Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites
Joint Authors
Kurt, Halil Ibrahim
Oduncuoglu, Murat
Source
International Journal of Polymer Science
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-11
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
In the current study, the effect of applied load, sliding speed, and type and weight percentages of reinforcements on the wear properties of ultrahigh molecular weight polyethylene (UHMWPE) was theoretically studied.
The extensive experimental results were taken from literature and modeled with artificial neural network (ANN).
The feed forward (FF) back-propagation (BP) neural network (NN) was used to predict the dry sliding wear behavior of UHMWPE composites.
Eleven input vectors were used in the construction of the proposed NN.
The carbon nanotube (CNT), carbon fiber (CF), graphene oxide (GO), and wollastonite additives are the main input parameters and the volume loss is the output parameter for the developed NN.
It was observed that the sliding speed and applied load have a stronger effect on the volume loss of UHMWPE composites in comparison to other input parameters.
The proper condition for achieving the desired wear behaviors of UHMWPE by tailoring the weight percentage and reinforcement particle size and composition was presented.
The proposed NN model and the derived explicit form of mathematical formulation show good agreement with test results and can be used to predict the volume loss of UHMWPE composites.
American Psychological Association (APA)
Kurt, Halil Ibrahim& Oduncuoglu, Murat. 2015. Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites. International Journal of Polymer Science،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1066753
Modern Language Association (MLA)
Kurt, Halil Ibrahim& Oduncuoglu, Murat. Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites. International Journal of Polymer Science No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1066753
American Medical Association (AMA)
Kurt, Halil Ibrahim& Oduncuoglu, Murat. Application of a Neural Network Model for Prediction of Wear Properties of Ultrahigh Molecular Weight Polyethylene Composites. International Journal of Polymer Science. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1066753
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1066753