Please use this identifier to cite or link to this item: http://nopr.niscair.res.in/handle/123456789/30525
Title: Prediction of effect of MoS2 content on wear behavior of sintered Cu-Sn composite using Taguchi analysis and artificial neural network
Authors: Kumar, P Senthil
Manisekar, K
Keywords: Solid lubricants;Taguchi method;Neural networks;Sliding wear;Scanning electron microscope
Issue Date: Dec-2014
Publisher: NISCAIR-CSIR, India
Abstract: The present investigation aims to develop MoS2 added, self lubricated copper-tin hybrid composite with different weight fractions of MoS2 and characterize tribological properties. In order to evaluate the behavior of composites satisfying multiple performance measures, Taguchi approach has been adopted. An orthogonal array and an analysis of variance are employed to the influence of parameters like as wt% of MoS2, load, sliding speed and sliding distance on dry sliding wear of the composites. Results show that sliding distance has the highest influence followed by a load and reinforcement. Confirmation tests are carried out to verify the experimental results.┬áThe morphology of the worn-out surfaces is examined to understand the wear mechanisms. The responses have been predicted using both Artificial Neural Network (ANN) and Taguchi method so that a comparative evaluation can be made. From this study, it is concluded that neural network predicts the responses more accurately than Taguchi prediction.
Page(s): 657-671
URI: http://hdl.handle.net/123456789/30525
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Appears in Collections: IJEMS Vol.21(6) [December 2014]

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