The use of machine learning methods to predict the processes and results of high-voltage electric discharge processing of titanium powder in kerosene with the implementation of volume-distributed multi-spark discharge

Abstract

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. 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 volume-distributed multi-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.