Predict the surface free energy of Ti/Zr/V chemical conversion coating on nickel by machine learning approach: Trees vs. neurons

Abstract

The surface properties of chemical conversion treated materials have received increasing attention and surface free energy (SFE) is an important indicator of it. In this study, an environment-friendly Ti/Zr/V chemical conversion coating was deposited on the nickel and Artificial Neural Network (ANN), Random Forest (RF), and Extra Trees (ET) models were applied to further investigate the relationship between the SFE and the compositions of the conversion bath. The accuracy of SFE prediction of three machine learning models was quantified, and the obtained results reveal that the correlation coefficients for the ANN, RF, and ET models are 0.958, 0.979, and 0.963 for the prediction of dispersion components and 0.964, 0.942 and 0.921 for the prediction of polar components, respectively, and the ANN model shows more outstanding performance in general. The errors in the prediction of the ANN model for the polar and dispersion components of SFE are 0.336 and 0.421, respectively, which are smaller than the extended uncertainties obtained from conventional experimental methods. The obtained machine learning models can accurately predict the variations of SFE for modest changes in the conversion bath components, thus linking the formation mechanism of coating with the variations of SFE.