Abstract
This paper reports on the critical analysis of the dry sliding wear characteristics of polyester-based composites through systematic experimentation integrated with artificial neural networks (ANN) and response surface methodology (RSM). In this study, composites are fabricated with unsaturated polyester resin as the matrix material and micro-sized walnut shell powder (WSP) as the particulate filler. Such composites with different filler concentrations have been prepared by simple hand layup technique. The composite characterizations regarding density, porosity and tensile strength are made and validated with the results obtained from a numerical tool Digimat-FE. Further, wear trials are conducted using a standard pin-on-disk machine according to design of experiment based on response surface methodology (RSM). The data obtained from the experiments are considered to train and test an ANN model, which prognosticates the impact of various control factors on wear behavior of the composites. Among all the factors considered for analysis, sliding velocity, filler content, and normal load are found to be the significant factors in that sequence affecting the wear rate of the composites.
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