An Analysis of the Sliding Wear Characteristics of Epoxy-Based Hybrid Composites Using Response Surface Method and Neural Computation

Abstract

ABSTRACT Hybrid composites are advanced composites in which the required characteristics of the material can be obtained easily by choice of suitable combinations of fibers and fillers. In this study, sliding wear characteristics of a class of hybrid composite consisting of epoxy, short human hair fibers (SHF), and solid glass microspheres (SGM) are being investigated. SGM (10wt. %) filled epoxy matrix composites are prepared by solution casting method with four distinct fiber loading. Dry sliding wear tests are performed on these composites using a pin-on-disc wear test rig by following ASTM G99-05. A mathematical model is created as per face-centered central composite design (FCCCD) in Response Surface Methodology (RSM), and the adequacy of the model was verified using analysis of variance (ANOVA). An artificial neural network (ANN) approach is also applied to predict the wear rate of the composite. The test results are compared with those obtained for composites with only SHF reinforcement under similar test conditions. It is found that while reinforcement of short hair fiber enhances the wear performance of epoxy, the addition of SGM further improves it. Worn surfaces of selected samples are investigated by scanning electron microscopy (SEM) to identify the wear mechanism of the composite.