International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This study investigates the hydrodynamic and thermal characteristics of spark discharge during electric discharge treatment of Al–Ti powder mixtures in ethanol. Machine learning methods were applied to identify relationships between mixture composition, interelectrode gap, and discharge parameters. Among the tested algorithms, the Gradient Boosting model demonstrated the highest predictive accuracy for describing nonlinear plasma-dynamic processes.
The analysis revealed a linear increase in plasma channel pressure from approximately 270 to 450 MPa as the interelectrode gap increased from 5 to 35 mm, regardless of the Al–Ti ratio. In contrast, the pressure acting on the discharge chamber walls was strongly dependent on powder composition. Titanium-rich mixtures exhibited a decrease in wall pressure with increasing gap, whereas mixtures containing 80 wt.% Al showed the opposite trend.
The developed Gradient Boosting model demonstrated that thermal characteristics of the discharge are governed primarily by pulse and liquid parameters, while peripheral hydrodynamic effects are controlled by the composition of the powder mixture. The obtained results provide new insights into the optimization of electric discharge processing of metal powder systems.

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

The possibility of using machine learning methods to predict the results of high-voltage electric discharge treatment of titanium powder in a hydrocarbon liquid is studied. As a result of the work, distribution surfaces for the average particle diameter of Titanium powder. the amount of Titanium carbide formed during processing, and the number of spherical particles of titanium powder depending on the interelectrode gap and the number of pulses, when using spark discharge and with Titanium powder concentration in kerosene of 0.07 kg / dm3, pulse repetition frequency 0.3 Hz and the energy of single discharge of 1 kJ, were obtained.