Optimization of Drilling Characteristics using Grey Relational Analysis (GRA) in Medium Density Fiber Board (MDF)

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ScienceDirect Materials Today: Proceedings 2 (2015) 1541 – 1551

4th International Conference on Materials Processing and Characterization

Optimization of drilling characteristics using Grey Relational Analysis (GRA) in Medium Density Fiber Board (MDF) S.Prakasha*,J.LillyMercyb, Manoj Kumar Saluguc, K.S.M.Vineethd b

#a Professor & Head, Faculty of Mechanical Engineering, Sathyabama University, Chennai, 600119, India Assistant Professor, Department of Mechanical and Production Engineering, Sathyabama University, Chennai, 600119, India c,d UG Student, Department of Mechanical and Production Engineering, Sathyabama University, Chennai, 600119, India

Abstract Medium Density Fiberboard (MDF) has been one of the most rapidly growing composite panel products to enter world market in recent years. Optimization of drilling parameters for Medium Density Fiberboard (MDF) panel with multiple performance characteristics using Grey Relational Analysis (GRA) method was done. Drilling parameters, such as feed rate, spindle speed, drill diameters were considered for the study. By analyzing grey grade matrix, the degree of influence for each controllable process factor on to individual targets can be found. An optimal parameter combination of drilling operation was obtained via GRA. The feed rate was identified to be the most influencing factor on surface roughness and delamination. Additionally, ANOVA (Analysis of Variance) is used to find the interaction between parameters and the possible error in the experiments © 2014 The Authors. Elsevier Ltd. All rights reserved. © 2015 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the conference committee members of the 4th International conference on Selection and peer-review responsibility of the conference committee members of the 4th International conference on Materials Materials Processing andunder Characterization. Processing and Characterization.

Keywords:Medium Density Fiber board, Grey Relational Analysis, Optimization, Drilling, Surface roughness, Delamination

1. Introduction 1.1 Medium Density Fibreboard and its applications The combination of increasing population and decreasing prime timber supply suggests a continuing shift to the use of composite panels, among which MDF offers many advantages. The surface of MDF panel is smooth, flat and uniform all of which make finishing operations easier and consistent, especially for demanding uses such as

* Corresponding author. Tel.: 09940390301; fax: 044 - 2450 2344. E-mail address:[email protected]

2214-7853 © 2015 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the conference committee members of the 4th International conference on Materials Processing and Characterization. doi:10.1016/j.matpr.2015.07.080

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direct painting and thin laminates. MDF’s stability and strength are important assets, with stability contributing to holding better tolerance in cut parts [1]. MDF is an excellent substitute for many other panel products in many interior applications. Thin MDF is used for back panels for furniture and base panels in drawers, becoming an indispensable component in furniture production. However, an even bigger drive to MDF demand turned out to be the rapidly growing market for laminate flooring. The general estimate is that about 90% of MDF produced is for the office and residential market for flooring, wall panels, kitchen cabinets and other uses. 1.2. Machining process in MDF panels Drilling is a cutting process that uses multi-point cutting edges to penetrate the surface of a work piece and produce a round hole. It can be considered as the most common metal cutting process. Drilling is the most unavoidable operation in wood composites during the assembly of parts. With the proper selection of equipment and cutting tools, MDF can be machined into intricate patterns as easily as natural wood. Due to homogeneous nature, MDF is made with different wood species and various binders and is available in different densities. It is strongly recommended that only the highest quality tools be used on MDF since it is generally denser than most natural woods and contains thermosetting resins which are abrasive. Correctly, selecting the tool geometry and cutting parameters that affect surface roughness are important factors for any machining process [2]. Nowadays, drilling is a machining operations frequently used in manufacturing parts of MDF. Metal turning has been studied extensively in the literature, and most of the researches on the machining of composites have focused on turning and facing, but MDF drilling has not received much attention[3, 4] 1.3 Literature review on response variables -surface roughness and delamination Surface roughness is a widely used index of product quality and in most cases a technical requirement for mechanical and furniture products. Roughness is a measure of the fine irregularities on a surface. The height and shape of the irregularities establish the surface quality of the product. Achieving the desired surface quality is of great importance for the functional behaviour of a part. The mechanism behind the formation of surface roughness is very dynamic, complicated and process dependant. Kilic et al[5] evaluated five roughness parameters in sawn, planed and sanding process. According to their results, the average roughness Ra, and mean peak to valley height Rz in solid wood are the most commonly used parameters for evaluating surface qualities. During the machining process, any surface irregularities reduce their final quality of the product and value when cutting parameters are not properly controlled. Davim et al[6] found Ravalues are progressively increasing with feed rate, while decreasing of the cutting speed. The trends for Rawere similar for mean peak to valley height Rzand maximum peak to valley Rtparameters. Wood-based composites are complex materials exhibiting important anisotropic properties. Commonly observed damage in these materials are: delamination between plies, debonding of wood–adhesive layers, or wood fibre fracture. Delamination is the debonding of two adjoining layers in the laminated wood-based composite, is probably the most frequently observed damage. Delamination is defined as the separation of layers in a laminate because of failure of the adhesive, either in the adhesive itself or at the interface between the adhesive and the adherent. Mohan et al [7] evaluated the factors and combination of factors that influence the delamination in drilling of GFRP composites using Taguchi methodology and they achieved the optimal machining conditions that would result in minimum delamination in drilling GFRP composite materials.

Fig. 1. Interrelations between the Input and Output response variables

Fig. 2. Purpose of Grey relational analysis

Paulo Davim et al[8] present the milling of medium density fiber board panels and the important role that the spindle speed plays on surface roughness as a function of material removal rate is discussed. Prakash et al[9] have

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successfully applied Taguchi methodology for the prediction of delamination in drilling of Medium Density Fiber board (MDF) composites 1.4. Literature review on optimization methodology and need for Taguchi Grey relational Analysis Optimization of process parameters is an important aspect in manufacturing engineering. Multiobjective optimization of process parameters is a complex process. Grey relational analysis is useful for multiple performance characteristics in machining process.Most of published works focused on optimization of parameters for machining of metals. The machining parameters are usually selected based on either the experience or the proposed guidelines of the manufacturers. This selection procedure does not lead to the optimal and economically effective use of the machines and the quality of the surface generated. Figure 1 shows the input parameters chosen for the study and the output responses measured. If there are multiple responses for the same set of independent variables, the methodology provides a different set of optimum operating conditions for each response variable. For example, in machining, optimum condition for minimizing cutting forces need not be the same as for minimizing surface roughness. In such conditions, obtaining a solution that gives the best possible surface finish at the lowest possible cutting forces are necessary in some instances. In such circumstances, the Taguchi method may not provide appropriate solution. For a complex and multivariate system such as machining, the relationships between various factors are unclear. Such systems often are called grey that give poor, incomplete, and uncertain information. To solve such kind of problem, the grey relational analysis is necessary. Deng [10] proposed applications of principle of grey relational analysis toengineering problems. It measures the degree of approximation among sequences using grey relational grade. The grey relational grade can provide knowledge of the factors affecting response variables. Most of the published works focus on optimization of parameters for machining of materials. Tzeng et al (11) investigated the optimization of CNC turning operation parameters for high carbon high chromium alloy tool steel dies using the Grey relational analysis method. The surface properties of roughness average and roughness maximum as well as the roundness were selected as the quality targets. An optimal parameter combination of the turning operation was obtained via Grey relational analysis. Noorlhaq et al (12) proposed for the optimization of drilling parameters on drilling Al/SiC metal matrix composite with multiple responses based on orthogonal array with grey relational analysis. Tosun et al [13] used grey relational analysis to optimize the drilling parameters for the multi-performance characteristics (surface roughness and burr height) in the drilling process. In his study the grey relational approach can be applied successfully to other operations in which performance is determined by many parameters at multiple quality requests used Grey based Taguchi methods to predict surface roughness of drilled holes and drill flank wear into a single characteristic response. Vikas et al (14) proposed for the optimization of machine process parameters on the Surface roughness in EDM for an EN41 material using Grey-Taguchi method In their study their predicted values are confirmed experimentally.Palanikumar also applied successfully GRA for optimizing the drilling parameters in drilling GFRP composites. Based on his studies, feed rate (f) and spindle speed (N) is the most influential factor in drilling GFRP. Grey relational analysis can effectively be recommended as a method for optimizing the complicated interrelationships among multiple performance characteristics [15].Through the grey relational analysis, a grey relational grade is obtained to evaluate the multiple performance characteristics. As a result, optimization of the complicated multiple performance characteristics can be converted into the optimization of a single grey relational grade (Figure 2).The grey – Taguchi method was established for combining both grey relational analysis and the Taguchi method. The grey – Taguchi method was successfully applied to optimize the multiple performance characteristics of complicated problems in manufacturing processes [16]. 2. Apparatus and method In this experimental work, a Taguchi based Grey Relational Analysis (TGRA) has been used to establish a correlation between the independent variables and the performance characteristics; therefore, the experiments were performed according to a Taguchi design of experiments. 2.1. Material The sample material of MDF panel with specification of 200 mm length and 120 mm width and thickness of 12 mm as per IS 12406 is used for experimentation. It’s procured from ASIS India Limited.

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2.2 Drilling tool A commercially available solid carbide step drill is used for the investigation. It has point angle with 140°, shank length of 36 mm and 45 mm with different overall length as 107 mm. The general step drill nomenclature and its specification is given in Table 1 Table 1.Step drill nomenclature and its specifications used for experimentation Drill bit diagram Type of Drill

Step Drill

Step Drill

Step Drill

Point angle

140°

140°

140°

Helix angle

30°

30°

30°

Diameter

4 mm

8 mm

12 mm

Shank length

36

36

45

Overall length

62 mm

89 mm

107 mm

Coating type

Solid carbide

Solid carbide

Solid carbide

2.3. Experimental Design, Cutting condition and Surface roughness, Delamination measurements Taguchi’s L27 (313) orthogonal array is applied for drilling on MDF panel with carbide step drill of different diameters. It uses an orthogonal array to study the entire parametric space with a limited number of experiments. The three drilling parameters (control factors) considered in this study are: spindle speed, feed rate, and drill diameter. All of them were set at three different levels which are presented in Table 2.After the suitable orthogonal array has been selected, the experiments are conducted using the standard hardware listed as follows and the experimental set up is presented in Figure 3 Table 2 Process input parameters of drilling process and their respective values (Prakash and Lilly Mercy [4])

Process input parameter

Level 1

Level 2

Level 3

100

300

500

Unit mm/min

Notation

Feed rate, Spindle speed, Drill diameter

1000 4

3000 8

5000 12

rpm mm

N d

x x x x x

f

The drilling experiments were carried out in dry cutting conditions using ARIX –CNC (VMC 100) Machining centre manufactured by ARIX-CNC Machine Co.Ltd., Taiwan. This machine has a spindle speed range of 60 to 5000 rpm and a table size 1279 x 254 mm. Surface roughness measurement device: Talysurf- Travelling length,0.1 to 50 mm, Stylus-Diamond, 2μm tip radius Software -Form Ultra software Delamination measurement: Profile Projector Models V40, Size of Table Traverse. 400 x 300mm 275 x 175mm of telemetric lens : 10X / 20X Drilling tools of carbide type step drill - 4,8 and 12 mm diameter

Statistical software package Minitab 16 is used for analyzing the output got from the experiments. The L 27 array contains 27 experimental runs and has 13 columns. To check the Degrees Of Freedom (DOF) in the experimental design, for the three levels test, the three main factors take 6 DOFs (3 x 2) and the remaining DOFs are taken by interactions. The experimental parameters the corresponding responses and their (S/N) ratio are given in Table 3. The first column of the table is assigned to the feed rate (f), the second to the spindle speed (N), the third to the drill diameter (d). The response variables chosen for the present investigation are: Delamination and Surface roughness. The lower-the better quality characteristic has been used for calculating the signal to noise (S/N) ratio for these responses. 3 Experimental results and discussion Statistical analysis was carried out on the experimental data obtained through Taguchi experimental design using statistical software MINITAB 16. Analysis of means (AOM) and analysis of variance (ANOVA) were performed to determine the influence of input parameters on the output response variables. Since the major focus of this study is on grey relational analysis, the results of Taguchi experiments have not been elaborated here.

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Fig3 Experimental sequences of drilling and testing the responses

3.1 Statistical results based on Taguchi analysis Statistical analysis of the individual performance characteristics was carried out to determine the influence of the control factors on the response variables. Table 4 shows the ANOVA results for the chosen performance characteristics surface roughness (Ra ,Rz, Rt) and delamination (Fd). 3.2 Grey relational analysis Grey data processing must be performed before calculating the grey correlation coefficients. In this study, a linear normalization of the experimental results (S/N ratios) for Delamination and Surface Roughness were performed in the range of 0 and 1, which is also called the grey relational generation. In our case, the problem has four performance characteristics that need to be minimized by choosing appropriate processing conditions. They are: three components of the surface roughness and delamination. In such cases, the problem is converted into a single objective problem using grey relational analysis 3.3 Definition of the problem and flow chart for GRA procedure The multi-objective optimization problem under investigation can be stated as: Minimize: Delamination (Fd), Surface Roughness (Ra, Rz, Rt) Subject to independent decision variables as: Spindle speed, N (rpm); feed rate, f, (mm/min); drill diameter and d, (mm). Where the range of the independent decision variables should be: N, 1000 ≤ 3000 ≤ 5000; f, 100 ≤ 300 ≤ 500; d, 4 ≤ 8 ≤ 12. Thus the, the above multi-objective problem can be converted into a single optimization problem using grey relational grade as: “Maximize GRG; 0 ≤ GRG ≤ 1”, subjected to independent decision variables in the range mentioned above. In order to optimize the above variable the following steps are executed such as data preprocessing, computation of grey relational coefficient and ranking of grey relational grade. From the grey grade the optimum solution is obtained based of ranking and response graph 3.4 Data pre-processing Generally, in GRA data pre-processing is the first step in order to normalize the raw data for analysis. The Grey data pre-processing must be performed before Grey relational Coefficient can be calculated. A linear normalization of the experimental results for the responses surface roughness and delamination is performed in the range between 0 and 1, which is called grey relational co efficient.In this paper, a linear data pre-processing method for surface roughness and delamination can be expressed and calculated using in Equation 1

Zij

=

max( yij ,i 1,2 ,... n )  yij max( yij ,i 1,2 ,... n )min( yij ,i 1,2 ,... n )

(1)

Where Yij is the ith experimental results in the jth experiment. According to Deng, larger the normalized value corresponding to the better performance and the best-normalized value should be equal to 1. Using the Equation 1, output responses are normalized and presented in Table.5 Table 3 Orthogonal array L27 (33) of the experimental results of output responses and their S/N values

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Run

Notation

Output Responses (Experimental values)

Output Responses (S/N ratio values)

1

f1N1d1

5.48

39.51

29.65

Delaminat ion (Fd) 1.14

2

f1N1d2

6.17

45.06

33.91

1.14

-15.81

-33.08

-30.61

-1.14

3

f1N1d3

7.54

54.36

40.89

1.21

-17.55

-34.71

-32.23

-1.66

4

f1N2d1

5.45

39.29

29.58

1.13

-14.73

-31.89

-29.42

-1.06

5

f1N2d1

6.81

49.10

36.84

1.21

-16.66

-33.82

-31.33

-1.66

6

f1N2d3

7.93

57.54

44.17

1.24

-17.99

-35.20

-32.90

-1.87

7

f1N3d1

4.41

31.80

23.86

1.19

-12.89

-30.05

-27.55

-1.51

8

f1N3d2

5.48

39.51

29.75

1.25

-14.78

-31.93

-29.47

-1.94

9

f1N3d3

6.63

48.80

35.87

1.26

-16.43

-33.77

-31.09

-2.01

10

f2N1d1

7.69

56.60

42.47

1.28

-17.72

-35.06

-32.56

-2.14

11

f2N1d2

8.53

61.50

46.15

1.20

-18.62

-35.78

-33.28

-1.58

12

f2N1d3

9.29

66.98

50.26

1.23

-19.36

-36.52

-34.02

-1.80

13

f2N2d1

6.62

47.73

35.81

1.20

-16.42

-33.58

-31.08

-1.58

14

f2N2d2

7.52

55.08

41.33

1.23

-17.52

-34.82

-32.33

-1.80

15

f2N2d3

8.34

60.13

45.22

1.31

-18.42

-35.58

-33.11

-2.35

16

f2N3d1

5.65

40.74

30.57

1.29

-15.04

-32.20

-29.70

-2.21

17

f2N3d2

6.35

45.78

34.35

1.34

-16.06

-33.21

-30.72

-2.54

18

f2N3d3

7.49

54.10

40.72

1.36

-17.49

-34.66

-32.20

-2.67

19

f3N1d1

9.47

68.28

51.23

1.38

-19.53

-36.69

-34.19

-2.80

20

f3N1d2

10.29

74.29

55.57

1.38

-20.25

-37.42

-34.90

-2.80

21

f3N1d3

11.53

83.13

62.38

1.40

-21.24

-38.40

-35.90

-2.92

22

f3N2d1

8.57

61.79

46.36

1.32

-18.66

-35.82

-33.32

-2.41

23

f3N2d2

9.13

66.93

49.29

1.39

-19.21

-36.51

-33.86

-2.86

24

f3N2d3

9.93

71.60

53.72

1.47

-19.94

-37.10

-34.60

-3.35

25

f3N3d1

7.74

55.81

41.87

1.38

-17.77

-34.93

-32.44

-2.80

26

f3N3d2

8.45

60.92

45.81

1.43

-18.54

-35.70

-33.22

-3.11

27

f3N3d3

8.96

64.70

48.57

1.47

-19.05

-36.22

-33.73

-3.35

(Ra) μm*

(Rz) μm*

(Rt) μm*

(Ra) μm*

(Rz), μm*

(Rt) μm*

-14.78

-31.93

-29.44

Delaminat ion (Fd) -1.14

Then the deviation sequence is computed for calculating the grey relational coefficient. The deviation sequence, Δ 0

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i (k) is the absolute difference between the reference sequence x 0 ѽ(k) and the comparability sequence x iѽ(k) after normalization. It is determined using Eq. 3 as: (2) and presented in Table 5. Referring the result from Table 5, the minimum surface roughness occurs at the 7th trial and is taken as reference sequence equivalent to 1(4.41microns). The maximum surface roughness occurs at the 21 st trial (11.53 microns) and the normalized value for the 1st trial (5.48 microns) can be calculated as follows:

max( yij ,i 1,2,... n )  yij max( yij ,i 1,2,... n )min( yij ,i 1,2,... n )

=(11.53 – 5.48) (11.53 4.41)

=0.8497

(3)

3.5. Calculation of grey relational coefficient From the data, the deviation sequence is computed and presented in Table 5. In grey relational analysis after calculating deviation sequence, the grey relational coefficient is calculated and presented in Table 6. For calculating the grey relational coefficient the following formula is used(Equation 4) ' min  [' max J ( y (k ), yi (k )) 'ij ( k )  [' max (4) Where (a).j=1, 2,3,. n and k=1, 2,. m.(n is the number of experimental data and m is the number of responses). (b).yo(k) is the reference sequence (yo(k)=1, k=1,2..m: yj(k) is the specific comparison sequence. (c)Δij=|| yo(k)- yj(k)||= The absolute value of the difference between yo(k) and yj(k) (d). Δ min = min min || yo(k)- yj(k)|| is the smallest value of yj(k) νj€iνk (e).Δ max = max max || yo(k)- yj(k)|| is the largest value of yj(k) νj€iνk (f) ζ is the distinguish coefficient. A value of the ζ is the smaller and the distinguished ability is the larger ζ = 0.5 is generally used. The deviation sequence Δ0i, Δ max (k) and Δ min (k) for i = 1–27, k = 1–2 can be calculated as follows: (5) Δ01(1) = | yo*(1) – y1*(1)| = |1.00- 0.8497| = 0.1503 (6) Δ01 (2) = | yo*(2) – y1*(2)| = |1.00- 0.7416| = 0.2584 ij

Using Table 5, Δmax and Δmin can be found. The values for Δmax = 1 and Δmin=0.The reference sequence used for calculating grey relational coefficient is chosen to be all ones. The grey relational coefficient, grey relational grade and its corresponding rank order is presented in Table.6.From the Table 6 the grey relational coefficient for all the four performance characteristics and the grey relational grade along its order is computed. The highest value of grey relational grade 0.8351 indicates that experiment number 7 is the optimum combination of drilling parameters in order to produce minimum value of surface roughness and delamination. 4. Results and discussions: The obtained grey grade across different input parameters are shown in Figure4. Grey relational grade gives the output which is inversely proportional to the optimized real data. Ranks are given to each run depending on the grey relational grade obtained as shown in Table 6. The levels of inputs corresponding to rank 1 are the optimized experimental set up among the 27 experiments conducted. It can be seen that the 7 th experimental run corresponding to the feed rate of 100mm/min, Spindle speed of 5000rpm and drill diameter of 4mm.Figure 4 shows the increase or decrease in grey relational grade under the varied levels of input parameters. It is evident from these graphs that feed rate is the most influencing factor on various parameters of surface roughness and delamination, followed by drill diameter and spindle speed. Figure 5 shows the percentage contribution of the input parameters with respect to output response. The percentage contributions are calculated using ANOVA table which is presented in Table 4. The percentage contribution for feed rate (Ra is 58.44%, Rz is 58.01%, Rt is57.05%, Fd is 72.55), spindle speed (Ra is 16.19%, Rzis 16.37%, Rt is 16.62%, Fd is 7.88) and drill diameter (Ra is 20.19%, Rzis 20.31%, Rt is 20.63 %, Fd is 8.68) respectively.

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Table 4 Analysis of variance (ANOVA) for output variables

Source DF Seq SS Surface roughness (Ra), μm f 1 44.08861 N 1 12.2183 d 1 15.2352 f2 1 0.4648 N2 1 0.2204 d2 1 0.0294 f 1 1.0034 fd 1 0.3745 Nd 1 0.0161 Error 17 1.775 Total 26 75.4232 Surface roughness (Rz), μm f 1 2277.41 N 1 642.67 d 1 797.4 f2 1 22.84 N2 1 13 d2 1 0.79 fn 1 53.94 fd 1 22.81 Nd 1 0.06 Error 17 93.46 Total 26 3927.37 Surface roughness (Rt,) μm f 1 1254.94 N 1 365.66 d 1 453.95 f2 1 12.12 N2 1 8.02 d2 1 0.96 fn 1 26.82 fd 1 13.31 Nd 1 0.14 Error 17 60.93 Total 26 2196.86 Delamination (Fd,) f 1 0.19 N 1 0.02 d 1 0.022 f2 1 0.0048 N2 1 0.002 d2 1 0.0002 fn 1 0.00067 fd 1 0 Nd 1 0.003

Adj SS

Adj MS

F-ratio

%contribution

44.08861 12.2183 15.2352 0.4648 0.2204 0.0294 1.0034 0.3745 0.0161 1.775

44.08861 12.2183 15.2352 0.4648 0.2204 0.0294 1.0034 0.3745 0.0161 0.1044

422.24 117.02 145.92 4.45 2.11 0.28 9.61 3.59 0.15

58.44 16.19 20.19 0.61 0.29 0.03 1.33 0.49 0.02 2.38 100

2277.41 642.67 797.4 22.84 13 0.79 53.94 22.81 0.06 93.46

2277.41 642.67 797.4 22.84 13 0.79 53.94 22.81 0.06 5.5

414.26 116.9 145.05 4.16 2.36 0.14 9.81 4.15 0.01

58.01 16.37 20.31 0.58 0.33 0.01 1.37 0.58 0.01 2.42 100

1254.94 365.66 453.95 12.12 8.02 0.96 26.82 13.31 0.14 60.93

1254.94 365.66 453.95 12.12 8.02 0.96 26.82 13.31 0.14 3.58

350.12 102.02 126.65 3.38 2.24 0.27 7.48 3.71 0.04

57.05 16.62 20.63 0.55 0.36 0.04 1.21 0.6 0.06 2.86 100

0.19 0.02 0.022 0.0048 0.002 0.0002 0.00067 0 0.003

0.19 0.02 0.022 0.0048 0.002 0.0002 0.00067 0 0.003

189.67 20.62 22.7 4.8 2.01 0.27 0.67 0.01 3

72.55 7.88 8.68 1.836 0.76 0.1 0.25 0.003 1.113

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Error Total

17 26

0.017 0.2614

0.017

0.001

6.827 100

Table 5 The sequence after data pre-processing and the deviation sequence

Exp run 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Comparability sequence

Deviation sequence

Ra,μm

(Rz), μm

(Rt), μm

(Fd),

(Ra), μm

(Rz), μm

(Rt), μm

(Fd),

0.8497 0.7416 0.5604 0.8539 0.6629 0.5000 1.0000 0.8497 0.6882 0.5169 0.4213 0.3146 0.6615 0.5492 0.4480 0.8258 0.7275 0.5674 0.2893 0.1742 0.0000 0.4157 0.3371 0.2247 0.5323 0.4326 0.3610

0.8498 0.7416 0.5604 0.8540 0.6630 0.4986 1.0000 0.8498 0.6688 0.5169 0.4214 0.3146 0.6897 0.5464 0.4481 0.8259 0.7276 0.5655 0.2893 0.1722 0.0000 0.4157 0.3157 0.2247 0.5323 0.4326 0.3590

0.8498 0.7390 0.5579 0.8514 0.6630 0.4727 1.0000 0.8472 0.6883 0.5169 0.4214 0.3147 0.6897 0.5464 0.4455 0.8259 0.7276 0.5623 0.2894 0.1768 0.0001 0.4158 0.3397 0.2248 0.5324 0.4300 0.3584

0.0294 0.3529 0.3824 0.0000 0.2353 0.3235 0.0294 0.0588 0.2353 0.4706 0.6176 0.6765 0.2059 0.2941 0.5294 0.4412 0.2059 0.2941 0.2941 0.7353 0.8824 1.0000 0.5588 0.7353 0.7353 0.8824 0.7941

0.1503 0.2584 0.4396 0.1461 0.3371 0.5000 0.0000 0.1503 0.3118 0.4831 0.5787 0.6854 0.3385 0.4508 0.5520 0.1742 0.2725 0.4326 0.7107 0.8258 1.0000 0.5843 0.6629 0.7753 0.4677 0.5674 0.6390

0.1502 0.2584 0.4396 0.1460 0.3370 0.5014 0.0000 0.1502 0.3312 0.4831 0.5786 0.6854 0.3103 0.4536 0.5519 0.1741 0.2724 0.4345 0.7107 0.8278 1.0000 0.5843 0.6843 0.7753 0.4677 0.5674 0.6410

0.1502 0.2610 0.4421 0.1486 0.3370 0.5273 0.0000 0.1528 0.3117 0.4831 0.5786 0.6853 0.3103 0.4536 0.5545 0.1741 0.2724 0.4377 0.7106 0.8232 0.9999 0.5842 0.6603 0.7752 0.4676 0.5700 0.6416

0.9706 0.6471 0.6176 1.0000 0.7647 0.6765 0.9706 0.9412 0.7647 0.5294 0.3824 0.3235 0.7941 0.7059 0.4706 0.5588 0.7941 0.7059 0.7059 0.2647 0.1176 0.0000 0.4412 0.2647 0.2647 0.1176 0.2059

Reference sequence =1.000

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f

N

0.36

Grey relational grade

0.33 0.30 0.27 0.24 100

300 d

4

8

500

1000

3000

5000

0.36 0.33 0.30 0.27 0.24 12

Gre y re lational grade graph

Fig 4.Greygrade graph

Fig 5Percentage contribution

Table 6 The calculated grey relational coefficient and grey relational grade Exp Run

Coded values of the parameters

Grey relational coefficient

Grey relational grade

Rank

A

B

C

Ra

Rz

Rt

Fd

1

1

1

1

0.7689

0.7690

0.7690

0.34000

0.6617

5

2

1

1

2

0.6593

0.6593

0.6571

0.43590

0.6029

6

3

1

1

3

0.5321

0.5322

0.5307

0.44737

0.5106

14

4

1

2

1

0.7739

0.7740

0.7709

0.33333

0.6630

3

5

1

2

2

0.5973

0.5973

0.5974

0.39535

0.5468

13

6

1

2

3

0.5000

0.4993

0.4867

0.42500

0.4778

21

7

1

3

1

1.0000

1.0000

1.0000

0.34000

0.8351

1

8

1

3

2

0.7689

0.7690

0.7659

0.34694

0.6627

4

9

1

3

3

0.6159

0.6015

0.6160

0.39535

0.5572

9

10

2

1

1

0.5086

0.5086

0.5086

0.48571

0.5029

17

11

2

1

2

0.4635

0.4635

0.4636

0.56667

0.4893

19

12

2

1

3

0.4218

0.4218

0.4218

0.60714

0.4681

22

13

2

2

1

0.5963

0.6170

0.6170

0.38636

0.5542

10

14

2

2

2

0.5258

0.5243

0.5243

0.41463

0.4973

18

15

2

2

3

0.4753

0.4753

0.4742

0.51515

0.4850

20

16

2

3

1

0.7417

0.7417

0.7417

0.47222

0.6743

2

17

2

3

2

0.6473

0.6473

0.6473

0.38636

0.5821

8

18

2

3

3

0.5361

0.5350

0.5332

0.41463

0.5048

16

19

3

1

1

0.4130

0.4130

0.4130

0.41463

0.4134

27

20

3

1

2

0.3771

0.3766

0.3779

0.65385

0.4464

26

21

3

1

3

0.3333

0.3333

0.3333

0.80952

0.4524

25

22

3

2

1

0.4611

0.4611

0.4612

1.00000

0.5959

7

23

3

2

2

0.4300

0.4222

0.4309

0.53125

0.4536

24

24

3

2

3

0.3921

0.3921

0.3921

0.65385

0.4575

23

25

3

3

1

0.5167

0.5167

0.5167

0.65385

0.5510

12

26

3

3

2

0.4684

0.4684

0.4673

0.80952

0.5534

11

27

3

3

3

0.4390

0.4382

0.4380

0.70833

0.5059

15

5. Conclusions: In the present work, machining parameters of drilling MDF panel composite has been studied. Grey

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S. Prakash et al. / Materials Today: Proceedings 2 (2015) 1541 – 1551

relational analysis (GRA) along with Taguchi method has been successfully implemented in optimizing the multi output responses in drilling of medium density fibre board. Table 7.Response table for grey relational grade

Level 1 2 3 Delta Rank

f 0.3715 0.2992 0.2406 0.1309 1

N 0.2744 0.2904 0.3466 0.0722 3

d 0.3502 0.3003 0.2608 0.0895 2

Based on the results the response table and response graph for each level of the drilling parameters is obtained. The Table 7 shows the response table for the grey relational grade. From the table feed rate and drill diameter is most significant parameter affecting the multiple process response. While the greater grey relational grade is the best, so level 1 for feed rate, drill diameter and level 3 for spindle speed is proposed. GRA can be extended to more number of drilling characteristics,provided accurate weights for different characteristics to calculate grade values. Thus the solutions from this method will be useful for tool manufacturer who are willing to search for an optimal solution of process parameters. References [1] Blackman “Who needs a bunch of trees to make MDF? Not this mill”, International Journal of Wood Technology,129 (2000).20–23 [2]Dippon, J., Ren, H., Amara, F.B., Altintas, Y, “Orthogonal cutting mechanics of medium density fibreboards. Forest. Prod. Journal 50 (2000), 25–30 [3] Engin, S., Altintas, Y., Amara, F.B, “Mechanics of routing medium density fibreboard”. Forest. Prod. Journal. 50. (2000):65–69 [4]S.Prakash, A.Krishnamoorthy, J.Lilly Mercy,S.Ramesh “Empirical Modeling of process parameters on drilling of Medium Density Fiber Board panel by Carbide step drill using Yate’s algorithm”, International Journal on Design and Manufacturing Technologies, 6, No1, (2012) 31-40 [5]Kilic, M.; Hiziroglu, S.; Burdurlu, E. “Effect of machining on surface roughness of wood”.Building and Environment41 :(2006) 1074-1078 [6]Davim, J.P.; Clemente, V.C.; Silva, S.“Surface roughness aspects in milling MDF (medium density fibreboard)”.International Journal of Advanced ManufacturingTechnology 40(1-2): (2009) 49-55. [7] N.S. Mohan, S.M.Kulkarni, A.Ramachandra“Delamination analysis in drilling process of glass fiber reinforced plastic (GFRP) composite materials”, Journal of Materials Processing Technology, 186,(2007),1–3, 265–271. [8]J.PauloDavim , V. C. Clemente &Sérgio Silva, “Surface roughness aspects in milling MDF (medium density fibreboard)”, International Journal of Advanced Manufacturing Technologies 40(2009), 49–55 [9] S.Prakash, K.Palanikumar,N.Manoharan “Optimzation of delamination factor in drilling medium density fiberboards(MDF) using desirability based approach”, International Journal of Advanced Manufacturing Technology, 45(2009),370-381’

[10].Deng, J.L.,“Introduction to grey system theory” Journal Grey System,1(1989a) 24-30 [11] Tzeng, C. J., Lin, Y. H., Yang, Y. K., Jeng, M. C., Optimization of turning operation with multiple performance characteristics using theTaguchi method and Grey relational analysis. Journal of materials processing technology 209 (2009), 2753–2759 [12]A.NoorulHaq, , P.Marimuthu, and R.Jeyapaul , “Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using greyrelational analysis in the Taguchi method”, International Journal of Advanced Manufacturing Technology, 37(2008), 250– 255. [13]Tosun, N. “Determination of optimum parameters for multiperformance characteristics in drilling by using grey relational analysis”. International Journal of Advanced Manufacturing Technology,28( 2006) 450–455. [14]Vikas, Apurba Kumar Roy , Kaushik Kumar, “Effect and Optimization of various Machine Process Parameters on the Surface Roughness in EDM for an EN41 Material using Grey-Taguchi”3rd International Conference on Materials Processing and Characterisation (ICMPC 2014) Procedia Materials Science,6 (2014) 383 – 390 [15]K. Palanikumar,B.Latha.V.S.Senthilkumar“Analysis on drilling of glassfiber reinforced polymer(GFRP) Composites using Grey Relational Analysis’ Materials and Manufacturing Process 27(2012),297-305 [16]G. Taguchi, Introduction to Quality Engineering, Asian Productivity Organization: Tokyo, 1990 [17]Reddy Sreenivasulu,Srinvasa Rao, “Modelling and optimization of Thrust force and torque during drilling of Aluminium 6061Alloy using Taguchi-Grey Analysis Approach”, Advanced Materials Manufacturing &Characterization 3(2013)1,413-418