investigation of cutting parameters effect for minimization of sur face roughness in internal...

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 1, pp. 121-127 FEBRUARY 2011 / 121

DOI: 10.1007/s12541-011-0015-x

NOMENCLATURE

Ra,n = Surface roughness with new insert

Ra,u = Surface roughness with slightly used insert

ηn = Signal to Noise ratio of Ra,n

1. Introduction

The demand to manufacture low cost products with better

quality has forced the manufacturing industry to continuously

progress in machining technologies. Surface roughness is a measure

to determine the quality of a product and is one of the desired

quality characteristics in boring for cams and crankshaft holes in

engines blocks. Boring is an internal turning process and differs

from external turning operations in many ways. In external turning,

a tool is normally short and rigidly clamped, whereas in boring

operations a long and slender tool is used. So the mechanism behind

the formation of the surface roughness in boring is very dynamic,

complicated and process dependent. The dynamic nature and

widespread usage of boring operations in general engineering

applications has raised a need for seeking a systematic approach

that can help to set up boring operation in a timely manner and also

to help achieve the desired surface roughness quality with less cost.

Long and slender boring bars statically and dynamically deform

under the cutting forces acting on the rake face of the tool during

boring operations. Due to this deflection, dimensional accuracy and

surface roughness do suffer as depth of cut may vary, making this

process complicated in nature.

Though the surface roughness in machining processes such as

turning,1 drilling,2,3 and milling4 has been studied widely, the boring

process is investigated by few researchers. In boring, some

researchers modeled the mechanics and dynamics of a boring

process for single point boring bar5-8 and multi inserts boring head9

using the computer simulation packages. These models were not

general enough for the general industrial applications. They claimed

that these models could be used in the process planning of boring

operations to predict the surface roughness and dimensional

accuracy within an accuracy of 15%. To understand these models

Investigation of Cutting Parameters Effect for Minimization of Sur face Roughness in Internal Turning

Muhammad Munawar1,#, Joseph Ching-Shihn Chen2 and Nadeem Ahmad Mufti1

1 Department of Industrial and Manufacturing Engineering, University of Engineering and Technology Lahore, 54000, Pakistan2 Department of Industrial and Manufacturing Engineering and Technology, Bradley University, Peoria, IL 61625, USA

# Corresponding Author / E-mail: [email protected], TEL: +92-300-9569106, FAX: +92-42-99250202

KEYWORDS: Minimization, Rake angle, Surface roughness, Taguchi

Minimizing the surface roughness is one of the primary objectives in most of the machining operations in general and in

internal turning in particular. Poor control on the cutting parameters due to long boring bar generates non conforming

parts which results in increase in cost and loss of productivity due to rework or scrap. In this study, the Taguchi method is

used to minimize the surface roughness by investigating the rake angle effect on surface roughness in boring performed on a

CNC lathe. The control parameters included besides tool rake angle were insert nose radius, cutting speed, depth of cut, and

feedrate. Slight tool wear was included as a noise factor. Based on Taguchi Orthogonal Array L18, a series of experiments

were designed and performed on AISI 1018 steel. Analysis of variance, ANOVA, was employed to identify the significant

factors affecting the surface roughness and S/N ratio was used to find the optimal cutting combination of the parameters. It

was concluded that tool with a high positive rake angle and smaller insert nose radius produced lower surface roughness

value in an internal turning operation. It was also concluded that high feedrate and low cutting speed has produced the

lowest surface roughness.

Manuscript received: July 14, 2010 / Accepted: November 4, 2010

© KSPE and Springer 2011

122 / FEBRUARY 2011 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 1

requires considerable knowledge and experience to utilize this

approach, which is generally not a case for the people in

manufacturing industry. Since the boring bar is long and flexible, it

becomes very important to select the cutting parameters carefully.

Unlike the case of turning and milling operations, for example,

large depth of cuts in boring may create stability problem which can

cause chattering. So to select the cutting parameters properly,

analytical models for predicting the stability limits of boring

processes has been constructed by researchers.10,11 They showed

that the insert nose radius effect on the stability limit was critical.

So by using inserts with smaller insert nose radius could increase

the stability limit in boring operations by avoiding the chattering.

These mathematical models again are only to predict the stability

limit to avoid the chatter problems in boring operation.

A few researchers have also investigated the dynamics of a

boring process experimentally. Lazoglu I. et al.12 have shortened the

cycle time and improved the part quality for multi inserts boring

head by controlling the cutting forces for engine cylinders. Multi

inserts boring heads are dedicated tools and are not suitable for

general boring diameters applications. Mustafa et al.13 investigated

the rake angle effect on surface roughness in external turning

operation. They changed the tool’s rake angle by swing mechanism

of the tooling system. Beauchamp Y. et al.14 investigated

experimentally the cutting parameters effect on surface roughness

with a single point boring tool using full factorial design of

experiment. The control factors used were cutting speed, depth of

cut, feedrate, and insert nose radius. Small nose radius, low cutting

speed, and large depth of cut were the optimized control parameters.

Moreover, a large number of experiments were performed to draw

this conclusion which definitely would have increased the cost and

time. The effect of cutting fluid is investigated in boring for cutting

of AISI 1030 low carbon steel.15 It was found that use of cutting

fluid has not been useful for the improvement of surface roughness

in boring operations except chip color changing. So having shown

an insignificant effect on surface roughness, the impact of cutting

fluid was not considered in this study.

Following the review above, this study included an alternative

approach based on the Taguchi method16,17 to determine the rake

angle and cutting parameters effect for minimization of surface

roughness in boring operations for a long and slender boring bar. To

select the levels of rake angle with boring bar is not as simple as in

external turning operation.13 The boring bar is normally long, round,

and fixed so could not be swung easily and accurately as shown in

Fig. 1. In this study, inserts with only two levels of rake angle were

selected. The two levels of rake angle were selected to have two

levels of effective rake angle, one positive and one negative, as

explained in Para 3.2. Further studies can be carried out by

increasing insert nose radius and rake angle levels. Rest of all

cutting parameters, used in this study, were with three levels each.

2. Procedure and purpose of research

The Taguchi design, developed by Dr. Genichi Taguchi, is

widely used by researchers17-19 for process analysis and

optimization.

The beauty of Taguchi design is that multiple factors can be

considered at once. Moreover, it seeks nominal design points that

are intensive to variations in production and user environments to

improve the yield in manufacturing and reliability in the

performance of a product.20 Therefore, it would not only include the

controlled factors but also the noise factors. So slightly used inserts

were used as the noise factor in the Taguchi design to simulate the

impacts that a slightly used tool’s wear has on the surface

roughness.16 Each first cut on the new workpiece was carried with

new insert and corresponding surface roughness was recorded. The

same insert used for first cut was employed for making the cut on

new workpices and this surface roughness was declared as

machined with slightly used insert.

Although it is similar to the design of experiments (DOE), the

Taguchi design uses a special orthogonal array to design the

experiment. By doing so it reduces the experimental time and cost

making it even more effective than the fractional design. Taguchi

proposed that design stages of any product or process must consist

of the three stages: system design, parameter design, and tolerance

design. Of the three design stages, the second stage-the parameter

design-is the most important stage.21 In this stage parameters

affecting quality characteristics in the manufacturing process are

identified. Then the major goal of this stage is to identify the level

of the parameters or factors that provide the optimal quality

characteristics for that process or product.

The following steps have been carried out for applying the

Taguchi parameter design stage to the current study:

• Selecting the factors with their levels that are affecting

quality characteristics.

• Selecting the proper orthogonal array according to

controllable factors and their levels.

• Carrying the experimental runs as given by the orthogonal

array (OA), without and with noise factor.

• Analyzing the data collected for determining the optimal

levels of the controllable factors.

• Conducting the confirmation run to verify the predicted

surface roughness with experimental results.

Fig. 1 Boring bar clamped in the turret

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 1 FEBRUARY 2011 / 123

The questions that this study will address include the following:

• To find the rake angle effect on surface roughness in boring

operation with a single edge cutting tool using Taguchi

method.

• If so, then to find the optimal levels of others controllable

factors taken in this study.

• To find the effect of noise factor on the surface roughness.

3. Experimental design

3.1 Orthogonal array and controllable factors

For conducting the tests, the orthogonal array L18 (2^2x3^6) has

been selected. Orthogonal array L18 (2^2x3^6) has eight columns

(i.e., factors or parameters) and 18 rows (i.e., runs) as shown in

Table 1. Of these factors, the first two are with two levels each and

rest six factors with three levels each as shown in Table 1. The

column 1 of Table 1 was assigned to insert nose radius, column 2 to

effective rake angle, column 3 to cutting speed, column 4 to

feedrate, and column 5 to depth of cut. Columns 6 to 8 of the

orthogonal array, L18, were left empty. Orthogonality is not lost by

letting the three columns of this orthogonal array remain empty. The

dependent or response variables used in this study is surface

roughness.

Table 2 shows the selected factors with their levels as discussed

above with their applicable codes and values for using in this

Taguchi parameter design study.

3.2 Experimental setup and procedure

The second step after selecting the proper orthogonal array is to

run the experiment. The following hardware as listed below was

included:

• CNC YAM turning lathe with a maximum power of 10 KW.

• Cutting tools/ inserts:

a. Screw on standard boring bar NK7 A10-SVQBL2

(Kennametal). The length of the bar was set

approximately equal to 92 mm so that L/D ratio was 5.8

and was greater than 4.14

b. PVD, TiAlN coated carbide throw away inserts having

grade KC5010 (Kennametal) as written below:

- VBGT 11 03 02 LF and VBGT 11 03 04 LF

- VBGT 11 03 02 HP and VBGT 11 03 04 HP

Where, LF, light finishing, and HP, high positive, indicate the

chip control or cutting edge conditions of the inserts.

• Low carbon steel pipe with ID = 49 mm and OD = 60 mm,

length of each sample = 50 mm

• Portable surface roughness Tester: Mitutoyo Surftest SJ-301

• Microsoft Excel and Design expert software packages for

charting data and statistical analysis

The two levels of the rake angles were achieved in the

following way. The screw on standard boring bar used has a ‘-6’

degree rake angle and the inserts used with the LF chip breaker

geometry have a 5 degree rake angle. So the first level of rake angle

was achieved by seating this insert on the boring bar. It was called

effective rake angle having a value of -1 degree as shown in Table 2.

Similarly for second level of rake angle, the inserts with HP chip

breaker geometry have a 15 degree rake angle. So the effective rake

angle for this insert was 9 degrees. The chipbreaker geometry of LF

and HP inserts is shown in Fig. 2. Moreover, all inserts with LF and

HP chipbreakers having the same clearance angle of 5 degree.

A rough cut at an inner diameter of 50 mm on each workpiece

sample was taken in order to ensure the same diameter before

carrying the experimentation and also to remove the run out error.

The working range of cutting parameters was selected according to

Table 1 The basic Taguchi L18(2^2x3^6) orthogonal array

Run Control factors and levels

A B C D E F G H

1 1 1 1 1 1 1 1 1

2 1 1 2 2 3 2 2 2

3 1 1 3 3 2 3 3 3

4 1 2 1 2 2 2 3 1

5 1 2 2 3 1 2 1 3

6 1 2 3 1 3 3 2 1

7 1 2 1 3 3 3 1 2

8 1 2 2 1 2 1 3 2

9 1 2 3 2 1 1 2 3

10 2 1 1 3 2 1 2 2

11 2 1 2 1 1 2 3 3

12 2 1 3 2 3 3 1 1

13 2 2 1 1 3 3 3 2

14 2 2 2 2 2 3 2 3

15 2 2 3 3 1 1 3 1

16 2 2 1 2 1 1 1 3

17 2 2 2 3 3 2 2 1

18 2 2 3 1 2 2 1 2

Table 2 Factors, units, codes, and level used for the orthogonal

array L18(2^2x3^6)

Factors Units Code Level 1 Level 2 Level 3

Insert nose radius, r mm A 0.2 0.4 -

Effective rake angle, γnet ° B -1 9 -

Cutting speed, v m/min C 175 200 225

Feedrate, f mm/rev D 0.05 0.075 0.10

Radial depth of cut, doc mm E 0.20 0.40 0.60

Noise Factor

Slightly used tool X - - -

Fig. 2 Chip breaker geometry for HP and LF inserts


the cutting tool catalogue supplier specification for low carbon steel.

A total of eighteen experiments were carried out by changing

control factors as shown in Table 3. After cutting 20 mm length on

each sample for randomly selected runs, surface roughness was

measured with a Mitutoyo Surftest-301 profilometer. The stylus of

the profilometer was allowed to move back and forth with a cutoff

length of 0.8 mm over an evaluation sampling length of 4 mm

according to JIS-1994 standard. The profilometer accuracy was

periodically verified during the surface roughness measurement

process. In order to keep all condition constant, each experiment

was performed with a new tool. For noise factor, the same slightly

used insert was used on the new workpiece. A full factorial design

with the same number of factors and levels would require two

hundred and sixteen workpieces (2 levels of r × 2 levels of γnet × 3

levels of v × 3 levels of f × 3 levels of d × 2 levels of replicates).

This study therefore cuts the number of workpieces and

experimental runs into 1/6th.

4. Results and discussion

Statistical treatment of the data has been made into three phases.

Following sub-sections describe ANOVA, S/N ratio analysis, and

optimization applied to the experimental results. In the 1st phase

analysis of variance (ANOVA) has been carried out for knowing the

significant factors. In the 2nd phase, S/N ratio analysis was carried

out for knowing the optimal levels of the controlling variables. In

the 3rd and final phase, based on the results of the ANOVA and S/N

ratio analyses, optimal settings of the parameters for minimization

of Ra were obtained and verified through a confirmation test.

Table 3 Experiments results for Surface roughness

4.1 ANOVA for average surface roughness, Ra and t-test

The response values of Ra range from 2.418 µm to 12.013 µm

as shown in Table 4 and provide the ratio of maximum to minimum

that is equal to 4.968. Table 4 presents the ANOVA detail for the

average surface roughness. From this table it is clearly observable

that the effect of factors insert nose radius (A), effective rake angle

(B), cutting speed (C), and feedrate (D) are found significant. Depth

of cut (E) had shown insignificant effect on the Ra. It was also

revealed from Table 4 that the insert nose radius and cutting speed

variables were strongly significant. The effective rake angle and

feedrate variables were found moderately significant on the surface

roughness. The average surface roughness produced with an insert

having positive rake angle is lower in value than with an insert

having negative rake angle. Therefore, it was concluded that tool

with positive effective rake angle has significant influence on

producing minimum surface roughness. Or an insert with a high

positive rake angle produces lower value of surface roughness. The

reason for having lower value of Ra with a insert having high

positive rake angle can be explained due to easy flow of chips. An

easy flow of chips results in less cutting forces and thereby less

vibration. Also a mathematical model is not developed for the

current study as two out of four parameters selected are with two

levels each, so showing a linear behavior.

From a practical point of view, the results obtained in table 4

can be interpreted in the following way. When a long boring bar

with length to diameter ratio greater than four is used, the surface

roughness is always good, ranging from 2-3 µm if the cutting speed

is low, insert nose radius used is small and having high positive

rake angle. The feedrate should also be from medium to high. As

the depth of cut has insignificant effect on surface roughness so it

can run at either level but better if used with medium to high range

values.

The effect of the noise “slightly used tool” can be determined

using both informal and formal statistical means. It can be seen in

Table 3 that the mean ‘µn ‘for the new tool condition tended to be

slightly lower than the mean ‘µu’ for the slightly used tool. So the t-

test, Table 5, resulted in a confidence interval of differences

between the mean for new and slightly used tools that includes zero,

Inner Control Factors Array Outer Array

# r γnet v f doc Ra,n Ra, u ηn

µm µm dB

1 0.2 -1 175 0.050 0.2 12.01 13.10 -21.593

2 0.2 -1 200 0.075 0.6 5.85 5.05 -15.349

3 0.2 -1 225 0.100 0.4 8.95 9.30 -19.034

4 0.2 9 175 0.075 0.4 2.87 3.11 -9.158

5 0.2 9 200 0.100 0.2 3.13 3.45 -9.911

6 0.2 9 225 0.050 0.6 11.65 13.09 -21.332

7 0.2 9 175 0.100 0.6 2.47 2.75 -7.676

8 0.2 9 200 0.050 0.4 8.81 9.71 -18.901

9 0.2 9 225 0.075 0.2 7.91 8.95 -17.966

10 0.4 -1 175 0.100 0.4 10.28 10.84 -20.242

11 0.4 -1 200 0.050 0.2 10.78 9.53 -20.653

12 0.4 -1 225 0.075 0.6 11.51 11.37 -21.225

13 0.4 9 175 0.050 0.6 6.83 6.67 -16.697

14 0.4 9 200 0.075 0.4 9.43 9.79 -19.490

15 0.4 9 225 0.100 0.2 11.34 13.21 -21.098

16 0.4 9 175 0.075 0.2 7.67 8.16 -17.706

17 0.4 9 200 0.100 0.6 8.05 10.03 -18.123

18 0.4 9 225 0.050 0.4 9.89 8.56 -19.907

Overall mean 8.27 8.71 -17.556

Table 4 Results of ANOVA for surface roughness

SourceSum

squaresd.f.

Mean

squares

F-

value Prob>F Significance

A 28.25 1 28.25 6.32 0.0272 Significant

B 23.55 1 23.55 5.27 0.0406 Significant

C 31.75 1 31.75 7.10 0.0202 Significant

D 21.76 1 21.76 4.86 0.0477 Significant

E 3.92 1 3.92 0.88 0.3676 Insignificant

Error 53.68 12 4.47

Total 162.92 17

Table 5 Noise factor t-test

t d.f.p-

value

Mean

Difference

Std. Error

Difference

Equal variance 0.38 34 0.710 0.438 0.065

Unequal variance 0.38 33 0.710 0.438 0.065


and p value of 0.719, assuming that statistical difference would

yield a p-value of 0.05 or less. So, it can be concluded that a

difference between the means µn and µu is not statistically different.

Therefore, it was not determined from this experiment that this

noise condition significantly affects the surface roughness. This

could be because of pipe material AISI 1018 is soft. So the

conformation tests were carried out with a slightly used insert

which saved cost and time.

4.2 Analysis of the S/N ratio

Taguchi recommends the use of Signal-to-Noise (S/N) ratio to

measure the quality characteristics deviation from the desired

values. The term ‘Signal’ represents the desired value (i.e., mean)

for the response and the term ‘Noise’ represents the undesired value

(i.e., Standard deviation, S.D). Therefore S/N ratio is the ratio of the

mean to S.D. Usually there are three categories of quality

characteristics in the analysis of S/N ratio, i.e., the larger-the-better,

the smaller-the-better, and the nominal-the-better. Regardless of the

category of the quality characteristic, a greater S/N ratio

corresponds to better quality characteristics. Table 4 shows the

values of S/N ratio, η, corresponding to the average surface

roughness of each run calculated using the following equation,19

( )2

i10log y / n η = − ∑ (1)

Where,

η= the S/N ratio;

yi = surface roughness measurements in a run

n = the number of replicates

In this case the S/N ratio is based on the Taguchi smaller-the-

better loss function, as the idea is to minimize the response, i.e.,

surface roughness.

Since the experimental design is orthogonal, it is then possible

to separate out the effect of each parameter at different levels.

For example, the mean S/N ratio for the insert nose radius at

levels 1 and 2 can be calculated by averaging the S/N ratio for the

experiments 1-9, and 10-18 respectively. The mean S/N ratio for

each of the other parameters can be computed in a similar manner.

The mean S/N ratio for each level of the cutting parameters is

summarized and called the mean S/N response table for the surface

roughness. The S/N response table and S/N response graph are

shown in Table 6 and Fig. 3, respectively.

4.3 Optimization followed by confirmation tests

The statistical method, analysis of variance (ANOVA), is

performed to see which process parameters are statistically

significant. Further, the optimal level of the significant process

parameters is the level with the greatest S/N ratio. So with ANOVA

and the S/N ratio analyses, the optimal combination of the process

parameters then can be predicted. Therefore, based on the ANOVA

and S/N analyses, the optimal cutting parameters for the surface

roughness are the insert nose radius at level 1, effective rake angle

at level 2, cutting speed at level 1, feedrate at level 3, and depth of

cut at level 3. As the optimal level of the design parameters has

been selected, the final step is to predict and verify the

improvement in the surface roughness using the optimal level of the

design parameters. The predicted S/N ratio, ηpred, using Taguchi

method,20,21 with the optimal levels of the input parameters can be

calculated as follows:

( ) ( ) ( )

( ) ( )

pred. m m m m

D m E m

CΑ Βη = η + η − η + η − η + η − η

+ η − η + η − η

(2)

Where, ηm is the overall mean S/N ratio and ηA, ηB, ηC, ηD, and ηE

are the S/N ratios of the factors A, B, C, D, and E respectively at

their optimal levels. The predicted S/N ratio for the surface

roughness at the optimal cutting parameters levels can then be

obtained. The corresponding surface roughness to this predicted

S/N ratio can be calculated by using the Eq. (1).

Table 7 shows a comparison of the predicted surface roughness

with the actual surface roughness at the optimal cutting parameters

levels. The increase in value of the S/N ratio from the initial cutting

parameters level to the optimal cutting parameters levels is 10.719

dB. Therefore the surface roughness value is improved by about

54% of the initial surface roughness value. In other words, the

experimental results confirm the suitability of Taguchi design for

analyses and optimization of the response variable and cutting

parameters. Therefore, surface roughness in internal turning

operation is greatly improved through this technique in internal

turning process.

Table 7 Results of conformation experiment for Ra

Initial cutting parameters levels Optimal cutting parameters levels

Prediction Experiment

Level A2B2C2D2E2 A1B2C1D3E3 A1B2C1D3E3

Ra (µm) 9.97 2.91 2.75

S/N ratio (dB) -19.816 -9.891 -9.097

Improvement of S/N ratio =10.719 dB (exp-initial)

Table 6 S/N response table for surface roughness

Symbol Cutting

parameter

Mean S/N ratio (dB)

Level 1 Level 2 Level 3 max-min

A r -15.656 -19.456 - 3.80

B γnet -19.681 -16.493 - 3.188

C v -15.508 -16.069 -20.091 4.583

D f -19.844 -16.812 -16.012 3.832

E doc -18.151 -17.788 -16.729 1.422

Fig. 3 S/N ratio graph for surface roughness, Ra,n


5. Concluding remarks

This study provided the profound analysis of dependence of

surface roughness on insert rake angle through the use of the

Taguchi parameter design process. The following conclusions can

be summed up on the basis of the experimental results obtained in

this study:

• The use of L18 orthogonal array, with five control parameters

required only eighteen runs to conduct the experiment, one

sixth the runs required for a full factorial design of

experiment.

• Insert nose radius and cutting speed have the highest effect on

the surface roughness.

• Insert rake angle and feedrate has shown moderate effect on

the surface roughness.

• Smaller insert nose radius and high positive rake angle has

produced minimum surface roughness which also support the

literature reviewed.

• Low cutting speed and medium to large depth of cut has

produced minimum surface roughness.

• The inclusion of noise factor, new tool and slightly used tool,

was not found to have a statistical significant effect

• The verification cuts were made with slightly used inserts, so

the inclusion of this noise factor helps make this experiment

robust by saving cost and time.

• The improvement of the surface roughness from initial

cutting parameters to the optimal cutting parameters was

about fifty four percent.

• Further studies on minimization of surface roughness can be

carried out by increasing number of levels of effective rake

angle and insert nose radius.

ACKNOWLEDGEMENTS

The authors are indebted to Higher Education Commission

(HEC) of Pakistan and Bradley University, Peoria, IL, 61625, USA

for having made this research possible. The authors are also

thankful to the Mr. Ron Jones, Manufacturing Lab Technician,

Industrial and Manufacturing Engineering and Technology

Department, Bradley University, Peoria, IL, USA for his valuable

suggestions and help in this research.

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