The performance of turning operations gradually depends on the machining parameters, and the most important parameter is the surface roughness quality. In this study, the effect of cutting speed and feed rate on the surface roughness in aluminium alloy 6082 (AA6082) machining, which is widely used in the automotive and aerospace sectors, was investigated experimentally and by machine learning prediction. In the experiments, three different cutting speeds (240, 300, and 360 m/min), three different feed rates (0.05, 0.1, and 0.15 mm/rev), and a constant depth of cut (0.5 mm) were used as machining parameters. In addition to machining parameters, the temperature, cutting forces, revolution, current, voltage, and power were measured. The workpiece was machined using uncoated cemented carbide cutting tools. Experimental results showed that the surface roughness increased with increasing feed rate and decreased with increasing cutting speed. The complete dataset was created from experiments by selecting measurements and machining parameters as inputs and surface roughness as output. Various machine learning models were implemented on this dataset, and different metric scores were used to select the best prediction performance of the machine learning models. Gradient Boosting (GB) exhibited superior prediction performance compared to the other tested algorithms, with an R2 score of 0.98560. The GB model emerged as the most precise and accurate, characterized by the highest R2 score, the lowest root mean squared error (0.12095), the lowest mean absolute error (0.09804), and the lowest mean squared error (0.01463) scores, respectively.
Zeyvelı et al. (Sun,) studied this question.