optimization of abrasive machining of ductile cast …umpir.ump.edu.my/11169/1/optimization of...

IRON USING TIO

ABSTRACT

This study was carried out

study is to investigate the performance of grinding of ductile iron based on response surface method and to develop

optimization model for grinding parameters u

selected was surface grinding and it was carried out two different coolants which are conventional coolant and titanium

dioxide nanocoolant. The selected inputs variables are table sp

single pass and multiple pass. The selected output parameters are temperature rise, surface roughness and material removal

rate. The ANOVA test has been carried out to check the adequacy of the devel

mathematical model for MRR, surface roughness and temperature rise are developed based on response surface method.

The artificial neural network model has been developed and analysis the performance parameters of g

using two different types of coolant including the conventional as well as TiO

that nanofluids as grinding coolants produces the better surface finish, good value of material removal rate and act

effectively on minimizing grinding temperature. The developed ANN model can be used as

Key

INTRODUCTION

Grinding is a material removal and surface generation

process used to shape and finish components made of

metals and other materials. The precision and surface

finish obtained through grinding can be up to ten times

better than with either turning or milli

and Sluga, 2005; Shen, Shih

employs an abrasive product, usually a rotating wheel

brought into controlled contact with a work surface

(Kadirgama, Rahman, Ismail and

and

2014; Walsh, Baliga

wheel is composed of abrasive grains held together in a

binder. Heat generation is an i

grinding process. It can degrade the integrity of the wheel

matrix and/or abrasive, reduce workpiece surface quality

by causing thermal cracks or burning of the surface,

introduce strength reducing tensile residual stresses, and

crea

Malkin

the grinding mechanism either by softe

by introducing phase transformations. This is one of the

important output parameters that will be observed where it

will be influenced widely on the usage of nano

large volume of grinding fluid is most commonly used to

floo

productivity targets while often neglecting the seemingly

fewer tangible environmental safety hazards. In addition,

OPTIMIZATION OF ABRASIVE MACHINING OF DUCTILE CAST

IRON USING TIO

ABSTRACT













Keywords: Grinding

INTRODUCTION










and Kadirgama, 2014; Rahman, Kadirgama

2014; Walsh, Baliga







creates dimensional inaccuracies

Malkin and Guo, 2007)






flood the grinding zone, hoping to achieve tangible



VOL. X, NO. X


IRON USING TIO

M.M. R













Grinding Multilayer perceptron A

INTRODUCTION Grinding is a material removal and surface generation









Kadirgama, 2014; Rahman, Kadirgama

2014; Walsh, Baliga and Hodgson, 2002)







tes dimensional inaccuracies

Guo, 2007). Temperature may also influence






d the grinding zone, hoping to achieve tangible



X, XXXXXXXX

ARPN Journal of Engineering and

©2006-2015


IRON USING TIO2 NANOPARTICLES: A MULTILAYER PERCEPTRON

M.M. RAHMAN, K. KADIRGAMA, M.M. NOOR and

Faculty of Mechanical Engineering, Universiti Malaysia Pahang

Phone: +6094246239; Fax: +6094246222

to study the effects of using nanofluids as abrasive machining coolants. The objective of this












Multilayer perceptron A





better than with either turning or milling

and Sluga, 2005; Shen, Shih and Simon, 2008)



(Kadirgama, Rahman, Ismail and Bakar, 2012; Ra

Kadirgama, 2014; Rahman, Kadirgama

Hodgson, 2002)


binder. Heat generation is an important factor in the





tes dimensional inaccuracies (Chen

. Temperature may also influence

the grinding mechanism either by softening the material or








Journal of Engineering and

15 Asian Research Pub

www.a


NANOPARTICLES: A MULTILAYER PERCEPTRON

APPROACH

AHMAN, K. KADIRGAMA, M.M. NOOR and

of Mechanical Engineering, Universiti Malaysia Pahang

26600 Pekan, Pahang, Malaysia

Email: [email protected]

Phone: +6094246239; Fax: +6094246222



optimization model for grinding parameters using artificial neural network technique. The abrasive machining process










Multilayer perceptron Approach





ng (Krajnik, Kopac

Simon, 2008). Grinding



Bakar, 2012; Rahman

Kadirgama, 2014; Rahman, Kadirgama and Ab Aziz,

Hodgson, 2002). The grinding


mportant factor in the





(Chen and Rowe, 1996;


ning the material or



will be influenced widely on the usage of nano-coolants. A






rch Publishing Network (ARPN).

www.arpnjournals



APPROACH





Phone: +6094246239; Fax: +6094246222



sing artificial neural network technique. The abrasive machining process


dioxide nanocoolant. The selected inputs variables are table speed, depth of cut and type of grinding pattern which are








pproach TiO2 Nanofluid





(Krajnik, Kopac

. Grinding



hman

Ab Aziz,

. The grinding


mportant factor in the





Rowe, 1996;


ning the material or



coolants. A





the inherent high cost of disposal or recycling of the

grinding fluid becomes another major

as the environmental regulations get stricter. Minimizing

the quantity of cutting fluid is desirable in grinding.

performance and contain lower

aluminum oxide (Al

super

high

nitride (CBN)

Vinayagam, 2010)

industries cannot achieve their productivity goals with

conventional grinding wheels. The use of a super abrasive

grinding wheel is prohibitively expensive

many machine shops. Therefore, a limited number of

manufacturing companies are using super

in their grinding operations

abrasives used in industry are synthetic. Aluminum oxide

is used in three quarters of all grinding operations, and is

primarily used to grind ferrous metals. Next is silicon

carbide, which

metals and high density materials, such as cemented

carbide or ceramics. Super abrasives, namely cubic boron

nitride or "CBN" and diamond, are used in about five

percent of grinding. Hard ferrous materials are grou

with "CBN" while non

are best ground with diamond. The grain size of abrasive

materials is important to the process.

Journal of Engineering and Applied Sciences

Network (ARPN). All righ

s.com



APPROACH





Phone: +6094246239; Fax: +6094246222





eed, depth of cut and type of grinding pattern which are


rate. The ANOVA test has been carried out to check the adequacy of the developed mathematical model. The second order



using two different types of coolant including the conventional as well as TiO2



anofluid Ductile Cast I





The conventional grinding wheels are low


aluminum oxide (Al

super-abrasive wheels are higher performance and contain

high-cost abrasives consisting of diamond or cubic boron

nitride (CBN)(Lee, Nam, Li

Vinayagam, 2010)






in their grinding operations




carbide, which is used for grinding softer, non





with "CBN" while non



pplied Sciences

hts reserved.



AHMAN, K. KADIRGAMA, M.M. NOOR and D. RAMASAMY


Phone: +6094246239; Fax: +6094246222







oped mathematical model. The second order



nanocoolant. The obtained results shows


effectively on minimizing grinding temperature. The developed ANN model can be used as a basis of grinding processes

Ductile Cast Iron







aluminum oxide (Al2O3) and s

abrasive wheels are higher performance and contain

cost abrasives consisting of diamond or cubic boron

(Lee, Nam, Li and

Vinayagam, 2010). In many applications, manufacturing






in their grinding operations (Krueger et al., 2000)




is used for grinding softer, non





with "CBN" while non-ferrous materials and non



ISSN 1819-6



D. RAMASAMY









The artificial neural network model has been developed and analysis the performance parameters of grinding processes



a basis of grinding processes


grinding fluid becomes another major concern, especially




performance and contain lower-cost abrasives such as

) and silicon carbide (SiC). The



and Lee, 2010; Prabhu

. In many applications, manufacturing



grinding wheel is prohibitively expensive and complex for


manufacturing companies are using super-abrasive wheels

(Krueger et al., 2000)




is used for grinding softer, non





ferrous materials and non



6608

1











rinding processes



a basis of grinding processes.


concern, especially




cost abrasives such as

ilicon carbide (SiC). The



Lee, 2010; Prabhu and




and complex for


abrasive wheels

(Krueger et al., 2000). Most




is used for grinding softer, non-ferrous




percent of grinding. Hard ferrous materials are ground

ferrous materials and non-metals










rinding processes


that nanofluids as grinding coolants produces the better surface finish, good value of material removal rate and acts


concern, especially



cost abrasives such as

ilicon carbide (SiC). The



and




and complex for


abrasive wheels

. Most




ferrous




nd

metals


consisting of solid nanoparticles with sizes typically

100 nm suspended in liquid. Nanofluids have attracted

great interest recently because of reports of greatly

enhanced thermal properties

Kadirgama, 2013; Mahendran, Lee, Sharma

2012; Syam Sundar

particle

(>10%) of particles to achieve such enhancement

Choi, Wenhua

Roetzel, 2003)

so far include thermal conductivities exceeding those of

traditional solid/liquid suspensions; a nonlinear

relationship between thermal conductivity and

concentration in the case of nanofluids containing carbon

nanotubes; strongly temperature

conductivity; and a significant increase in critical heat flux

in boiling heat transfer

2013; Hussein, Bakar, Kadirgama

et al., 2011; Ravisankar

Sundar

highly desirable for thermal systems; a stable and easily

synthesized fluid with these attributes and

viscosity would be a strong candidate for the next

generation of liquid coolants

Risby, 2013; Ravisankar

increasing interest in using artificial neural networks

(ANNs) for modelling and optimization of machining

process

2011; Rahman

are developed based on many simplified assumptions. It is

sometimes difficult to adjust the parameters of the above

mentioned models according to the actual situation of the

machining process. Therefore, an artificial neural

networks

possess massive parallel computing capability, have

attracted much attention in research on machining

processes. ANN provides significant advantages in solving

processing problems that require real

interpretation of relationships among variables of high

dimensional space

2012; Rahman, 2012; Rahman, Mohyaldeen, No

Kadirgama

applied in modeling many metal

as turning, milling and drilling. The general ability of the

network is actual

factors are the selection of the appropriate input/output

parameters of the system, the distribution of the dataset,

and the format of the presentation of the dataset to the

network. The selection of the neuron number,

layers, activation function and training algorithm are very

important to obtain the best results. The objectives of this

study are to investigate the effect of titanium dioxide

(TiO

develop optimiza

multilayer perceptron technique.

Nanofluids are solid






2012; Syam Sundar

particle-liquid suspensions require high concentrations


Choi, Wenhua

Roetzel, 2003)







in boiling heat transfer


et al., 2011; Ravisankar

Sundar and Sharma, 2011b)





Risby, 2013; Ravisankar



process (Kadirgama et al., 2012; Madic

2011; Rahman





networks can map the input/output relationships and






dimensional space


Kadirgama and



network is actual








(TiO2) nanocoolant on precision surface grinding and to

develop optimiza


VOL. X, NO. X

Nanofluids are solid






2012; Syam Sundar and Sharma, 2011a)

liquid suspensions require high concentrations


Choi, Wenhua and Pradeep, 2008; Das, Putra, Thiesen

Roetzel, 2003). Key features of nanofluids that reported







in boiling heat transfer(Azmi, Sharma, Mamat


et al., 2011; Ravisankar and

Sharma, 2011b)





Risby, 2013; Ravisankar and



(Kadirgama et al., 2012; Madic

2011; Rahman and Kadirgama, 2014)





can map the input/output relationships and






dimensional space (Khan, Rahman, Kadirgama


and Bakar, 2011)



network is actually dependent on three factors. These








) nanocoolant on precision surface grinding and to

develop optimization model for grinding parameters using


X, XXXXXXXX


©2006-2015

Nanofluids are solid-liquid composite materials




enhanced thermal properties (Hussein, Sharma, Bakar


Sharma, 2011a)



Pradeep, 2008; Das, Putra, Thiesen

Key features of nanofluids that reported





nanotubes; strongly temperature-dependent


(Azmi, Sharma, Mamat

2013; Hussein, Bakar, Kadirgama and Sharma, 2013;

and Tara Chand, 2013; Syam

Sharma, 2011b). Each of these features is




generation of liquid coolants (Fadhillahanafi, Leong and

and Tara Chand, 2013)



(Kadirgama et al., 2012; Madic and

Kadirgama, 2014). A









processing problems that require real-time encoding


(Khan, Rahman, Kadirgama


Bakar, 2011). ANN has been extensively

applied in modeling many metal-cutting operations such


ly dependent on three factors. These









tion model for grinding parameters using

multilayer perceptron technique..



www.a

liquid composite materials

consisting of solid nanoparticles with sizes typically of 1



(Hussein, Sharma, Bakar

Kadirgama, 2013; Mahendran, Lee, Sharma and Shahrani,

Sharma, 2011a). Conventional


(>10%) of particles to achieve such enhancement (Das,

Pradeep, 2008; Das, Putra, Thiesen

Key features of nanofluids that reported





dependent thermal


(Azmi, Sharma, Mamat and Anuar,

Sharma, 2013;

Tara Chand, 2013; Syam

. Each of these features is


synthesized fluid with these attributes and acceptable


(Fadhillahanafi, Leong and

Tara Chand, 2013). There is



and Radovanovic,

. Analytical models









time encoding


(Khan, Rahman, Kadirgama and Bakar,


. ANN has been extensively

cutting operations such






network. The selection of the neuron number, hidden








www.arpnjournals

liquid composite materials

of 1-



(Hussein, Sharma, Bakar and

Shahrani,

Conventional


(Das,

Pradeep, 2008; Das, Putra, Thiesen and

Key features of nanofluids that reported to





thermal


Anuar,

Rao

Tara Chand, 2013; Syam

. Each of these features is


acceptable


(Fadhillahanafi, Leong and

. There is



Radovanovic,

nalytical models









time encoding and

interpretation of relationships among variables of high-

Bakar,

2012; Rahman, 2012; Rahman, Mohyaldeen, Noor,

. ANN has been extensively

cutting operations such






hidden






METHODS AND MATERIALS

Supertec precision grinding machine, model STP

102ADCII

wheel (PSA

grains was used. The workpiece material was block ductile

iron with a carbon content of 3.5

hardness of 110

workpiece surface for grinding were 35 mm and 80 mm,

respectively. First, the workpiece was clamped onto a

clamper jaw since cast iron is not attracted to the magnet

field. Then the zero point of the Z

grinding the disc slowly until

After that, the coolant was sprayed directly onto the

workpiece to ensure the temperature of the workpiece was

equivalent to the temperature of the coolant and as a

precaution to achieve an exact value of rising temperature.

Then t

tachometer. The model STP

and uses a hydraulic system to move left and right. The

speed is controlled by a control valve; however, there is no

speed display. So, in this research, calibra

speed using a tachometer had to be undertaken and the

speed was set at 20 mm/min, 30 mm/min and 40 mm/min.

The design of experiments techniques enables designers to

determine simultaneously the individual and interactive

effects of many f

The central composite design

There is good commercial software available to help with

designing and analyzing response

Table

software.

Specimen

An

and different types of coolant: titanium oxide

nanocoolant

20% volume concentration conventional soluble oil water

based coolant. Constant grinding wheels, of vitrified bond

aluminum ox

grinding were considered: single pass and multiple pass

set to ten passes.

Nanofluid Preparation

selected. A



s.com


The grinding process was undertaken using a


102ADCII. A vitrified bond aluminum oxide grinding

wheel (PSA-60JBV) with an average abrasive size of 60



hardness of 110-










Then the workpiece speed was calibrated using a

tachometer. The model STP








effects of many f




Table 1 shows the DOE table generated using

software.

Table

Specimen Table speed (

A

B

C

D

E

F

G

H

I

An experiment


nanocoolant with a 0.10% volume concentration and a



aluminum oxide (PSA


set to ten passes.


Titanium oxide nanoparticle materials were

selected. A two

pplied Sciences

hts reserved.




. A vitrified bond aluminum oxide grinding

60JBV) with an average abrasive size of 60



Rockwell C. The width and length o










he workpiece speed was calibrated using a

tachometer. The model STP-102ADCII can be controlled








effects of many factors that could affect the output results.




1 shows the DOE table generated using

Table 1: Design of experiment.

Table speed (m/min)

20

20

20

30

30

30

40

40

40

experiment was conducted


with a 0.10% volume concentration and a



ide (PSA-60JBV) were used


set to ten passes.



two-step method

ISSN 1819-6







iron with a carbon content of 3.5–3.9% and average

Rockwell C. The width and length o




field. Then the zero point of the Z-axis was found by

grinding the disc slowly until there were some sparks.






102ADCII can be controlled



speed display. So, in this research, calibration of the table





actors that could affect the output results.

The central composite design (CCD) is the most popular.


designing and analyzing response-surface experiments.

1 shows the DOE table generated using

Design of experiment.

m/min) Depth of cut (µm)

was conducted based on the DOE table





60JBV) were used.



step method was used to

6608

2






3.9% and average

Rockwell C. The width and length of the




axis was found by

there were some sparks.









tion of the table






CCD) is the most popular.


surface experiments.

1 shows the DOE table generated using Minitab

Design of experiment.

Depth of cut (µm)

20

40

60

20

40

60

20

40

60

based on the DOE table

and different types of coolant: titanium oxide (TiO




. Two types of



was used to prepare the


Supertec precision grinding machine, model STP-




3.9% and average

f the




axis was found by

there were some sparks.









tion of the table






CCD) is the most popular.


surface experiments.

Minitab

based on the DOE table

TiO2)


20% volume concentration conventional soluble oil water-


Two types of



prepare the

nanofluid. T

liquid form with a

concentration with a 30

level and density equal to 5600 kg/m³. It is diluted to a

0.10% volume concentration. The conversion of t

weight percent concentration to volume concentration is

expressed

determine how much distilled water is required to dilute

the initial

where

ϕpercent of nanoparticles,

ρ

have to be faced. One of the most important issues is the

stability of the nanofluids, and it remains a considerable

challenge to achieve the desired stability of the

The stability of the mixture is ensured by maintaining the

pH of the aqueous solution of nano

sonication for about two hours resulting in no settling of

particles observed for the machining period.

stability in the dil

continuously for

rpm.

of surfactants is an important technique in enhancing the

stability of nanoparticles in fluids. However

functionality of the surfactants under high temperature is

also a major concern, especially for high

applications. Therefore, no

study.

Multilayer Perceptron Approach

analysis method under Artificial Neural Networks. In this

study, the analysis is performed using the Neuro Solutions

6 software. It is done by keying the sets of the

experimental data obtained from the experiments done in

the lab. The columns of depth o

tagged as input while the columns of temperature rise,

MRR and surface roughness are tagged as desired. The

tagged input parameters to develop the MLP model. The

hidden layer for the optimization process is set to 1. The

processin

selected for transfer function. Momentum is selected for

learning rule at 1.00000 value of step size and 0.7 for

momentum value. Maximum epochs is set 30000 and

Termination is set at MSE, minimum with Threshold

0.000001.the data are then tested for regression for each

training, cross validation and testing options. From then,

the

RESULTS AND DISCUSSION

desired output value and actual network.

nanofluid. The dispersed

liquid form with a

concentration with a 30




expressed as equation


the initial nanofluid

1ϕ =

where

1ϕ is the initial volume concentration,

percent of nanoparticles,

2TiOρ is the density of the nanoparticles,

For a two








stability in the dil

continuously for

rpm. Nanoparticles






study.


Multilayer perceptron (MLP) approach is










processing elements are set to 4 while SigmoidAxon is







the optimization model is obtained


Figure


VOL. X, NO. X

he dispersed

liquid form with a volume

concentration with a 30–40




as equation (1). It shows the dilution formula to


nanofluid.

1100

wρω

ωρ

−+

is the initial volume concentration,

percent of nanoparticles, ρis the density of the nanoparticles,

For a two-phase system, some important issues








stability in the dilution, the solution needs to be stirred

continuously for two hours

Nanoparticles have a tendency to aggregate. The use

















g elements are set to 4 while SigmoidAxon is







optimization model is obtained


Figure 1 represents the comparison between


X, XXXXXXXX


©2006-2015

he dispersed nanoparticles which

volume of one liter have 20% weight

40 nm particle size, an 8.9 pH




(1). It shows the dilution formula to


2100TiO

w

ρω

ωρ

−

is the initial volume concentration,

wρ is the density of water, and

is the density of the nanoparticles,

phase system, some important issues





pH of the aqueous solution of nano-particles and ultra



ution, the solution needs to be stirred

two hours with the mixture set to 1000

have a tendency to aggregate. The use





applications. Therefore, no surfactant is applied in this

Multilayer Perceptron Approach Multilayer perceptron (MLP) approach is





the lab. The columns of depth of cut and table speed are












optimization model is obtained.


1 represents the comparison between




www.a

nanoparticles which come in

of one liter have 20% weight

particle size, an 8.9 pH






is the initial volume concentration, ω is the weight

is the density of water, and




challenge to achieve the desired stability of the nanofluids.


particles and ultra


particles observed for the machining period. To achieve


the mixture set to 1000



stability of nanoparticles in fluids. However, the


also a major concern, especially for high-temperature

is applied in this






f cut and table speed are













desired output value and actual network. Figure



www.arpnjournals

come in

of one liter have 20% weight

particle size, an 8.9 pH


0.10% volume concentration. The conversion of the




(1)

is the weight

is the density of water, and




luids.


particles and ultra-


To achieve


the mixture set to 1000



, the


temperature

is applied in this

Multilayer perceptron (MLP) approach is an





f cut and table speed are









Termination is set at MSE, minimum with Threshold of




Figure 2

represents the sensitivity about the mean for single pass

grinding pattern.

which are table speed and depth of cut highly affects t

temperature rise of the workpiece followed by MRR while

the surface roughness is the least affected. Table

the comparison between the output parameters of desired

value (predicted) and actual value (experimental) for

single pass grinding patter

of varied input value towards all three output parameters

for single pass grinding pattern. It is observed that as the

table speed increases, temperature rise and MRR value

increases steadily while the surface roughness val

increases but in small increment. As the depth of cut

increases, the temperature rise increases a lot while MRR

and surface roughness increases steadily in small portions.

Figure

Figure

value and actual network output for TiO

multiple pass grinding pattern

sensitivity about the mean for multiple pass grinding

pattern. As shown in the figure, the increment of both

input variables which are table speed and depth of cut

highly affects the temperature rise of the workpiece. After

that i

second followed by surface roughness as table speed

increases. But for the increment of depth of cut, it is the

other way around where surface roughness is the second

most affected and the least affected



s.com


grinding pattern.






single pass grinding patter








Figure 1: Desired output and actual network output for

Figure 2: Sensitivity analysis for single pass.

Figure 4 represents the comparison between desired output







that it differs where after temperature rise, MRR is the





pplied Sciences

hts reserved.


grinding pattern. The increment of both input variables






single pass grinding pattern. Figure 3 indicates the effect








Desired output and actual network output for

single pass grind

Sensitivity analysis for single pass.

4 represents the comparison between desired output







t differs where after temperature rise, MRR is the





ISSN 1819-6


he increment of both input variables






n. Figure 3 indicates the effect









single pass grinding.



value and actual network output for TiO2nanocoolant with

multiple pass grinding pattern. Figure 5 represents the









most affected and the least affected is MRR.

6608

3



which are table speed and depth of cut highly affects the


the surface roughness is the least affected. Table 1 shows







increases steadily while the surface roughness value also







nanocoolant with

. Figure 5 represents the









is MRR.



he


1 shows







ue also






nanocoolant with

. Figure 5 represents the









Table 1: Comparison between experimental value and predicted value (0.1% TiO

Figure 3: Effect of

Table 2

Table

Speed

(m/min)

20

20

20

30

30

30

40

40

40

Table

Speed

(m/min)

20

20

20

30

30

30

40

40

40

VOL. X, NO. X

Comparison between experimental value and predicted value (0.1% TiO

(a) Table speed

(b) Depth of cut

Effect of network outputs for single pass

grinding.

Table 2: Comparison between experimental value and predicted value (0.1% TiO

Depth

of Cut

(µm) Experimental

20

40

60

20

40

60

20

40

60

DOC

(µm)

Experimental

20

40

60

20

40

60

20

40

60

X, XXXXXXXX


©2006-2015


(a) Table speed

(b) Depth of cut

network outputs for single pass

grinding.


Temperature rise

(°C)

Experimental

0

0

1

0

1

1

0

1

1

Temperature rise

(°C)

Experimental

0

0

1

0

1

1

1

1

1



www.a




Temperature rise

(°C)

Predicted

-0.054

0.079

0.946

-0.028

0.969

1.052

0.952

1.041

1.051

Temperature rise

(°C)

Predicted

-0.050

0.011

0.988

0.003

0.998

1.055

0.869

1.052

1.055



www.arpnjournals



between desired value (predicted) and actual value

(experimental) for multiple pass grinding patterns.

6 indicates the effect of varied input value towards all

three output parameters for multiple pass grinding

patterns. It is observed that as the table speed and depth of

cut increases, temperature rise increases steadily while the

MRR and surface roughness values increases but in small

increment.

Figure


Experimental

0.178

0.435

0.541

0.312

0.781

0.813

0.714

0.952

1.310

Experimental

0.023

0.052

0.063

0.038

0.085

0.091

0.107

0.190

0.214



s.com


Table 2 shows the comparison of output value




hree output parameters for multiple pass grinding




increment.

Figure 4: Desired output and actual network output for

multiple pass grinding pattern.


MRR

(g/sec)

Experimental Predicted

0.219

0.383

0.562

0.326

0.769

0.815

0.802

0.830

0.827

MRR

(g/sec)

Experimental Predicted

0.023

0.052

0.063

0.038

0.085

0.090

0.086

0.087

0.087

pplied Sciences

hts reserved.

Comparison between experimental value and predicted value (0.1% TiO2) for single pass grinding pattern.

2 shows the comparison of output value










Comparison between experimental value and predicted value (0.1% TiO2) for multiple pass grinding.

Predicted Experimental

0.219 0.201

0.383 0.264

0.562 0.310

0.326 0.251

0.769 0.281

0.815 0.385

0.802 0.237

0.830 0.303

0.827 0.489

Predicted Experimental

0.023 0.226

0.052 0.276

0.063 0.336

0.038 0.229

0.085 0.284

0.090 0.369

0.086 0.233

0.087 0.316

0.087 0.401

ISSN 1819-6

) for single pass grinding pattern.











) for multiple pass grinding.

Surface Roughness

(µm)

Experimental

0.201

0.264

0.310

0.251

0.281

0.385

0.237

0.303

0.489

Surface Roughness

(µm)

Experimental

0.226

0.276

0.336

0.229

0.284

0.369

0.233

0.316

0.401

6608

4

) for single pass grinding pattern.



(experimental) for multiple pass grinding patterns.Figure








) for multiple pass grinding.

Surface Roughness

(µm)

Predicted

0.218

0.251

0.317

0.242

0.289

0.372

0.320

0.324

0.364

Surface Roughness

(µm)

Predicted

0.226

0.276

0.336

0.228

0.284

0.367

0.263

0.299

0.325



Figure-







Figure 6

CONCLUSIONS

parameters including the grinding pattern, depth of cut and

table speed have been studied towards the output

parameters including the temperature rise, surface

roughness and material removal rate for both conventional

coola

based on desired minimize the temperature rise, minimum

surface roughness and maximum material removal rate.

For single pass grinding patterns, all three output

parameters are more affected by depth of

the table speed. As table speed increases, grinding

temperature and MRR increases steadily while surface

roughness is nearly constant. However, as the depth of cut

increases, grinding temperature increases the most

followed by MRR and lastl

multiple pass grinding patterns, the grinding temperature

and surface roughness are more influenced by the depth of

cut compared to table speed while MRR is more affected

by varying table speed compared to depth of cut. The

incr

Figure 5: Sensitivity about the mean for multiple pass

Figure 6: Network outputs for varied input depth of cut for

CONCLUSIONS





coolant and titanium dioxide nanocoolant. The selection is














increase in table speed causes the grinding temperature to

VOL. X, NO. X

Sensitivity about the mean for multiple pass

grinding pattern.

(a)

(b)

Network outputs for varied input depth of cut for

multiple pass grinding

CONCLUSIONS

The effects of selected of input





nt and titanium dioxide nanocoolant. The selection is














ease in table speed causes the grinding temperature to

X, XXXXXXXX


©2006-2015


grinding pattern.

Table speed

Depth of cut


multiple pass grinding.















followed by MRR and lastly is surface roughness. For








www.a












parameters are more affected by depth of cut followed by





y is surface roughness. For








www.arpnjournals












cut followed by





y is surface roughness. For






increase dramatically while MRR and surface roughness

are significantly affected. On the other hand, the increase

of depth of cut also highly affects temperature difference,

MRR also increases by a sma

roughness is nearly constant.

ACKNOWLEDGMENTS

Malaysia Pahang for financial support under university

research project no. RDU120310.

REFERENCE

Azmi,

Chen, X.

Das, S.K., Choi, S.U.S., Wenhua, Y.W.

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s.com






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The authors would like to thank Universiti



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