INTERACTIVE MULTIFACTOR MODELING OF THE COMPOSITION AND PROPERTIES OF CR₃C₂-NICR COATINGS OBTAINED BY THE HVOF METHOD

Authors

  • Zh. Makhasheva Международный казахско-турецкий университет им. Х.А. Ясави
  • Sh. Kurbanbekov Международный казахско-турецкий университет имени Х.А. Ясави
  • B. Seitov Международный казахско-турецкий университет имени Х.А. Ясави
  • Y. Amangeldiyeva Международный казахско-турецкий университет имени Х.А. Ясави
  • N. Ertayev
  • S. Kambarbekov Международный казахско-турецкий университет им. Х.А. Ясави

Keywords:

Cr3C2-NiCr coatings, HVOF method, modeling, interactive software suite, phase composition, elemental analysis

Abstract

Received: 10 April 2026

Revised: 30 April 2026

Accepted: 1 May 2026

Published: 25 May 2026

https://doi.org/ххххххххххх.

 

This study presents the results of comprehensive modeling and experimental verification of the effect of High-Velocity Oxygen-Fuel (HVOF) spraying parameters on the microstructure and adhesion strength of Cr3C2-NiCr coatings. Based on an extensive literature dataset, an interactive multi-factor modeling platform was utilized to virtually predict coating characteristics. To verify the modeling trends, the coating samples deposited on 65G steel substrates were characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and pull-off adhesion testing. The SEM and EDS cross-sectional analyses revealed the formation of a dense, non-columnar Cr3C2-NiCr layer with a uniform thickness ranging from 70 to 80 m, demonstrating continuous bonding with the steel substrate.Mechanical testing showed that the coating produced under optimal parameters achieved a maximum adhesion strength of 11.2 MPa, whereas increasing the air pressure by 0.2 bar in the second mode reduced the adhesion strength to 10.0 MPa. This 12% decrease in bonding quality is attributed to flame temperature reduction and partial in-flight particle oxidation, which formed weakening oxide films at the interface. The findings demonstrate that the developed platform effectively predicts qualitative quality trends and serves as a reliable digital tool for optimization of HVOF parameters, significantly reducing expensive experimental trials.

References

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Published

2026-09-30

Issue

Vol. 2 No. 2 (2026): International Journal of Energy and Applied Sciences

Section

Articles

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