407

________________

Corresponding author: Reddy Sreenivasulu,

E-mail address: : reddysri1431@yahoo.co.in

Doi: http://dx.doi.org/10.11127/ijammc.2013.02.076

Copyright@GRIET Publications. All rights reserved.

Advanced Materials Manufacturing & Characterization Vol3 Issue 1 (2013)

Advanced Materials Manufacturing & Characterization

journal home page: www.ijammc-griet.com

Experimental Investigation on Influence of Nano Fluids in Drilling of Al 6061

Alloy using Grey Based- Taguchi Approach

Reddy Sreenivasulu1, Ch.Srinivasa Rao2

1Assist Professor, Department of Mechanical Engineering, R.V.R. &J.C.College of Engineering(Autonomous), Guntur, Andhra Pradesh, India. 2 Associate Professor, Department of Mechanical Engineering ,University College of Engineering (Autonomous), Andhra University, VisakhapatnamAndhra

Pradesh, India.

A R T I C L E I N F O Article history: Received 16Oct 2012 Accepted 26 Dec 2012 Keywords: Drilling, Nano fluids, Orthogonal Array, Taguchi-Grey relational analysis

Introduction

A B S T R A C T Drilling, a hole making process is especially important because it accounts for a large portion of overall machining operations. Amongst all machining operations, drilling using twist drill is most commonly applied method for generating holes for riveting and fastening of structural assemblies.This paper deals with multi performance characteristics in drilling of Aluminum 6061 alloy with water based Nano fluid (small amount of Al2O3 Nano particles in water) using Grey based – Taguchi method. Five parameters namely cutting speed, feed rate, point angle, clearance angle and variable volume fraction of Al2O3

Nanoparticles in water were identified and their ranges for the present investigation were determined from preliminary experiments. Thrust force and surface roughness are recorded using well calibrated equipments at 3 levels with L27 orthogonal array as per Taguchi experimental plan. Optimal combination of process parameters was obtained to minimize the recorded responses. In the case where experiments are ambiguous or when the experimental method cannot be carried out exactly, grey analysis helps to compensate for the shortcoming in statistical regression. Grey based – Taguchi method is an effective tool for analyzing the relationship between sequences with less data and can analyze many factors that can overcome the disadvantages of statistical method.

Most of manufacturing industries perform a huge number of drilling operations in machine shops. The drilling technology has been studied to improve the cutting performance with optimizing the cutting parameters and the tool geometry. Some researchers have developed mathematical models of drilling to estimate thrust and torque. Williams [1] showed that during cutting there are three identifiable zones of interest at the drill point, the main cutting edges, the secondary cutting edges at the chisel edge and an indentation zone about the drill center. Zhang et al. model was based on mechanics of vibration and the continuous distribution of thrust and torque along the lip and the chisel edge of the twist drill [2]. The mean thrust and torque

increased as feed increases under constant vibration parameters [3–4]. They concluded that vibration drilling is different from conventional drilling and it is a dynamic cutting process. Another model was presented for drilling processes by Yang et al. [5]. A statistical analysis of hole quality was performed by Furness et al. [12]. They found that feed and speed have a relatively small effect on the measured hole quality features. With the expectation of hole location error, the hole quality is not predictably or significantly affected by the cutting conditions. Although the authors did not expect these results, they have the important positive implication that production rates may be increased without sacrificing hole quality. Rincon and Ulsoy[15] showed that the changes in the relative motion of the drill do affect the variations of the forces. An increase in the ranges of drill motion results in an increase in the ranges of torque and thrust.

The 6061 alloy of aluminum is primarily composed of magnesium and silicon. Some other elements of 6061 aluminum alloy include small amounts of iron, copper, manganese,

408

magnesium, chromium, zinc and titanium. The 6061 composition of aluminum is an extensively used material for the construction of a wide variety of materials. Bicycles, airplane parts, automotive parts and aluminum cans are all constructed utilizing 6061 aluminum. In many cases, the foil interior wrapper on food containers is also made with 6061 aluminum alloy. Because the material is extremely workable, it is an ideal material for use in these products. Due to its good mechanical properties such as machinability and low density, Aluminum is commonly used in a wide range of industries and constitutes about 40% of all metal-cutting operations [16].

Based on the literature review it was concluded that experimental studies on the effect of thrust force and surface roughness in drilling of Al6061 alloy with nanofluids has not been reported yet. This research was aimed at studying the performance of thrust force and surface roughness during drilling of Al6061 alloy experimentally on Grey based Taguchi method .The paper also highlight the result of ANOVA to confirm the validity and correctness of the Grey based Taguchi method.

Experimental procedure

The standard high- speed steel twist drills with different point and clearance angles were used in the presentinvestigation. Drilling operation was carried out on a Universal Milling Machine with vertical head attachment, Sutlej make, Ludhiana, Punjab, India. Sensing signaled (thrust force (0-500Kgf) and surface roughness (0.025-50 µ-m) were measured using drill tool dynamometer (IEIOS Bangalore make, Model: 651) and TalySurf-50, Taylor Hobbson, England respectively. The photograph of experimental setup is shown in figure1.The experiments were conducted at RVR&JC College of Engineering (Autonomous), Guntur, Andhra Pradesh, India. Drilling of exercises was carried out for each experimental condition to drill 10mm depth blind holes on the Al6061 work piece and for each experimental condition five holes were drilled, with water based Nano fluid (small amount of Al2O3 Nano particles in water).

Figure1: Experimental setup and measurement system

Grey based – Taguchi method:

The integrated grey based taguchi method combines advantages of both grey relational analysis [9]and taguchi method [10]. This method was successfully applied to optimize the multi response of complicated problems in manufacturing processes. Furthermore, ANOVA is performed to see which process parameters are statistically significant. The integrated grey based taguchi method combines the algorithm of taguchi method and grey relational analysis to determine process parameters for multiple responses as shown in figure2. In grey relational analysis, black represents having no information and white represents having all information. A grey system has a level of information between black and white. This analysis can be used to represent the grade of correlation between two sequences so that the distance of two factors can be measured discretely. In the case when experiments are ambiguous or when the experimental method cannot be carried out exactly, grey analysis helps to compensate for the shortcoming in statistical regression .Grey relation analysis is an effective means of analyzing the relationship between sequences with less data and can analyze many factors that can overcome the disadvantages of statistical method. In grey relational analysis, when the range of the sequence is large or the standard value is enormous, the function of factors is neglected. However, if the factors goals and directions are different, the grey relational might produce incorrect results. Therefore, one has to pre-process the data which are related to a group of sequences, which is called ‘grey relational generation’. Data pre-processing is a process of transferring the original sequence to a comparable sequence. For this purpose the experimental results are normalized in the range between zero and one. In the grey relational analysis, the grey relational grade is used to show the relationship among the sequences. If the two sequences are identical, then the value of grey relational grade is equal to 1. The grey relational grade also indicates the degree of influence that the comparability sequence could exert over the reference sequence. Therefore, if a particular comparability sequence is more important than the other comparability sequences to the reference, then the grey relational grade for that comparability sequence and reference sequence will be higher than other grey relational grades.

Figure.2. Structure of Grey based – Taguchi method

409

Nanofluids

Nanofluids are a relatively new class of fluids which consist of a base fluid with nano-sized particles (1–100 nm) suspended within them. These particles, generally a metal or metal oxide, increase conduction and convection coefficients, allowing for more heat transfer out of the coolant [11]. Serrano et al. [12], provided excellent examples of nanometer in comparison with millimeter and micrometer to understand clearly as can be seen in figure.3.In the past few decades, rapid advances in nanotechnology have lead to emerging of new generation of coolants called “nanofluids”. Nanofluids are defined as suspension of nanoparticles in a base fluid. Some typical nanofluids are ethylene glycol based copper nanofluids and water based copper oxide nanofluids, Nanofluids are dilute suspensions of functionalized nanoparticles composite materials developed about a decade ago with the specific aim of increasing the thermal conductivity of heat transfer fluids, which have now evolved into a promising nano technological area. Such thermal nanofluids for heat transfer applications represent a class of its own difference from conventional colloids for other applications.

Figure 3.Length scale and some examples related to nano particles

Vital role of NanoFluid in drilling application

Drilling fluid are used in drilling operations to coal and lubricate the drill bit, remove rock debris and drill cuttings from the site and to counteract down hole formation pressures, but if the fluid lacks in any of functional requirements could lead to severe drilling problems such as lost circulation, pipe sticking, formation damage, erosion of the borehole, poor hole cleaning and torque and drag that significantly reduces the efficiency of drilling. The rheology of the drilling fluid determines the serviceability of the fluid and proportionally the probability of encountering undesirable predicaments in drilling operation. The viscosity, density and the gelling strength of the drilling fluid are the key factors that determine the functional specifications at drilling fluids with the obvious inference of the fact that they should remain constant over a wide range of operating condition. Research is being conducted to develop nanoparticle-amended drilling fluid with enhanced functionalities. Such enhancements include improved rheological, thermal, mechanical, magnetic and optical profiles. These drilling fluids will have close to realtime responsiveness (i.e. viscosity) to changing conditions downhole. Cutting fluids with inclusion of nanoparticles have enhanced heat

transfer capacity up to 6%. Nano fluids samples (figure5) are prepared and obtained from Vignan Engineering College, Vadlamudi, and Guntur.

Figure 4.Samples of Al2O3 nanofluids (without any stabilizer) stability

change with time

Results and discussions

Table2 shows the experimental runs according to the selected orthogonal table. After drilling, two quality objectives of the work pieces are chosen, including the thrust force and surface roughness. Typically, small values of thrust force and surface roughness are desirable for the drilled hole, grey relational coefficient and grade for each experiment of the L27orthogonal array was calculated. Experiment No.4 has the highest grey relational grade. Thus the experiment 4 gives the best multi performance characteristics among the 27 experiments. The response table of Grey based -Taguchi method was employed here to calculate the average grey relational grade for each factor level. The procedure was to group the relational grades firstly by factor level for each column in the orthogonal array and then to average them. Since the grey relational grades represented the level of correlation between the reference and comparability sequences, the larger grey relational grade means the comparability sequence exhibits a stronger correlation with reference sequence. Therefore, the comparability sequence has a larger value of grey relational grade for the thrust force and surface roughness. Based on this premise the study selects the level that provides the largest average response. In table3, A2 B1 C2 D1 E1 shows the largest value of grey relational grade for factors A, B, C, D and E respectively. Therefore A2 B1 C2 D1 E1 is the condition for the optimal parameter combination of the drilling of a hole to minimize thrust force and surface roughness. The influence of each cutting parameter can be more clearly presented by means of the grey relational grade graphs. It shows the change in the response, when the factors go for their level 1 to level 3. The response graph for the drilling parameters is presented in fig.5.

410

Table1. Factors and Levels of the Experiment

Levels

Cutting Speed

(rpm)

A

Feed rate (mm/rev)

B

Volume fraction of

Al2O3nanofluids in

water

C

Point angle(◦)

D

Clearance Angle(o)

E

1

350

0.3

0.3

136

4

2

550

0.5

0.5

126

6

3

750

0.6

0.7

118

8

Table2. Grey – Taguchi Response Table as per L27 Orthogonal Array

Exp A B C D E Measured Responses Grey Relational Grade

Ft ( Kg-f )

Ra(µ-m)

1 1 1 1 1 1 108.36 1.58 0.9159

2 1 1 1 1 2 119.21 1.36 0.7185

3 1 1 1 1 3 127.00 3.54 0.5819

4 1 2 2 2 1 124.00 1.85 0.9225

5 1 2 2 2 2 223.00 3.16 0.6013

6 1 2 2 2 3 241.00 2.82 0.6747

7 1 3 3 3 1 328.00 3.48 0.5283

8 1 3 3 3 2 127.00 3.25 0.5209

9 1 3 3 3 3 225.00 1.89 0.6226

10 2 1 2 3 1 329.68 3.03 0.5034

11 2 1 2 3 2 331.00 2.65 0.5020

12 2 1 2 3 3 226.42 1.56 0.7773

13 2 2 3 1 1 218.52 3.36 0.4697

14 2 2 3 1 2 293.00 3.41 0.5430

15 2 2 3 1 3 303.46 1.76 0.6715

16 2 3 1 2 1 367.00 3.62 0.5201

17 2 3 1 2 2 315.00 2.95 0.4702

18 2 3 1 2 3 296.00 3.11 0.4706

19 3 1 3 2 1 125.81 1.88 0.6611

20 3 1 3 2 2 127.80 1.95 0.5872

21 3 1 3 2 3 224.00 2.76 0.4874

22 3 2 1 3 1 122.00 2.54 0.5717

23 3 2 1 3 2 302.00 2.15 0.4655

24 3 2 1 3 3 268.74 3.28 0.4936

25 3 3 2 1 1 217.32 2.84 0.4979

26 3 3 2 1 2 340.00 2.45 0.5166

27 3 3 2 1 3 233.00 3.38 0.4670

411

Table 3: Optimal Response table for average grey relational grade

Levels A B C D E

1 0.5093 0.6598 0.5509 0.5713 0.5762

2 0.6762 0.5518 0.5680 0.5545 0.5488

3 0.4831 0.4571 0.5497 0.5427 0.5436

Figure5. Response graphs for the drilling parameters

Conclusions

The Grey relational analysis, based on an orthogonal

array of the Taguchi methods was a way of optimizing the

process parameters in drilling for A16061 From the response

table of the average grey relational grade, it is found that the

largest value of the GRA for the cutting speed of 550rpm, the feed

rate of 0.5 mm/rev, 0.5 Volume fraction of Al2O3nanofluids in

water, point angle 136 degrees and clearance angle 4degrees. It is

the recommended levels of the controllable parameters for the

process of drilling as the minimization of thrust force and surface

roughness. Due to applying nano fluids as cutting fluid, it causes

increase of thrust force and better surface roughness, observed

during experimentation.

References

1) R.A. Williams, A study of the drilling process, Journal of Engineering for Industry (1974); 1207–1215.

2) L.B. Zhang, L.J. Wang, X.Y. Liu, H.W. Zhao, X. Wang, H.Y. Lou, Mechanical model for predicting thrust and torque in vibration drilling fibre-reinforced composite materials, International Journal of Machine Tools and Manufacture 41 (2001) 641–657.

0

0.5

1

1 2 3 Ave

rage

Gre

y R

ela

tio

nal

Gra

de

Factor Levels

Cutting Speed

0

0.5

1

1 2 3

Ave

rage

Gre

y R

ela

tio

nal

Gra

de

Factor Levels

Feed Rate

0.54

0.56

0.58

1 2 3

Ave

rage

Gre

y R

ela

tio

nal

Gra

de

Factor Levels

Volume fraction of

Al2O3 Nano particles

with water

0.52

0.54

0.56

0.58

1 2 3 Ave

rage

Gre

y R

ela

tio

nal

G

rad

e

Factor Levels

Point Angle

0.52

0.54

0.56

0.58

1 2 3

Ave

rage

Gre

y R

ela

tio

nal

G

rad

e

Factor Levels

Clearance Angle

412

3) L.P. Wang, L.J. Wang, Y.H. He, Z.J. Yang, Prediction and computer simulation of dynamic thrust and torque in vibration drilling, Proceedings Institution of Mechanical Engineers 212 (Part B) (1998) 489–497.

4) L.P. Wang, J.S. Wang, P.Q. Ye, L.J. Wang, A theoretical and experimental investigation of thrust and torque in vibration micro drilling, Proceedings Institution of Mechanical Engineers 215 (Part B) (2001) 1539–1548.

5) J.A. Yang, V. Jaganathan, R. Du, A new dynamic model for drilling and reaming processes, International Journal of Machine Tools and Manufacture 42 (2002) 299–311.

6) R.J. Furness, C.L. Wu, A.G. Ulsoy, Statistical analysis of the effects of feed, speed, and wear of hole quality in drilling, Journal of Manufacturing Science and Engineering 118 (1996) 367–375.

7) D.M. Rincon, A.G. Ulsoy, Effects of drill vibration on cutting forces and torque, Annals of the CIRP 43 (1994) 59–62.

8) Hamade, R.F., Ismail, F. (2005). “A case for aggressive drilling of aluminum.” Journal of Materials Processing Technology, Vol. 166, pp. 86-97

9) Deng, J. Introduction to Grey System. J. Grey System, 1(1), 1-24 (1989)

10) Montgomery, D. C., Design and Analysis of Experiments ,5th Ed., Wiley, New York, (2000)

11) Serrano E, Rus G, Martínez JG. Nanotechnology for sustainable energy. Renew Sust Energy Rev 2009; vol. 13, 2373–84.

12) Wasan, D.T. and Nikolove, A.D. Spreading of Nanofluids on Solid, Nature 423, pp.156-159, 2003

13) Srikan RR, Rao DN, Subrahmanyam MS, Vamsi KP. Applicability of cuttingfluids with nanoparticle inclusion as coolants in machining. ProcIMechE Part J: EngTribol 2009; 223.