PROCESS OPTIMIZATION IN DRY TURNING OF STEEL, CAST … · for minimum cutting force in turning of...

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International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 1, January 2017, pp. 213–234, Article ID: IJMET_08_01_024

Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=1

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication

PROCESS OPTIMIZATION IN DRY TURNING OF

STEEL, CAST IRON AND BAKELITE USING LOW

COST TOOL MATERIAL

Tapas Banerjee

Mechanical Engineering Department, Jadavpur University,

Kolkata, West Bengal, India

Asish Bandyopadhyay



Pradip Kumar Pal



ABSTRACT

The present work includes a methodical study of the effects of input parameters viz. spindle

speed, longitudinal feed and depth of cut on surface roughness (Ra) of machined components and

vibration generated during machining. Vibration is an output response produced in metal cutting

operation. This vibration acts directly as a response which takes part in roughness formation on

work materials. There is an impact on both surface finish and productivity. The design of

experiment plan is based on L16 orthogonal array with three factors and four levels for each

variable as per Taguchi method. The experiments are conducted on Low Carbon steel, Grey Cast

Iron and Bakelite on Centre Lathe using HSS single point turning tool under dry cutting condition.

Response variables viz. surface roughness (Ra) and cutting vibration (dB) in the three directions

are recorded by using accelerometer placing on the periphery of work holding chuck end bearing

housing. An attempt is made to optimize the cutting parameters in respect of multi-response

variables viz. surface roughness (Ra) and cutting vibration (dB) in three directions to achieve a

breakeven level of both the quality and yield by using low cost tool material. CQL perception in

WPCA based Taguchi technique is used to evaluate optimal input parameters.

Key words: Surface Finish; Cutting Vibration; Multi-objective optimization; WPCA; CQL.

Cite this Article: Tapas Banerjee, Asish Bandyopadhyay and Pradip Kumar Pal. Process

Optimization in Dry Turning of Steel, Cast Iron and Bakelite Using Low Cost Tool Material.

International Journal of Mechanical Engineering and Technology, 8(1), 2017, pp. 213–234.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=1

Tapas Banerjee, Asish Bandyopadhyay and Pradip Kumar Pal


1. INTRODUCTION

The lathe is a flexible machine tool. In lathe, most common process of material removal is turning. Surface

roughness is one of the most important output parameters in metal cutting processes. Shape, waviness and

roughness or irregularities are the three basic criterion of a surface. Straightness error is the result of

changing shape of a cylindrical work piece during turning. Vibration develops waviness. The combined

result of feed, tool nomenclature, tool material, work piece material and temperature of tool work piece

interface generates roughness or irregularities. Moreover by achieving good surface finish in turning, time

is substantially reduced for finishing operations like grinding, polishing, electro polishing etc. It also saves

assembly time in precision fits. The interaction of vibrations and surface roughness are studied in turning

operation on different materials (Low Carbon Steel, Grey Cast Iron, Bakelite etc.). It is observed in cutting

operation that apart from chatter (indicates severe vibration) self excited vibration (oscillations present

when no periodic forces are available in the system) also developed. These vibrations normally affect both

surface roughness and dimensional accuracy. One parameter is dependent on other parameters. If one

change others are affected.

Major influential factors which affect surface roughness has been outlined by the following block

diagram (Figure 1)

Figure 1 Major influential factors which affect surface roughness

Both shop floor engineers and operators apply their individual experience and skill to select effective

parameters for obtaining desired surface finish by minimizing the effect of vibration generated during

machining processes. Moreover, nowadays, various data ranges on cutting parameters are available from

different experimentations on different metals with turning operation in centre lathe. But still those data are

not sufficient enough to cover the entire spectrum of metal cutting application for obtaining desired surface

finish. So more and more experimentation are being designed and machining operation performed with

different metals and non metals with or without coolant by changing various input parameters to optimize

the outputs. So that all the assimilated data are accessible as well as acceptable during operation in

manufacturing industries without any hesitation.

AISI 1020 steel is utilized for uncomplicated structural application like cold headed bolts and also for

manufacturing of axles, general engineering components and spares for machinery, shafts, camshafts,

gudgeon pins, ratchets, light duty gears, worm gears, spindles etc. Case harden, more than HRC 65, is

possible for tiny section as well as for bulky section without influencing the core strength. Carbonitriding

can be performed for attaining certain benefits over normal carburizing.

IS210 –FG200 grey cast iron is used for outstanding resistance to sliding friction and wear in cylinder

bores, piston rings and sideways on machine tools. Percentage of graphite flakes presence in cast iron

increases the capacity of damping vibration.

Process Optimization in Dry Turning of Steel, Cast Iron and Bakelite Using Low Cost Tool Material


DIN7735 Type-Hgw2008 bakelite is preferred for a wide variety of components where finest quality is

required. It is used for intricate appearance as well as for superior finish and accuracy of machined

components. Typical components contain fine jagged gears & pinions, geneva wheels, cams, seal retaining

rings, insulating sleeves and bushes, actuating arms, spares related to cryogenic temperatures, medium and

low voltage insulations, a broad range of other precision machined parts etc.

Choudhury et al.[8] achieved vibration control by relative vibration sensation between the tool and the

work piece by producing a force which leans to counterbalance the excitation. Choudhury et

al.[9]developed a system for on-line vibration control on a turning lathe by introducing a closed loop

feedback circuit and suitable varying its phase difference and gain. Su et al.[41] proposed an effective

procedure based on principle component analysis (PCA) to optimize the Taguchi method’s multi-response

problems. Abouelatta et al.[1] observed the possibility of predicting roughness parameters on the basis of

tool vibrations and cutting parameters in turning operation. Four models were used to predict the roughness

parameters (Ra , Rt , Rsk) as functions of the cutting parameters and tool vibrations. The predicted models

were depend on both cutting parameters and tool vibrations. Risbood et al.[30] predicted dimensional

deviation and surface roughness by measuring vibrations and cutting forces in turning process by using

neural net work to locate effective approximates on surface finish and dimensional deviation. Benardos et

al.[6] reviewed different approaches for predicting surface roughness with RSM, Taguchi techniques,

analyzing experimental results by using ANOVA, Artificial neural networks (AAN), Neuro-fuzzy systems,

Genetic Algorithms etc. Noordin et al.[23] performed cutting tests of AISI 1045 Steel with constant depth

of cut under dry cutting conditions using multilayer tungsten carbide tool. Also investigated the most

significant factor, feed which influenced the response variables viz. tangential force and surface roughness.

Luo et al.[18] found that the feed rate and depth of cut played significant role on machined surfaces. Hung-

Chang L [13] proposed weighted principal components (WPC) method to overcome the shortcomings in

principal component analysis (PCA). Mahapatra et al.[19] conducted study of hard turning operation on an

engine lathe using P-10 grade tungsten carbide for machining of S45C steel. Taguchi methodology had

been used to find out the effective performance output and machining conditions. Stoic et al.[40] studied

on surface quality which was affected by cutting unsteadiness during hard turning. Antic et al.[4]

developed an appropriate model with Neural-Networks for monitoring tool-wear during hard turning.

Pusavec et al.[25] conducted study on high speed cutting of soft grey cast iron with advanced tool material

such as CBN. Mazid A.M. [20] developed a methodology to correlate between accuracy of MFTW

(machine tools, jig-fixture, cutting tool and workpiece) elastic system and the dimensional output of the

machined product. Thamizhmanii et al.[42] analyzed surface roughness on dry turning of SCM440 alloy

steel with Al2O3 + TiC golden coated cutting tool using Taguchi method. Taguchi method had shown that

the depth of cut played a significant role in producing lower surface roughness followed by feed. Bajic et

al.[5] investigated the effect of turning on both the surface roughness and cutting force. They also studied

the sway of cutting parameters viz. feed, cutting speed and depth of cut. Both regression analysis and

neural network methodology were used and compared for better prediction. Lu. Chen [17] investigated on

prediction of surface quality in machining process on a CNC turning lathe with stainless steel 304L as

workpiece (diameter 95.5 mm) and carbide coated inserts as tool. The developed RBF neural network

model supported on cutting speed, feed & depth of cut. It was reflected the prediction of surface contour

with low cost, high accuracy and high speed. Sahoo et al.[32] investigated optimal parametric combination

for minimum cutting force in turning of low carbon steel with HSS cutting tool by using RSM. It was

observed that the feed force and cutting force were high at low cutting speed and moderately low at high

cutting speed due to thinner chips and higher trim angle. Velchev et al.[44] investigated the cutting speed

on the specific cutting force during turning of steel (40CrMnMo7), bronze & aluminium alloy with P30

brazed carbide inserts. They developed new mathematical models which deal with the approximation in a

broad range of the dependence of the specific cutting force on the cutting speed in turning of different

materials. Gokkaya Hasan[11] used CNC turning machine and uncoated carbide tools for orthogonal

cutting of aluminium alloy (T4) using fixed depth of cut to study the properties of machining parameters

on surface roughness, cutting forces, built-up layer (BUL) and built-up edge (BUE). It was also observed



that average surface roughness was increasing for increasing feed rate. Selvaraj et al.[34] investigated the

sway of cutting parameters (cutting speed, feed, and depth of cut) on the surface roughness of austenitic

stainless steel during dry turning with TiC and TiCN coated tungsten carbide cutting tool. They had

applied Taguchi optimization method to find out the optimal process parameters to improve surface

roughness in dry turning of Austenitic Stainless Steel (AISI 304).

Ramesh et al.[27] investigated the cutting conditions in turning of Duplex Stainless Steel 2205 using

CVD triangular carbide insert. They also established correlation between cutting speed, feed rate, depth of

cut etc. and optimized the turning conditions based on surface roughness by regression analysis.

Abuthakeer et al.[3] investigated the spindle vibration’s effect on surface roughness of workpiece in dry

turning using Artificial Neural Networks (ANN). They conducted the experiments on CNC lathe with

carbide turning insert. They attempted to estimate the surface finish value and vibration level using Multi-

layer perceptions (MPL) architecture. Neseli et al.[22] investigated the tool geometry’s influence on

surface finish. It was obtained in turning of AISI 1040 steel with Al2O3 coated insert tools. They found that

nose radius was the most significant factor for obtaining better surface roughness than other tool geometry

like approach angle, rake angle etc. Sharma et al.[35] investigated, analyzed and optimized cutting

parameters specifically, depth of cut, insert radius, feed & cutting speed with consideration of surface

roughness by turning of AISI 410 grade steel on a CNC lathe with P-20 and P-30 TiN coated inserts. They

found that feed rate had effected on Ra. Krishankant et al.[15] optimized turning process by the effect of

machining parameters using Taguchi methods to develop the value of manufactured goods. Kumar et

al.[16] investigated the effect of process parameters in turning of carbon alloy steel (SAE 8620, EN8,

EN19, EN24 and EN47 ) in a CNC lathe using carbide tip tool in wet condition. Finer surface finish

attained in turning of alloy carbon steel with higher spindle speeds at low feed rate.

Eze et al.[10] investigated experimentally the correlation of induced vibration with surface roughness

in turning of 41Cr4 alloy steel with F30 type carbide cutting tool by using response surface methodology

RSM. Wang et al.[47] investigated the sway of tool-tip vibration on surface irregularity. Rogov et al.[31]

used two cutting tools made of AISI 5140 and Tic coated carbide insert for turning of alluminium alloy

(AA2024) in lathe to ascertain the effect of cutting parameters on both surface irregularity and free

vibration. They also determined the percentage contribution of various parameters influencing natural

frequency and surface roughness using Taguchi Technique. Hessainia et al.[12] investigated the combined

effects of cutting parameters (speed, feed, depth of cut etc.) and tool vibration on surface roughness by

employing the analysis of variance (ANOVA) in hard turning of 56HRC hardened 42CrMo4 steel (74 mm

dia. × 380 mm long) with Al2O3 / TiC mixed ceramic tool, type SNGN 120408 T01020. They also

developed completed and reduced experimental model to show a relationship between the parameters of

surface roughness with machining ones and tool vibrations. Rajasekaran et al.[26] investigated diverse

process parameters such as feed, cutting speed, depth of cut etc. and their importance in deciding the

surface roughness during machining of carbon fiber reinforced polymer material with cubic boron nitride

(CBN) tool. They found that response surface methodology (RSM) was well matched for predicting the

surface roughness of carbon fiber reinforced polymer composites. Rao et al.[28] investigated the influence

of feed, speed, and depth of cut on surface roughness and cutting force, on industrial CNC lathe, during

turning of AISI 1050 steel having hardness of 484 HV by using ceramic cutting tool [Al2O3 + TiC matrix

(KY1615)]. They also determined a combination of feed rate and depth of cut for achieving best possible

surface finish. Kayastha et al.[14] optimized process parameters for turning operation by using both

Taguchi and Principal Component Analysis method. Saraswat et al.[33] studied the performance

characteristics in turning of unalloyed medium carbon steel (EN9) by using Analysis of Variance

(ANOVA) and Signal to Noise ratio. Cutting parameters optimized to obtain better surface finish in turning

of medium carbon steel. Singarvel et al.[37] analyzed the optimum machining parameters on turning of

EN25 steel for minimizing of cutting force, surface irregularity, and rate of material removal by using

Taguchi based utility concept coupled with Principal Component Analysis (PCA). CVD and PVD coated

carbide tools were used during the experimental work. Nayak et al.[21] investigated the sway of machining

parameters (speed, depth of cut, and feed) on diverse performance measured in dry turning of AISI 304



austenitic stainless steel with ISO P30 grade uncoated cemented carbide inserts. They optimized the

machining parameters for three important characteristics like material removal rate, cutting force, surface

roughness etc. using grey relational analysis. Vinayagamoorthy et al.[46] analyzed the performance of

precision turning by using conventional lathe on Ti-6Al-4V under dry working condition. They used

Analysis of Variance (ANOVA) to understand the influence of various cutting parameters on surface

roughness, tool ware, cutting force and cutting tool temperatures while precision turning of titanium alloy.

They also identified optimal levels of parameters using grey relational analysis. Abhang et al.[2]

investigated the effect of machining parameters such as feed rate, cutting speed, tool nose radius, depth of

cut, and lubricant (boric acid mixed with base oil SAE-40) on surface roughness during turning of EN31

steel on heavy duty lathe machine with diamond shape carbide tool (CNMA 120404, CNMA 120408,

CNMA 120412). They found that the surface roughness had been increased due to enhanced feed rate,

subsequently depth of cut, but surface roughness had been decreased with enhanced cutting speed and tool

nose radius respectively. Venkataramaiah et al.[45] investigated the sway of feed rate and tool

nomenclature on feed force, cutting force and radial force. during turning of aluminium workpiece with

HSS cutting tool using Taguchi Method and Fuzzy Logic. They examined the test result with ANOVA and

found that influencing factor on all the cutting forces were rake angle, feed rate and approach angle.

Valera et al.[43] investigated on power consumption and roughness attributes of surface generated in

operation of turning of EN31 alloy steel on general purpose lathe machine with TiN+Al2O3+TiCN coated

tungsten carbide under different cutting parameters. They concluded that spindle speed, feed and depth of

cut significantly affected the surface roughness and power consumption. They also suggested for further

work to find out most significant cutting parameter for EN31 ally steel work material. Sharma et al.[36]

investigated the optimization of cutting parameters (cutting speed, depth of cut and feed rate) for metal

removal rate and surface roughness in CNC turning operation of AISI 8620 steel using coated carbide

insert using Taguchi and Grey Taguchi analysis. Feed rate was found the most considerable factor for both

the material removing rate and surface roughness. Ojha et al.[24] optimized the input parameters in dry

turning and studied the effects of foremost input factors on the output using Taguchi technique and

response surface methodology. Srithar et. al.[39] analyzed the surface roughness parameters in machining

of AISI D2 steel (having 66 HRC hardness) by coated carbide insert. They found that feed rate influenced

the surface roughness parameters in machining of AISI D2 steel. Silberschmidt et. al.[38] investigated on

surface roughness and compared response of various alloys and metals – from ones with well known good

machinability to hard-to-machine ones having ultrasonic assisted turning. They noticed the remarkable

improvement of surface roughness of all the studied alloys (Inconel 718, Ti 15 3 3 3, X2CrNi18-9, ASTM

A 48 class 20, AlMg1SiCu, CuZn37Pb2, CuSn11P, X2CrNi18-9 etc.) with hybrid turning technique UAT

(ultrasonic assisted turning). Bhuiyan et. al.[7] investigated tool wear, chip formation and surface

roughness of workpiece under various cutting conditions in machining utilizing acoustic emission (AE) for

vibration signature and tool condition monitoring(TCM) in turning. They found that amplitude of vibration

components had been increased due to increase of feed rate, depth of cut and cutting speed respectively.

Reddy et al.[29] conducted experiments on CNC lathe with tungsten carbide tool and EN16 steel as work

material to optimized the turning parameters such as cutting speed, feed rate and depth of cut for best

output responses such as surface finish and material removing rate. They used Taguchi’s L27 orthogonal

array for experimental investigation. Experiments were analyzed using analysis of variance to find

significance of each input parameter on the evaluation of process performances, surface roughness and

material removal rate.

Within the scope of literature survey for the present work, it has been observed that most of the

investigators had worked with advanced productive machine tools with latest developed tool materials.

They had also experimented with special materials for special applications purpose. Very few had worked

with widely used common materials (ferrous, non-ferrous, non-metal etc.) as well as commonly low cost

available tools materials to bring down the cost of production. Moreover they had not attempted any

redesign work on cutting tool angles on which both surface quality and tool life depends. They had

followed either tool manufactures’ or handbooks’ guide line only.



The present study’s focus is to uncover a correlation between cutting vibrations and surface roughness

in turning machines by collecting and analyzing data generated during turning of low carbon steel, grey

cast iron and bakelite samples. Input parameters have been optimized using WPCA coupled Taguchi

method for multi-objective optimization to improve surface quality and reduce cutting vibration.

In the present study optimization has been done by using combined quality loss (CQL). This CQL is

finally optimized by Taguchi method.

2. MATERIAL AND METHODS

All the AISI 1020 Low Carbon steel, IS210-FG200 Grey Cast Iron, and DIN 7735 Type-Hgw 2008

bakelite have been selected for present study for its wider application in both the Industries and general

engineering.

Chemical compositions, Physical properties and Mechanical properties of the test pieces are listed

below in Table 1,2,3, & 4.

Table 1 Chemical Composition

Materials

Content %

C Fe Si Mn P S

AISI1020, Low carbon steel 0.17 – 0.23 99.08 – 99.53 - 0.30 – 0.60 ≤ 0.04 ≤ 0.05

FG200, Grey cast iron 3.0 – 3.3 - 1.6 – 2.0 0.8 – 1.0 ≤ 0.15 ≤ 0.12

Table 2 Physical & mechanical properties

Materials

Properties

Density

g/cc

Tensile strength

Mpa

Yield strength

Mpa

Modulus of elasticity

Gpa

Hardness

RC

AISI1020, Low carbon

steel

7.87 395 295 200 64

Table 3 Physical & mechanical properties

Materials

Properties

Density

g/cc

Tensile strength

Mpa

Comp. strength

Mpa

Modulus of elasticity

Gpa

Hardness

HBW

FG200, Grey cast iron 7.10 200 720 114 160-220

Table 4 Physical properties of Bakelite round bar

Properties Metric

Tensile strength

60 MPa

Compressive strength 200 MPa

Flexural Strength

170 MPa

Water assimilation 2.5 mg/cm2

Insulation Resistance after dipping in water 5 X 108 Ohms

Relative Density 1.35

In turning operation, some parameters involved in the metal cutting process are strongly related with

other parameters. It is not possible to consider all the parameters. Important machining parameters

optimized for turning operation is speed (N), longitudinal feed (f) and Depth of cut(d). Parameters have

been optimized for output responses like surface roughness (Ra) and vibrations in ‘X’, ‘Y’ and ‘Z’ axis.

Machining data are constantly reliant on the cutting data, the actual operation, and the machine tool. The



machining parameter may have to be adjusted the actual conditions of a specific machining operation.

Process parameters and selected process variables are listed in Table 5. Input parameters viz, spindle speed

(N), longitudinal feed (f), depth of cut (d) etc. have been selected on the basis of available machine power,

trial and error approach based on shop floor experience and reference data available from both the

machinery handbook and past work of researchers. Moreover selection of the values of variables is limited

by the capacity of machine used for experimentation. Four levels in the operating range have been selected

for each of the factor. L16 orthogonal array (OA) design has been considered in present work.

a) Checking of spindle speed and longitudinal feed range of the lathe by using Prestige Counter

Instruments Pvt. Ltd. make calibrated hand held ‘Tachometer’ and Mitutoyo make analog precision

‘Dial Gauge’. Figure 2 shown the recorded feed curve.

b) Turning of all the AISI 1020 low carbon steel , IS210-FG200 grey cast iron & DIN7735 Type Hgw

2088 bakelite rounds to size ф30 mm × 30 mm long in lathe for proper clamping of samples rigidly on

dead end supported threaded mandrel for mounting on three jaw self centering chuck as well as to maintain

constant cutting speed for performing turning operation. Figure 3 & Figure 4 shown the experimental

arrangement.

c) Measuring vibration along ‘X’(longitudinal), ‘Y’ (cross) and ‘Z’(vertical) axis of general purpose lathe

machine with the help of Accelerometer of Kistler-USA make and data logging at 60MHz, Tektronik make

two channel digital Oscilloscope.

d) Measuring surface roughness ‘Ra’ with the help of stylus type profilometer of Taylor Hobson UK make,

Surtronic 3+.

Figure 2 Calibration curve of feed (mm/rev)



Present work includes a methodical study and optimization of input parameters viz. spindle/chuck

speed, longitudinal feed and depth of cut to achieve good surface finish of machined samples. Table 5

represents process variables and their levels. Mitutoyo make analog precision ‘Dial Gauge’ has been used

to measure depth of cut during experiments.

Table 5 Process variables and their levels

Parameter

unit

Symbol

Factor Levels

1 2 3 4

Spindle Speed rpm N 120 280 460 800

Feed mm/rev f 0.05 0.07 0.10 0.12

Depth of cut mm d 0.05 0.10 0.15 0.20

Centre Lathe of Parmar Mechanic Works, Surendranagar make, Type MKP3, has been used for turning

of work pieces. Moreover acceptability for experimentation of the machine tool has been based on the

study of the prevailing condition of machine tool as shown in feed curve in Figure 2. Commonly used High

Speed Steel (HSS) square tool bit, manufactured by Miranda Tools as per ISO5421, has been selected for

turning work of test pieces. A common tool nomenclature (shown in Table 6) has been designed for

turning of work pieces.

Table 6 Designed angles for high speed cutting tools

Materials Front/Back

Rake Angle

Front

Clearance

Angle

Side

Rake

Angle

Side

Clearance

Angle

Side

Cutting

Angle

End

Cutting

Angle

Nose

Radius

(mm)

Low Carbon Steel

Grey Cast Iron

Bakelite

7 9 10 10 15 8 1.2



Table 7 DOE and corresponding experimental data of AISI 1020 Low Carbon Steel

Table 8 DOE and corresponding experimental data Of IS210-FG200 Grey cast iron

Run

No.

Parameters Measured Responses

N

(rpm)

f

(mm/rev)

d

(mm)

Avg.

Ra

µm

RMS

Vibration

(dB)

X Y Z

1 1 1 1 1.38 90.67 88.22 90.12

2 1 2 2 1.97 90.88 89.56 90.77

3 1 3 3 2.92 89.64 89.76 91.63

4 1 4 4 3.07 86.08 90.71 89.77

5 2 1 2 1.75 84.16 82.03 85.02

6 2 2 1 1.56 85.02 83.25 84.56

7 2 3 4 2.50 82.89 82.64 85.04

8 2 4 3 3.60 84.11 82.54 84.70

9 3 1 3 1.33 82.14 81.36 82.23

10 3 2 4 2.76 81.89 80.80 82.81

11 3 3 1 2.40 81.43 80.86 82.83

12 3 4 2 2.79 81.78 81.25 82.83

13 4 1 4 1.41 78.38 78.48 79.31

14 4 2 3 1.42 78.36 77.74 78.49

15 4 3 2 1.76 78.00 77.89 78.40

16 4 4 1 1.80 78.51 79.59 79.59

Run

No.


N

(rpm)

f

(mm/rev)

d

(mm)

Avg.

Ra

µm

RMS

Vibration

(dB)

X Y Z

1 1 1 1 2.04 81.39 83.58 84.39

2 1 2 2 1.87 84.14 84.47 85.02

3 1 3 3 1.98 82.77 83.31 84.39

4 1 4 4 2.07 84.37 83.77 84.59

5 2 1 2 1.78 82.72 80.03 84.23

6 2 2 1 1.54 84.70 82.15 82.09

7 2 3 4 1.95 84.66 84.52 83.03

8 2 4 3 2.26 84.76 84.40 84.54

9 3 1 3 1.78 82.84 82.19 80.92

10 3 2 4 1.41 82.62 81.80 80.90

11 3 3 1 1.70 82.68 81.73 81.38

12 3 4 2 2.42 83.09 81.90 81.40

13 4 1 4 1.91 80.04 79.80 77.77

14 4 2 3 2.41 79.21 78.68 77.82

15 4 3 2 1.88 80.22 78.92 79.60

16 4 4 1 2.15 79.32 77.85 78.27



Table 9 DOE and corresponding experimental data of DIN7735 Type-Hgw2008 Bakelite

DOE and corresponding experimental data shown in Table 7, 8 & 9.

3. RESULTS

Normalization is a part of optimization processes, (Su and Tong, 1997). It is the process to fit data within

1. Data values are to be in between 0.1 to 1. Since at zero, model perhaps crumple. The original

experimental data must be normalized to eliminate such an effect. The three types of normalization are

represented by the following equations.

a) LB (Lower-the-Best), ��∗�� = � �� (1)

HB (Higher-the-Better), ��∗ �� = �� (2)

c) NB (Nominal-the-Best), ��∗�� = �� , �� , �� (3)

� = 1, 2, … … … … … . , �

Here

� = 1, 2, … … … … … , �

Where Xi*(k) is the normalized data of the k

th factor in the i

th series.

Run

No.


N

(rpm)

f

(mm/min)

d

(mm)

Avg.

Ra

µm

RMS

Vibration

(dB)

X Y Z

1 1 1 1 1.39 84.68 85.30 84.37

2 1 2 2 1.24 84.26 83.75 84.36

3 1 3 3 1.51 84.93 85.50 83.63

4 1 4 4 2.46 84.83 84.59 83.17

5 2 1 2 2.70 85.19 84.59 83.08

6 2 2 1 1.39 84.75 81.19 83.39

7 2 3 4 2.21 84.95 85.03 83.10

8 2 4 3 1.30 83.96 82.57 82.37

9 3 1 3 1.68 82.68 80.36 80.85

10 3 2 4 1.96 82.84 82.09 81.14

11 3 3 1 1.30 82.59 81.77 83.22

12 3 4 2 1.59 82.72 81.84 81.24

13 4 1 4 1.85 82.15 79.64 80.11

14 4 2 3 2.04 83.46 80.61 81.94

15 4 3 2 1.27 78.98 78.46 78.14

16 4 4 1 1.56 78.94 77.01 80.39



and X0b(k) is the desired value of the k th

quality attribute. The value of Xi*(k) will be between ‘0’ and ‘1’

after data normalization. The series Xi*, i = 1,2,3..m can be viewed as the proportional series used in the

present case.

All the response data have been normalized using the equation no.(1) i.e. Lower-the-Best. Normalized

response data are shown in Table 10, 11 & 12 for low carbon steel, grey cast iron and bakelite respectively.

Table 12 Normalized experimental data of DIN7735 Type-Hgw2008 Bakelite

Sl.

No.

Normalized Data of Responses

Ra X Y Z

Ideal 1.0000 1.0000 1.0000 1.0000

1 0.8921 0.9322 0.9028 0.9262

2 1.0000 0.9369 0.9195 0.9263

3 0.8212 0.9294 0.9007 0.9343

4 0.5041 0.9306 0.9104 0.9395

5 0.4593 0.9266 0.9104 0.9405

6 0.8921 0.9314 0.9485 0.9370

7 0.5611 0.9293 0.9057 0.9403

8 0.9538 0.9402 0.9327 0.9486

9 0.7381 0.9548 0.9583 0.9664

10 0.6326 0.9529 0.9381 0.9630

11 0.9538 0.9558 0.9419 0.9389

12 0.7798 0.9543 0.9409 0.9618

13 0.6703 0.9609 0.9669 0.9754

14 0.6078 0.9458 0.9553 0.9536

15 0.9764 0.9995 0.9815 1.0000

16 0.7949 1.0000 1.0000 0.9720



A check has to be made for correlation of the responses, (Su and Tong, [41])

� !, "� = ��#∗��, �$∗��, �%∗��, … … … … … … . �∗ �� (4)

&ℎ ( , � = 1, 2, … … … … … , �

It is the normalized sequence of the ith

quality attribute. The correlation of constant quantity (coefficient)

between two quality attribute is calculated by the following equation:

)* = +,-./0,/12340 � 341

(5)

5 = 1, 2, 3, … … … … … , �

here � = 1, 2, 3, … … … … … , �

5 ≠ �

Here, ρjk is the correlation coefficient between quality attribute j and quality attribute k ;

Cov(Qj ,Qk) is the positive covariance of quality attribute j and quality attribute k ;

σQj and σQk are the standard deviation of quality characteristic j and quality characteristic k

correspondingly ;

The correlation is checked by examining the following proposition:

H0 : ρjk = 0 (There is no correlation)

(6)

H1 : ρjk ≠ 0 (There is correlation)

Table 13,14 & 15 Shown the Pearson’s correlation coefficient. Non-zero value in all the cases of

correlation coefficient shows that all the response features are correlated to each other.

Table 13 Table 14

Check for correlation between responses Check for correlation between responses

of AISI 1020 Low carbon steel of IS 210-FG200 Grey Cast Iron

Table 15 Check for correlation between responses of DIN 7735 Type-Hgw2008 Bakelite

Sl.

no.

Correlation

between

responses

Pearson

correlation

coefficient

Observation

1 Ra and X 0.001 Both are correlated

2 Ra and Y 0.160 Both are correlated

3 Ra and Z 0.152 Both are orrelated

4 X and Y 0.877 Both are correlated

5 X and Z 0.824 Both are correlated

6 Y and Z 0.858 Both are correlated

Sl.

no.

Correlatio

n between

responses

Pearson

correlation

coefficient

Observation



3 Ra and Z 0.436 Both are correlated




Sl.

no.

Correlatio

n between

responses

Pearson

correlation

coefficient

Observation



3 Ra and Z 0.169 Both are correlated






Now Principal Component Analysis (PCA) has been applied to eliminate response correlation. PCA

results viz. Eigenvalue, Eigenvector, accountability proportion (AP) and cumulative accountability

proportion (CAP) have been shown in Table 16, 17 & 18. PCA done with MINITB 16 software.

Table 16 Table 17

Computation of Principal Component Computation of Principal Component

Analysis (PCA) Analysis (PCA)

or AISI 1020 Low carbon steel for IS 210-FG200 Grey cast iron

Table 18 Computation of Principal Component Analysis (PCA) for DIN 7735 Type-Hgw 2008 Bakelite

From the result of Table 16 (low carbon steel), it has been found that the four principal components i.e.

Ψ1 , Ψ2 , Ψ3 & Ψ4 can take care of 78.3%, 20.2% , 1.3% and 0.2% variability in data respectively. The

cumulative accountability proportion (CAP) for the four principal components (Ψ1 , Ψ2 , Ψ3 & Ψ4) has

been found 100%.

Similarly, from the result of Table 17 (grey cast iron), it has also been found that the four principal

components i.e. Ψ1 , Ψ2 , Ψ3 & Ψ4 can take care of 68.1%, 25.0%, 4.2% and 2.7% variability in data

respectively. The cumulative accountability proportion (CAP) for the four principal components (Ψ1 , Ψ2 ,

Ψ3 & Ψ4) has been found 100%.

Frther, from the result of Table 18 (Bakelite), it has been found that the four principal components i.e.

Ψ1 , Ψ2 , Ψ3 & Ψ4 can take care of 72.9%, 22.2%, 3.2% and 1.7% variability in data respectively. The

cumulative accountability proportion (CAP) for the four principal components (Ψ1 , Ψ2 , Ψ3 & Ψ4) has

been found 100%.

PCA Results Ψ1 Ψ2 Ψ3 Ψ4

Eigenvalue,

λK

3.1311 0.8093 0.0532 0.0064

Eigenvector,

βK

0.294

0.542

0.552

0.561

-0.949

0.261

0.142

0.105

-0.074

-0.652

0.751

-0.069

0.086

0.461

0.334

-0.818

AP 0.783 0.202 0.013 0.002

CAP 0.783 0.985 0.998 1.000


Eigenvalue,

λK

2.7251 0.9998 0.1690 0.1061

Eigenvector,

βK

0.104

0.569

0.583

0.571

-0.984

0.180

0.001

-0.000

-0.097

-0.527

-0.256

0.805

0.110

0.650

-0.771

0.164

AP 0.681 0.250 0.042 0.027

CAP 0.681 0.931 0.973 1.000


Eigenvalue, λK 2.9164 0.8891 0.1271 0.0674

Eigenvector, βK

0.266

0.572

0.560

0.538

-0.943

0.040

0.104

0.314

-0.143

-0.017

0.729

-0.669

0.142

-0.819

0.380

0.405

AP 0.729 0.222 0.032 0.017

CAP 0.729 0.951 0983 1.000



Next step is to calculate the principal component scores of the normalized comparative sequence and

reference sequences using the equation shown below, (Su and Tong, 1997):

8�� = 9 ��∗�5�: *, � = 0, 1, 2, … … … … … , �, � = 1, 2, … … … … … , �

<=$ �7�

Where, Yi (k) is the principal component score of the kth

element in the ith

series. Xi*(j) is the normalized

value of the jth

element in the ith

sequence, and βkj is the jth

element of eigenvector βk..

Now, all the correlated responses have been converted into independent (uncorrelated) quality indices

i.e. individual principal components using equation no. (7) and have been shown in Table 19, 20 & 21 for

low carbon steel, grey cast iron and bakelite respectively.

Table 19 Table 20

Independent Principal Component Independent Principal Component

for AISI 1020 Low carbon steel for IS210-FG200 Grey cast iron

Sl.

No.

Independent Principal Component

Ψ1 Ψ2 Ψ3 Ψ4

Ideal 1.9490 -0.4410 -0.0440 0.0630

1 1.7241 -0.4736 -0.0305 0.0622

2 1.6274 -0.2027 -0.0173 0.0371

3 1.5636 0.0077 -0.0096 0.0297

4 1.5815 0.0388 -0.0395 0.0268

5 1.7662 -0.2479 -0.0124 0.0548

6 1.7835 -0.3397 -0.0239 0.0497

7 1.7029 -0.0289 -0.0100 0.0396

8 1.6504 0.1224 0.0115 0.0167

9 1.8709 -0.4654 -0.0413 0.0630

10 1.7201 0.0273 0.0005 0.0275

11 1.7438 -0.0400 -0.0088 0.0361

12 1.7163 0.0318 -0.0039 0.0260

13 1.9181 -0.3910 -0.0429 0.0621

14 1.9272 -0.3822 -0.0362 0.0564

15 1.8761 -0.2094 -0.0274 0.0413

16 1.8475 -0.1998 -0.0369 0.0420

Sl.

No.

Independent Principal Component

Ψ1 Ψ2 Ψ3 Ψ4

Ideal 1.8270 -0.8030 -0.0750 0.1530

1 1.6949 -0.5040 -0.0765 0.1416

2 1.6737 -0.5716 -0.0688 0.1343

3 1.6896 -0.5275 -0.0708 0.1310

4 1.6718 -0.5003 -0.0586 0.1194

5 1.7216 -0.6061 -0.0872 0.1109

6 1.7208 -0.7316 -0.0616 0.1333

7 1.6787 -0.5422 -0.0459 0.1309

8 1.6596 -0.4448 -0.0486 0.1158

9 1.7274 -0.6064 -0.0496 0.1359

10 1.7533 -0.8105 -0.0720 0.1571

11 1.7324 -0.6427 -0.0599 0.1363

12 1.7027 -0.4008 -0.0331 0.1075

13 1.7796 -0.5473 -0.0379 0.1363

14 1.7773 -0.3947 -0.0326 0.1154

15 1.7728 -0.5593 -0.0591 0.1240

16 1.7868 -0.4646 -0.0460 0.1132



Table 21 Independent Principal Component for DIN 7735 Type-Hgw 2008 Bakelite

Accountability proportion of individual principal component has been treated as individual priority

weight, Hung-Chang L (2006). Multi-response performance index (MPI) has been computed using the

following equations and shown in Table 22, 23 & 24 for low carbon steel , grey cast iron and bakelite

respectively.

?@A = 9 B<C<

<=$

Where WJ is the weight of the jth

principal components and ΨJ principal components of jth

no.

MPILCS = Ψ1 X 0.783 + Ψ2 X 0.202 + Ψ3 X 0.013 + Ψ4 X 0.002 (8)

MPICI = Ψ1 X 0.681 + Ψ2 X 0.250 + Ψ3 X 0.042 + Ψ4 X 0.027 (9)

MPIBk = Ψ1 X 0.729 + Ψ2 X 0.222 + Ψ3 X 0.032 + Ψ4 X 0.017 (10)

N.B. : LCS stands for low carbon steel, CI stands for cast iron and BK stands for bakelite

The larger the MPI is the higher the quality.

Combined Quality Loss (CQL) is defined as the deviation of individual/independent principal

component from its ideal value. Now, CQL has been treated as single objective function for optimization

in order to minimize it. These calculated CQL on the basis of MPI are the optimal parametric combination

to obtain quality surface. Lastly, Taguchi method has been used with ‘Smaller-the-Best’, S/N criteria for

final optimization. The calculated values of MPI and CQL are laid down in the Table 22, 23 & 24 for low

carbon steel , grey cast iron and bakelite respectively.

Sl.No. Independent Principal Component

Ψ1 Ψ2 Ψ3 Ψ4

Ideal 1.9360 -0.4850 -0.1000 0.1080

1 1.7744 -0.4192 -0.1049 0.0813

2 1.8151 -0.5190 -0.1083 0.0993

3 1.7571 -0.3501 -0.1017 0.0761

4 1.6816 -0.0484 -0.0528 0.0359

5 1.6680 -0.0060 -0.0469 0.0332

6 1.8054 -0.4111 -0.0788 0.1038

7 1.6938 -0.1025 -0.0649 0.0436

8 1.8242 -0.4670 -0.1071 0.1040

9 1.7991 -0.2547 -0.0697 0.0784

10 1.7568 -0.1585 -0.0671 0.0559

11 1.8330 -0.4685 -0.0943 0.0908

12 1.7977 -0.2974 -0.0852 0.0763

13 1.7942 -0.1868 -0.0598 0.0707

14 1.7507 -0.1366 -0.0445 0.0609

15 1.9191 -0.4647 -0.1101 0.0980

16 1.8663 -0.3004 -0.0519 0.0675



Table 22 Table 23

Calculated MPI and CQL Calculated MPI and CQL

for AISI 1020 Low carbon steel for IS210-FG200 Grey cast iron

Table 24 Calculated MPI and CQL for DIN7735 Type-Hgw2008 Bakelite

Sl.

No. MPI CQL

S/N Ratio

of CQL

Ideal

Ideal 1.0444 0.0000 -

1 1.0288 0.0156 36.1394

2 0.9976 0.0468 26.5971

3 1.0193 0.0251 31.9968

4 1.0142 0.0302 30.3875

5 1.0202 0.0242 32.3123

6 0.9899 0.0545 25.2764

7 1.0093 0.0351 29.0869

8 1.0201 0.0243 32.2874

9 1.0264 0.0180 34.8732

10 0.9926 0.0518 25.7059

11 1.0202 0.0242 32.3257

12 1.0609 0.0165 35.6714

13 1.0772 0.0328 29.6905

14 1.1134 0.0690 23.2217

15 1.0683 0.0239 32.4284

16 1.1018 0.0573 24.8293

Sl.

No. MPI CQL

S/N Ratio

of CQL

Ideal

Ideal 1.4365 0.0000 -

1 1.2540 0.1825 14.7735

2 1.2331 0.2034 13.8324

3 1.2258 0.2107 13.5248

4 1.2457 0.1908 14.3864

5 1.3328 0.1037 19.6822

6 1.3277 0.1089 19.2605

7 1.3275 0.1091 19.2472

8 1.3172 0.1194 18.4627

9 1.3706 0.0659 23.6135

10 1.3525 0.0841 21.5059

11 1.3573 0.0793 22.0177

12 1.3502 0.0863 21.2805

13 1.4224 0.0141 37.0064

14 1.4315 0.0051 45.8953

15 1.4264 0.0101 39.8948

16 1.4058 0.0307 30.2571

Sl.

No. MPI CQL

S/N Ratio

of CQL Ideal

Ideal 1.3023 0.0000 -

1 1.1985 0.1038 19.6729

2 1.2062 0.0961 20.3481

3 1.2013 0.1010 19.9113

4 1.2141 0.0882 21.0890

5 1.2137 0.0886 21.0522

6 1.2241 0.0782 22.1346

7 1.2107 0.0916 20.7641

8 1.2245 0.0778 22.1792

9 1.2541 0.0482 26.3354

10 1.2443 0.0579 24.7342

11 1.2308 0.0715 22.9112

12 1.2431 0.0592 24.5518

13 1.2658 0.0365 28.7533

14 1.2456 0.0567 24.9250

15 1.2939 0.0083 41.6033

16 1.2934 0.0089 40.9998



S/N ratio plots of CQL have been shown in Fig. 5, 6 & 7 considering SB (Smaller-the-Better) criterion.

Analysis of means are also laid down in Table 25, 26 & 27 for low carbon steel ,grey cast iron and bakelite

respectively.

Figure 5 Level of factors





Table 28 Results of conformity experiments Table 29 Results of conformity experiments

AISI 1020 Low carbon steel IS210-FG200 Grey cast iron

Table 30 Results of conformity experiments DIN7735 Type-Hgw2008 Bakelite

4. DISCUSSION

The following discussion has been highlighted to analyze the above results:

4.1. Low Carbon Steel

Optimal setting has been appraised from the main effects plot for S/N ratio and response table for signal to

noise ratio (Fig. 5 & Table 25). Optimal combination has been studied as N4f2d3 i.e. N=800 RPM, f = 0.07

mm/rev and d = 0.15 mm. This result has been verified in the course of conformity test. Taguchi predicted

value of S/N ratio for combined quality loss (CQL) which has been computed as 41.9309, where as in

verification of experimental test it has been observed as 45.8953 (Table 28) at optimal setting (N4f2d3).

Responses viz. average surface roughness ‘Ra’ and RMS vibration in ’X’ , ‘Y’and ‘ Z’ axis have been

recorded as 1.42 µm, 78.36 dB, 77.74 dB and 78.49 dB respectively against the optimal parameters which

are the same as experiment run no.14 (Table 22). Further it has been observed that S/N ratio is 45.8953

against experiment run no. 14 in Table 22 corresponding to parameters viz. N=800 RPM, f =0.07 mm/rev,

and d = 0.15 mm. Hence , run no. 14 as per Table 22 is the optimal parameter offered, better S/N ratio and

surface finish with a reduction of machine vibration has been achieved.

4.2. Grey Cast Iron

Optimal setting has been assessed from the main effects plot for S/N ratio and response table for signal to

noise ratio (Fig. 6 & Table 26). Optimal combination has been studied as N3f1d2 i.e. N = 460 RPM, f =

0.05 mm/rev, and d = 0.10 mm. This result has been verified through conformity test. Taguchi predicted



Responses viz. average surface roughness ‘Ra’ and RMS vibration in ‘X’, ‘Y’ and ‘Z’ axis have been

recorded as 1.38 µm, X = 81.95 dB, Y= 81.56 dB and Z = 81.60 dB respectively against the optimal

parameters which are better than experiment run no. 01 (Table 23) corresponding parameters viz. N = 120

RPM, f = 0.050 mm/rev, d = 0.05 mm. Responses obtained at this parameters are Ra = 2.04µm, X = 81.39

dB, Y = 83.58 dB and Z = 84.39 dB. Here, S/N ratio obtained (37.8525) in practical verification is higher

than predicted value (36.7964) and quality of surface finish has been improved with a reduction of

machine vibration. Moreover S/N ratio (37.8525) observed in confirmation test, has been improved than

experiment run no.01 as per Table 23 (36.1394).

Description Optimal Setting (Grey

Prediction Experiment

Level of N3f1d2 N3f1d2

SN ratio for 36.7964 37.8525

Description Optimal Setting (Low


Level of N4f2d3 N4f2d3

SN ratio for 41.9309 45.8953

Description Optimal Setting (Bakelite)


Level of factors N4f4d2 N4f4d2

SN ratio for 37.9185 42.2721



4.3. Bakelite

Optimal setting has been assessed from the main effects plot for S/N ratio and response table for signal to

noise ratio (Fig. 7 & Table 27). Optimal combination has been studied as N4f4d2 i.e. N = 800 RPM, f =

0.12 mm/rev, and d = 0.10 mm. This result has been verified through conformity test. Taguchi predicted



Responses viz. average surface roughness ‘Ra’ and RMS vibration in ‘X’, ‘Y’ and ‘Z’ axis have been

recorded as 1.19 µm, X = 77.96 dB, Y= 77.72 dB and Z = 77.22 dB respectively against the optimal

parameter which are better than experiment run no. 15 (Table 24) corresponding parameters viz. N = 800

RPM, f = 0.10 mm/rev, d = 0.10 mm. Responses obtained at this parameters are Ra = 1.27 µm, X = 78.98

dB, Y= 78.46 dB and Z = 78.14 dB. Here, S/N ratio obtained (42.2721) in practical verification is higher

than predicted value (37.9185) and quality of surface finish has been improved with a reduction of

machine vibration. Moreover S/N ratio (42.2721) observed in confirmation test, has been improved than

experiment run no.15 as per Table 22 (41.6033).

In the above confirmative study, it has been observed that material removing rate (MRR) has been

substantially improved by reducing time and keeping the quality of the test surface better by reducing the

machine vibration.

5. CONCLUSIONS

The foregoing study deals with optimization of surface roughness by reducing machine vibration of AISI

1020 low carbon steel, DIN 7735 Type-Hgw 2008 Bakelite and IS210-FG200 grey cast iron in turning

operations for the expedition of optimal parameters. The study recommends an integrated optimization

approach utilizing combined quality loss (CQL) perception in WPCA based Taguchi technique. Some

conclusions can be drawn up based on the experiments themselves and the analysis of the experimental

data.

• PCA application is recommended to eradicate response correlation by altering correlated responses into

uncorrelated quality indices known as principal components that are treated as independent response

variables for optimization.

• Accountability proportion (AP) is taken as weight of individual response. Weighted Principal Component

analysis (WPCA) combines individual principal constituents into a single multi-response performance index

(MPI). It is utilized for optimization. Application of this can optimize simultaneously a large number of

responses.

• CQL concept involves considerable physical analysis to the objective function. CQL always has a positive

value. Thus, SN ratio that is in Taguchi technique can be calculated easily.

• For unhindered quality as well as productivity improvement in small variable batch production, and even the

off-line quality control of a product or a process, the technique of multi-objective optimization is suggested.

6. ACKNOWLEDGEMENTS

The work illustrated in this paper was entirely supported by the Mechanical Engineering Department,

Jadavpur University, Kolkata.

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