Optimization and design of ANN with Levenberg-Marquardt algorithm to increase the accuracy in predicting the viscosity of SAE40 oil-based hybrid nano-lubricant

Abstract

In recent years, the use of artificial neural networks (ANNs) has increased due to their good performance and high accuracy in predicting laboratory data. In this study, experimental data of viscosity ( μ nf ) of MWCNT-Al 2 O 3 (10:90)/SAE40 nano-lubricant in volume fraction φ =0.0625%–1% and temperature range T = 25 to 50 °C were designed and checked by ANN. For modeling by ANN, a multilayer perceptron (MLP) ANN with the Levenberg-Marquardt (ML) algorithm, which is one of the most accurate ANN modeling methods, is used. To design the ANN, the parameters of φ , temperature and shear rate are considered input variables and μ nf is considered as an output variable. Finally, the values of the R regression coefficient and the mean square error (MSE) for the optimal structure were 0.9999 and 0.000615042, respectively. The range of the margin of deviation (MOD) of the predicted values is −2% < MOD <2%. A comparison of three data sets, namely laboratory data, correlation output and ANN output, shows that The ANN method is much more powerful and more accurate than mathematical relationship data and laboratory data. • Design of an ANN and experimental evaluation of the viscosity of a nano-lubricant are investigated. • A multilayer perceptron network with Levenberg-Marquardt algorithm is used. • φ , temperature and shear rate are considered as input variables and μ nf as an output variable. • The range of the margin of deviation (MOD) of the predicted values is −2% < MOD <2%. • the ANN method is much more capable than the new relation and estimates laboratory data more accurately.