Optimization of accuracy in estimating the dynamic viscosity of MWCNT-CuO/oil 10W40 nano-lubricants

Artificial neural network (ANN) is one of the best models with good performance for predicting labora-tory data, Due to its high accuracy, this design can be a suitable alternative to frequent and costly testing. In this study, the viscosity (mu nf ) of MWCNT-CuO (10-90)/Oil 10W40 nano-lubricant is modeled by ANNs by experimental data. mu nf is measured in (p 1/4 SVF =(0.05-1% and temperature range T=5 to 55 degrees C to train the ANNs. To check the precision of predicted data by ANN, mean square error (MSE), regression coeffi-cient, and also margin of deviation (MOD) are used. The optimal structure was selected from among 400 ANN samples for MWCNT-CuO (10:90)/Oil 10W40 nano-lubricant, which has two hidden layers and the number of 4 and 8 neurons, as well as tansig and logsig transfer functions. The inputs of the ANN model are solid volume fraction (SVF or (p), temperature (T), and shear rate (SR), and the output of the ANN is the mu nf. A comparison shows that the ANN calculates the laboratory data more accurately.(c) 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intel-ligence, Cairo University. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

Automated summary

In this study, the viscosity of MWCNT-CuO (10-90)/Oil 10W40 nano-lubricant is modeled by artificial neural network (ANN) using experimental data. The accuracy of the predicted data by ANN is verified through mean square error (MSE), regression coefficient, and margin of deviation (MOD) analysis.