Abstract
In-flight particle sensors for thermal spraying are used for real-time monitoring of coating manufacture. However, such tools do not offer facilities to tune the processing parameters when the monitoring reveals fluctuations or instabilities in the thermal jet. To complete the process control, any diagnostic sensors need to be coupled with a predictive system to separate the effect of each processing parameter on the in-flight particle characteristics. In this work, a nonlinear dynamic system based on an artificial neural network (ANN) model is proposed to play this role. It consists of a method that relates the processing parameters to the particle emitted signal characteristics recorded with a DPV2000 (TECNAR Automation, St-Bruno, QC, Canada) optical sensing device. In such a way, a database was built to train and optimize an ANN structure. The in-flight particle average velocity, temperature, and diameter of an alumina-13wt.%titania feedstock were correlated to the injection and power parameters. Correlations are discussed on the basis of these predictive results.
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