ANN modeling of anticorrosive performance of paint systems on steel

In this investigation, the corrosion behavior of 1020 steel plates in term of corrosion potential wasmodeled using the ANN approach. Input variables were the surface pre-treatments, anticorrosive coatingsand the time of immersing in the corrosive environment. feedforward multi-Layer perceptron neuralnetwork was used. The reliability and speed of Levenberg–Marquardt Scaled conjugate gradient ,and Resilient backpropagation algorithms were also compared, and it was concluded that theLevenberg–Marquardt is the most accurate and the fastest algorithm for modeling. The results showedthat the estimated corrosion potentials of samples are in good agreement with the actual data.

کلیدواژه ها:

Corrosion Potential ، Coating ، Artificial Neural Network (ANN) ، Modeling