Thermal spraying is a complex physical process consisting of three main sub-systems: flame/plume generation, powder/flame/plume interaction and coating build-up. While mathematical and CFD models provide valuable insight about the individual modules of a thermal spray process, it is very difficult to gain overall insight of the whole process and dependencies between different inputs and outputs using mathematical and CFD analysis, due to very complex and interconnected nature of the thermal spray process. In this work, a sophisticated experiment has been conducted to collect enough data for the sake of developing data-driven model of a plasma spray process. Metco 204 powder feedstock material and F4 gun have been used. An optimized number of data samples has been chosen by applying common industrial input parameters in the experiment. The developed neural network model is able to predict the coating quality parameters with acceptable average accuracy of above 90% on test data by considering all relevant measurement error deviations of the process analysis methods. A sophisticated user-interface has been developed to enable the use of the model for coating parameter development as well as the designing recipe for target coating characteristics. The developed model can be used for different purposes: parameter development, off-line coating quality control, and eventually adaptive coating control.

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