Prediction of surface roughness using different features in vortex cooled turning process of Ti6Al4V alloy

Despite their superior mechanical properties, titanium alloys cause excessive temperatures in turning operations due to their low thermal conductivity. This situation reduces workpiece quality and shortens tool life. Therefore, it is necessary to determine the type of coolant that will provide the desired product quality at the optimum cost. Surface roughness (Ra) is one of the most important indicators of product quality. The goal of this work is to accurately predict the Ra in various cooling conditions prior to turning the Ti6Al4V alloy. Models built using various machine learning algorithms were used to compare the cutting parameters and features taken from sensor signals. While the lowest Ra values were obtained in dry cutting, the highest Ra values were obtained with vortex CO2. The highest performance success was achieved with the XGBoost algorithm. The model created by enhancing the cutting parameters performed better in predictions than the models created using statistical information taken from the sensor signals. The suggested approach eliminates the need for extra sensors and allows for the low computational cost and high success rate estimation of Ra. © 2024 Elsevier Ltd