A study on Edge Milling Operation of NEMA G11 GFRP Composites based on Grey-Taguchi method

Hari Vasudevan1, a, Ramesh Rajguru2,b* and Naresh Deshpande3,c 1Principal, D.J. Sanghvi College of Engineering, Mumbai, India

2Department of Mechanical Engineering, D.J. Sanghvi College of Engineering, Mumbai, India

3Department of Production Engineering, D.J. Sanghvi College of Engineering, Mumbai, India

aharivasudevan@iitb.ac.in, bramesh.rajguru9@gmail.com, cncdeshpande72@yahoo.co.in

Keywords:Grey relational analysis, CNC Milling, Polycrystalline Diamond Tool, Taguchi Methodology, Woven Fabric

Abstract.Milling is one of the most practical machining processes for removing excess material to

produce high quality surfaces. However, milling of composite materials is a rather complex task,

owing to its heterogeneity and poor surface finish, which includes fibre pullout, matrix

delamination, sub-surface damage and matrix polymer interface failure. In this study, an attempt has

been made to optimize milling parameters with multiple performance characteristics in the edge

milling operation, based on the Grey Relational Analysis coupled with Taguchi method. Taguchi’s

L18 orthogonal array was used for the milling experiment. Milling parameters such as milling

strategy, spindle speed, feed rate and depth of cut are optimised along with multiple performance

characteristics, such as machining forces and delamination. Response table of grey relational grade

for four process parameters is used for the analysis to produce the best output; the optimal

combination of the parameters. From the response table of the average GRG, it is found that the

largest value of the GRG is for down milling, spindle speed of 1000 rpm, feed rate of 150 mm/min

and depth of cut 0.4 mm.

Introduction

Materials, such as Fiber reinforced plastics (FRPs) like carbon fiber reinforced plastics (CFRPs)

and glass fiber reinforced plastics (GFRPs) have been widely used in applications for aerospace,

defense and transportation structures as well as sports and leisure goods owing to their high specific

stiffness, high specific strength, high damping, high corrosion resistance and low coefficient of

thermal expansion. Among several industrial machining processes, milling is a fundamental

machining operation. Edge milling is one of the most common metal removal operations

encountered in such applications. It is widely used in a variety of manufacturing industries,

including the aerospace and automotive sectors. The quality of the surface plays a very important

role in the performance of milling, as a good-quality milled surface significantly improves fatigue

strength, corrosion resistance and creep life.

Puw and Hocheng [1-2] were the first to report some preliminary results of milling uni-

directional carbon fibre-reinforced plastic (CFRP) composites. However, only a handful of

researchers, have reported experimental results on limited aspects of GFRP's milling machinability

characteristics, such as machining forces and delamination damage. Even though Davim et al.[3-4]

have reported some promising results with regard to improving surface quality and reducing

delamination damage of machining GFRP composites, their studies were limited to only two

machining parameters, namely, feed rate and the cutting speed. Furthermore, performance of

machinability in terms of tool wear and tool life were not disclosed. On the other hand, Sheikh

Ahmad et al. [5] developed a force prediction model during end milling of uni-directional CFRP

composites with different fibre orientations, yet their study was limited to relatively low machining

conditions.

Applied Mechanics and Materials Vols. 592-594 (2014) pp 18-22Online available since 2014/Jul/15 at www.scientific.net© (2014) Trans Tech Publications, Switzerlanddoi:10.4028/www.scientific.net/AMM.592-594.18

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Experimental Details

The experiments were conducted according to L18 (OA). The four cutting parameters selected

for this investigation are the milling parameters such as milling strategy, spindle speed, feed rate

and depth of cut. The milling experiments were carried out on a vertical HAAS TM-2 CNC Milling

machine. The set up for milling is as shown in Fig.1 (a).

Fig.1. (a) Milling set up; (b) PCD end mill; (c) NEMA G-11 GFRP/E Work specimen.

The work specimens are of rectangular shape of dimension 150mm x 60mm x 8mm. The cutting

tool used is polycrystalline diamond (PCD) end mill and is shown in Fig. 1(b) with two flutes and

10mm diameter. The work material selected for the study is NEMA (National Electrical

Manufacturers Association) G-11 glass fibre reinforced epoxy composite, shown in Fig. 1(c).

Components of machining forces in the workpiece such as Fx, Fy and Fz as shown in Fig.2 (a) were

measured with Kistler piezoelectric dynamometer of type-5233A with built in, charge amplifier up

to 10KN and a least count of 1mN. Data acquisition was accomplished by connecting this

dynamometer to computer and using Kistler Dynoware type- 2825A software. Delamination was

evaluated with 2D microscope as shown in Fig.2 (b). The damage caused on both NEMA G-11

GFRP/E composite materials was measured perpendicular to the feed rate with 2D microscope.

After the measurement of the maximum damage suffered by the material, the damage normally

assigned by delamination factor DF was determined. This factor is defined as the quotient between

the width of maximum damage and the nominal width of the cut.The machining parameters used

and their levels are shown in Table 1.

Fig.2 (a) Sample graph of cutting forces; (b) 2D microscope

Applied Mechanics and Materials Vols. 592-594 19

Table 1: Experimental parameters and their levels

Symbol Cutting parameter Unit Level1 Level2 Level3

A Milling strategy -- Down Up -

B Spindle Speed rpm 1000 1500 2000

C Feed Rate mm/min 100 150 200

D Depth of cut mm 0.2 0.4 0.6

Grey-Taguchi Optimization

In grey relational analysis, the experimental data, i.e. measured features of quality characteristics

of the product, are first normalized ranging from zero to one. This process is known as grey

relational generation. Next, based on normalized experimental data, grey relational coefficient is

calculated to represent the correlation between the desired and actual experimental data. Then,

overall grey relational grade is determined by averaging the grey relational coefficient

corresponding to selected responses. The overall performance characteristic of the multiple

response process depends on the calculated grey relational grade. This approach converts a multiple

response, process optimization problem into a single response optimization situation, with the

objective function as overall grey relational grade. The optimal parametric combination is then

evaluated by maximizing the overall grey relational grade. Calculated GRC and GRG are listed in

Table 2.

Table 2: The calculated grey relational coefficient and grey relational grade

Expt

Run

Factors Measured parameters Normalization

Grey relational coefficient & GRG

A B C D F(N) DF F(N) DF F(N) DF GRG Rank

1 1 1 1 1 137.60 1.018 0.2766 1 0.3294 0.6438 0.4866 7

2 1 1 2 2 168.03 1.023 0.0538 0.6624 0.3282 0.9029 0.6155 1

3 1 1 3 3 109.07 1.027 0.4855 0.4458 0.3275 0.5074 0.4174 10

4 1 2 1 2 142.53 1.028 0.2405 0.3821 0.3273 0.6752 0.5013 5

5 1 2 2 3 99.84 1.034 0.553 0 0.3260 0.4748 0.4004 12

6 1 2 3 1 72.73 1.028 0.7515 0.3821 0.3273 0.3995 0.3634 14

7 1 3 1 3 146.13 1.029 0.2141 0.2738 0.3269 0.7002 0.5136 4

8 1 3 2 1 100.84 1.028 0.5457 0.3503 0.3272 0.4781 0.4027 11

9 1 3 3 2 127.65 1.030 0.3494 0.2292 0.3268 0.5887 0.4577 8

10 2 1 1 2 167.46 1.023 0.0579 0.6815 0.3283 0.8962 0.6123 3

11 2 1 2 3 175.38 1.025 0 0.5730 0.3279 1.0000 0.6150 2

12 2 1 3 1 50.41 1.021 0.9149 0.828 0.3288 0.3534 0.3411 16

13 2 2 1 1 49.90 1.029 0.9187 0.2738 0.3269 0.3524 0.3397 17

14 2 2 2 2 71.34 1.018 0.7617 0.9808 0.3293 0.3963 0.3628 15

15 2 2 3 3 38.80 1.023 1 0.6878 0.3283 0.3333 0.3308 18

16 2 3 1 3 141.57 1.019 0.2475 0.9363 0.3292 0.6689 0.4990 6

17 2 3 2 1 80.69 1.024 0.6932 0.5987 0.3280 0.4190 0.3735 13

18 2 3 3 2 125.16 1.023 0.3676 0.6942 0.3283 0.5763 0.4523 9

20 Dynamics of Machines and Mechanisms, Industrial Research

Grey relational coefficient for all the sequences expresses the relationship between the ideal

(best) and actual normalized experimental data. If the two sequences agree at all points, then their

grey relational coefficient is 1.The grey relational analysis, based on the grey system theory is

adopted for solving the complicated interrelationships among the multiple responses. For every

response, the grey relational coefficient ( ( )

( )) can be expressed by following equation.

( ( )

( ))

( ) (

( ) ( )) ( )

Where, ( ) is the deviation sequence of the reference sequence ( ) and comparability

sequence ( ). is the distinguishing coefficient . The distinguishing coefficient ( )

value is chosen to be 0.5.

Analysis of the experimental results

As seen from the response table (Table 3), the difference between the maximum and the minimum

value of the GRG of the milling parameters is as follows: 0.0204 for type of milling, 0.1397 for

spindle speed, 0.1452 for feed rate and 0.1417 for depth of cut. The most significant factor affecting

multiple performance characteristics is determined by comparing these values. This comparison will

present the level of significance of the controllable factors over the multiple performance

characteristics. The most significant controllable factor was the maximum of these values. Here, the

maximum value is 0.1452. This value indicates that the feed rate has the strongest effect on the

multiple performance characteristics among the other milling parameters. In response table

parameter setting A1B1C2D2 shows the largest value of GRG. Therefore A1B1C2D2 is the

condition for the optimal parameter combination of the edge milling process.

Table 3: Response table for the grey relational grade:

Conclusion

The following conclusions can be drawn based on the results of this study:

The grey relational analysis technique converts the multiple performance characteristics into

single performance characteristics and it simplifies the optimization procedure.

From the response table of the average GRG, it is found that the largest value of the GRG is

for the down milling, spindle speed of 1000 rpm, feed rate of 150 mm/min and depth of cut

0.4 mm.( A1B1C2D2)

Feed rate and depth of cut have the most dominant role in influencing the delamination.

A minimum value of delamination has been obtained for down milling area.

As seen in this study, the Grey-Taguchi method provides a systematic and efficient

methodology for the design optimization of the process parameters, resulting in the

minimization of machining forces and delamination.

Level1 Level2 Level3 Max-Min Rank

A 0.4621 0.4417 - 0.0204 4

B 0.5228 0.3831 0.4498 0.1397 3

C 0.4921 0.5939 0.4487 0.1452 1

D 0.3845 0.5262 0.4709 0.1417 2

Applied Mechanics and Materials Vols. 592-594 21

Acknowledgements

The authors would like to thank the Dr. Babasaheb Ambedkar Technology University (BATU),

Lonere, Maharashtra and Institute for Design of Elecrtical Measuring Instruments (IDEMI),

Chunabhatti, Sion, Mumbai for granting the permission and providing the support during the

experimental work.

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[3] Davim JP, Reis P (2005) Damage and dimensional precision on milling carbon fiber

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[4] Davim JP, Reis P, Antonio CC (2004) A study on milling of glass fiber reinforced plastics

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[5] Sheikh-Ahmad J, Twomey J, Kalla D, Lodhia P (2007) Multiple regression and committee

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22 Dynamics of Machines and Mechanisms, Industrial Research

Dynamics of Machines and Mechanisms, Industrial Research 10.4028/www.scientific.net/AMM.592-594 A Study on Edge Milling Operation of NEMA G11 GFRP Composites Based on Grey-Taguchi Method 10.4028/www.scientific.net/AMM.592-594.18