Machines. Technologies. Materials.
Vol. 19 (2025), Issue 7, pg(s) 252-256

TECHNOLOGIES

Using machine learning methods to predict processes and outcomes of high-voltage electrical discharge treatment of titanium powder in alcohol with implementation of volume-distributed multi-spark discharge

  • 1 Institute of Pulse Processes and Technologies of NAS of Ukraine, Ukraine
  • 2 Mykolaiv National Agrarian University, Ukraine

Abstract

High-voltage electrical discharge (HVED) treatment of powder mixtures is a modern, efficient, and economically advantageous method for both particle size reduction and modification of the material’s phase composition. The primary mechanisms of particle destruction within the discharge zone include shock waves, microcavitation, ablation, collisions with chamber components, and mutual abrasion between particles.
The application of machine learning methods to model HVED processes for titanium—a promising material for composite applications— enables more accurate predictions and optimization of the technological workflow.
The data used for modeling were obtained between 2013 and 2021 and include results from the treatment of the initial titanium powder (with an average diameter of d₀ = 60 μm) in ethanol. This setup enabled the formation of a volume-distributed multi-spark discharge (VMD) within the ethanol–powder dispersed system. The dataset includes information on the number of treatment pulses, discharge gap, pressure in the discharge channels, pressure on the chamber walls, and the amount of titanium carbide formed during the treatment process.
It was shown that the concentration of TiC gradually rises with the increase of specific treatment energy, regardless of the interelectrode gap. Specifically, at a specific energy (Ws) of 5 to 15 MJ/kg, the amount of titanium carbide reaches 10%; at 15 to 30 MJ/kg, it increases to 20%; and at energy levels above 30 MJ/kg, the TiC content reaches 30%.
Keywords: Ethanol, Titanium, Titanium Carbide, High-Voltage Electrical Discharge, Volume-Distributed Multispark Discharge, Electric Discharge Dispersion, Plasma Technologies, Machine Learning, Logistic Regression, Random Forest

Keywords

References

  1. https://www.mscsoftware.com/sites/default/files/hexagon _mi_ msc_whitepaper-ai_a4_ru_2021.pdf
  2. G. N. Gavrilov, G. G. Gorovenko, P. P. Malyushevskii, A. G. Ryabin, Razryadnoimpulsnaya tehnologiya obrabotki mineralnyh sred (Naukova Dumka, Kyiv, 1979). (In Russian).
  3. V. I. Kurets, A. F. Usov, V. A. Tsukerman, Elektroimpulsnaya dezintegratsiya materialov (Publishing house of the Kola Scientific Center of the Russian Academy of Sciences, Apatity, 2002). (In Russian).
  4. M. S. Prystash, S. F. Prystash, A. S. Torpakov, Ye. V. Lypian, O. M. Syzonenko, IOP Conf. Ser.: Mater. Sci. Eng. 1277, 012001 (2023). https://doi.org/10.1088/1757-899X/1277/1/012001
  5. M. Prystash, S. Prystash, A. Torpakov, Y. Lypian, O. Syzonenko, R. K. Janutienė, Machines. Technologies. Materials. 16(8), 267–269 (2022).
  6. O. M. Syzonenko, P. I. Loboda, A. D. Zaichenko, Ye. V. Solodkyi, A. S. Torpakov, M. S. Prystash, V. O. Trehub, Journal of Superhard Materials. 39, 243–250 (2017).
  7. O. M. Sizonenko, M. S. Prystash, E. I. Taftai, A. S. Torpakov, Ye. V. Lypian, Materials Science. 57, 209–214 (2021).

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