Journal
INTERNATIONAL CONFERENCE ON MODELLING OPTIMIZATION AND COMPUTING
Volume 38, Issue -, Pages 1218-1232Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.proeng.2012.06.153
Keywords
Dry sliding wear; Rice-husk; Epoxy; Taguchi design; ANN
Ask authors/readers for more resources
Artificial neural network (ANN) is a technique which can be used to simulate a wide variety of complex nonlinear engineering problems such as tribological performance of polymer composites. This article reports the implementation of ANN in analyzing the sliding wear performance of a new class of epoxy based composites filled with rice husk. Composites of four different compositions (5,10, 15 and 20 wt.% of rice husk reinforced in epoxy resin) are prepared in simple hand-lay-up technique. Physical, chemical and mechanical tests are conducted on these composites. Dry sliding wear experiments are conducted as per Taguchi's orthogonal array design. Significant control factors affecting specific wear rate are identified. Based on the data obtained from experiments, an ANN model is trained and tested to predict the effect of wear behaviour on various control factors. Factors like sliding velocity, filler content and normal load, in this sequence, are the significant factors affecting the specific wear rate. This work shows that rice husk possesses good filler characteristics, as it improves the sliding wear resistance of the polymer resin. (C) 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available