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Journal of Thermal Spray Technology

, Volume 28, Issue 1–2, pp 76–86 | Cite as

Experiments, Statistical Analysis, and Modeling to Evaluate the Porosity Influence in SPS Coatings

  • Yongli ZhaoEmail author
  • François Peyraut
  • Marie-Pierre Planche
  • Jan Ilavsky
  • Hanlin Liao
  • Audrey Lasalle
  • Alain Allimant
  • Ghislain Montavon
Peer Reviewed
  • 126 Downloads

Abstract

Suspension plasma spray (SPS) is far more complicated than conventional plasma spray and requires a deep knowledge about the influence of process parameters and their correlations. In this study, YSZ coatings were manufactured by SPS with six different process parameters such as plasma power, suspension mass load, original powder size, substrate surface topology, spray distance, and spray step. Afterward, the porosity of as-prepared coatings was investigated by image method and x-ray transmission technique. A multivariate analysis on the collected experimental data was carried out by employing mathematical statistics methods. The results showed that: (1) coating porosity has a negative correlation with plasma power and suspension mass load and a positive correlation with the original powder size, spray distance, spray step, and substrate roughness; (2) spraying distance is the main factor affecting to coating porosity, followed by suspension mass load and substrate surface roughness, respectively. A linear model for porosity prediction was developed and was verified by experiments. The mechanism by which process parameters influence coating porosity is also discussed.

Keywords

image method multivariate statistics analysis process parameter porosity suspension plasma spray x-ray transmission 

Notes

Acknowledgments

This research used resources of the Advanced Photon Source, a US Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357.

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Copyright information

© ASM International 2018

Authors and Affiliations

  • Yongli Zhao
    • 1
    Email author
  • François Peyraut
    • 1
  • Marie-Pierre Planche
    • 1
  • Jan Ilavsky
    • 2
  • Hanlin Liao
    • 1
  • Audrey Lasalle
    • 3
  • Alain Allimant
    • 3
  • Ghislain Montavon
    • 1
  1. 1.ICB, UMR 6303, CNRS, Université de Bourgogne Franche-Comté, UTBMBelfortFrance
  2. 2.Advanced Photon Source, Argonne National LaboratoryArgonneUSA
  3. 3.Saint-Gobain CREECavaillonFrance

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