Modeling of Plasma Spray Coating Process Using Robust Solutions Based on New Intelligence methods

Mehdi Abedi-Varaki, Shahram Mollaiy- Berneti


This study adopts two computational intelligence techniques namely least-squares support vector machine (LS-SVM) and group method of data handling (GMDH) type polynomial neural network to model the plasma spray coating process. The coating qualities were evaluated by determining its thickness, porosity and micro-hardness. Four parameters including primary gas flow rate, stand-off distance, powder flow rate and arc current that affecting the coating properties were chosen as input variables in model development. The performances of the developed models were evaluated by calculating the deviations between the predicted and the actual values based on the  performance indices of mean absolute error (MAE), root mean  square  error  (RMSE) and coefficient of determination (R2). The results demonstrate the

high capability of the LS-SVM and GMDH-type polynomial neural network for prediction of thickness and micro-hardness values with lowest MAE, RMSE and highest R2. Due to the high non-linearity behavior of porosity in coating process, the results obtained by these models for porosity are not very well.


Plasma spray coating; Coating properties; Least-squares support vector machine; GMDH-type polynomial neural network.

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