Modeling and prediction of corrosion inhibition efficiency of 5-Aminotetrazole on AA6065-AZ31 alloy using electrochemical noise and artificial neural networks with different transfer functions
Year: 2025
Type: article
Abstract: Different configurations of artificial neural network (ANN) models were developed and evaluated to predict the corrosion inhibition efficiency of 5-Aminotetrazole on AA6065-AZ31 alloy exposed to saline conditions (0.1 M and 0.05 M NaCl) using electrochemical noise. The training dataset consisted of 302,400 measurements from immersion tests with inhibitor concentrations of 2 mM, 4 mM, 6 mM, 8 mM, and 10 mM. The variables time, inhibitor concentration, and electrolyte concentration were used as input variables, while the output variable was electrochemical resistance. A comprehensive analysis was performed using different transfer functions in the hidden layer, including TanSig, LogSig, ElliotSig, Radbas, Softmax, dSiLU, Sqsinc, ReLU, and SoftPlus, all trained with the Levenberg-Marquardt algorithm. Among these configurations, the model employing a 9-neuron hidden layer architecture and dSiLU as transfer function achieved the best performance. The determination coefficient (R²) of 0.9983 obtained by the best model demonstrated an excellent correlation between simulated and experimental data. The corrosion inhibition efficiency predicted by the best ANN model obtained less than 4 % error, confirming the ANN's potential for accurately modeling electrochemical noise. • Corrosion inhibition efficiency predicted using artificial neural networks and electrochemical noise. • Electrochemical resistance modeled with artificial neural networks employing nine transfer functions. • Comparative analysis performed on electrochemical resistance models. • dSiLU transfer function achieved the best results in modeling electrochemical noise with artificial neural networks.
Authors: J.M. Angeles, A. Parrales, Sung-Hyuk Cha, D.E. Millán-Ocampo, R. López-Sesenes, J.A. Hernández
Institutions: Universidad Autónoma del Estado de Morelos, Pace University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,670
OpenAlex ID: W4409259156