International Journal of Engineering Materials and Manufacture (2020) 5(2) 40-49 https://doi.org/10.26776/ijemm.05.02.2020.02 Abutu, J. 1 , Lawal, S. A. 2 , Ndaliman, M. B. 2 , Lafia-Araga, R. A. 3 , Oluleye M.A. 4 1 Department of Mechanical Engineering, Taraba State University, Jalingo, Taraba State, Nigeria 2 Department of Mechanical Engineering, Federal University of Technology, Minna, Niger State, Nigeria 3 Department of Chemistry, Federal University of Technology, Minna, Niger State, Nigeria 4 Department of Mechanical Engineering, Ekiti State University, Ado-Ekiti, Ekiti State, Nigeria E-mail: mbndaliman@futminna.edu.ng Reference: Abutu, J., Lawal, S. A. Ndaliman, M. B., Lafia-Araga, R. A. and Oluleye,M. A. Tribological Properties of Friction Materials Developed from Non- Asbestos Materials using Response Surface Methodology. International Journal of Engineering Materials and Manufacture, 5(2), 40-49. Tribological Properties of Friction Materials Developed from Non- Asbestos Materials using Response Surface Methodology Abutu Joseph, Lawal Sunday Albert, Ndaliman Mohammed Baba, Lafia-Araga Ruth Anayimi and Oluleye Atinuke Modupe Received: 22 April 2020 Accepted: 09 May 2020 Published: 30 June 2020 Publisher: Deer Hill Publications © 2020 The Author(s) Creative Commons: CC BY 4.0 ABSTRACT Over many years, asbestos has been used as reinforcement material in the production of brake pads production but it has lost favour due to its carcinogenic nature, as a result, there is need to investigate other possible substitute which can offer similar tribological properties as the carcinogenic material (asbestos). Several works has been carried out using different reinforcement material with the aim of finding a possible replacement for asbestos. In this work, Rule of mixture (ROM) was ustlised for sample formulation and the tribological properties of natural based material (coconut shell and seashell) were investigated using experimental design (response surface methodology) and multi- response optimisation technique (Grey relational analysis). The multi-response performance of the formulated brake pads samples was compared with a commercial brake pad sample. The research findings revealed that sample can be produced using 52% reinforcement, 35% binder, 8% abrasive and 5% friction modifier while the Grey relational analysis (GRA) showed that optimum multi-response performance of the developed coconut shell based sample can be achieved using MP, MT and CT and HTT of 12MPa, 100 o C, 6mins and 2hrs respectively while that of the developed seashell based brake pad can be achieved using MP, MT and CT and HTT of 10MPa, 160 o C, 12mins and 2hrs respectively. Also, the Analysis of variance (ANOVA) results show a percentage error of less than 5% indicating minima noise effect. In addition, the optimized coconut shell-based brake pads falls within the category of class H (µ >0.55) type of brake pads while seashell based sample falls within the class G (µ: 0.45-0.55) type of brake pads. It therefore concluded that the use of coconut shell can serve as a better substitute for asbestos-based brake pads. Keywords: Brake pad, Response surface methodology, tribological properties, Grey relational analysis. 1 INTRODUCTION Friction materials are materials used for the development of automobile parts such as clutch and brake pads which are utilised in the transmission and braking of various machineries like cars, aircraft, motorcycles and other automobile systems. The constituents kept varying with the aim of meeting up with environmental technology and emerging. Blau (2001) reported that friction materials can be classified as semi-metallic, organic and carbon-based, depending on the elemental composition. Mechanics working on automobile are in most cases exposed to asbestos dust in several ways. This include, grinding of friction brake or clutch, repair work on brakes and clutch, where accumulated dusts are always wiped off before the old ones are replaced using brush (Abutu et al., 2018). All these methods are capable of causing asbestos particles to become airborne which is very hazardous to the environment. Dagwa and Ibhadode (2008) reported that if old brake pads are still hard enough to be applied, automobile mechanics working on them often utilize a bench grinder to normalize the surface, or dissolve the dirts of the lining which often lead to the release of the particles of asbestos, thereby putting human at risk of contacting diseases such as pleural, peritoneal or pericardial mesothelioma, asbestos related cancer and asbestosis (Norton, 2001). Also, Mutlu et al. (2009) reported that tribological properties are very important properties in the performance of brake pads and a relatively high friction coefficient in the range of 0.3-0.7 and lower wear rate is normally desirable when using brake lining materials. Several works have been carried out with the aim of replacing asbestos as inclusion in brake pads production. Tribological Properties of Friction Materials Developed from Non- Asbestos Materials using Response Surface Methodology 41 Gabriel (2016) utilized periwinkle/palm shell as reinforcement material using Taguchi experimental design and reported that optimal performance of the developed friction material can be obtained using moulding pressure (140 KPa), moulding temperature (150 ˚C) and curing time (10 minutes) as process parameter. Also, Abutu et al. (2018) with the aim of finding a substitute for asbestos developed a friction material using seashell as reinforcement material and utilizing response surface-central composite design (RSM-CCD) technique and grey relational analysis (GRA). The authors reported that seashell can serve as good substitute for asbestos in friction material production and that multi- response performance of the developed material can be obtained using 1 hour heat treatment time, 12 minutes curing time, 160 ºC moulding temperature and 14MPa moulding pressure. Also Ruzaidi et al. (2011) formulated a non- asbestos containing brake pad with varying composition of Polychlorinated Biphenyls (PCB) and palm ash waste along with thermoset resin as a binder and metal filler as abrasive. Five samples were produced using moulding pressure, moulding temperature and curing time of 122 MPa, 150 ̊C and 5 minutes respectively and were tested to examine its compression strength, water absorption rate, wear rate, and morphological properties. Experimental findings indicated that the optimum performance (mechanical and wear properties) of the brake pads was obtained using higher percentage of palm ash and also, wear properties of the developed brake pads compared satisfactorily with conventional brake pad. Also, Mutlu et al. (2009) with the aim of finding a possible replacement for asbestos whose dust is hazardous developed friction materials using Rice Husk Dust (RHD) and Rice Straw Dust (RSD) to study the tribological behaviour of brake pads. The materials in each brake pad were composed of RHD, RSD, copper particles, barite, brass, cashew, steel fibres, graphite and alumina. The newly formulated brake pads were tested in order to study their tribological performance and the results revealed a mean friction coefficient of 0.315 – 0.381 which is very low to be applied in heavy duty automobiles brake pads as specified in the work of Dagwa and Ibhadode (2008) while the wear rate varies from 0.000853 – 0.001041 g/mm 2 . In addition, Fono–Tamo and Koya, (2011), also developed friction materials using palm kernel shell combined with other materials and observed that the optimal mechanical properties of the developed material showed a hardness of 32.34 and shear strength of 40.95 MPa while the optimum coefficient of friction of was found to be 0.43. Also, Ikpambese et al. (2014) developed an asbestos–free friction material using palm kernel fibres as reinforcement material and reported that sample with 10% palm kernel fiber, 6% Al2O3, 40% epoxy–resin, 29% graphite gave and 15% calcium carbonate gave the optimum performance. Similarly, Bashar et al. (2012) utilized coconut shell powder as reinforcement material to develop friction material and found that high inclusion of coconut powder may results in brittleness of the composite and also samples with 60 % matrix and 10% reinforcement as well as 50% matrix and 10% reinforcement can be utilized in the production of friction materials. Yawas et al. (2016) also developed an asbestos–free friction material using periwinkle shell as reinforcement material and revealed that optimal periwinkle shell reinforced sample produced using sieve size of125μm possessed specific gravity (1.01 g/cm3), coefficient of friction (0.41), hardness (116.7 HRB), compressive strength (147 N/mm2), and thickness swell in water (0.39 %) and thickness swell in SEA oil (0.37 %). Also, Ibhadode and Dagwa, (2008) used palm kernel shell (PKS) as reinforcement material along with other constituents in the production of friction materials and found that optimal sample produced using 56% reinforcement, 24% binder, 14% abrasives and 6% friction modifier compared favourably with commercial samples as a result can served as replacement for asbestos in friction lining production. Therefore, in this study, locally sourced coconut shell and seashell were separately used as reinforcement material along with other constituents to develop an environmentally friendly brake pad samples using Central composite design (CCD)-Response surface methodology (RSM) experimental design technique. The multi-response performance of the two brake pad samples was compared with commercially available brake pad by examining their tribological properties (wear rate and friction coefficient). 2. MATERIALS AND METHODS 2.1 Materials Coconut shells (Figure 1a) were sourced locally from a local coconut trader in Sabon Tasha market, Kaduna – Nigeria, seashells (Figure 1b) were collected from a local seafood vendor in Lagos bar beach, Lagos-Nigeria. Also, alumina and commercial brake sample (Figure 1c) designed for Mazda 323 and produced by Ibeto Group of Companies were sourced from a commercial shop situated in Kaduna-Nigeria while epoxy resin used together with hardener (Sikadur 42T) were purchased from a chemical store located in Onitsha-Nigeria while graphite (Figure 1d) sourced from dry cell batteries (1.5 volt, TIGER). 2.2 Method Development of brake pads constitute the preparation of sourced materials, experimental design using Minitab 17 software, compression moulding process, testing of developed samples and analysis of experimental results using signal to noise (S/N), analysis of variance (ANOVA) and Grey relational analysis (GRA). 2.2.1 Materials preparation Materials preparation involved the preparation of the coconut shells, seashells and graphite powder. These involved washing, cleaning using tissue paper, drying under the sun for 24 hours followed by crushing with metallic mortar and pestle and thereafter, grinding using grinding machine situated at Samaru, Zaria and then sieving using a sieve size of 10µm. https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0ahUKEwiAsZPDx6DLAhXCmw4KHbNZAT4QFggsMAM&url=https%3A%2F%2Fwww3.epa.gov%2Fepawaste%2Fhazard%2Ftsd%2Fpcbs%2F&usg=AFQjCNFWsfEomOr1b1tJ2WhBu3E8OoF0JA Abutu et al.: International Journal of Engineering Materials and Manufacture, 5(2), 40-49 42 2.2.2 Design of Experiment using RSM Design Technique In this study, CCD via RSM experimental design consisting of moulding pressure (MP), moulding temperature (MT), curing time (CT) and heat treatment (HTT) was built in accordance to standard L27(2) 4 using Minitab 17 statistical software. The factor levels of process parameters and experimental design matrix are shown in Table 1 and 2 respectively. Figure 1: Reinforcement and friction modifier used (a) crushed coconut shells (b) seashells (c) commercial brake pad sample (d) extracted graphite rods Table 1: Factor level of process parameters Factor MP (MPa) MT ( o C) CT (min) HTT (hr) High 16 160 10 4 Low 12 120 6 2 Table 2: RSM-CCD experimental design matrix Run MT ( o C) MP (MPa) CT (minute) HTT (hour) 1 120 12 6 2 2 120 16 6 2 3 160 12 6 2 4 160 16 6 2 5 120 12 10 2 6 120 16 10 2 7 160 12 10 2 8 160 16 10 2 9 120 12 6 4 10 120 16 6 4 11 160 12 6 4 12 160 16 6 4 13 120 12 10 4 14 120 16 10 4 15 160 12 10 4 16 160 16 10 4 17 140 10 8 3 18 140 18 8 3 19 100 14 8 3 20 180 14 8 3 21 140 14 4 3 22 140 14 12 3 23 140 14 8 1 24 140 14 8 5 25 140 14 8 3 26 140 14 8 3 27 140 14 8 3 a c b d Tribological Properties of Friction Materials Developed from Non- Asbestos Materials using Response Surface Methodology 43 2.2.3 Samples Formulation and Production Sample formulation was carried out using Rule of mixture (ROM) technique outlined in the work of Askeland (1985) utilising density (𝜌) as a criteria while production was carried out on a compression moulding machine situated at Federal College of Chemical and Leather Technology (FCCLT), Samaru, Zaria (Polymer workshop) and was conducted using the procedure adopted by Abutu et al. (2019) which involved utilising varying process parameters shown in Table 2 with constant percentage composition as obtained from ROM. This procedure further involved pouring and mixing epoxy resin and hardener (catalyst) in the ratio of 2:1 in a separate container, followed by filling of mould cavity with total mixture of mixed binder and filler materials, then, the withdrawal of cured samples from the moulding machine, cooling of samples and removal of the cooled samples from the mould. 2.2.4 Sample Characterization Tribological properties (wear rate and friction coefficient) of the developed and commercial samples were evaluated using a Tribometer (ANTON PAAR GmbH, CSM Instrument, Strasse 20, 8054 Graz-Austria) and experiment was conducted in accordance with ASTM G99 testing procedure using the test conditions presented in Table 3. 3. RESULTS AND DISCUSSION 3.1 Samples formulation and Production The results of sample formulation indicate that samples should be produced using 52% reinforcement material (seashell or coconut shell), 35% binder (epoxy resin and hardener), 8% abrasive (Alumina) and 5% friction modifier (graphite). 3.1 Experimental Results The average values of experimental result along with the individual signal-to noise (S/N) ratios for wear rate (Wr) and friction coefficient (µ) of the developed brake pad samples are shown in Table 4. S/N ratios of friction coefficient were calculated using larger-the better (Eqn. 1) while that of wear rate was calculated using smaller-the better (Eqn. 2).          N N rN NS 1 2 11 log10/ (1)    NN r N NS 1 21 log10/ (2) r = response value of given factor level combination, N = number of factor level combination From the experimental results presented in Table 4, it can be observed that the values of the friction coefficient varied from 0.477 to 0.788 for coconut shell-based samples and 0.43 to 0.61 for seashell-based samples. These values falls within the class F (0.35 – 0.45), G (0.45 – 0.55) and H ( 0.55) type of brake pads recommended for use in automobile by Society of Automobile Engineers (SAE) and reported by Blau (2001) as well as Dagwa and Ibhadode (2006).These results are in good agreement with the earlier work of Roubicek et al. (2008) who reported that friction coefficient that falls within the range of 0.30–0.70 is desirable in brake pads. Therefore, seashell and coconut shell- based brake pads are suitable for use in automobiles. 3.2 Multi-response optimisation Multi-response optimisation of experimental results was carried using Grey relational analysis (GRA) technique. This technique was adopted to investigate the optimal process parameters that will produce the multi-response performance of the coconut and seashell-based brake pads. The procedure for GRA include using the values of S/N ratios shown in Table 3 to calculate the Grey relational grade (GRG) for friction coefficient and wear rate using larger the better (Eqn. 3) and smaller the better (Eqn. 4) attribute respectively. This is followed by the calculation of Grey relational coefficient (GRC) using Eqn. 5 and finally, the calculation using Grey relational grade using Eqn. 6. Larger-the-better attributes (wij) = ji iij aa aa   (3) Smaller-the-better attributes (wij) = jj ijij aa aa   (4) Where, ai = the performance value of alternative i attribute j and ja = max{aij , i = 1, 2, . . . , x}.response and ja = min{lij , i = 1, 2, . . . , x}. GRC, (x0j, xij) = max maxmin DD DD ij     (5) Abutu et al.: International Journal of Engineering Materials and Manufacture, 5(2), 40-49 44 j = 1, 2, . . . , 27 and i = 1, 2, . . . , 27, D= x0j –xij, , = min (Dij, i = 1, 2, . . . , x; j = 1, 2, . . . , y), naxD and minD = maximum and minimum GRG value for response and  is the distinguishing coefficient,  ∈ (0, 1). The aim of distinguishing coefficient is to reduce or increase the range of the grey relational coefficient and 0.5 is the widely accepted value (Chin, 2003). Grade = responsesofNo GRC .  (6) The results of GRG, GRC and grades obtained from GRA are presented in Table 5 and the resulting factor effects of process parameters are shown in Table 6 while the main effect plots for coconut shell and seashell based brake pads are shown in Figure 2 and 3 respectively. Table 3: Test parameters/conditions for tribology test Ball Diameter 10 mm Speed 10 cm/s Load 7 N Ball Material Stainless Steel Duration of Test 223 seconds Humidity of environment 55 % Temperature of environment 25 °C Table 4: Experimental results and S/N ratios of developed samples Run MP (MPa) MT ( o C) CT (min) HT T (hr) Coconut shell based Seashell based µ µ (dB) Wr (mg/m) Wr (dB) µ µ (dB) Wr (mg/m) Wr (dB) 1 12 120 6 2 0.788 -2.07 0.2620 11.64 0.492 -6.16 0.3340 9.525 2 16 120 6 2 0.652 -3.72 1.0936 -0.78 0.447 -6.99 0.2685 11.42 3 12 160 6 2 0.686 -3.27 0.1310 17.66 0.548 -5.22 0.2358 12.55 4 16 160 6 2 0.688 -3.25 0.1703 15.38 0.529 -5.53 0.1834 14.73 5 12 120 10 2 0.685 -3.29 0.2030 13.85 0.556 -5.10 0.3995 7.970 6 16 120 10 2 0.601 -4.42 0.1244 18.10 0.525 -5.60 0.3536 9.029 7 12 160 10 2 0.601 -4.42 0.3995 7.970 0.58 -4.73 2.6130 -8.34 8 16 160 10 2 0.651 -3.73 0.0393 28.11 0.593 -4.54 0.2881 10.81 9 12 120 6 4 0.566 -4.94 0.6614 3.590 0.565 -4.96 0.2358 12.55 10 16 120 6 4 0.533 -5.47 0.2161 13.31 0.572 -4.85 0.1637 15.72 11 12 160 6 4 0.688 -3.25 0.0720 22.85 0.573 -4.84 0.2554 11.86 12 16 160 6 4 0.496 -6.09 0.0655 23.68 0.568 -4.91 0.1113 19.07 13 12 120 10 4 0.558 -5.07 0.6549 3.677 0.430 -7.33 0.2096 13.57 14 16 120 10 4 0.649 -3.76 0.7138 2.928 0.610 -4.29 0.2816 11.01 15 12 160 10 4 0.568 -4.91 0.6680 3.505 0.534 -5.45 0.1899 14.43 16 16 160 10 4 0.477 -6.43 0.7204 2.849 0.496 -6.09 0.7924 2.021 17 10 140 8 3 0.668 -3.50 0.1441 16.83 0.567 -4.93 0.1834 14.73 18 18 140 8 3 0.571 -4.87 0.0720 22.85 0.546 -5.26 0.4977 6.061 19 14 100 8 3 0.617 -4.19 0.6483 3.764 0.521 -5.66 0.1244 18.10 20 14 180 8 3 0.648 -3.77 0.0589 24.59 0.491 -6.18 0.2816 11.01 21 14 140 4 3 0.650 -3.74 0.0197 34.13 0.571 -4.87 0.2161 13.31 22 14 140 12 3 0.550 -5.19 0.0786 22.09 0.544 -5.29 2.1545 -6.67 23 14 140 8 1 0.720 -2.85 0.0066 43.68 0.509 -5.87 0.1048 19.59 24 14 140 8 5 0.614 -4.24 0.0720 22.85 0.519 -5.70 0.6549 3.677 25 14 140 8 3 0.503 -5.97 0.7138 2.928 0.498 -6.06 0.2227 13.05 26 14 140 8 3 0.528 -5.55 0.8382 1.533 0.511 -5.83 0.2096 13.57 27 14 140 8 3 0.521 -5.66 0.7924 2.021 0.497 -6.07 0.2030 13.85 Tribological Properties of Friction Materials Developed from Non- Asbestos Materials using Response Surface Methodology 45 Table 5: Results of Grey relational analysis (GRA) Senario GRG GRC Grade Coconut shell based Seashell based Coconut shell based Seashell based Coconu t shell based Seashell based µ Wr µ Wr µ Wr µ Wr Xo 1.00 1.00 1.00 1.00 - - - - - - 1 1.00 0.72 0.39 0.36 1.00 0.64 0.45 0.44 0.82 0.45 2 0.62 1.00 0.11 0.29 0.57 1.00 0.36 0.41 0.79 0.39 3 0.72 0.59 0.69 0.25 0.64 0.55 0.62 0.40 0.60 0.51 4 0.73 0.64 0.59 0.17 0.65 0.58 0.55 0.38 0.62 0.47 5 0.72 0.67 0.73 0.42 0.64 0.60 0.65 0.46 0.62 0.56 6 0.46 0.58 0.57 0.38 0.48 0.54 0.54 0.4\5 0.51 0.50 7 0.46 0.80 0.86 1.00 0.48 0.72 0.78 1.00 0.60 0.89 8 0.62 0.35 0.92 0.31 0.57 0.43 0.86 0.42 0.50 0.64 9 0.34 0.90 0.78 0.25 0.43 0.84 0.70 0.40 0.64 0.55 10 0.22 0.68 0.82 0.14 0.39 0.61 0.73 0.37 0.50 0.55 11 0.73 0.47 0.82 0.28 0.65 0.48 0.74 0.41 0.57 0.58 12 0.08 0.45 0.80 0.02 0.35 0.48 0.71 0.34 0.42 0.53 13 0.31 0.90 0.00 0.22 0.42 0.83 0.33 0.39 0.63 0.36 14 0.61 0.92 1.00 0.31 0.56 0.86 1.00 0.42 0.71 0.71 15 0.35 0.90 0.62 0.18 0.43 0.84 0.57 0.38 0.64 0.48 16 0.00 0.92 0.41 0.63 0.33 0.86 0.46 0.57 0.60 0.52 17 0.67 0.60 0.79 0.17 0.60 0.56 0.71 0.38 0.58 0.55 18 0.36 0.47 0.68 0.48 0.44 0.48 0.61 0.49 0.46 0.55 19 0.51 0.90 0.55 0.05 0.51 0.83 0.53 0.35 0.67 0.44 20 0.61 0.43 0.38 0.31 0.56 0.47 0.45 0.42 0.52 0.44 21 0.62 0.21 0.81 0.23 0.57 0.39 0.73 0.39 0.48 0.56 22 0.28 0.49 0.67 0.94 0.41 0.49 0.60 0.89 0.45 0.75 23 0.82 0.00 0.48 0.00 0.74 0.33 0.49 0.33 0.54 0.41 24 0.50 0.47 0.54 0.57 0.50 0.48 0.52 0.54 0.49 0.53 25 0.11 0.92 0.42 0.23 0.36 0.86 0.46 0.40 0.61 0.43 26 0.20 0.95 0.49 0.22 0.39 0.91 0.50 0.39 0.65 0.45 27 0.18 0.94 0.41 0.21 0.38 0.89 0.46 0.39 0.64 0.43 Table 6: Resulting factor effects of process parameters Level Coconut shell based Seashell based MP (MPa) MT ( o C) CT (min) HTT (hr) MP (MPa) MT ( o C) CT (min) HTT (hr) 1 0.5800 0.6700 0.4800 0.5400 0.5500 0.4400 0.5600 0.4100 2 0.6400 0.6565 0.6200 0.6325 0.5475 0.5088 0.5038 0.5513 3 0.5405 0.5190 0.5562 0.5419 0.5095 0.5410 0.4795 0.5324 4 0.5812 0.5687 0.6012 0.5887 0.5388 0.5775 0.5825 0.5350 5 0.4600 0.5200 0.4500 0.4900 0.5500 0.4400 0.7500 0.5300 Figure 2: Main effect plots for coconut shell based sample Abutu et al.: International Journal of Engineering Materials and Manufacture, 5(2), 40-49 46 Figure 3: Main effect plots for seashell based sample The main effect plots shown in Figure 2 indicates that optimum multi-response performance of the developed coconut shell based brake pad samples can be achieved using MP, MT and CT and HTT of 12MPa, 100 o C, 6mins and 2hrs respectively while Figure 3 revealed that optimum multi-response performance of the developed seashell based brake pads can be achieved using MP, MT and CT and HTT of 10MPa, 160 o C, 12mins and 2hrs respectively. Any change in these optimal parameters may lead to poor bonding between the resin and its constituent fillers (Abutu et al., 2018). 3.3 Production of optimized samples The optimised brake pad samples were produced using standard compression moulding process described in the earlier section. Production of the coconut shell-based sample was done using MP, MT and CT and HTT of 12MPa, 100 o C, 6mins and 2hrs respectively while the seashell-based sample was produced using MP, MT, CT and HTT of 10MPa, 160 o C, 12mins and 2hrs respectively as obtained from GRA. The percentage composition of the brake pad samples remains constant throughout the moulding process. 3.4 Characterisation of optimised and commercial samples In order to study and compare the tribological properties of the optimised and commercial (control) brake pads, samples were characterised using the testing methods discussed in the previous section. The results of experimental findings compared with the control are summarised in Table 7. The results presented in Table 7 revealed that friction coefficient and wear rate of the commercial brake pads are 0.634 and 0.04184 mg/m respectively while that of coconut shell and seashell reinforced samples are 0.614 and 0.03156 mg/m as well as 0.542 and 0.07252 mg/m respectively. These results indicate that the coconut shell-based brake pads possesses lower wear rate compared to commercial and seashell-based samples and also falls within the category of class H (µ >0.55) type of brake pads. Thus, is recommended for use in heavy duty automobile by the Society of Automobile Engineers (SAE) as reported in the work of Blau (2001) and Dagwa and Ibhadode (2006). Also, the samples reinforced with seashell falls within the class G ( : 0.45–0.55) type of brake pads and thus suitable for use in light weight automobile. The morphology of the wear track section of the optimized and commercial sample is shown in Figure 4(a-c). The part labeled ‘A’ represent the wear track section of the test specimen. As shown in Figure 4a, it can be observed that the coconut shell-based sample has the least track section with an area of 154597.2 μm2. This is followed by commercial sample (Figure 4b) with track sectional area of 204896.1 μm2 and finally the seashell-based sample (Figure 4c) which showed the widest track section (355165.0 μm2). These differences in the wear track area may be attributed to the variation in the hardness and flexural strength of the samples. This is in agreement with the earlier work of Zum-Gahr (1987) who reported that wear rate of materials are strongly dependent on the size, hardness, shape and flexibility of the abrasive particles as a results harder materials tend to have lower wear track sectional area. Tribological Properties of Friction Materials Developed from Non- Asbestos Materials using Response Surface Methodology 47 Figure 4: Morphology of wear track section on tribometer Table 7: Tribological properties of optimised and control sample S/N Properties Commercial product (X) Coconut shell based (C) Seashell based (S) 1. Wear rate (mg/m) 0.04184 0.0315 0.0725 2. Coefficient of friction 0.634 0.614 0.525 Table 8: ANOVA for Friction Coefficient Factor Coconut shell based Seashell based DOF SS MS F P (%) DOF SS MS F P (%) MP (MPa) 4 0.0297 0.0074 9.715 19.388 4 0.0116 0.0029 16.422 24.440 MT (ºC) 4 0.0177 0.0044 5.787 11.549 4 0.0142 0.0035 20.037 29.819 CT (min) 4 0.0252 0.0063 8.249 16.464 4 0.0117 0.0029 16.586 24.684 HTT (hr) 4 0.0729 0.0182 23.855 47.609 4 0.0082 0.0021 11.648 17.335 Error 10 0.0076 0.0008 4.989 10 0.0018 0.0002 3.721 Total 26 0.1532 0.0059 100.0 26 0.0476 0.0018 100.0 Table 9: ANOVA for wear rate Factor Coconut shell based Seashell based DOF SS MS F P (%) DOF SS MS F P (%) MP (MPa) 4 0.492 0.123 12.365 17.65 4 1.332 0.333 15.608 14.93 MT (ºC) 4 0.916 0.229 23.046 32.9 4 1.518 0.38 17.788 17.01 CT (min) 4 0.603 0.151 15.169 21.65 4 4.93 1.233 57.755 55.23 HTT (hr) 4 0.675 0.169 16.975 24.23 4 0.932 0.233 10.917 10.44 Error 10 0.099 0.01 3.569 10 0.213 0.021 2.391 Total 26 2.786 0.107 100 26 8.926 0.343 100 3.5 Analysis of Variance (ANOVA) ANOVA was conducted on the experimental responses in order to study the significant effects and percentage contribution of individual process parameters. This analysis was carried out using confidence level of 99 % and significance level of 1 %. ANOVA table shown in Table 8-9 consist of degree of freedom (DOF), sum of square (SS), mean square (MS), f-value and percentage contribution (P). The ANOVA for the coefficient of friction of coconut shell-based composite shown in Table 9 indicates that HTT with percentage contribution of 47.609% provides the greatest impact on the friction coefficient of the friction materials. This is followed by MP (19.388%) and CT (16.464%), finally, the least significance, MT with percentage contribution of 11.549%. Also, the ANOVA for the coefficient of friction of seashell-based composite shown in Table 9 indicates that MT with percentage contribution of 29.819% provides the greatest influence on the friction coefficient of the friction materials, followed by CT (24.684%) and MP (24.440%) and HTT (17.335%). In addition, the ANOVA for wear rate of coconut shell-based composite shown in Table 10 revealed that MT with percentage contribution of 32.896% provides the greatest impact on the wear rate of the friction materials, followed by HTT (24.231%), CT (21.653%) and finally, MP (17.651%). Also, the ANOVA for the wear rate of seashell-based composite shown in Table 10 showed that CT with percentage contribution of 55.232% provides the greatest impact on the wear rate of the friction materials. This is followed by MT (17.011%), MP (14.93%) and finally, MP (10.442%). Also, the percentage error obtained for this analysis were less than 5% which indicate that the experimental processes were conducted with minimum influence of noise (Lawal et al., 2016; Zaharudin et al., 2011). (a) Optimised coconut shell -based sample (b) Optimised seashell- based sample (c) Commercial sample .A .A .A Abutu et al.: International Journal of Engineering Materials and Manufacture, 5(2), 40-49 48 3.2.4 Empirical regression analysis Empirical regression equation was obtained using the experimental data with the aim of predicting the value of the investigated responses. The regression equations along with correlation coefficients (Rsq) for coconut shell and seashell based sample are shown in Equ. 7-11. For coconut shell reinforced composite, µ = 1.128 - 0.01223 MP - 0.000240 MT - 0.01056 CT - 0.0429 HTT (7) R-sq = 85.52% and R-sq (adj) = 75.62%. Wear rate = 0.4095- 0.0011 MP - 0.00592 MT + 0.0202 CT + 0.0607 HTT (8) R-sq = 66.77% and R-sq (adj) = 51.63% For seashell reinforced composite, µ = 0.471 + 0.00042 MP + 0.000342 MT - 0.00050 CT + 0.00408 HTT (9) R-sq = 53.28% and R-sq (adj) = 50.11% Wear rate = 0.6536- 0.0292 MP+ 0.00570 MT + 0.0303 CT - 0.082 HTT (10) R-sq = 76.47% and R-sq (adj) = 63.10%. As shown in Eqn. 7-10, it can be observed that the value of R-sqadj (correlation coefficient) falls below the recommended of 80 % as a result of noise which could occur from experimental uncertainty (Asuero et al., 2006). 4. CONCLUSIONS This study was carried out with the aim of investigating the tribological properties of natural based (coconut shell and seashell) friction materials as a substitute for asbestos in the production of brake pads using response surface methodology and multi-response optimisation technique (GRA). From the results obtained, the following conclusion can be drawn; i. Changes in experimental factors affects the tribological properties of the developed friction materials as all the samples produced with varying parameters gave different performance characteristics. ii. Multi-response optimization results indicate that optimum multi-response performance of the developed coconut shell based brake pad can be achieved using MP, MT and CT and HTT of 12MPa, 100 o C, 6mins and 2hrs respectively while optimum multi-response performance of the developed seashell based brake pad can be achieved using MP, MT and CT and HTT of 10MPa, 160 o C, 12mins and 2hrs respectively, iii. The optimized coconut shell based brake pads falls within the category of class H (µ>0.55) type of brake pads while seashell based sample falls within the class G ( : 0.45–0.55) type of brake pads as a result can be recommended for use in heavy and light duty automobile as specified by the Society of Automobile Engineers (SAE) standards. iv. 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