Advanced Materials Manufacturing & Characterization Vol 5 Issue 1 (2015)
Advanced Materials Manufacturing & Characterization journal home page: www.ijammc-griet.com
Study of mechanical and tribological properties of ABS/ZnO polymer composites J. Sudeepana, K. Kumarb, T. K. Barmanc, P. Sahooc* aDepartment
of Chemical Engineering & Technology, BIT, Mesra, India of Mechanical Engineering, BIT, Mesra, India cDepartment of Mechanical Engineering, Jadavpur University, Kolkata, India *Corresponding author bDepartment
ARTICLE
INFO
Article history: Received 12-12-2014 Accepted 30-01-2015 Keywords: ABS; ZnO; Grey relational analysis; Friction; Wear rate.
A B S T R A C T The mechanical and tribological properties of acrylonitrile-butadiene-styrene (ABS) polymer filled with micron-sized zinc oxide (ZnO) are studied in this paper. The mechanical properties viz. tensile modulus, tensile strength, flexural modulus, flexural strength and micro-hardness are studied. For mechanical tests, ABS/ZnO composite materials are developed with 0, 5, 10, 15 and 20 wt% of filler. It is seen from the results that the tensile and flexural moduli increase with increase up to considered filler content, but the tensile strength and flexural strength increase up to 15 wt% and then start decreasing. The tribological behavior (friction coefficient and specific wear rate) of ABS composites filled with ZnO filler sliding against the steel counter face are investigated varying filler content (A), normal load (B) and sliding speed (C) with three levels of each parameter. The experiments are conducted on a multi-tribotester (block-on-roller configuration) using L27 orthogonal array (OA) of Taguchi analysis. To optimize the multiple responses (friction coefficient and specific wear rate), grey relational analysis is performed for the experimental results. It is seen from the analysis that the highest level of design parameters (A3B3C3) provides minimum friction coefficient and specific wear rate. The most influential factor which affects the tribological properties is normal load (B) followed by sliding speed (C) and filler content (A). Finally, a confirmation test is also carried out to validate the optimized results and it is seen that the grey relational grade is increased about 22% from initial to optimum conditions. The worn surfaces of ABS filled with micron-sized ZnO are also investigated by using scanning electron microscopy (SEM) images. It is seen that there are longitudinal grooves caused by micro-cutting effect and the wear mechanism is mainly abrasive in nature
1. Introduction
Nowadays, polymers play an important role in engineering applications with their desired physical and mechanical properties. Polymers alone cannot satisfy the required properties for applications so in order to improve properties and to lower the cost of polymer products, inorganic particulate fillers are employed. Inorganic-filled polymer composites have become attractive in polymer field due to its ________________
Corresponding author: P. Sahoo E-mail address:
[email protected] Doi: http://dx.doi.org/10.11127/ijammc.2015.03.01 Copyright@GRIET Publications. All rights reserved.
various advantages such as easiness in processing, cost
effectiveness and excellent performance over the metals as well as improved properties such as tensile modulus, strength, heat deflection temperature, hardness, fracture toughness etc. [1]. Polymer composite has a special property of self-lubrication as well as low friction coefficient, better wear resistance, lower weight alternatives to metallic components and these make composites suitable in tribological applications such as gears, cams, clutches, bearings, wheels, bushes etc. [2]. Acrylonitrile–butadiene–styrene (ABS) is one of the engineering thermoplastic terpolymer widely used over the past decades and finds applications in many fields like automotive, aerospace, business machines, computers, telephone handsets etc. Acrylonitrile gives chemical resistance and heat stability, butadiene gives toughness and impact strength and the styrene
125
gives rigidity and easiness of processability. The advantages of ABS polymer are good abrasion resistance, toughness and stiffness but they are having poor weathering resistance and heat resistance. Many researchers have used different fillers to improve the properties of polymer composites such as calcium carbonate (CaCO3) [3], titanium di-oxide (TiO2) [4], fumed silica [5] and kaolin [6]. Out of many inorganic filler, zinc oxide (ZnO) has been identified as functional inorganic filler which has great potential to alter the properties because of its prominent physical and chemical properties [7]. It has been widely used in areas such as optical materials, cosmetics and functional devices [8-10]. Zinc oxide filled polymers are studied in many research articles related to the mechanical properties. Mechanical properties of the HDPE/ZnO-Mg (OH)2-CaCO3 polymer composites are investigated and it is found that tensile modulus and strength decrease with increasing filler content [11]. It is seen that by adding 1 wt% of nano-ZnO filler into polypropylene (PP) matrix has enhanced the tensile strength, tensile modulus and elongation of the composites [12]. Some researchers have also studied mechanical properties of ABS polymer composites [1316]. Recently, efforts have been made to study the tribological properties of polymer based composites. Chang et al. [17] have investigated polyether-ether ketone (PEEK) and polyether-imide (PEI) reinforced with short carbon fibres, sub-micro TiO2, ZnS and graphite and reported that the conventional fillers enhance both the wear resistance and load carrying capacity of base polymers. Further, Jiang et al. [18] have revealed the tribological properties of polyphenylenesulfide (PPS) reinforced with submicro TiO2 and short carbon fibres (SCF) and found that 15 vol % SCF and 6 vol % TiO2 provide the lowest coefficient of friction based on artificial neural network (ANN) prediction. Xiang and Gu [19] have reported friction and wear behaviour of poly-tetrafluoro-ethylene (PTFE) with ultra-fine kaolin particles. The incorporation of kaolin particles reduces the wear rate by two orders of magnitude as compared to the unfilled PTFE, but friction coefficient increases over unfilled PTFE at filler concentrations of 10 wt %. Difallah et al. [20] have studied the tribological properties of ABS with the incorporation of graphite and found that the friction and wear decrease with the increase in filler content. Further, Wang et al. [21] have studied the mechanical and tribological properties of ABS filled with graphite and carbon black and found that the fillers can effectively decrease the COF and wear rate. It is seen from the literature review that there is scarcity of literatures related to tribological performances of ABS/ZnO composites. In the present study, ABS terpolymer has been selected as matrix material because of its industrial importance and wide applications in structural component. As the filler material, micron-sized ZnO is selected. For the preparation of composite materials, compression molding technique is used. Mechanical properties of the composites are evaluated considering different filler content. Friction and wear properties are also investigated conducting tests on multi-tribotester varying three design parameters viz. filler content, normal load and sliding speed. Using grey relational analysis, multiple responses (friction coefficient and specific wear rate) are optimized. Analysis of variance (ANOVA) is also carried out to study the level of significance of factors and their interactions on the overall grey relational grade. A confirmation experiment is conducted to verify the optimal test parameter combination as predicted by the analysis. Finally, wear behaviour of polymer
composites is studied with the help of scanning electron microscopy (SEM) images. 2.
Experimental details
2.1. Materials The filler selected for this study is zinc oxide (ZnO) supplied in the form of powder by Central drug Ltd, India with a mean particle size of 0.3 – 0.4 µm and bulk density of 5.61 g / cm3. The matrix selected is acrylonitrile-butadiene-styrene (ABS) of Absolac-920 grade, which is supplied in pellets form by Styrolution ABS limited, India with a density of 1.04 g / cm3 and melt flow index of 21 g/10 min. 2.2. Specimen preparation The materials are weighed in the proportions of 5, 10, 15 and 20 wt% of ZnO filler and the mixture is extruded by using a Haake single screw extruder (Rheocord – 9000) with a screw diameter of 18mm and L/D ratio of 24:1. ABS pellets and powders of ZnO filler are dried at 60°C in a vacuum oven for 6 hours to remove moisture and ABS/ZnO with different compositions are premixed manually with a zip-lock bag before extrusion. The extruder is fitted with a rod die and screw speed of 60 rpm is employed for preparing polymer composites. The temperature profile of the extruder is shown in Table 1 and the mixing for different compositions is carried out in a continuous manner. The extruded composites in the shape of rod are immediately cooled with water followed by air cooling. The composites in the form of rod are pelletized into granule form in uniform size by using a pelletizer machine. Again, the pelletized composites are dried at 60°C in a vacuum oven for 6 hours to remove moisture before compression molding process. The compression molding test rig is shown in Fig. 1. The pelletized granules are placed in a rectangular mold of size 150 x 100 x 8 mm3 and subjected to hot compression mold (Carver Press, Germany) with a temperature of 260°C and load of 8 metric tonnes kept for 1 min and then lowered the load to 6 metric tonnes to allow the entrapped air out from the mold and kept for 15 min. Then, heat is turned off and mold is allowed to cool in the compression machine itself at room temperature for 2 hrs and then the composites are removed from the mold. Table 1 Temperature profile along the extruder barrel Feed Zone
Compression Zone
Metering Zone
Die
210°C
220°C
230°C
240°C
2.3. Mechanical tests The mechanical properties of tensile and flexural tests are performed on Instron Universal Testing Machine (Instron Ltd, UK) with the maximum load capacity of 1 kN at room temperature and the instrument is shown in Fig. 2. The tensile tests are carried out according to ASTM D-638 with a specimen dimension of 100 x 12 x 8 mm3 with 50 mm gauge length and the cross head speed of 2.5 mm / min. According to ASTM D-790, flexural tests are carried out for the specimen dimensions of 100 x 12 x 8 mm3 with a span length of 50 mm and cross head speed of 2.5 mm / min. The mechanical tests are carried out for three samples for each composition and the average results are recorded. Micro hardness testing of composites is carried out in a UHL micro hardness tester (Model- VMHT MOT, Sl. No. 1002001, Technische Mikroskopie) with a Vickers diamond indenter. The dwell time is kept at 10s while the speed of indentation is set at
126
50µm/s and indentation load at 100 gf. The micro hardness tester is controlled through a touch screen based system which is part of the tester. The hardness numbers are obtained by processing the indention image. An average of at least three hardness values for each sample is reported.
Fig. 1 Compression molding machine
selected, sliding velocity etc., which will affect the test results of friction coefficient and wear rate. Based on the literature review [13-15, 22-23], the filler content (5, 10 and 15 wt %), normal load (15, 25 and 35 N) and sliding speed (80, 100 and 120 rpm) are selected as design parameters and are shown in Table 2. In order to study the influence of parameters and its interactions, a predesigned orthogonal array (OA) of L27 is used in this study. The selection of OA is based on degrees of freedom (DOF) for the experiments. The main factors have 2 (no of levels minus 1, i.e., 31) DOF and for two way interaction of the factors, the DOF is 4. Therefore, total DOF will be (3 x 2) + (3 x 4) = 18. The total DOF of the OA should be greater than the experimental DOF of the factors according to design of experiment. Hence, L27 OA having 26 DOF is chosen for this study. The tests are conducted as per the experimental design shown in Table 3 at room temperature. Here, each row represents the test conditions and column represents the test parameter. According to the linear graph shown in Fig. 3, the first column is assigned to filler content (A), the second column is assigned to normal load (B) and the fifth column is assigned to sliding speed (C). The third and fourth column are assigned to the interaction of filler content and load (A x B), sixth and seventh column assigned to the interaction of load and speed (A x C), eighth and eleventh column is assigned to the interaction of filler content and speed (B x C) and the remaining columns are assigned to error terms [24]. The response variables selected for the tribological characteristics are coefficient of friction (COF) and specific wear rate. 2.5. Friction and wear tests The friction and wear tests of ABS / ZnO with different compositions are performed on a block-on-roller multitribotester TR25 (Ducom, India) under dry condition with a constant time of 300 sec. A schematic diagram of the test rig is shown in Fig. 4. The rotating steel roller serves as a counter face and the stationery block serves as the test specimen. The surfaces of specimen and roller are cleaned with a soft paper before each test to ensure proper contact with the counter face. The composite samples (20 X 20 X 8 mm3) are pressed against a rotating steel roller (diameter 50 mm, thickness 50 mm and material EN8 steel) of hardness 55 HRc. A loading lever is used to apply a normal load on the top of the specimen. The frictional force is measured by a frictional force sensor that uses a beam type load cell of capacity 1000N. The experimental data of coefficient of friction (COF) are recorded on a computer attached to the testing apparatus. The weight loss of the composite is used to calculate the specific wear rate. The samples are weighed before and after the experiments to an accuracy of 0.0001 g in a mettler toddler electronic balance. The specific wear rate (W s) is calculated using Equation (1) [21].
W1 W 2 1000 W s ρ* P* υ* t (1) where Ws is the specific wear rate in mm3 / N.m, W1 is the weight Fig. 2 Universal testing machine
2.4. Design of experiments The selection of design parameters is the important stage for the design of experiment. There are many design factors such as filler content, normal load, sliding speed, temperature, materials
ρ
before the test in g, W2 is the weight after the test in g, is the computed density of composites in g / cm3, P is the applied normal load in N, υ is the relative sliding velocity in m / s and t is the experimental time in sec.
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Table 2 Design factors with different levels Levels Design factors Unit 1 2 3 Filler content (A)
%
5
10
15
Load (B)
N
15
25
35
Speed (C)
rpm
80
100
120
ζ (k) Δ where, i is the grey relational coefficient, min and Δmax is
Δ the minimum and maximum values of absolute differences ( oi ) of all comparing sequences,
Δoi X o(k) X i(k) is the X o(k) X i(k)
difference of the absolute value between
and
,
ζ is the distinguishing coefficient 0 ζ 1 . The distinguishing coefficient ζ depends X o(k)
is the reference sequence and
upon the weightage of responses. For this study, equal weightage are given to the responses, so ζ is 0.5 is taken for this study. After calculating grey relational coefficients, the grey relational
γ
grade i can be calculated by using Equation (4). It is calculated by averaging the grey relational coefficient corresponding to each performance characteristics. Fig. 3 Linear graph of L27 (313) orthogonal array
3. Grey relational analysis Taguchi method [25] is used to optimize single response optimization problem. But for multi-response optimization problems, Taguchi method alone cannot solve because higher S/N ratio for one performance characteristic may correspond to a lower S/N ratio for another. It is important to optimize all the responses simultaneously to achieve best results in manufacturing industries. To overcome this problem, Deng [26] proposed grey relational analysis, which is an efficient tool for solving inter-relationships among multiple-performances. This method can convert several responses into an equivalent single response function. The first step in solving the grey relational analysis is the grey relational generation. The responses of friction coefficient and specific wear rate are to be normalized in the range between 0 and 1 based on the Equation (2). Here, lower-the-better criterion is selected since minimum values of responses are required [27].
xi (k)
max yi (k) yi (k) max yi (k) min yi (k) (2)
x (k) is the normalized grey relational value for the kth where i max yi(k) is the largest value of yi(k) for the kth min yi(k) is the smallest value of yi(k) for the kth response, yi(k) th response ,
response, is the experimental value for the k response and i = 1 to 27, which is experiment number, k = 1 to 3 depends on the number of factors. The second step is to calculate the grey relational coefficients of the responses from the normalized values which represent the relationship between the desired and actual experimental data according to Equation (3).
ζ i(k)
Δmin ζΔmax Δoi(k) ζΔmax (3)
γi
1 n ζ (k) n k 1 i
(4)
where, n is the number of responses. The overall evaluation of multiple performances is based on the grey relational grade. As a result, optimization of the complicated multiple performance characteristics is converted into optimization of a single grey relational grade. The optimal level of the process parameters is the level with the highest grey relational grade. The optimal factor setting for maximizing overall grey relational grade can be performed by Taguchi method. Taguchi method is one of the powerful statistical tools used in the application of design and analysis for experiments adopted to optimize the design parameters and it is an effective approach to produce high-quality products at a relatively low cost [25]. Taguchi method uses a statistical measure of performance characteristic called signal-to-noise ratio (S/N), which is logarithmic functions of desired output to serve as objective functions for optimization. The S/N ratio is the ratio of the mean (signal) to the standard deviation (noise). Usually, the standard S/N ratio has three categories of the performance characteristics, namely, smaller-the-better, nominal-the best and higher-the-better. The S/N ratio of each level of design parameter is computed based on the S/N analysis. According to Taguchi, the parameter level combination with the maximum S/N ratio is the optimal setting irrespective of the performance characteristics. Analysis of variance (ANOVA) is a statistical technique used to predict the process parameters and its interactions which significantly affect the quality characteristics [28]. This is done by separating the total variability of S/N ratio, which is measured by the sum of squared deviations from the total mean of S/N ratio, into contributions for each process parameter and the error. The percentage contribution can be used to determine the significant parameters which affect the performance characteristics. ANOVA calculates F-ratio [29] which is the ratio between the mean square and the mean square error. F-ratio is used to measure the significance of parameters at the desired confidence level. If the calculated F-ratio value is greater than the tabulated value, then the factor is significant at a desired confidence level.
128
Table 3 Experimental design
4.
Results and discussion
3
1Fil ler co nt en t 1 (A 1) 1
4
1
2
2
2
1
1
1
2
2 2
3
3
3
5
1
2
2
2
2
2
2
3
3 3
1
1
1
6
1
2
2
2
3
3
3
1
1 1
2
2
2
7
1
3
3
3
1
1
1
3
3 3
2
2
2
8
1
3
3
3
2
2
2
1
1 1
3
3
3
9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7
1
3
3
3
3
3
3
2
2 2
1
1
1
2
1
2
3
1
2
3
1
2 3
1
2
3
2
1
2
3
2
3
1
2
3 1
2
3
1
2
1
2
3
3
1
2
3
1 2
3
1
2
2
2
3
1
1
2
3
2
3 1
3
1
2
2
2
3
1
2
3
1
3
1 2
1
2
3
2
2
3
1
3
1
2
1
2 3
2
3
1
2
3
1
2
1
2
3
3
1 2
2
3
1
2
3
1
2
2
3
1
1
2 3
3
1
2
2
3
1
2
3
1
2
2
3 1
1
2
3
3
1
3
2
1
3
2
1
3 2
1
3
2
3
1
3
2
2
1
3
2
1 3
2
1
3
3
1
3
2
3
2
1
3
2 1
3
2
1
3
2
1
3
1
3
2
2
1 3
3
2
1
4.1.3. Micro-hardness test results
3
2
1
3
2
1
3
3
2 1
1
3
2
3
2
1
3
3
2
1
1
3 2
2
1
3
3
3
2
1
1
3
2
3
2 1
2
1
3
3
3
2
1
2
1
3
1
3 2
3
2
1
3
3
2
1
3
2
1
2
1 3
1
3
2
The effect of ZnO filler particles filled with ABS on hardness is also investigated in this study. Fig. 7 shows the hardness values of the composites obtained from Vicker’s micro-hardness test. It is seen that the presence of ZnO fillers increases the hardness value of ABS from 27 Hv0.1 to 42 Hv0.1 with increasing filler content. This may be due to the rigidity of ZnO filler and increased modulus withstand the depth of penetration, hence micro-hardness increases with increase in filler content.
N o . 1 2
2Lo ad (B )
3( A x B )
4(A xB )
5Sp ee d (C)
6( B x C )
7( B x C )
8( A x C)
1 9 0 -
1 1(A xC )
1 2 -
1 3 -
1
1
1
1
1
1
1
1 1
1
1
1
1
1
1
2
2
2
2
2 2
2
2
2
1
1
1
3
3
3
3
3 3
3
3
3
4.1. Mechanical tests results 4.1.1. Tensile test results Tensile tests of ABS/ZnO composites with different compositions (0, 5, 10, 15 and 20 wt %) of filler are carried out in this study and the results for tensile modulus and tensile strength are shown in Fig. 5 (a, b). From Fig. 5 (a), it is noticed that the tensile modulus increases with the addition of ZnO filler up to 20 wt% of filler and this may be due to the fact that composites withstand deformation given by the filler rigidity and thus increase in modulus occurs [30]. In case of tensile strength of ZnO filled composites, it increases up to 15 wt% ZnO filler, and then it decreases with increased filler content. It may be due to the fact that the interface bonding between the matrix and filler is not good enough to transfer the tensile stress at high filler content, which indicates that rigid fillers has a negative effect on tensile strength [31]. 4.1.2. Flexural test results Three point bending tests are conducted for ABS/ZnO composites with different compositions of ZnO filler (0, 5, 10, 15 and 20 wt %) in this study. The flexural properties of composite materials are of great importance which is used in structural elements is prone to fail in bending, so the development of new composites with improved flexural properties is essential. Fig. 6 (a, b) shows the results of flexural modulus and flexural strength. From the flexural modulus figure, it is seen that the modulus increases with the addition of ZnO filler up to 20 wt%. In case of flexural strength (Fig. 6b), it shows that the strength is increased up to 10 wt% and then starts to decrease from that point. This may be due to the strength generally depends on the weakest part of the composites i.e., the interfacial bonding between filler and matrix and the bonding between the composites may be poor to transfer the stresses to the composites, hence decrease in strength is noticed [14-15].
129
(a) Fig. 4 Schematic diagram of multi-tribometer
(b) (a)
(b)
Fig. 6 Mechanical test results (a) Effect of flexural modulus of ABS/ZnO; (b) Effect of flexural strength of ABS/ZnO
Fig. 7 Effect of Vickers micro-hardness of ABS/ZnO
Fig. 5 Mechanical test results (a) Effect of tensile modulus of ABS/ZnO; (b) Effect of tensile strength of ABS/ZnO
130
Table 4 Experimental results of COF and specific wear rate along with normalized and grey relational coefficient
E x p R u n
COF
Specific wear rate
Norma lized values (COF)
Normali zed values (sp. wear rate)
1
0.3944
0.00268
0.3350
0.0000
0.4292
0.3333
2 3
0.3508 0.3466
0.00268 0.00235
0.4587 0.4706
0.0000 0.1684
4
0.2695
0.00196
0.6894
0.3673
0.4802 0.4857 0.6168
0.3333 0.3755 0.4414
5
0.2483
0.00134
0.7495
0.6837
6
0.2152
0.00121
0.8434
0.7500
0.6662 0.7615
0.6125 0.6667
7
0.2056
0.00137
0.8705
0.6684
8 9 1 0 1 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 2 0 2 1 2 2 2 3 2 4 2 5 2 6 2 7
0.1976 0.2097
0.00121 0.00091
0.8935 0.8591
0.7500 0.9031
0.7943 0.8244
0.4379
0.00251
0.2115
0.0867
0.4017 0.4787
0.00245 0.00224
0.3144 0.0959
0.1173 0.2245
0.2267
0.00158
0.8107
0.5612
0.2404
0.00125
0.7720
0.7296
0.2102
0.00116
0.8575
0.7755
0.2075
0.00110
0.8652
0.8061
0.2230
0.00105
0.8212
0.8316
0.1600 0.5125
0.00083 0.00240
1.0000 0.0000
0.9439 0.1429
0.4023
0.00090
0.3127
0.9082
0.4848
0.00072
0.0786
1.0000
0.2863
0.00129
0.6417
0.7092
0.2766
0.00117
0.6691
0.7704
0.2723
0.00108
0.6815
0.8163
0.2683
0.00100
0.6927
0.8571
0.2271 0.2647
0.00090 0.00072
0.8098 0.7030
0.9082 1.0000
Grey coeffici ent (COF)
Grey coeffici ent (Sp. wear rate)
0.7802 0.3880 0.4217
4.2. Tribological test results The friction and wear tests are conducted based on L27 OA with three design parameters viz. filler content (5, 10 and 15 wt %), applied load (15, 25 and 35 N) and sliding speed (80, 100 and 120 rpm) on multi-tribotester (block-on-roller configuration) and results are shown in Table 4.
Table 5 Grey relational grade and its orders
Exp Run
Grey relational grade
Orders
1
0.3813
24
0.6013 0.6667
2
0.4068
22
3
0.4306
21
0.8377 0.3538
4
0.5291
20
5
0.6394
16
6
0.7141
9
7
0.6978
11
0.7455
6
0.3616
0.3561
0.3920
8 9
0.8089
3
0.7254
0.5326
10
0.3709
26
0.6868
0.6490
11
0.3917
23
12
0.3741
25
0.7782
0.6901
13
0.6290
18
0.7876
0.7206
14
0.6679
14
15
0.7342
8
0.7366
0.7481
16
0.7541
5
17
0.7424
7
18
0.9496
1
19
0.3509
27
20
0.6330
17
21
0.6759
12
22
0.6074
19
23
0.6435
15
24
0.6711
13
25
0.6986
10
26
0.7846
4
27
0.8137
2
1.0000
0.8991
0.3333
0.3684
0.4211
0.8449
0.3518
1.0000
0.5825
0.6323
0.6018
0.6853
0.6109
0.7313
0.6193
0.7777
0.7244
0.8449
0.6274
1.0000
4.2.1. Grey relational analysis The multiple performance characteristics of COF and specific wear rate are performed using grey relational technique. This method can be used to convert several responses into a single response to find out the optimal process parameter. In grey
131
relational analysis, experimental results are normalized ranging from zero to one using Equation (2) and the normalized results of COF and specific wear rate are shown in Table 4. Based on normalized results, grey relational coefficient is calculated to represent the correlation between the desired and actual data using Equation (3) and is shown in Table 4.
Table 6 Response table for each factor levels of mean
Average mean for each factor level (Grey relational grade) A B
Level
By putting the equal weights to the responses, the grey relational grade and its order are determined using Equation (4) and are shown in Table 5. The overall performance characteristic of multiple response process depends on the calculated grey relational grade. The optimal parameter combination is determined by the highest grey relational grade and is found as A2B3C3 (10 wt% filler content, 35 N applied load, 120 rpm speed) followed by A3B3C3.
C
1
0.5948
0.4461
0.5577
2
0.6238
0.6484
0.6283
3
0.6532*
0.7772*
0.6858*
Delta
0.0584
0.3311
0.1281
Rank
3
1
2
Note: The total mean S/N ratio = 0.6239 *Indicates optimal condition Filler content
Normal load
Sliding speed
0.8
S 1 1 ratio 10 * log10 ( * ) N n y2 (5) where, y represents experimental data for grey relational grade and n denotes the number of experiments.
0.7
Means
Since the design of experiment is orthogonal, so the best optimal parameter combination obtained from the highest grey relational grade is independent. Hence, the mean grey relational grade for each level is computed and is shown in Table 6 and main effects plot shown in Fig. 8. The optimal setting for maximum overall grey relational grade is found by using Taguchi higher-the-better criterion based on Equation (5).
0.6
0.5
0.4 5
10
15
15
25
35
80
100
120
Fig. 8 Main effects plot for grey relational grade
The highest mean grey relational grade for each factor level will be the optimum parameters for this study. It is seen from Table 6 and Fig. 8, the optimal design parameter combination for minimizing COF and wear rate is found as A3B3C3 (15 wt% filler content, 35 N normal load, 120 rpm speed). Using Minitab 16 software [32], the analysis is carried out. 4.2.2. Analysis of variance (ANOVA) In order to understand the effect of design factors like filler content (A), normal load (B) and sliding speed (C) on the experimental data, analysis of variance (ANOVA) is studied at 95% level of confidence. The results of ANOVA for grey relational grade are presented in Table 7. It is seen from the table that factor B (normal load) has most significant effect on grey relational grade followed by factor C (sliding speed). From the interaction plots shown in Fig. 9 (a-c), it is seen that none of the interactions of design parameters is statistically significant on grey relational grade.
(a)
(b)
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(c) Fig. 9 Interaction plot for grey relational grade (a) AxB (b) AxC (c) BxC
(c) Fig. 10 Surface and contour plots for grey relational grade as a function of (a) AxB (b) AxC (c) BxC
4.2.3. Effect of design parameters on grey relational grade The surface and contour plots are drawn with the help of regression equation in Matlab software to know the influence of filler content, normal load and sliding speed on grey relational grade and is shown in Fig. 10 (a-c). It is seen from the plots, the grey relational grade increases with increase in filler content, normal load and sliding speed. This confirms the optimization result of grey relational analysis which is found to be A3B3C3.
4.2.4. Confirmation test A confirmation test is carried out to verify the accuracy of the analysis. The estimated S/N ratio, (6) [33].
γ
is calculated using Equation
0 γ γ m (γi γ m ) i 1
γ
(6)
γ
where m is the total mean of COF and wear rate, i is the mean COF and wear rate at the optimal testing parameter level and 0 is the number of main design process parameters that significantly affect the performance of polymer composites. The grey relational grade is compared with an initial condition of A2B2C2 and it is found that the best optimal parameter combination enhances grey relational grade of about 22% from initial condition to optimum condition. The comparisons of the estimated and the actual grey relational grade are shown in Table 8.
(a)
4.2.5. Scanning electron microscopy (SEM) To study the morphology of wear track, SEM examinations are carried out on the composite surfaces coated with thin platinum film on the worn out surface by sputtering to get a conducting layer on a JEOL (model JSM 6390LV, Japan) microscope. Fig. 11 (a, b) shows the SEM micrograph of the initial and optimal conditions after tribology test. From the SEM micrograph, it is observed that the sliding surface is mainly composed of longitudinal grooves along the sliding direction and it indicates the micro-cutting and micro-ploughing effect of the counterface. The arrow mark in the micrographs indicates the sliding direction. It is also seen that ZnO filler covers the matrix region which enhances the modulus and due to this the composite material withstands the shear action. Hence, increase in wear resistance is noted after the incorporation of filler with ABS polymer.
(b)
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Table 8 Confirmation test for estimated and actual grey relational grade
Level COF Specific wear rate Grey relational
Initial parameter A2B2C2
Predicated Optimal A3B3C3
Experimental
Wedge
A3B3C3
0.2404
0.2647
0.00125
0.00072
0.6679 0.8391 0.8137 grade Note: Improvement in grey relational grade = 0.1458 5. Conclusion The mechanical and tribological behaviors have been carried out for ABS matrix filled with ZnO filler. The tribological properties of friction coefficient and wear rate are analyzed by using grey relational analysis (multiple performance characteristics). Analysis of variance (ANOVA) is conducted to study the influence of each process parameter on the responses. Confirmation test is carried out to evaluate the accuracy of optimal results. Finally, SEM micrographs are investigated to support the optimal parameters. It is found that with the addition of ZnO filler with ABS polymer, the tensile and flexural moduli increase with increase in filler content up to 20 wt% and tensile and flexural strength increase up to 15 wt% and start decreasing afterwards. From the micro-hardness test, it is seen that hardness value increases with increasing filler content. From the analysis of grey relational technique, it can be concluded that the addition of filler decreases the friction coefficient and wear rate with an increase in normal load and sliding speed. The optimal condition for grey relational grade is found to be 15 wt% filler content, 35 N normal load and 120 rpm sliding speed (A3B3C3). The factor B (normal load) has the major contribution on tribological property followed by sliding speed (C) and filler content (A). The confirmation test shows the improvement of grey relational grade analysis is about 0.1458 (22%). SEM images shows that longitudinal grooves caused by micro-cutting effect and it can be the possible reason for reduction in friction and wear rate. Finally, it can be concluded from this study that with the addition of micron-sized ZnO filler with the ABS polymer at the right combination of normal load and sliding speed, the mechanical and tribological properties will get improved.
(a)
(b)
Fig. 11 SEM images after tribological testing: (a) Initial condition (A2B2C2) (b) optimal condition (A3B3C3)
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