Basalt hybrid composite

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ScienceDirect Materials Today: Proceedings 5 (2018) 5544–5552

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ICMPC 2017

Parametric Optimization in Drilling of Bamboo/Basalt Hybrid Composite Pulakesh Chetiaa1, Sutanu Samantab, Thingujam J. Singhc b

a Research Scholar, Department of Mechanical Engineering, NERIST, Nirjuli, Arunachal Pradesh Associate Professor, Department of Mechanical Engineering, NERIST, Nirjuli, Arunachal Pradesh c Research Scholar, Department of Mechanical Engineering, NERIST, Nirjuli, Arunachal Pradesh

Abstract In recent years, natural fiber reinforced composites have captured the attention of the researcher due to their specific properties, non carcinogenic and bio-degradable nature. Bamboo and Basalt are the example of some such material, which are cost effective and having excellent mechanical properties comparable to uni-directional glass reinforced plastic. Drilling is a machining operation that is often used for making holes to facilitate the assembly of parts. However, drilling is often associated with damage like delamination and other cutting parameters. In the present investigation, Taguchi L9 orthogonal array with grey relational analysis has been performed to determine the optimum combination of process parameters to minimize delamination factor and maximize tensile strength. ANOVA analysis is also performed. The results revealed that the cutting speed and feed rate are the two important parameters that make largest contribution to the delamination and tensile strength. Confirmation experiments are performed to verify the predicted results with the experimental results. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization.

Keywords: Bamboo; Basalt; Taguchi; Grey Relational Analysis; ANOVA

1. Introduction Now-a-days, natural fiber reinforcements are an alternative to synthetic fibers because of the properties like low cost, light weight, low density, high specific strength, non corrosive, easy to manufacture, etc. Because of the availability of these properties, natural fibers have gained a wide area of application in industries like automotive, aerospace and other transportation industries [1-4]. Bamboo is the example of one such fiber. Lakkad and Patel [5] reported that bamboo possesses much higher specific tensile strength than mild steel, polyester resin and glass * Corresponding author. Tel.: +91 9678797132; fax: +91 (0)360 2257872. E-mail address:[email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization.

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reinforced plastic. On the other hand, Basalt has exceptional properties. Basalt fiber is obtained from basalt-based molten igneous volcanic rock that is being found in flowing lava. It is obtained after extrusion [6]. Basalt is an inorganic fiber which possesses the properties like good modulus, high strength, good chemical resistance, improved strain to failure, excellent stability, eco-friendly, non toxic and inexpensive [7-12]. A lot of work has been done by researchers on the composites based on these fibers. But hybridization of these fibers is not much explored in the field of composites.

Nomenclature v Cutting Speed f Feed rate Fd Delamination factor.

These fibers are combined in the same matrix to impart properties that cannot be obtained with single kind of reinforcement. Drilling is one of the most frequently used and acceptable machining process for making holes. But drilling operation causes damages in the composite laminates in the form of delamination, fiber pull out, interlaminar cracking or thermal damages [13-14]. The damages caused by drilling operation may affect the mechanical behaviour of the composites. Babu [15] et al. performed drilling operation on hemp fiber reinforced composites and reported that drilling not only gives delamination damages but also affects tensile strength of the composite. They used large number of machining experiments to analyze it. In this paper, Taguchi based Grey relational analysis is performed to optimize the machining parameters of drilling and to reduce delamination factor and also to maximize the tensile strength of the bamboo/basalt reinforced hybrid composites. 2. Experimental Procedure 2.1. Specimen Preparation The composite material is being fabricated with the help of bamboo and basalt fiber. Epoxy is used as a resin for the hybrid composite. The composite is being fabricated by the hand lay-up technique. For the present experimental work, all the specimens are fabricated and cut according to the ASTM standard D3039. Four layered hybrid composite is being fabricated in sandwich form. Of which the outer layers are basalt fibers and the inside layers are of bamboo fibers. The specimens are cut and made ready according to the required size for machining operation. 2.2. Machining set-up The drilling operation was carried out on a radial drilling machine. A standard twist drill of diameter 6 mm was used. The drill material was solid tungsten carbide. After drilling, the tensile strength was performed on INSTRON 8081 machine. 2.3. Process parameters The process parameters selected for the present study include cutting speed and feed rate. The cutting speed and feed rate are the two most important parameters for drilling operation. So, these two parameters are selected for the present investigation. The number of levels used is three as shown in below table 1.

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Table 1. Process parameters and their levels Code A B

Process parameters

Unit

Cutting speed Feed rate

m/min Mm/rev

Level 1

2

3

450 0.08

710 0.125

1120 0.20

3.Design of Experiments Taguchi L9 orthogonal array method is implemented for this experiment. The L9 orthogonal array includes nine experimental runs. This type of design minimizes the number of experiments so as to economize cost and also time.

Measurement of delamination factor and tensile strength The most frequent defect caused by drilling is delamination. After drilling, the specimens are put under microscope to observe the delamination damage of holes. The value of delamination factor is obtained by the following equation Fd =

(1)

Where Dmax is the maximum diameter of the damage and D is the diameter of the drill. Also, the tensile strength of the drilled specimens was tested. The strength is being tested on INSTRON 8081 machine at 2 mm/min speed. The readings of both delamination factor and residual tensile strength were taken as process response. The experimental assignment with their measurement results are shown in Table 2. Table 2. Experimental assignment and measurement results Number

Process parameters A B

Delamination Factor (cm)

Tensile Strength (MPa)

1

450

0.08

1.25

73.64

2

450

0.125

1.33

69.19

3

450

0.20

1.50

61.81

4

710

0.08

1.18

82.60

5

710

0.125

1.27

74.65

6

710

0.20

1.37

68.71

7

1120

0.08

1.10

81.65

8

1120

0.125

1.21

77.91

9

1120

0.20

1.26

73.25

As per the Taguchi signal-to-noise (S/N) ratio, a high value of S/N ratio implies better setting of process parameters. Generally, there are three categories of quality characteristics. They are namely, smaller the better, nominal the better, larger the better. For the present experimental work, the objective is to minimize the delamination factor and to maximize the tensile strength. So, smaller the better criteria must be appropriate to minimize the delamination factor and larger the better criteria of quality characteristic to maximize the tensile strength.

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The equation for calculating S/N ratio value for smaller the better type characteristic is given as S/N = - 10 log ( ∑

)

(2)

The equation for calculating S/N ratio value for larger the better type characteristic is given as S/N = - 10 log (

∑

)

(3)

Where yi is the experimental result of ith experiment, i is the number of experiments, and n is the total number of experiments.

Table 3. S/N ratio results Number 1 2 3 4 5 6 7 8 9

Delamination factor S/N (dB) -1.94 -2.47 -3.52 -1.43 -2.07 -2.73 -0.83 -1.66 -2.00

Tensile strength S/N (dB) 37.34 38.80 35.82 38.34 37.46 36.74 38.24 37.83 37.30

4. Results and Discussion 4.1. Signal-to-noise ratio analysis For the present study, from Table-4 it can be seen that the delamination factor is mainly influenced by feed rate followed by cutting speed. From fig.-1, it is seen that the optimal process parameters for minimum delamination factor is A3B1 which means 1120 m/min cutting speed and 0.08 mm/rev feed rate. From Table-5, it is also seen that the most significant factor is the feed rate followed by cutting speed. From fig.-2, it is seen that the optimal process parameters for maximum tensile strength is also A3B1 i.e. 1120 m/min cutting speed and 0.08 mm/rev feed rate.

Table 4. Response table mean S/N ratio for delamination factor Level 1 2 3 Delta Rank

Cutting speed -2.64 -2.08 -1.50 1.14 2

Feed rate -1.4 -2.07 -2.75 1.35 1

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4.2. Multi-objective optimization using grey relational analysis Generally, Taguchi S/N ratio analysis is used to solve single-objective optimization problem. But optimization of multi-objective i.e. delamination factor and residual tensile strength, Taguchi design with grey relational analysis is used to choose the optimal solution of process parameters. Taguchi design with grey relational analysis is a powerful method for multi-objective optimization [16]. The multi-objective problems are converted to single objective problems with the help of grey relational analysis. The steps involved in grey relational analysis are as follows: The first step of GRA is normalization. For the present study, since there are two different objectives i.e. lower the better for delamination factor and larger the better for tensile strength, the different methods adopted for normalization are as follows:

Smaller the better normalization method is expressed as: °( )

∗

( )=

°

( )

°( )

(4)

°( )

Larger the better normalization method is expressed as: ∗

°( ) °( )

( )=

°

( )

(5)

°( )

Where ° ( ) is the measured result, max ° ( ) is the maximum value of ° ( ), min ° ( ) is the minimum value of ° ( ), i is the number of experiments, and k is the quality characteristic. The normalized values are shown in Table 6.

Main Effects Plot for SN ratios

Main Effects Plot for SN ratios

Data Means

Data Means

Speed

Speed

Feed

-1.50

37.75

Mean of SN ratios

Mean of SN ratios

Feed

38.00

-1.75

-2.00 -2.25 -2.50

37.50 37.25 37.00 36.75

-2.75

36.50 450

710

1120

0.080

0.125

Signal-to-noise: Smaller is better

Fig. 1. Mean S/N graph for delamination factor

0.200

450

710

1120

0.080

0.125

0.200

Signal-to-noise: Larger is better

Fig-2: Mean S/N graph for Tensile strength

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Table 6. Normalized values for delamination factor and tensile strength Number 1

Normalized value Delamination factor Tensile strength 0.63(0.37) 0.57(0.43)

2 3

0.43(0.57) 0(1)

0.35(0.65) 0(1)

4 5

0.80(0.20) 0.58(0.42)

1(0) 0.62(0.38)

6 7 8 9

0.33(0.67) 1(0) 0.73(0.27) 0.60(0.40)

0.33(0.67) 0.95(0.05) 0.77(0.23) 0.55(0.45)

The second step of GRA is to calculate the grey relational coefficient which is given as follows

( )=

∆

∆

∆ ( )

∆

(6)

Where ∆ ( ) is the offset in the absolute values, is the distinguishing coefficient and is selected as 0.5, ∆ is the smallest and largest values of ∆ ( ). The grey relational coefficients are shown in Table 7. ∆

and

Table 7. Grey relational coefficients along with grey grades Number

1 2 3 4 5 6 7 8 9

Grey relational coefficient Delamination factor 0.57 0.47 0.33 0.71 0.54 0.43 1 0.65 0.56

Tensile strength 0.55 0.43 0.33 1 0.57 0.43 0.91 0.68 0.53

Grey relational grade Value

Order

0.57 0.45 0.33 0.86 0.56 0.43 0.96 0.67 0.55

4 7 9 2 5 8 1 3 6

The last step is to calculate the grey relational grade. It is done by taking the average of the sum of grey relational coefficients and it is given by =

∑

( )

(7)

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Where changes in the range of 0 to 1 and n is the number of experiments. The higher is the grey relational grade, the more ideal is the characteristic. From Table- 8 it is seen that the higher grey relational grade corresponds to higher order. Table 7 shows the response table for grey relational grade. From Fig-3 , it can be shown that the higher is the grey relational grade; the better is the multiple quality characteristic. The optimal process parameters obtained from the fig. is A3B1 where cutting speed is 1120 m/min and feed rate is 0.08 mm/rev. Table 8. Response Table for grey relational grade Level

Grey relational grade Cutting Speed Feed rate 0.45 0.79 0.62 0.56 0.73 0.44 0.28 0.35 2 1

1 2 3 Delta Rank

Main Effects Plot for Means Data Means

Speed

Feed

Mean of Means

0.8

0.7

0.6

0.5

0.4 450

710

1120

0.080

0.125

0.200

Fig. 3. Grey relational grade graph 4.2. ANOVA analysis The ANOVA analysis is used to determine the most influencing characteristic. The results of ANOVA with respect to grey relational grade showed that the most dominant factor in drilling operation is the feed rate and it is being displayed as on Table 9. Table 9. The results of ANOVA analysis Source

DF

Adj SS

Adj MS

F-Value

P-Value

% contribution

Speed

2

0.11940

0.059700

15.18

0.014

35.96

Feed

2

0.19687

0.098433

25.03

0.005

59.29

Error

4

0.01573

0.003933

Total

8

0.33200

4.73

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4.3. Confirmation experiment The confirmation experiment is performed after the optimal process parameters are confirmed by grey relational analysis. It is conducted to verify and predict the quality characteristics. The expression for estimated grey relational grade is given by =

+ ∑

(

−

)

Where is the total mean of grey relational grade, number of machining parameter.

(8)

is the optimum level grey relational grade, and n is the

As determined, the optimal process parameter combination is A3B1 and estimated grey relational grade is 0.96. The predicted grey relational grade is 0.92 as determined by Equation (8). From the confirmation experiment, it can be shown that the quality characteristics are promoted. 5. Conclusion An attempt has been made to optimize the drilling parameters of Bamboo/Basalt reinforced hybrid composite. The following conclusions are drawn from the obtained results: (1) From Taguchi’s S/N ratio analysis, it is observed that higher cutting speed and lower feed rate exhibits superior output responses. (2) Using GRA, the multi-objective quality characteristics are converted to single objective quality characteristic. (3) From GRA, it is observed that the effect of feed rate on the output response is more pronounced than the cutting speed. (4) From ANOVA analysis, it is found that the percentage contribution of cutting speed and feed rate is 35.96% and 59.29% respectively. References [1] K. G. Satyanarayana, G.G.C. Arizaga, F.Wypych, Biodegradable composites based on lingo cellulosic fibers—an overview, Progress in Polymer Science. 34 (2009) 982–1021 [2] S. Mukhopadhyaya, R. Srikanta, Effect of ageing of sisal fibres on properties of sisal Polypropylene composites, Polymer Degradation and Stability. 93 (2008) 2048–2051. [3] H. Larbig, H. Scherzer, B. Dahlke, R. Pol-trock, Natural fiber reinforced foams based on renewable resources for automotive interior applications, J. Cell. Plast. 34 (1998) 361–379. [4] A.M. Eleiche, G.M.Amin, The effect of unidirectional cotton fibre reinforcement on the friction and wear characteristics of polyester, Wear. 112 (1986) 67-78. [5] C. Lakkad, J.M. Patel, Mechanical properties of bamboo, a natural composite, Fibre Sci. Technol. 14 1981 319–322 [6] Banibayat P, Patnaik A. Variability of mechanical properties of basalt fiber reinforced polymer bars manufactured by wet-layup method. Mater Des 2014;56:898–906. [7] Lopresto V, Leone C, Iorio ID. Mechanical characterisation of basalt fibre reinforced plastic. Composite Part B – Eng 2011;42(4):717–23 [8] Wei B, Cao HL, Song SH. Degradation of basalt fibre and glass fibre/epoxy resin composites in sea water. Corros Sci 2011:53(1):426-31. [9] Wang X, Wu Z, Wu G, Zhu H, Zen F. Enhancement of basalt FRP by hybridization for long span cable-stayed bridge. Compos Part B – Eng 2013; 44(1):184–92. [10] Fiore V, Bella GD, Valenza A. Glass-basalt/epoxy hybrid composites for marine applications. Master Des 2011;32(4):2091–9. [11] Borhan TM. Properties of glass concrete reinforced with short basalt fiber. Mater Des 2012;42: 265–71. [12] Larrinaga P, Chastre C, Biscaia HC, San-Jose JT. Experimental and numerical modeling of basalt textile reinforced mortar behavior under uniaxial tensile stress. Mater Des 2014; 55:66–74

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[13] Wern CW, Ramulu M, Shukla A. Investigation of stresses in the orthogonal cutting of fibre reinforced plastics. Exp Mech 1994:33-41. [14] Rao GVG, Mahajan P, Bhatnagar N. Micro-mechanical modelling of machining of FRP composites – cutting force analysis. Compos Sci Technol 2007; 67:573–9. [15] G Dilli Babu, K Sivaji Babu, B Uma Maheswar Gowd. Optimization of machining parameters in drilling hemp fiber reinforced composites to maximize the tensile strength using design experiments. International Journal of Engineering & Materials Sciences Vol. 20, October 2013, pp. 385-390 [16] Kaining Shi1 & Dinghua Zhang1 & Junxue Ren. Optimization of process parameters for surface roughness and microhardness in dry milling of magnesium alloy using Taguchi with grey relational analysis. Int J Adv Manuf Technol DOI 10.1007/s00170-015-7218-8