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In this study, comparative analysis of multiple linear regression (MLR) and artificial neural network (ANN) for prediction of wear rate and coefficient of friction brake pad produced from palm kernel shell was carried out. The inputs parameters used for the two models generated using inertia dynamometer were the percentages of palm kernel shell, aluminium oxide, graphite, calcium carbonate, epoxy resin, interface temperature of the brake pad, and work done by brake application. Two model equations were developed using MLR model for predicting wear rate and coefficient of friction while the neural network architecture 2 was used to predict wear rate and coefficient of friction. The predicted wear rate and coefficient of friction by MLR model were compared with ANN model along with the measured values using statistical tools such as means square absolute error (MAE), root means square error (RMSE), and Nash-Scutcliffe efficiency (NSE). The results revealed that the MLR model outsmarts the ANN model with the values of MAE and RMSE reasonably low and NSE reasonably higher. The best MAE and RMSE values of 0.000 were observed at the three values of measured wear rates and coefficient of friction that matched with the predicted values using MLR compared to -0.0300 and 0.0740 for ANN model. However, the ANN model was equally found suitable for the prediction of wear rate and coefficient of friction of brake pads developed. The implication of these results is that the two models have the capabilities of being used simultaneously when estimating the wear and coefficient of friction of brake pads.

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This study presents the evaluation of the mechanical, physical and dynamic mechanical properties of luffa-banana fibre reinforced polyester hybrid composites. The luffa fibre and banana fibres were extracted from luffa plant and banana stem respectively by manual stripping into strands. The luffa and banana fibres were then blended in the ratio of 50:50 for the production of the hybrid composites using hand lay-up method. Polyester-resin was used as binder and the percentages of luffa-banana fibres used were 3, 5, 6, and 9 %. The tensile strength, impact strength, flexural strength, density, water absorption, and the dynamic mechanical analysis (DMA) (storage modulus, loss modulus damping factor) of the produced luffa-banana hybrid composites were evaluated. The results of the density and water absorption obtained varied from 0.84-1.23 g/cm3 and 0 - 0.35 % respectively. The tensile and impact strengths (3.46 - 9.27 MPa and 0.66-3.26 J/cm2) of the produced hybrid composites were observed to increase with increasing fibre content from 3 - 6 % and decreased at 9 %. The results of DMA revealed that loss modulus of the hybrid composites and pure polyester were found to increase with increasing temperature up to glass transition temperature and then decreased. The damping factor was observed to increase with increasing temperature and goes at maximum level in transition region and while decreasing the in rubbery region. The properties of the produced hybrid luffa-banana composites showed that luffa and banana fibres can be used in synergy as raw materials for composites manufacture. As the properties evaluated were in agreement with standard composites used as interior design of cars.