Artificial Intelligence Models for the Mass Loss of Copper-Based Alloys under Cavitation

Abstract

Cavitation is a physical process that produces different negative effects on the components working in conditions where it acts. One is the materials’ mass loss by corrosion–erosion when it is introduced into fluids under cavitation. This research aims at modeling the mass variation of three samples (copper, brass, and bronze) in a cavitation field produced by ultrasound in water, using four artificial intelligence methods—SVR, GRNN, GEP, and RBF networks. Utilizing six goodness-of-fit indicators (R2, MAE, RMSE, MAPE, CV, correlation between the recorded and computed values), it is shown that the best results are provided by GRNN, followed by SVR. The novelty of the approach resides in the experimental data collection and analysis.