Aeroacoustics and Artificial Neural Network Modeling of Airborne Acoustic Emissions During High Kinetic Energy Thermal Spraying

This work describes an online, non-destructive monitoring technology for thermal spray coating processes based on the airborne acoustic emissions (AAE) in the booth. First, numerical simulations were carried out to probe into the relationship between AAE signals and the frequency spectrum generated during high velocity-oxy-fuel thermal spray. The experimental part consisted of spraying a plane substrate. The torch was traversed in front of the substrate at a constant speed, 90° impact angle and for different combinations of standoff distance and powder feed rate. The AAE signals were acquired using a broadband piezoelectric sensor positioned at a fixed point near the torch, and the experimental power spectrum of the signal was processed and compared with model predictions. A neural network-based model was implemented capturing and representing the complex relationships between the power spectrum of the AAE and the resulting coating microhardness. The research outcomes demonstrate that the sound contains detectable information associated with spray parameters such as powder feed rate, spray distance and the resulting coating microhardness. The proposed technology can be used to detect process flaws so that deviations from the optimum spraying conditions can be detected and corrected promptly.