Fuzzy Logic Based-Modeling and Parameter Optimization for Improving the Corrosion Protection of Stainless Steel 304 by Epoxy-Graphene Composite

Epoxy-graphene composites were fabricated and evaluated as corrosion resistance coatings on stainless steel 304 (SS304). Graphene-based composites coatings were synthesized using in situ approach at various levels of synthesis parameters, such as load of graphene, thickness of coating and mixing time between filler, and polymer resin. Corrosion resistance properties of the prepared coatings were examined using potentiodynamic polarization, where the variation in corrosion current represents the influences of synthesis parameters. Furthermore, the collected dataset was utilized to create an accurate model that simulates the corrosion resistance properties of the coatings using a fuzzy logic approach. Moreover, an optimization process was carried out to determine the optimal levels of synthesis parameters that may deliver supreme corrosion protection property. The resulting plots from fuzzy modeling demonstrated a well-fitting between the fuzzy model and the experimental data. The root-mean-squared errors (RMSEs) of the model prediction are found to be 8.1146e
^-08
and 0.0084724 for training and testing, respectively. The coefficient of determination (R-squared) of the fuzzy output is found 0.99758. The application of the PSO optimizer based on the fuzzy modeling leads to a significant drop in the current density by 7.52 % over that obtained experimentally without changing the system design or the materials used.