Use of ANN for Prediction of Erosive Wear Behaviour of Epoxy Composites
Keywords:
Polymer composite, Bio fibres, Fly Ash, Taguchi method, Erosion, ANN CorrespondingAbstract
This paper includes a study on erosion wear response of fly ash filled epoxy composites reinforced with short fibres obtained from the scales of a typical freshwater fish. Erosion characteristics are studied using an air jet type erosion test rig employing the Taguchi’s design-of-experiment approach. Further, an Artificial Neural Networks (ANN) approach is
implemented taking into account the training and test procedure to simulate the erosion wear process and thereby to predict the wear rate under different operating conditions.
It is evident from the existing literature that polymer composites reinforced with natural fibres like jute, sisal etc. have long been used in various structural applications. Bio-fibres like animal whiskers and poultry feather have also recently drawn the attention of researchers.
But the potential use of fish scale fibre in the composite making has not adequately been explored so far. Similarly, fly ash is such an industrial waste (generated during the combustion of coal), the reinforcing potential of which needs to be explored. Moreover, Artificial Neural Network (ANN) is comparatively a new modelling technique, which can be used to solve and predict performance output in a complex non-linear problem like the erosion wear process. Because of the above, the present work is undertaken to study the erosion wear study of this new class of bio-fibre composites.
The findings of this work show that the erosion rate of these composites is greatly influenced by various control factors like fly ash content, impingement angle and erodent size. ANN technique is successfully applied in this investigation to predict and simulate the wear response of the composites under various test conditions within and beyond the experimental domain. The predictions of wear rates as functions of filler content and testing conditions thus prove a remarkable capability of well-trained neural networks for modelling concern. This technique helps in saving time and resources for a large number of experimental trials. These composites are expected to find potential applications as suitable materials for conveyor belt rollers, pipes carrying pulverized coal in power plants, pump and impeller blades and also as low-cost housing materials.
How to cite this article: Pradhan MK. Use of ANN for Prediction of Erosive Wear Behaviour of Epoxy Composites. J Adv Res Mfg Mater Sci Met Engr 2020; 7(4): 9-13.
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