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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/26365
Title: Prediction of tribological behaviour of WC-12Co nanostructured microwave clad through ANN
Authors: Zafar S.
Sharma, Apurbba Kumar
Published in: Tribology Online
Abstract: In the present work an artificial neural network (ANN) model was developed to predict the wear rate and coefficient of friction of WC-12Co nanocomposite microwave clads. Various combinations of the transfer function and number of neurons in the hidden layer was used to optimise the neural network. The influence of nature of reinforcement, normal load and sliding distance on the wear rate of the conventional and nanostructured microwave clads was evaluated using the ANN model. The mean square error of 500 epochs was considered to evaluate the performance of the ANN model. The wear rate and coefficient of friction predicted through the ANN model was then compared with experimental results. The predictions of the ANN model were consistent with experimental results. It can be therefore concluded that ANN is an effective modeling technique to predict the wear rates of the WC-12Co microwave clads. Copyright © 2016 Japanese Society of Tribologists.
Citation: Tribology Online, 11(2): 333-340
URI: https://doi.org/10.2474/trol.11.333
http://repository.iitr.ac.in/handle/123456789/26365
Issue Date: 2016
Publisher: Japanese Society of Tribologists
Keywords: ANN
Hybrid heating
Microwave
WC-12Co
Wear
ISSN: 1881218X
Author Scopus IDs: 56345797700
55434064300
Author Affiliations: Zafar, S., Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, 247667, India
Sharma, A.K., Mechanical and Industrial Engineering Department, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, 247667, India
Funding Details: 
Corresponding Author: Sharma, A.K.; Mechanical and Industrial Engineering Department, India; email: akshafme@iitr.ac.in
Appears in Collections:Journal Publications [ME]

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