Artificial neural network based prediction on tribological properties of polycarbonate composites reinforced with graphene and boron carbide particle

2020 ◽  
Vol 26 ◽  
pp. 296-304 ◽  
Author(s):  
Mohamed Zakaulla ◽  
Fathima Parveen ◽  
Amreen ◽  
Harish ◽  
Nehal Ahmad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Boron Carbide ◽  
Tribological Properties ◽  
Carbide Particle ◽  
Artificial Neural

scholarly journals Experimental Investigation and Prediction of Tribological Properties of Titanium Carbide and Multiwalled Carbon Nanotubes Reinforced Aluminium Composites using Artificial Neural Network

2018 ◽  
Vol 3 (8) ◽  
pp. 40
Author(s):  
Mohamed Zakaulla ◽  
Anteneh Mohammed Tahir ◽  
Seid Endro ◽  
Shemelis Nesibu Wodaeneh ◽  
Lulseged Belay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Tribological Properties ◽  
Friction And Wear ◽  
Back Propagation ◽  
Wear Loss ◽  
Ann Model ◽  
Multiwalled Carbon ◽  
Artificial Neural

In this study, the tribological properties of TiC particle and MWCNTs reinforced aluminium (Al7475) hybrid composite synthesized by stir casting method were investigated by experimental and artificial neural network (ANN) model. Al7475 metal matrix composites was produced with different wt% of TiC and MWCNTs. The composite samples were tested at 0.42 ms- 1, 0.84 ms- 1 and 1.68 ms- 1 under three different loads  (10N, 20N and 40N). The results indicated that Al7475+10%TiC+2%MWCNTs composite exhibit lower wear rate and reduced coefficient of friction in compare to other samples. TiC percent, MWCNTs percent, applied weight, sliding speed and Time were used as input values for the theoretical prediction model of the composite. coefficient of friction and Wear loss were the two outputs developed from proposed network. Back propagation neural network with 5 – 6 – 2 architecture that uses Levenberg –Marquardt training algorithm is used to predict the coefficient of friction and wear loss. After comparing experimental and ANNs predicted results it was noted that R2 was 0.992 for wear loss and 0.980 for coefficient of friction. This indicated that developed predicted model has a high state of reliability.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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