COMPUTER-AIDED PREDICTION OF CUTTING PARAMETERS FOR SURFACE MILLING OPERATION

In this study, a computer-aided approach has been proposed to predict cutting parameters in surface milling operations commonly used in metal processing. The aim is to demonstrate that modeling complex relationships between parameters based on experimental data can be used to improve the efficiency and surface quality of milling operations. Within the scope of the study, factors such as cutting speed, feed rate, and cutting angle, which affect the milling process and the surface quality of the workpiece, were parametrically evaluated, and surface milling operations were performed. The surface roughness of the processed parts was determined using a microscope, while surface hardness was measured using a microhardness tester. Some of the obtained experimental data were used to train a model created for computer-aided prediction, and the accuracy of the model was tested with the remaining experimental data. As a result of the evaluation of the findings, it was concluded that a computer-aided prediction model is an effective tool for accurately predicting cutting parameters in milling operations, and thus, the trained model can be effectively used to predict cutting parameters for a new milling operation. This approach, while allowing for reaching appropriate cutting parameters with fewer experiments, will also enable time and cost savings in production and facilitate more efficient and stable milling operations.