413

________________

Corresponding author: Reddy Sreenivasulu

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

Doi: http://dx.doi.org/10.11127/ijammc.2013.02.077

Copyright@GRIET Publications. All rights reserved.

Advanced Materials Manufacturing & Characterization Vol 3 Issue 1 (2013)

Advanced Materials Manufacturing & Characterization

journal home page: www.ijammc-griet.com

Modeling and Optimization of Thrust force and Torque during Drilling of Aluminum 6061 Alloy Using Taguchi–Grey Analysis Approach

Reddy Sreenivasulu1, Ch.Srinivasa Rao2

1 Assistant Professor, Department of Mechanical Engineering, R.V.R. &J.C.College of Engineering, Chowdavaram,Guntur, Andhra Pradesh, India. 2 Associate Professor, Department of Mechanical Engineering, University College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India

A R T I C L E I N F O Article history: Received 05 Dec 2012 Accepted 26 Dec 2012 Keywords: Drilling, Thrust force, Torque, Orthogonal Array, Grey relational analysis.

Introduction

A B S T R A C T Drilling, a hole producing process is especially important because it accounts for a large portion of overall machining operations. Amongst all machining operations, drilling using twist drill is the most commonly applied method for generating holes for riveting and fastening structural assemblies. It is well known that the drill point geometry has a significant effect of the thrust force of a twist drill. The present research initiative in an attempt to investigate the relative significance of the drilling parameters such as point angle, clearance angle, speed, feed rate and drill diameter on the thrust force and torque using Taguchi-Grey relational analysis. Drilling operations have been conducted over a wide a range of cutting condition. Spindle speed varied in the range 600 rpm to 1000 rpm in 3steps, Feed rate varied from 0.3 to 0.6mm /rev in 3 steps. High-speed steel (HSS) drills of 3 different diameters (8mm, 10mm and 12mm) and different point angles have been used for drilling of 27 through holes on 10mm depth with variable combination of soluble oil mixing with pure water on Aluminum 6061 alloy. A drill tool dynamo meter was used to record the thrust force and torque. In grey relational 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 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 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.

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. However, burrs are sometimes formed when the drill exits the work piece and the exit burrs have to be removed in the deburring process. The control of burr formation, therefore, has been strongly required to reduce the post process of the drilling operation. Many researchers have been made on burr formation so far. The earlier works associated the burr shape with the

cutting parameters experimentally. Some mathematical models based on the experiments were presented to determine the cutting conditions. A statistical analysis was presented to estimate the burr height in the drilling process. Some of researches tried to associate the drill geometries with burr formations. A drill shape, which removed the chisel edge and increased the wedge angle and the web thickness, was designed to suppress burr formation and evaluated in cutting of some materials . From the point of view of the cutting force, the thrust at the end of the cut promotes burr formation in drilling of the plates. When the cutting chip flows upward, a large burr is left at the edge of the hole. Therefore, the thrust and torque should be controlled to reduce burr formation.

414

Fig1. View of forces on the lip

The study of drilling has often presented some difficulties which are linked to the complex geometry of the twist drill (Fig.1). In practice, generally empirical equations are used to calculate thrust force and torque. These equations are very approximate, because they do not take all the cutting parameters into account. They often use only the feed speed and the diameter of the drill.

Williams recognized the significance of the feed on the resultant velocity and on altering the cutting geometry. In making predictions of torque and thrust, Williams argued that a portion of the drill acted as an orthogonal cutting edge because the cutting velocity is assumed to be perpendicular to the cutting edge. In 1972 Armarego and Cheng proposed an approach to predict thrust and torque during drilling for a conventional drill and a modified drill in order to simplify the calculations. The method of calculation used the orthogonal cutting model and the oblique cutting model, and was also used in 1979 by Wiriyacosol and Armarego . Basically, this method consists of dividing the cutting edges into a limited number of cutting elements. These elements were assumed to be oblique cutting edges on the cutting lip and orthogonal cutting edges on the chisel edge. The calculation used empirical equations established from orthogonal cutting tests. In most of the methods mentioned above, the major problem was to choose the number of cutting elements, and to determine the empirical equations for some cutting parameters. Watson initially used practically the same method, with a different geometry. He developed a model for the chisel edge and the lip from the orthogonal cutting model and the oblique cutting model, respectively. The author initially used the same principle which consisted of dividing drill edges into a number of elementary cutting edges. Watson recognized that the chips front the lips and the chisel edges are continuous across their width and that continuity imposes a restriction on the possible variation of the chip flow angle across those edges. Elhachimiet

al. assumed that the chisel edge model results are very small compared with where the cutting process takes places and they found that the thrust force is not sensitive to the variation of the spindle rotational speed. However, the effect of the spindle speed cannot be neglected on the torque. The power and the torque are proportional to the rotational speed. Moreover, thrust force, torque and power increase with the feed. Chandrasekharan et al. developed a theoretical method to predict the torque and thrust along the lip and chisel edge. A mechanistic force model can be used to develop models for cutting force system and a calibration algorithm to extract the cutting model coefficients. A statistical analysis of hole quality was performed by Furness et al. 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 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. They suggested that drill vibrations can have an effect on drilling performance because increasing vibration during entry can cause poor hole location accuracy and burr formulation. 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. 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.

Based on the literature review it was concluded that experimental studies on the effect of thrust force and torque on burr size in drilling of Al6061 alloy has not been reported yet. This research was aimed at studying the performance of thrust force and torque during drilling of Al6061 alloy to minimize exit burr size experimentally on Grey based Taguchi method.

2. CUTTING FLUIDS

Cutting fluids, sometimes referred to as lubricants or coolants are liquids and gases applied to the tool and work piece to assist in the cutting operations. They may be made from petroleum distillates, animal fats, plant oils, or other raw ingredients. Depending on context and on which type of cutting fluid is being considered, it may be referred to as cutting fluid, cutting oil, cutting compound, coolant, or lubricant. Most metalworking and machining processes can benefit from the use of cutting fluid, depending on work piece material. A common exception to this is machining cast iron or brass, which are machined dry. Also, interrupted cuts, such as milling with carbide cutters, are usually recommended to be used dry due to damage to the cutters caused by thermo shock.

415

Experimental procedure

The standard high- speed steel twist drills of 8mm,

10mm and 12mm with different point angles(118o,126o and

136o) ,variable cutting fluid mixture ratio[ (120ml of soluble oil +

880ml of water), (180ml of soluble oil + 820ml of water), (240ml

of soluble oil + 760ml of water)]was used with variable speeds

and feed rates for 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 (0-5000N) and torque (0-500Nm) were

measured using drill tool dynamometer (IEIOS Bangalore make,

Model: 651). Drilling of exercises was carried out for each

experimental condition to drill 10mm depth blind holes on the

Aluminum 6061alloy having the composition of 0.63% Si, o.466%

Fe, 0.096% Cu, 0.179% Mn, 0.53% Mg,0.091% Zn, 0.028%

Cr,0.028% Ti and remaining aluminum and for each experimental

condition five holes were drilled.

Fig2: Experimental Setup

Design of Experiments using Taguchi’s orthogonal array

technique:

Taguchi method has been widely employed in several industrial

fields, and research works. By applying the Taguchi technique,

the time required for experimental investigations can be

significantly reduced, as it is effective in the investigation of the

effects of multiple factors on performance as well as to study the

influence of individual factors to determine which factor has

more influence, which one less. The factor level combinations

selected for the design of experiments table1.An L27 OA lay out

was selected to satisfy the minimum number of experiments

condition. The experimental results are shown in table2.

Table1. Factors and Levels of the Experiment

Levels of factors

Cutting Speed (rpm) A

Feed rate (mm/rev) B

Drill diameter(mm) C

Point angle(◦) D

Cutting fluid mixing ratio(%)

E

1 350 0.3 8 118 12

2 550 0.5 10 126 18

3 750 0.6 12 136 24

Table2. Grey – Taguchi Response Table as per L27 orthogonal array

Grey Relational Analysis:

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

Exp

A

B

C

D

E

Measured Responses

Ft ( Kg-f ) Kg f

T(Kg-m)

Kg m 1 1 1 1 1 1 108.36 272

2 1 1 1 1 2 119.21 352

3 1 1 1 1 3 127.00 380

4 1 2 2 2 1 124.00 456

5 1 2 2 2 2 223.00 482

6 1 2 2 2 3 241.00 431

7 1 3 3 3 1 328.00 340

8 1 3 3 3 2 127.00 276

9 1 3 3 3 3 225.00 232

10 2 1 2 3 1 329.68 308

11 2 1 2 3 2 331.00 420

12 2 1 2 3 3 226.42 341

13 2 2 3 1 1 218.52 367

14 2 2 3 1 2 293.00 448

15 2 2 3 1 3 303.46 268

16 2 3 1 2 1 367.00 403

17 2 3 1 2 2 315.00 246

18 2 3 1 2 3 296.00 372

19 3 1 3 2 1 125.81 384

20 3 1 3 2 2 127.80 364

21 3 1 3 2 3 224.00 388

22 3 2 1 3 1 122.00 454

23 3 2 1 3 2 302.00 374

24 3 2 1 3 3 268.74 358

25 3 3 2 1 1 217.32 207

26 3 3 2 1 2 340.00 282

27 3 3 2 1 3 233.00 278

416

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 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.

Results and Discussions:

Table2 shows the experimental runs according to the

selected orthogonal table for smaller the better ratio of S/N

ratios. After drilling, two quality objectives of the work pieces are

chosen, including the thrust force and torque. Typically, small

values of thrust force and torque are desirable for the drilled

hole, grey relational coefficient and grades for S/N ratio for each

experiment of the L27orthogonal array was calculated. The

response table of Taguchi method was employed here to

calculate the average grey relational grade for each factor level.

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 torque.

Based on this premise the study selects the level that provides

the largest average response. In table3, A2 B1 C1 D3 E1 shows

the largest value of grey relational grade for factors A, B, C, D and

E shows the condition for the optimal parameter combination of

the drilling of a hole to minimize thrust force and torque. The

influence of each cutting parameter can be more clearly

presented by means of the grey relational grade graph. It shows

the change in the response, when the factors go for their level 1

to level 3. The response graphs for the drilling parameters is

presented in fig.3.

Table 3. Average Grey Relational Grade for Factor and Levels of the

Experiment

Levels

Factors

Cutting Speed (rpm)A

Feed rate (mm/rev)B

Drill diameter (mm) C

Point angle(◦) D

Cutting fluid ratio(%)E

1 0.4256 0.6532 0.5026 0.5487 0.6231 2 0.553

9 0.5902 0.4241 0.5523 0.5338

3 0.3983 0.5282 0.4610 0.5954 0.5647

750550350

-48.5

-49.0

-49.5

-50.0

-50.5

0.60.50.3 12108

136126118

-48.5

-49.0

-49.5

-50.0

-50.5

241812

CuttingSpeed(rpm)-A

Me

an

of

SN

ra

tio

s

FeedRate(mm/rev)-B Drilldiameter(mm)-C

PointAngle(degrees)-D CuttingFluidMixingratio(%)-E

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

750550350

-32

-34

-36

0.60.50.3 12108

136126118

-32

-34

-36

864

Cutting Speed (rpm)

Me

an

of

SN

ra

tio

s

Feed rate (mm/rpm) Drill diameter

Point angle(?) Clearance Angle(o)

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Fig4.Main Effects plots for S/N Ratio for thrust force & torque

750550350

30

28

26

24

22

0.60.50.3 12108

136126118

30

28

26

24

22

864

Cutting Speed (rpm)

Me

an

Feed rate (mm/rpm) Drill diameter

Point angle(◦) Clearance Angle(o)

main effcts plot for thrust force

750550350

70

60

50

40

0.60.50.3 12108

136126118

70

60

50

40

864

Cutting Speed (rpm)

Me

an

Feed rate (mm/rpm) Drill diameter

Point angle(◦) Clearance Angle(o)

main effcts plot for torque

417

300

200

100

241812

0.60.50.3 136126118

300

200

100

300

200

100

300

200

100

750550350

300

200

100

12108

CuttingSpeed(rpm)-A

FeedRate(mm/rev)-B

Drilldiameter(mm)-C

PointAngle(degrees)-D

CuttingFluidMixingratio(%)-E

350

550

750

CuttingSpeed(rpm)-A

0.3

0.5

0.6

FeedRate(mm/rev)-B

8

10

12

Drilldiameter(mm)-C

118

126

136

PointAngle(degrees)-D

12

18

24

CuttingFluidMixingratio(%)-E

Interaction Plot for Thrust Force(Kgf)-FData Means

420

360

300

241812

0.60.50.3 136126118

420

360

300

420

360

300

420

360

300

750550350

420

360

300

12108

CuttingSpeed(rpm)-A

FeedRate(mm/rev)-B

Drilldiameter(mm)-C

PointAngle(degrees)-D

CuttingFluidMixingratio(%)-E

350

550

750

CuttingSpeed(rpm)-A

0.3

0.5

0.6

FeedRate(mm/rev)-B

8

10

12

Drilldiameter(mm)-C

118

126

136

PointAngle(degrees)-D

12

18

24

CuttingFluidMixingratio(%)-E

Interaction Plot for Torque(Kgm)-TData Means

Fig5.Interaction plots for thrust force & torque

Confirmation test by ANOVA:

The analysis of variance (ANOVA) of the experimental data was done to statistically analyze the relative significance of the parameters under the investigation on the response variables. From the analysis of table4 we can observe that drill diameter factor (P=59.6%) has statistical and physical significance on the thrust force obtained for the standard HSS twist drill. Geometry of drill bit has more significance in the case of thrust force. From the analysis of table5 we can observe that clearance angle factor (P=51.2%) has statistical and physical significance on the torque obtained for the standard HSS twist drill. Geometry of drill bit and feed rate has more significance in the case of torque. The cutting speed factor does not present a statistical significance in both responses

Source DF SS MS F P Cutting

Speed

2 309.7 154.870 15.4

9

0.000

FeedRate 2 133.9 66.964 6.70 0.008

Drill

Diameter

2 10.69 5.345 0.53 0.596

Point

Angle

2 10.88 5.442 0.54 0.591

Clearance

Angle

2 21.86 10.930 1.09 0.359

Error 16 159.9 9.998

Total 26 647.0

Source DF SS MS F P

Cutting Speed

2 4303.19 2151.5 13.51 0.000

Feed Rate 2 453.63 226.81 1.42 0.270

Drill Diameter

2 797.63 398.81 2.59 0.113

Point Angle

2 437.63 218.81 1.37 0.281

Clearance Angle

2 222.74 111.37 0.70 0.512

Error 16 2548.6 159.3

Total 26 8763.4

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 750rpm, the feed rate of 0.03

mm/rev, drill diameter 8mm,the 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 torque.The order of the

importance of influential factors based on the taguchi response

table in sequence is drill diameter, Feed rate, Clearance angle,

point angle and cutting speed

References:

1. Pande, S. S., ReleKar, H. P., 1986, Investigations on Reducing Burr Formation in Drilling, International Journal of Machine Tool Design Researches, 26, 3339-348.

2. Lauderbaugh, L. K., 2008, Analysis of the effects of process parameters on exit burrs in drilling using a combined simulation and experimental approach, Journal of Material Processing Technology, 2008.04.062.

3. Ko, S. L., Lee, J, K, 2001, Analysis of burr formation in drilling with a new-concept drill,

4. Journal of Material Processing Technology, 113,392-398. 5. R.A. Williams, A study of the drilling process, Journal of

Engineering for Industry (1974); 1207–1215. 6. M. Elhachimi, S. Torbaty, P. Joyot, Mechanical modeling of

high speed drilling: 1 predicting torque and thrust, International Journal of Machine Tools and Manufacture 39 (1999) 553–568.

7. M. Elhachimi, S. Torbaty, P. Joyot, Mechanical modeling of high speed drilling: 2 predicted and experimental results, International Journal of Machine Tools and Manufacture 39 (1999) 569–581.

8. V. Chandrasekharan, S.G. Kapoor, R.E. Devor, A calibration procedure for fundamental oblique-cutting model coefficients based on a three-dimensional mechanistic drilling force model, Technical Papers of the NAMRI of ME, 24 (1996).

9. V. Chandrasekharan, S.G. Kapoor, R.E. Devor, A mechanistic approach to predicting the cutting forces in drilling: with application to fiber-reinforced composite materials, Journal of Engineering for Industry 117 (1995) 559–569.

10. 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.

418

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

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

13. K.F. Ehmann, S.G. Kapoor, R.E. Devor, I. Lazoglu, Machining process modeling: a review. Journal Manufacturing Science Engineering, 119, (1997): 655–663.

14. S.K. Bera, A. Bhattacharyya, Mechanics of drilling process, Journal Inst. Engineering (India) ME 6 46 ,(11), (1966): 265–276.

15. R.A. Williams, A theoretical and experimental study of the mechanics of the drilling process, PhD thesis, University of South Wales, 1968.

16. E.J.A. Armarego, C.Y. Cheng, Drilling with rake face and conventional twist drills. i: Theoretical investigation, Int. J. Mach. Tool Des. Res. 12 (1972) 17.

17. S. Wiriyacosol, E.J.A. Armarego, Thrust and torque prediction in drilling from a cutting mechanics approach, Annals of the CIRP, 28 (1), (1979) 87.

18. A.R. Watson, Geometry of drill elements, Int. J. Mach. Tool Des. Res. 25 (3) (1985) 209–227.

19. A.R. Watson, Drilling model for cutting lip and chisel edge ad comparison of experimental and predicted results initial cutting lip model, Int. J. Mach. Tool Des. Res. 25 (4) (1985) 347–365.

20. A.R. Watson, Drilling model for cutting lip and chisel edge ad comparison of experimental and predicted results revised cutting lip model, Int. J. Mach. Tool Des. Res. 25 (4) (1985) 367–376.

21. A.R. Watson, Drilling model for cutting lip and chisel edge ad comparison of experimental and predicted results drilling model for chisel edge, Int. J. Mach. Tool Des. Res. 25 (4) (1985) 377–392.

22. A.R. Watson, Drilling model for cutting lip and chisel edge and comparison of experimental and predicted results drilling tests to determine chisel edge contribution to torque and thrust, Int. J. Mach. Tool Des. Res. 25 (4) (1985) 394–404.