Evaluation and prediction of wear response of pine wood dust filled epoxy composites using neural computation

Inspired by the biological nervous system, an artificial neural network (ANN) approach is a fascinating computational tool, which can be used to simulate a wide variety of complex engineering problems such as tribo-performance of polymer composites. This paper, in this context, reports the implementation of ANN in analyzing the wear performance of a new class of epoxy based composites filled with pine wood dust. Composites of three different compositions (with 0, 5 and 10 wt.% of pine wood dust reinforced in epoxy resin) are prepared. Dry sliding wear trials are conducted following a well planned experimental schedule based on design of experiments (DOE). Significant control factors predominantly influencing the wear rate are identified. An ANN approach taking into account training and test procedure is implemented to predict the dependence of wear behavior on various control factors. This work shows that pine wood dust possesses good filler characteristics as it improves the sliding wear resistance of the polymeric resin and that factors like filler content, sliding velocity and normal load, in this sequence, are the significant factors affecting the specific wear rate. It is further seen that the use of a neural network model to simulate experiments with parametric design strategy is quite effective for prediction of wear response of materials within and beyond the experimental domain.