scholarly journals Artificial neural network-based modeling of surface roughness in machining of Multiwall Carbon Nanotube reinforced polymer (epoxy) nanocomposites

2020 ◽  
Vol 48 (3) ◽  
pp. 693-700
Author(s):  
Prakhar Kharwar ◽  
Rajesh Verma
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Carbon Nanotube ◽  
Multiwall Carbon Nanotube ◽  
Epoxy Nanocomposites ◽  
Reinforced Polymer ◽  
Artificial Neural ◽  
Multiwall Carbon

Prediction of Surface Roughness and Coefficient of Friction Using Artificial Neural Network in Tribotesting of Bio-Lubricants

2019 ◽  
Vol 895 ◽  
pp. 52-57 ◽  
Author(s):  
Prasanna Vineeth Bharadwaj ◽  
T.P. Jeevan ◽  
P.S. Suvin ◽  
S.R. Jayaram
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Coefficient Of Friction ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Trained Neural Network ◽  
Lubrication Conditions ◽  
Good Agreement

Tribotesting is necessary to understand the behaviour of the material under various operating lubrication conditions. This paper deals with the training of an artificial neural network (ANN) model with Bio-lubricant properties and machining conditions for prediction of surface roughness and coefficient of friction in Tribotesting by Tool chip Tribometer. Experimental results obtained from Tool chip tribometer for tested bio-lubricants are compared with those obtained by ANN prediction. A good agreement in results recommends that a well trained neural network is competent enough to predict the parameters in Tribotesting process.

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