Tribological Behaviour of Epoxy Composites Reinforced with Micro-Sized Red Mud Particles

Abstract

Although a variety of metals and ceramic powders are used as reinforcing fillers in polymeric resins, the use of industrial wastes for this purpose has not been adequately explored. Based on this, the present work reports on the development of the polymer composites using red mud as the filler content. Red mud is the solid waste obtained during the industrial production of alumina. Using this red mud in various proportions (0, 10, 20, 30 wt. %) in epoxy matrix base, epoxy composites are fabricated by the hand layup technique. In this work, by incorporating the micro sized red mud particles in the epoxy, composites are made and an attempt was made to study the sliding wear behavior of these epoxy composites. For this a standard pin-on-disk test setup and Taguchi’s orthogonal array are used. By using the design of experiments approach using Taguchi’s orthogonal array the number of repeated experiments can be reduced and thus saves time, cost and materials. The wear can be minimized by optimal parameter settings and this can be obtained by using the Taguchi’s approach. The S/N ratios can be obtained by utilizing the software Minitab 14 specifically utilized for design of experiment1applications. The S/N ratio response analysis gives that among1all of the factors, sliding velocity was the most important factor1followed by filler content, and the other two factors will have very less effect on the on wear rate of the red mud filled epoxy composites in this work. Scanning Electron Microscope (SEM) was utilized for examining the morphology of worn surfaces. The capability of ANN’s (Artificial Neural Networks) for the sliding wear properties prediction can be explored utilizing an experimental data set obtained from the sliding wear tests performed on epoxy composites. The ANN prediction1profiles for the1characteristic tribological properties1exhibited very good1agreement with the measured results, demonstrating1that a well-trained network was created.