Multivariate analysis and evaluation of the porous architecture of suspension plasma sprayed coatings

Monday, May 27, 2019
Dr. Yongli Zhao , ICB UMR 6303, CNRS, UTBM, Belfort, France
Mr. Yan wang , Université de Technologie Belfort-Montbéliard, Belfort, France
Prof. François Peyraut , ICB UMR 6303, CNRS, UTBM, Belfort, France
Dr. Marie-Pierre Planche , ICB-LERMPS, Univ. Bourgogne Franche-Comté, Belfort cedex, France
Dr. Jan Ilavsky , Advanced Photon Source, Argonne National Laboratory, Illinois, IL
Prof. hanlin liao , university of technology of belfort-montbéliard, belfort, FL, France
Prof. Ghislain Montavon , ICB UMR 6303, CNRS, UTBM, Belfort, France
Dr. Audrey Lasalle , Saint-Gobain CREE, Cavaillon, France
Dr. Alain Allimant , Saint-Gobain Research Provence, Cavaillon, France
The porous architecture has a significant influence on coating performances and then should be properly designed for the intended applications. In this study, YSZ coatings were manufactured by suspension plasma spray (SPS) with five different process parameters such as suspension mass load, original powder size, substrate surface topology, spray distance, and spray step. Afterwards, the total porosity and small pore content (<1 μm) in as-prepared coatings were investigated by X-ray transmission technique and Ultra small angle X-ray scattering (USAXS) respectively. A multivariate analysis on the collected experimental data was carried out by employing mathematical statistics methods. The results showed that: 1) Coating total porosity has a negative correlation with suspension mass load and a positive correlation with the original powder size, spray distance, spray step, and substrate roughness; 2) Small pore content has a negative correlation with suspension mass load and the original powder size, and a positive correlation with spray distance, spray step, and substrate roughness; 3) Spraying distance is the main factor affecting total porosity, while suspension mass load has the greatest effect on small pores. Finally, models for porosity prediction were developed and were verified by experiments.
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