Application of an Artificial Neural Network for Simulating Robust Plasma‐Sprayed Zirconia Coatings

This article presents the application of the artificial neural network (ANN) of a statistically designed experiment for developing a robust wear‐resistant zirconia coating. In this research, experimental design with orthogonal arrays efficiently provides enough information with the least number of experiments, reducing the cost and time. A radial basis function (RBF) network for the wear behavior is adopted. The friction and tribological properties of zirconia coatings were investigated. The microstructural feature of the coatings is also addressed in this study. It is found that the worn volumes of plasma‐sprayed zirconia coatings after wear tests are greatly improved by the optimal parameters. The relationships between the microstructure of the worn surface and their properties are examined, and the results reveal a higher wear resistance and a lower worn surface roughness with a large amount of plastic deformations. These wear resistant structures formed as a result of a dense lamellar formation during sprayed zirconia coatings. The RBF network can be established efficiently. A comparison of the predicted results with that of the RBF network and the Taguchi method predictor shows average errors of 2.735% and 9.191% for the RBF network and the Taguchi method, respectively. It is experimentally confirmed that the RBF network predictions are in agreement with the experiments, and it can be reliably used for the prediction of wear for plasma sprayings. The experimental results demonstrate that the RBF network used for a statistically designed experiment is an effective, efficient, and intelligent approach for developing a robust, high efficiency, and high‐quality zirconia coating process.