Sensors in Spray Processes

Abstract

This paper presents what is our actual knowledge about sensors, used in the harsh environment of spray booths, to improve the reproducibility and reliability of coatings sprayed with hot or cold gases. First are described, with their limitations and precisions, the different sensors following the in-flight hot particle parameters (trajectories, temperatures, velocities, sizes, and shapes). A few comments are also made about techniques, still under developments in laboratories, to improve our understanding of coating formation such as plasma jet temperature measurements in non-symmetrical conditions, hot gases heat flux, particles flattening and splats formation, particles evaporation. Then are described the illumination techniques by laser flash of either cold particles (those injected in hot gases, or in cold spray gun) or liquid injected into hot gases (suspensions or solutions). The possibilities they open to determine the flux and velocities of cold particles or visualize liquid penetration in the core of hot gases are discussed. Afterwards are presented sensors to follow, when spraying hot particles, substrate and coating temperature evolution, and the stress development within coatings during the spray process as well as the coating thickness. The different uses of these sensors are then described with successively: (i) Measurements limited to particle trajectories, velocities, temperatures, and sizes in different spray conditions: plasma (including transient conditions due to arc root fluctuations in d.c. plasma jets), HVOF, wire arc, cold spray. Afterwards are discussed how such sensor data can be used to achieve a better understanding of the different spray processes, compare experiments to calculations and improve the reproducibility and reliability of the spray conditions. (ii) Coatings monitoring through in-flight measurements coupled with those devoted to coatings formation. This is achieved by either maintaining at their set point both in-flight and certain spray parameters (spray pattern, coating temperature…), or defining a good working area through factorial design, or using artificial intelligence based on artificial neural network (ANN) to predict particle in-flight characteristics and coating structural attributes from the knowledge of processing parameters.