Prediction and analysis of the grit blasting process on the corrosion resistance of thermal spray coatings using a hybrid artificial neural network

Abstract

Grit blasting as a pretreatment process for the substrate surface before thermal spraying is of great importance for assuring the service performance of thermal spraying coatings. In this work, a novel hybrid artificial neural network (ANN) was presented to optimize the grit blasting process to improve the structural properties and corrosion resistance performance of thermal spraying coatings. Different grit blasting process parameters were combined to pretreat the substrate surface, and the corresponding surface roughness, interface adhesion strength and corrosion resistance performance were obtained. Hence, a backpropagation (BP) neural network model optimized by the genetic algorithm (GA) was presented to address the poor regression roughness and accuracy of the traditional fitting models; the grit blasting processing parameters were utilized as the inputs for the GA–BP model; the structural properties and the corrosion resistance performance were used as the outputs. The correlation coefficient R reached and exceeded 0.90, and three error values were less than 1.75 on the prediction of the service performance of random samples. All these indicators demonstrated convincingly that the obtained hybrid artificial neural network models possessed good prediction performance, and this innovative and time-saving grit blasting process optimization approach could be potentially employed to improve the comprehensive service performance of thermal spraying coatings.