Evaluation of Regression Models for Predicting Cutting Forces Based on Spindle Speed, Feed Speed and Milling Strategy During MDF Board Milling

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. Unlike previous studies that focus primarily on force measurement, this work integrates experimental analysis with machine learning-based predictive modelling to improve process understanding and prediction accuracy. The main objective was to experimentally measure orthogonal cutting force components (Fx, Fy, Fz) and electrical power consumption under varying spindle speeds (14,000, 16,000 and 18,000 rpm), feed speed (6, 8 and 10 m/min), and milling strategies (conventional and climb), and to evaluate the suitability of the obtained data for predictive modelling. Cutting forces were measured using a Kistler 9257B piezoelectric dynamometer, and power consumption was recorded by a three-phase power quality analyser. Statistical analysis confirmed significant effects of machining parameters on force components, total cutting force, and power consumption. Spindle speed showed the strongest influence on total cutting force and power consumption, while milling strategy predominantly affected Fx and Fy components. Power consumption increased with increasing spindle speed. Based on the measured data, several machine learning models were developed to predict the total cutting force. The Fine Tree algorithm demonstrated the best performance, achieving validation metrics of R2 = 0.9 and RMSE = 0.60 (MSE = 0.36, MAE = 0.48), and improved testing performance with R2 = 0.95 and RMSE = 0.44 (MSE = 0.20, MAE = 0.36). After model comparison using RMSE, R2, training time, and model size, a Fine Tree model was identified as the most suitable, achieving high prediction accuracy without signs of overfitting. The results confirm that experimentally obtained data on cutting force and electrical energy consumption are suitable for reliable predictive modelling in CNC milling of MDF boards. However, it is necessary to work with those components that have the greatest dependence on speed, feed, or type of milling, and these are the force components measured on the x and y axes.