorthy

2*, Dr. M.Pradeep Kumar

3

1Associate Professor, Government College of Technology, Coimbatore, India

2Research Scholar, Anna University, Chennai, India

3Associate Professor, Anna University, Chennai, India

[email protected],

[email protected],

[email protected]

Abstract

In High Speed Machining Processes, a major quality related output is surface

roughness of the machined surfaces. Machining of the difficult-to-cut

materials under high speed machining conditions results a drastic decrease in

tool-life. Huge cutting force and higher cut edge temperature leads to poor

surface finish. An attempt has been made in this paper to study the influence

of cutting parameters on surface integrity of Inconel 718 nickel based super

alloy under cryogenic cooling conditions during high speed machining.

Experiments were carried out using Taguchi‟s L18 mixed orthogonal array

based quality design concept to arrive the best optimal parametric combination

by varying the cutting speed, feed, depth of cut, tool nose radius, air / LN2

pressure and the head level of LN2 in the cryocan. Analysis of variance

(ANOVA) was done to find out the significance of the machining parameters

while machining Inconel 718. Confirmation experiments were conducted to

validate the experimental investigation.

Keywords: Cryogenic cooling, cryocan, surface integrity, Inconel 718, DoE,

Orthogonal Array

Introduction Nowadays manufacturing industries are facing challenges in enhancing the surface

integrity along with increased productivity of the machined parts particularly for the

heat resistant super-alloys that are employed in aerospace applications. Machining

super-alloys to the desired configuration continues to be a challenge in the shop-floor.

Researchers have been focusing on such challenges to come up with innovative

solutions [1]. Surface qualities of the machined components are being characterized

by the degree of roughness and metallurgical damage which are together termed as

„surface integrity‟. Surface integrity had significant influence on the surface sensitive

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4192 Dr.M.Nataraj

properties like fatigue, stress corrosion resistance and creep strength that are directly

related to the endurance life of the components [2]. Surface roughness is the most

important factor that affect the quality of a machined product. The poor surface

quality during machining is caused by the high cutting temperature developed during

machining at high cutting velocity and feed rate when the work material is difficult-

to-machine. Such high temperature causes dimensional deviation and premature

failure of cutting tools [3]. Exploring higher cutting speeds relay on the cutting tool

materials to a greater extent. The machining of heat resistant super alloys classified as

difficult-to-machine materials and hence the selection of cooling lubricant during the

machining operations is very important [4].

The surface finish of Inconel / Titanium alloys is very poor during the high speed

machining as the cutting fluids have no influence on cutting temperature and the tool

life. However high pressure soluble oil jet when applied at the chip–tool interface has

reduced the temperature in cutting zone and tool life is improved to some extent [5].

The conventional cutting fluids posed major ecological problem like environmental

pollution due to chemical break-down of the cutting fluid at high cutting temperature,

besides providing some marginal technological benefits. Further biologically

hazardous to the operator due to bacterial growth, requirements of additional system

for pumping, local storage, filtration, recycling, chilling and large space, water

pollution and soil contamination during final disposal were some other critical issues

to be faced [6]. Some works have been reported recently on cryogenic cooling by

liquid nitrogen jet in machining and grinding of various grades of steel for common

use. Cryogenic cooling provided less cutting forces, better surface finish and

improved tool life compared to dry machining [7]. Coated carbide and ceramics tools

were normally used for high speed machining of Inconel 718. But the right selection

of tungsten carbide cobalt alloy for substrate, associated coating materials, coating

procedure and cutting conditions are the main problems for the coated carbide tools

[8]. Many researchers have done investigation on the performance of TiALN coated

carbide as cutting tool for machining of Inconel 718 with LN2 as coolant [9].

Methodology

Workpiece and tool materials

In this research study Inconel 718 Nickel-based superalloy, a high-strength and

thermal-resistant material was chosen as workmaterial as it found applications in

aerospace, petroleum and nuclear energy industries and noted for its outstanding

corrosion resistance [10]. It possesses excellent mechanical properties at low and

intermediate temperatures (−250°C to 700 °C). High powered (6.6 kW) NAGMAT

175 universal lathe was used to machine the workpiece. Figure 1 illustrates the

experimental set up used for this research study.

Study on Surface Integrity of High Speed Turning of Inconel 718 Using et.al. 4193

Figure 1: Experimental setup with cryogenic cooling LN2 setup

A storage tank called cryocan was used to store the liquid nitrogen (LN2), the head

level of the tank is maintained at 0.6 m and 0.9 m during the experiments in order to

use Taguchi‟s mixed orthogonal array based design of experiments. The compressed

air is admitted into the cryocan and constant pressure is maintained using pressure

regulator mounted on the top of the cryocan. The removable insert was used which

encompass four working edges. For machining the work material, cutting tools with

three different tool nose radius were used viz., CNMP120404WM25CT (0.4 mm nose

radius) CNMP120408WM25PT (0.8 mm nose radius) and CNMP120412WM25PT

(1.2 mm nose radius). Tool holder is codified as PCLNR20-20K22 with common

active part tool geometry. Surface roughness (Ra) was measured off-line [11], Ra

value was measured for each of the cutting condition using a Mitutoyo Surftest 201

roughness meter. The length examined was 2.4 mm with a basic span of 3mm. The

values of Ra were measured within the range of 0.05–40 µm. This roughness was

directly measured on the workpiece without dismantling from the lathe in order to

reduce uncertainties due to resumption operations [11,12]. Measurements were made

three times at three reference lines equally positioned at 120o and an average of the

observation was taken into account.

Experimental design

Taguchi methods which combine the experiment design theory and the quality loss

function concept have been applied in process design since it has solved confusing

problems in manufacturing arena [13]. L18 Orthogonal Array (OA) based Design of

Experimental (DoE) approach was chosen for the experimental trail; it has good even

distribution of factorial interactions over the control factors [14]. Among the seven

influential cutting parameters, six parameters namely cut speed (ν), feed rate (f), depth

of cut (ap), tool nose radius (r), LN2 pressure (p), cutting time (t) each one was

4194 Dr.M.Nataraj

assigned with three levels and the remaining LN2 cryocan head level was assigned

with two level as shown in Table 1.

Table 1: Control Factors and its levels

Sl. No. Variables Control

factors

Levels

L 1 L 2 L 3

1 Cut speed (v) m/min A 44 73 102

2 Feed rate (f) mm/rev B 0.05 0.10 0.15

3 Depth of cut (ap) mm C 0.5 1.0 1.5

4 Nose radius (r) mm D 0.4 0.8 1.2

5 LN2 Pressure (p) bar E 0.5 1.0 1.5

6 Cutting time (t) sec F 4 7 10

7 Head of LN2 (h) m G 0.6 0.9 -

The surface roughness was measured as quality characteristics by adjusting the

parametric combinations. Experiments were analyzed using ANOVA to find the

significance of the machining variables.

Data Analysis Taguchi quality concept was used for the experiment plan as it employs a generic

Signal-to-Noise (S/N) ratio to quantify the variation. S/N ratios may be applicable

depending on the type of quality characteristics involved like “Smaller the Better”

(LTB), “Larger the Better” (LTB) and Nominal is the Best (NTP). The S/N ratios

were calculated using the equations [15]:

(1)

(2)

(3)

where η is the characteristic property, is standard deviation, y is observations

and n is the repeated number of the experiment which denotes the observed value in

decibel (dB). Table 2 shows the experimental plan showing the levels of control factor

and the corresponding S/N ratio obtained using Equation (3) for measuring the surface

roughness as response. The mean S/N ratio for each level of the cutting parameters is

summarized and is shown in Table 3. Figure 3 plotted using Table 3 shows the

response graph which indicates how far the changes in the control factor influence the

surface roughness. Further S/N curve enable to decide the optimal level of machining

variables. The optimal level of machining variables for better surface finish (least

roughness) obtained from the experiments is A3B1C3D2E3F1G2.


Table 2: Experimental results for surface roughness and S/N ratio

Sl No A B C D E F G

Surface roughness

(Ra) µm S/N

Ratio I II III

1 1 1 1 1 1 1 1 0.42 0.62 0.49 5.74

2 1 2 2 2 2 2 1 1.62 1.68 1.70 -4.44

3 1 3 3 3 3 3 1 1.90 1.67 1.98 -5.37

4 2 1 1 2 2 3 1 1.87 1.38 1.88 -4.73

5 2 2 2 3 3 1 1 1.89 1.46 1.86 -4.85

6 2 3 3 1 1 2 1 1.09 1.01 1.12 -0.62

7 3 1 2 1 3 2 1 0.98 0.94 0.93 0.45

8 3 2 3 2 1 3 1 0.91 1.09 0.96 0.09

9 3 3 1 3 2 1 1 1.67 1.62 1.76 -4.53

10 1 1 3 3 2 2 2 1.96 1.86 1.50 -5.02

11 1 2 1 1 3 3 2 1.08 1.10 1.02 -0.56

12 1 3 2 2 1 1 2 1.10 1.19 1.09 -1.05

13 2 1 2 3 1 3 2 0.62 0.54 0.74 3.88

14 2 2 3 1 2 1 2 1.80 1.12 1.80 -4.11

15 2 3 1 2 3 2 2 1.92 1.39 1.32 -3.90

16 3 1 3 2 3 1 2 0.39 0.32 0.47 8.00

17 3 2 1 3 1 2 2 0.50 0.55 0.53 5.57

18 3 3 2 1 2 3 2 1.25 1.76 1.25 -3.17

The experimental trail run 3 and 16 in Table 2 gives the minimum and maximum

surface roughness respectively. Table 2 shows the values of the surface roughness Ra

which falls within the range of 0.32 – 1.98 µm.

Table 3: Mean S/N ratio for each level of the cutting parameters

Level A B C D E F G

1 -10.71 8.31 -2.42 -2.28 13.61 -0.80 -18.27

2 -14.34 -8.30 -9.18 -6.03 -26.00 -7.96 -0.36

3 6.42 -18.63 -7.03 -10.31 -6.23 -9.86 -

Sum of SN ratio -18.63 -18.63 -18.63 -18.63 -18.63 -18.63 -18.63

Sum of squares of

SN ratio 361.54 485.07 139.52 147.99 900.09 161.33 333.77

Rank 3 2 7 6 1 5 4

4196 Dr.M.Nataraj

Figure.3 S/N Ratio graph for surface roughness

Results and Discussion

Analysis of variance

Table 4 gives ANOVA information for surface roughness obtained for all seven

control factors with the confidence level of 95%. The variance Fratio is evaluated from

the variance of each control factor (V) and the variance of error factor (e). The

expected value of the sum of square (SSe) and the total square sum (SSt) were used to

find the percent contribution (P %) of various control factors and error factors (Pe).

The contribution of the error factor (Pe) falls below 15% implies that the quality

characteristics of the experiment are under a precise control [15]. In contrast if the

percent contribution of the error factor exceeds 50%, it means that the certain

significant factor is overlooked and the experiments must be reviewed again [16].

Table 4: The ANOVA table for surface roughness

Sl

No

Source of

variation

Sum of

squares DoF

Mean

square Fo

Pure

var

%

Cont.

1 Cutting speed 40.98 2 20.49 2.049 20.98 10.59#

2 Feed rate 61.57 2 30.78 3.078 41.57 20.99#

3 Depth of cut 3.97 2 1.99 0.199

4 Nose radius 5.39 2 2.69 0.269

5 LN2 pressure 130.74 2 65.37 6.537 110.74 55.91#

6 Cutting time 7.61 2 3.80 0.380

7 Head of LN2 17.81 1 17.81 1.781 7.81 3.94

8 Error 40.00 4 10.00

9 e-pooled 16.97 10 1.70 16.97 8.57

TOTAL 308.05 17 198.06 100

# - Significant

Me

an

of

SN

ra

tio

s 21

3

0

-3

321 321

321

3

0

-3

321 321

321

3

0

-3

1-Dry / 2-LN2 Cut Speed Feed

Depth of Cut Nose radius LN2 pressure

Cutting time

Main Effects Plot (data means) for SN ratios

Signal-to-noise: Smaller is better


Statistical analysis

Variance analysis of the surface roughness (Ra) was made with the objective of

analyzing the influence of cutting speed, feed rate, depth of cut, tool nose radius,

Air/LN2 pressure, cutting time and head of LN2 on the results. This analysis was

arrived for a 5% significance level (95% confidence level). The last column of table

shows the factor contribution (%) on the total variation, indicating the degree of

influence. It is obvious from Table 4, the effect of feed rate (f) and Air/LN2 pressure

(p) seems to be the most significant factor allied with surface roughness having

55.91% and 20.99% contribution respectively. It is understood from the experimental

investigation that the increase in Air / LN2 pressure provide more coolant over the

machining zone particularly at the shearing zone leads to chip brittleness and

formation of discontinuous chips with tiny pieces. Similarly increase in feed rate

generates helicoids furrow ensuing helicoids movement tool–workpiece [17, 18]. For

this reason weak feed rate have to be employed with low pressurized Air/LN2 mixture

during turning operation. On other part, the depth of cut (ap), nose radius (r), cutting

time (t) and Head level of LN2 in cryocan do not show significant contribution on the

surface roughness evolution.

Mathematical Modeling of Surface Roughness The mathematical model (Eqn 4) which establish the relationship between the

machining variables and the surface roughness was obtained using Design Expert 9.0

software

Ra = 0.874-9.378x10-3

H -5.843x10-3

ν+4.156f

+0.102ap+0.335r+0.447p+0.018t (4)

In the above equation it is understood that the variables H, ν, f, ap, r, p and t will

not be equal to zero at any time during the cutting operation (H, ν, f, ap, r, p, t ≥ 0).

The predicted values are compared with the corresponding experimental values

depicted in Figure 4. The regression coefficient for the model is 91.89% which

indicates the effectiveness of experimental plan. Using the model, one can ascertain

the Ra value for a specific combination of the machining variable under consideration

for the high speed turning of Inconel 718.

Figure 4: Comparison of experimental and predicted values of surface roughness

0.00

1.00

2.00

3.00

1 3 5 7 9 11 13 15 17

Ra

valu

e

Experimental run orderExperimental Predicted

4198 Dr.M.Nataraj

Confirmation test

The selected optimal level of the design parameters have been validated through

confirmation tests. The estimated S/N ratio of the optimal level of the design

parameters Ypredicted is calculated using Equation (5)

(5)

where Ym is total mean S/N ratio, Yi is mean S/N ratio at the optimal level, and k is

number of main design parameters that affect the quality characteristics [19, 20]. The

predicted SN ratio from the confirmation tests was = 8.7626 dB which

shows that the optimal parametric combination obtained from the experimental study

close to the confirmation test.

Conclusion In this study the machining was done on Inconel 718 using TiALN carbide coated tool

with pressurized Air/LN2 as coolant by applying Taguchi‟s quality design concept

approach. Machining parameters were studied using Taguchi‟s OA based DoE

concept for investigating the influence of cutting variables on the machined surface

roughness. Comparison of experimental and predicted values of the surface roughness

shows a good agreement between them. This study shows that cut speed (ν), feed rate

(f), Air / LN2 pressure (p) have significant influences on the surface roughness while

the depth of cut (ap), tool nose radius (r), cutting time (t) and head level of LN2 (H) in

cryocan have insignificant contribution on the surface roughness evolution. During

the experiment, it is observed that increase in Air/LN2 mixture pressure results more

coolant over the shearing zone which leads to brittleness to the chip and formed

discontinuous chips with tiny pieces resulted in poor surface finish.

Acknowledgements The experimental work was carried in the Department of Mechanical Engineering,

Anna University, Chennai and authors would thank for their help and support during

the execution of this research work.

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Study on surface integrity of high speed turning of Inconel 718 using Taguchi DoE approach

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