Development of a Method for Increasing the Deposition Efficiency in Thermal Spraying

• Applicant: Professorin Dr.-Ing. Kirsten Bobzin
• Subject Area: Joining and Separation Technology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 506461824

In Plasma Spraying, the deposition efficiency is one of the most decisive factors, which is significantly influenced by the process parameters and consequently by the particle properties. Due to the complex interactions between a variety of influencing factors, a precise prediction of the deposition efficiency based on the process parameters is not state of the art. A promising solution for the in situ determination of the deposition efficiency is the determination of the mass flow rates of the particles flying in the direction of the substrate and bouncing off from the substrate. As part of the preliminary works, a methodology for determining the particle mass flow rate in the free-jet by means of particle diagnostics has already been developed at the IOT. This approach was then successfully used to determine the local deposition efficiency in Plasma Spraying. In addition, the particles near the substrate were successfully detected, which is the first step for the in situ determination of the deposition efficiency. The classical particle diagnostic devices, however, are not able to distinguish between the rebounding and incoming particles near the substrate. To make this possible, a so-called Particle Image Velocimetry (PIV) technique for the directionally resolved detection of the particles at the substrate will be developed within the scope of the present research proposal. The high-speed camera available at the IOT will be combined with a pulsed laser for illumination of the particles near the substrate. The new PIV measurement method should enable the following insights:- The successful detection of incoming and bouncing particle flows near the substrate- The detection of a statistically sufficient variety of particles at the substrate for the in situ determination of the deposition efficiency- The observation of mechanisms during particle impact that lead to adhesion or rebound of the particleBased on the measurements near the substrate using the new PIV measurement method, an artificial intelligence (AI) model will be developed to predict the mass flow rates as well as the impact behavior of the particles near the substrate for the applied feedstock materials. The AI model will then be validated by determining the deposition efficiency according to the corresponding DIN standard. Furthermore, the influence of the mechanisms during particle impact on the deposition efficiency will be studied. This investigation will be supplemented by experimental analyzation of the coating properties. The outcoming results are finally used to increase the deposition efficiency by minimizing the rebounding particle mass flow rates near the substrate.

• DFG Programme: Research Grants