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The abrasive wear performance of fly ash filled aramid fibre reinforced phenolic based composites has been investigated on a rubber wheel abrasive wear tester (RWAT). The design of the experiment approach which uses Taguchi’s orthogonal arrays is adopted to objectively evaluate and prioritize five influencing factors that are taken as experimental variables. A predictive mathematical model for damage assessment in wear rate is developed and validated by a well designed set of experiments. The study reveals that sliding distance, external abrading particle size and fly ash content show greater influence on the specific wear rate of the composites. An investigation on worn surface morphology with a scanning electron microscope (SEM) has been carried out to understand the plausible wear mechanisms. The study thus carried out has revealed the decisive role of quartz particles and photovoltaic (PV) conditions in terms of their influence not only on the alterations of topographical attributes, but also surface ploughing, micro-pitting and sub-surface damage as the various modes of wear of these composites.

The purpose of this study is to present a novel approach for the evaluation of tribological properties of brake friction materials (BFM).

In this study, a BFM was newly formulated with 16 different ingredients and produced using an industrial hot compression molding process. Experimentation was carried out on the brake tester, which was developed for this purpose according to SAE J661 standards. The braking applications, sliding speed and braking pressure were considered as performance parameters, whereas coefficient of friction (CoF) and wear rate as output parameters. The influence of the performance parameters on the output was studied using response surface plots. Analysis of variance and regression analysis was accomplished for post-experimental evaluation of results. Multi-criteria decision-making (MCDM) and multi-objective genetic algorithm (MOGA) were applied for estimating the most critical performance parameter combination to evaluate the BFM.

The present experimental model was significant and effectively used to predict the performance. MCDM generates the optimal values for the parameters braking applications, braking pressure (Bar) and sliding speed (rpm) as 1000, 30 and 915, whereas MOGA as 1008, 10.503 and 462.8202, respectively.

An efficient model for performance evaluation of the BFM considering maximum CoF and minimum wear rate was experimentally presented and statistically verified. Also, the two multi-objective optimization methodologies were implemented and compared. A comparison between the results of MCDM and MOGA reveals that MOGA yields 30% better results than MCDM.