Development of an Expert System for Prediction of Deposition Efficiency in Plasma Spraying

Abstract Deposition efficiency (DE) serves as a key performance indicator in plasma spraying, which is tailored by dozens of intrinsic and extrinsic influencing factors. Due to the nonlinear complex interdependencies factors, increasing DE has always been challenging undertaking process development spraying. Hence, employing modern computer-aided algorithms inevitable overcome these complexities. In this study, an expert system developed predict from parameters using adaptive neuro-fuzzy inference (ANFIS) support-vector machine (SVM). The consists two subsystems: (1) SVM-models previous work authors are used in-flight particle properties different based on simulation data sets (2) ANFIS experimental sets. results show that able estimate precisely with root-mean-square error (RMSE) about 1.1%. proposed enables sustainable cost-effective coatings through prediction for each set parameters.