31 TAGUCHI METHOD - iaeme.com OF SURFA… · INTRODUCTION Among various cutting processes, ... demonstrates the application of the Taguchi method for identifying the optimal

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

6340(Print), ISSN 0976 – 6359(Online) Volume 3, Issue 1, January- April (2012), © IAEME

332

OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING

HIGH CARBON HIGH CHROMIUM STEEL BY USING

TAGUCHI METHOD

R. R. Deshmukh1, V. R. Kagade2

1Department of Mechanical Engineering, Jawaharlal Nehru Engineering College,

Aurangabad-431 006

2Department of Mechanical Engineering, Shree Tuljabhavani College of

Engineering,Tuljapur -413 601

Email: [email protected]

ABSTRACT

The surface quality of the machined parts is one of the most important product quality

characteristics and one of the most frequent customer requirements. In this study the

Taguchi robust parameter design for modelling and optimization of surface roughness

in dry single-point turning of high carbon high chromium steel using TiCN,Al2O3,TiN

coated carbide inserts was presented. Three cutting parameters, the RPM speed (1753,

1984, 2215 rpm), the feed rate (0.017, 0.0285, 0.04 mm/rev), and the depth of cut

(0.7, 1.2, 1.7 mm), were used in the experiment. Each of the other parameters was

taken as constant. The average surface roughness (Ra) was chosen as a measure of

surface quality. The experiment was designed and carried out on the basis of standard

L9 Taguchi orthogonal array. The data set from the experiment was employed for

conducting the optimization procedures, according to the principles of the Taguchi

method. The results of calculations were in good agreement with the experimental

data. A certain discrepancy between the experimental results and calculations could

be interpreted as the presence of measurement errors, many irregularities and

deficiencies in the turning process, as well as environmental effects. The results

presented in this work confirm the effectiveness of Taguchi’s technique in

optimization of cutting processes.

Key words: turning process, surface roughness, Taguchi method, regression analysis,

ANOVA.

INTERNATIONAL JOURNAL OF MECHANICAL

ENGINEERING AND TECHNOLOGY (IJMET)

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

Volume 3, Issue 1, January- April (2012), pp. 332-342

© IAEME: www.iaeme.com/ijmet.html Journal Impact Factor (2011): 1.2083 (Calculated by GISI)

www.jifactor.com

IJMET

© I A E M E



333

1. INTRODUCTION

Among various cutting processes, turning process is one of the most fundamental and

most applied metal removal operations in a real manufacturing environment. The

surface roughness of the machined parts is one of the most significant product quality

characteristics. This characteristic refers to the deviation from the nominal surface of

the third up to sixth order. The actual surface profile is the superposition of error of

the form, waviness and roughness. The order of deviation is defined in international

standards. The surface roughness greatly affects the functional performance of

mechanical parts such as wear resistance, fatigue strength, ability of distributing and

holding a lubricant, heat generation and transmission, corrosion resistance, etc. The

perfect surface quality in turning would not be achieved even in the absence of

irregularities and deficiencies of the cutting process, as well as environmental effects.

There are various parameters used to evaluate the surface roughness. In the present

research, the average surface roughness (Ra) was selected as a characteristic of

surface finish in turning operations. It is the most used standard parameter of surface

roughness. In a machining process, there are two sharp and often conflicting

requirements. The first is high-quality surfaces and the second is high production rate.

An extremely high quality surface can produce higher production costs and time

consumption. Therefore, the machine tool operators would not push the machine tool

and/or cutting tool to its limit, rather using less risky process factors for that reason,

which neither guarantees the achievement of the desired surface quality nor attains

maximum production rate or minimum production cost [6]. Hence, it is of great

importance to exactly quantify the relationship between surface roughness and cutting

conditions. The materials having high-tensile strength and resistance to wear and

impact, which are frequently used for drawing dies, blanking dies, forming dies,

bushings, gauges, etc., are generally difficult to machine[5]. HCHCr-High carbon

high chromium steel is one of these materials. For the machining of High carbon high

chromium steels, the cutting tool materials must be harder than the work piece

materials. These types of materials can be machined with carbide tools [1]. Among

the cutting parameters affecting machining variables for steel, speed has maximum

effect & depth of cut has minimum effect. Tool tip temperature increases with

increase in cutting speed. At high speeds, surface finish is least affected. Surface

finish deteriorates at high feed rates; hence to obtain good surface finish, feed rate

may be kept low. At low speeds cutting force are high & tendency of work material to

form a built up edge is also stronger. At lower speeds, surface roughness increases

with increasing feed but at higher speeds surface roughness is less dependent on feed

[2]. The Taguchi parameter design method is an efficient experimental method in

which a response variable can be optimized, given various control and noise factors,

and using fewer experimental runs than a factorial design [3]. This paper

demonstrates the application of the Taguchi method for identifying the optimal

cutting parameters for surface roughness in dry turning of high carbon high chromium

steel.

2. LITERATURE REVIEW

Suresh Dhiman, et al [2008] studied effect of cutting parameters (feed, speed, depth of

cut) of AISI 1018 steel on various factors (tool tip temperature, surface roughness,

and cutting forces) that account for machining costs. A cylindrical bar of AISI 1018

steel (length 125 mm, diameter 25 mm) was used to carry out experiments on lathe by



334

HSS single point cutting tool without using any coolant. Among the cutting

parameters affecting machining variables for AISI 1018 steel, speed has maximum

effect & depth of cut has minimum effect. Tool tip temperature increases with

increase in cutting speed. At high speeds, surface finish is least affected. Surface

finish deteriorates at high feed rates; hence to obtain good surface finish, feed rate

may be kept low. Annealing & normalising AISI 1018 steel would improve

machinability by coarsening pearlite. At low speeds cutting force are high & tendency

of work material to form a built up edge is also stronger. At lower speeds, surface

roughness increases with increasing feed but at higher speeds surface roughness is

less dependent on feed [2].

E. Daniel Kirby, et al [2006] presented an application of the Taguchi parameter

design method to optimizing the surface finish in a turning operation. The Taguchi

parameter design method is an efficient experimental method in which a response

variable can be optimized, given various control and noise factors, and using fewer

experimental runs than a factorial design. The control parameters for this operation

included: spindle speed, feed rate, depth of cut, and tool nose radius. Noise factors

included varying room temperature, as well as the use of more than one insert of the

same specification, which introduced tool dimension variability. A total of 36

experimental runs were conducted using an orthogonal array, and the ideal

combination of control factor levels was determined for the optimal surface roughness

and signal to noise ratio. A confirmation run was used to verify the results, which

indicated that this method was both efficient and effective in determining the best

turning parameters for the optimal surface roughness [3].

3. EXPERIMENTAL DETAILS This experiment was conducted using the hardware listed in Table 1 on CNC

lathe machine as shown in the figure 1.

Table 1: Hardware list Sr.

No. Item Specifications

1 CNC lathe

Lakshmi Machine Works (LMW), Model- SMARTURN

Spindle: Max. Speed- 4500 rpm; Max. Power Rating – 7.5/5.5

KW; Spindle Nose- Flat (Dia. 140); Spindle Bore Dia.-53 mm

Travels & Feed rates: X-axis- 105 mm; Z-axis- 320 mm;

Rapids on X-axis- 20 m/min; Rapids on Z axis- 20 m/min

General: Weight- 2300 kg; Power requirement- 15 KVA ; 3-

phase : 415 V; wire-4; frequency- 50 Hz

2 Surface roughness

measurement device

Model: Surf test SJ201 P (Mitutoyo) Cut-off Values (lc):

0.8mm; Evaluation Length(es): 12.5mm; Drive

speed:Measuring:0.25mm/s,0.5mm/s),Returning:0.8mm/s;

Power Supply:100-240V AC, 50-60Hz,Via AC adapter/built-

in rechargeable battery; Roughness parameter: Ra in µm;

Stylus tip radius: 5 µm; Measuring force: 4 mN.

3 Cutting tool inserts

ISO Grade-TNMG 160408

MC;Grade:TT5100;Composition:TiCN,Al2O3,TiN;

Coat:MTCVD



335

The cutting parameters (design factors) considered in the present paper were

RPM speed (N), feed rate (f), and depth of cut (t). Other parameters were kept

constant for the scope of this research. The average surface roughness (Ra) was

chosen as the target function (response, output). Since it was obvious that the effects

of factors on the selected function were nonlinear, the experiment was set up with

factors at three levels (Table 2). The factor ranges were chosen with different criteria

for each factor, in order to use the widest possible ranges of values. Also, the

possibility of mechanical system and manufacturer's recommendation were taken into

account.

Figure 1: CNC Lathe

Table 2: Process variables and their limits

Process variables

Values in

coded

form

Spindle Speed (N)

(RPM)

Feed ( f )

(mm/rev)

Depth of cut (t)

(mm)

-1 1753 0.017 0.7

0 1984 0.0285 1.2

+1 2215 0.04 1.7

Based on the selected factors and factor levels, a design matrix was

constructed (Table 3) in accordance with the standard L9 Taguchi orthogonal array

(OA). The three levels of each factor were denoted by -1, 0 and 1. This design

provided uniform distribution of experimental points within the selected experimental

hyper-space and the experiment with high resolution.



336

Table 3: Taguchi’s L9 Orthogonal Array

Factorial combination

Sr.

No.

Spindle

Speed (N)

(RPM)

Feed ( f )

(mm/rev)

Depth

of cut

(t)

(mm)

1 -1 -1 -1

2 -1 0 0

3 -1 1 1

4 0 -1 0

5 0 0 1

6 0 1 -1

7 1 -1 1

8 1 0 -1

9 1 1 0

The cutting parameters in experiment were changed according to different cutting

conditions for each trial. All of the trials were conducted on the same machine tool,

with the same tool type and the same other cutting conditions. Longitudinal dry

turning of steel bars was performed on a CNC lathe. The cylindrical bars, with a

diameter of 34.5 mm and length of 100 mm, were fixed in the lathe with a three-jaw

chuck.

The workpiece material used in the experiment was high carbon high chromium steel

(C-1.5% or more than 2%, Cr-12%, Mo-1% & some traits of W & V; hardness 58-65

HRC) [4]. TiCN, Al2O3, TiN coated carbide inserts, type TNMG 160408 (Taegu Tec)

of TT5100 grade, were used for turning. The average surface roughness (Ra) of

machined workpieces was measured using Surf test SJ201 P (Mitutoyo) profilometer

(Figure 2).

Figure 2: Equipment for surface roughness measurement



337

The average surface roughness values shown in Table 4 are the arithmetical mean of

three measurements.

Table 4: Experimental data of surface roughness

Sr.

No.

Speed in

RPM

Feed in

mm/rev

Depth of cut

in mm

Surface

Roughness, Ra

in µm

1 1753 0.017 0.7 2.130

2 1753 0.0285 1.2 3.230

3 1753 0.04 1.7 4.240

4 1984 0.017 1.2 2.170

5 1984 0.0285 1.7 1.313

6 1984 0.04 0.7 1.410

7 2215 0.017 1.7 0.885

8 2215 0.0285 0.7 0.220

9 2215 0.04 1.2 0.326

4. RESULTS & DISCUSSIONS

From Table 4 which shows Experimental Data Related to Surface roughness Design

of Experiment [DOE] using Taguchi’s Analysis, Regression Analysis & ANOVA for

Main effects plot has been done using MINITAB 16.1 application software. Results of

the same with their respective graphs & interpretations are mentioned below in the

sequential order.

4.1 Taguchi Design

Taguchi Orthogonal Array Design

L9 (3**3)

Factors: 3

Runs: 9

Columns of L9 (3**4)

4.1.1 Taguchi Analysis: Ra in µm versus Speed in RPM, Feed in mm/rev, Depth of

cut in mm

Table 5: Response Table for Signal to Noise RatiosSmaller is better

Level N F T

1 -9.7663 -4.0786 1.1999

2 -4.0263 0.2007 -2.3925

3 7.9828 -1.9320 -4.6172

Delta 17.7491 4.2793 5.8170

Rank 1 3 2



338

221519841753

10

5

0

-5

-10

0.04000.02850.0170

1.71.20.7

10

5

0

-5

-10

N

Mean of SN ratios

f

t

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Graph 1:- Main Effects Plot for SN ratios [Ra]

Interpretation: The response table for the surface roughness data show that:

· For the S/N ratios, RPM speed is ranked 1, depth of cut 2 & feed 3 which are

justified using main effects plot for SN ratios as shown in the above graph 1.

4.2 Regression Analysis

4.2.1 Regression Analysis: Ra in µm versus Speed in RPM, Feed in mm/rev, Depth of

cut in mm

Box-Cox transformation of the response with specified lambda = 0

Regression Equation,

ln (Ra) = 8.50083 - 0.00442303 N - 10.7447 f + 0.669709 t…(i)

Table 6: Coefficients

Term Coef SE Coef T P

Const 8.5008 1.6220 5.24095 0.003

N -0.0044 0.0008 -5.85294 0.002

f -10.7447 15.1796 -0.70784 0.511

t

0.6697

0.3491

1.91822

0.113

Summary of Model

S = 0.427596 R-Sq = 88.49% R-Sq(adj) = 81.58%

PRESS = 3.02491 R-Sq(pred) = 61.91%



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Table 7: Analysis of Variance

Source DF Seq SS Adj SS Adj MS F P

Regression 3 7.02785 7.02785 2.34262 12.8125 .0008773

N 1 6.26347 6.26347 6.26347 34.2569 0.002062

f 1 0.09161 0.09161 0.09161 0.5010 0.510666

t 1 0.67276 0.67276 0.67276 3.6796 0.113187

Error 5 0.91419 0.91419 0.18284

Total 8 7.94204

1.00.50.0-0.5-1.0

99

90

50

10

1

Residual

Percent

10-1

0.50

0.25

0.00

-0.25

-0.50

Fitted Value

Residual

0.60.40.20.0-0.2-0.4

3

2

1

0

Residual

Frequency

987654321

0.50

0.25

0.00

-0.25

-0.50

Observation Order

Residual

Normal Probability Plot Versus Fits

Histogram Versus Order

Residual Plots for Ra

Graph 2: Residual Plots for Ra in µm

Interpretation of Graph 2:

From Histogram: - Histogram is skewed.

From Normal probability plot: - For the Ra data, the residuals appear to follow a

straight line although the negative tail falls slightly away from the line. No evidence

of nonnormality, skewness, outliers, or unidentified variables exists

From Residual versus Fits: - The residuals are scattered randomly about zero

From Residual versus Order: - For the Ra data, the residuals appear to be randomly

scattered about zero.



340

4.3 ANOVA-Main Effects Plot

221519841753

3

2

1

0.04000.02850.0170

1.71.20.7

3

2

1

NMean

f

t

Main Effects Plot for RaData Means

Graph 3: Main Effects Plot for Ra in µm

4.3.1 Interpretation of Main Effects Plot for Ra in µm

Speed has the most dominant effect on the observed surface roughness, followed by

depth of cut and feed rate, whose influences on surface roughness are smaller. The

surface roughness is continuously improved with the increase in speed, but increase in

feed rate and depth of cut causes a significant deterioration of surface finish.

4.4 Comparison of Experimental values with Regression Analysis equation values:

Table 8: Comparison of Experimental values with Regression Analysis equation

values

Experimental

Values

Regression

Values

Exp.

No. Ra in µm Ra in µm

1 2.130 2.130

2 3.230 3.230

3 4.240 4.240

4 2.170 2.170

5 1.313 1.313

6 1.410 1.410

7 0.885 0.885

8 0.220 0.220

9 0.326 0.326



341

From the above table 8 of comparison of Experimental values with Regression

Analysis equation values obtained from regression equation (i) it is clear that

regression equation (i) perfectly fits for experimental values shown in table 4.

Table 9: Optimization from S/N ratio

for Ra

N F t

2215 0.0285 0.7

Since the category smaller-the-better is adopted, it is evident from Graph 1 that the

optimal combination of factor levels, which gives the lowest value of the average

surface roughness (taking higher values of S/N ratio for each predictor), is as shown

in table 9 above & the same is checked from regression equation (i) as shown in table

10 below.

Table 10: Check from Regression Analysis equation

N F T Ra

2215 0.0285 0.7 0.321

5. CONCLUSION & FUTURE SCOPE

Among the cutting parameters affecting the surface roughness for HCHCr steel, speed

has maximum effect & feed has minimum effect. At high speeds, surface finish is

least affected. At low speeds surface roughness increases with increasing feed but at

higher speeds surface roughness is less dependent on feed.

Also from results of ANOVA main effects plots it is clear that these results fully

support the conclusions derived earlier.

The future scope for the further development of this experimental investigation is

recommended as follows:

1. Study the effects other predictors such cutting tool geometry, nose radius, etc. on

responses which we studied in this experimental investigations and other

responses like tool tip temperature, Material removal rate, tool wear, cutting

force, etc.

2. Various kinds of other cutting tool materials can be used for the experimental

investigation of turning operation on the same workpiece material.

NOMENCLATURE

List of Abbreviations and Symbols

ANOVA ----- Analysis of Variance

CNC ----- Computer Numerical Control

DOE ----- Design of Experiment

TiN ----- Titanium Nitride

TiCN ----- Titanium Carbo Nitride

Al2O3 ----- Aluminium Oxide

MTCVD ----- Medium Temperature Chemical Vapour Deposition



342

ACKNOWLEDGMENT

I offer my most sincere humble thanks to my esteemed guide Prof.R.R.Deshmukh,

Mechanical Engineering Department, Jawaharlal Nehru Engineering College,

Aurangabad. Not only for giving his valuable guidance, unflagging encouragement

and inspiration, but also for his never-ending willingness to tender generous. It is with

humble gratitude & sense of indebtedness; I thank my respected & esteemed Head of

Mech. Engg. Department Prof. A.B. Kulkarni & Principal Dr.S.D. Deshmukh for their

valuable guidance, suggestion & constant support which lead towards successful

completion of this work inspite of their busy departmental & academic schedule. I

am also thankful to Mr Gaike Sir & all other staff members at Swagati Engineering

Company, W-52, MIDC, Chikalthana, Aurangabad for making available all the

facilities for the successful completion of this work.

REFERENCES

[1] Tosun N., Ozler E. L, “Optimisation for hot turning operations with multiple

performance characteristics”, International Journal of Advanced Manufacturing

Technology, (2004) 23: 777–782 DOI 10.1007/s00170-003-1672-4

[2] Dhiman Suresh, Sehgal Rakesh, Sharma S K & Sharma Vishal S, “Machining

behaviour of AISI 1018 steel during turning”, Journal of Scientific & industrial

Research; vol.67, May 2008, pp.355-360

[3] Kirby E. Daniel, Zhang Zhe, Chen Joseph C, Chen Jacob “Optimizing surface

finish in a turning operationusing the Taguchi parameter design method”,

International Journal of Advanced Manufacturing Technology (2006) 30: 1021–1029

DOI 10.1007/s00170-005-0156-0

[4] V.D.Kodgire & S.V.Kodgire “Material Science & Metallurgy”, 16th Edition,

2005, Everest Publishing House, Pune, ISBN No.-81-86314-00-8, Pg. No. 438

[5] O.P.Khanna, “Material Science & Metallurgy”, 11th Reprint: 2007, Revised

Edition: 1999, Dhanpat Rai Publicatons (P) Ltd., New Delhi, Pg. No. 5-32

[6] Marinkovic Velibor & Madic Milos, “Optimization of surface roughness in

turning alloy steel by using Taguchi method”, Scientific Research and Essays Vol.

6(16), pp. 3474-3484, 19 August, 2011; Available online at

http://www.academicjournals.org/SRE ISSN 1992-2248 ©2011 Academic Journals

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31 TAGUCHI METHOD - iaeme.com OF SURFA… · INTRODUCTION Among various cutting processes, ... demonstrates the application of the Taguchi method for identifying the optimal