The present paper deals with the analysis of cross-section surface irregularities after CO₂ laser cutting. The surface irregularities of beech (Fagus sylvatica L.), oak (Quercus petraea), and spruce (Picea abies L.) wood were quantified by primary profile parameters using a digital microscope. The arithmetic mean height (Pa), used as the basic parameter, was supplemented by amplitude parameters (Pv, Pp, Pz) and the Psm parameter, through which the shape of the irregularity was specified in more detail. A statistically significant change was demonstrated when changing the values of the feed speed and the power of the CO₂ laser. The results of this article confirm that the surface irregularities increased with an increasing laser power and decreasing feed rate. The scanned topographic images also provide a more detailed explanation of the measured P-parameters and point out the risks associated with the evaluation of the cross-section with the primary profile.

Modern manufacturing technologies include wood processing using laser technologies. The most used laser for wood cutting is the CO₂ laser, which offers many advantages such as processing speed, efficiency, and minimal impact on the material’s structure after cutting. To achieve a high-quality cut, characterized by the cutting kerf parameters, it is necessary to know the appropriate combination of cutting parameters, primarily laser power (P) and the cutting speed (v). Therefore, this article investigates the effect of P, v, and cutting direction on the cutting kerf widths on the upper surface WKU, lower surface (WKL), and their ratio (WKR). The analysis was performed on samples of spruce, beech, and oak wood, while also evaluating the influence of the anatomical cutting direction. The correlation coefficient between the predicted values and the measured values is at least 0.94, with the mean square error not exceeding 4%. Consequently, the employed models demonstrate validity in predicting cutting kerf widths and optimizing the cutting process based on the type of timber, cutting direction, and the specified laser parameters.

The replacement of historical load-bearing wooden elements made from not commonly used species such as Populus tremula L. presents significant challenges. As these species are seldomly used in modern construction, a knowledge gap exists regarding their implementation in accordance with current building codes. This study investigates the mechanical properties of European aspen (Populus tremula L.) from central Slovakia as a potential replacement for historical structures. Notably, poplar species, including European aspen, have historically been utilized for construction across various landscapes in Europe. We conducted experimental testing on visually graded aspen timber to determine the dynamic modulus of elasticity (MOE_dyn,ultr), modulus of elasticity (MOE), modulus of rupture (MOR), and density. The results were analyzed and compared to established standards for structural timber. Notably, the 5th percentileof the strength distribution (f_0.05) was determined to be 28.78 MPa, while the characteristic strength (f_k) was 26.23 MPa, and the modulus of elasticity (E_g12) was 13.60 MPa. The correlation between MOR and dynamic MOE facilitated the determination of MOR by non-destructive testing (NDT) using the Sylvatest Duo^®. This simple linear model could grade 49% of boards into the higher strength class C30. The additional parameters and their interactions in multiregresssion models improved the predictability of the bending strength of aspen. The advanced model graded 68% of boards into C30. These characteristics, along with aspen’s growth potential, make it a promising candidate for replacing damaged structural elements in historical constructions. Our findings contribute to the understanding of the potential of European aspen as a structural timber, highlighting its viability as a fast-growing hardwood species.

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 (F_x, F_y, F_z) 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 F_x and F_y 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.

This study investigates the influence of selected technical and technological parameters on cutting forces and power consumption during the milling of medium-density fibreboards. The main objective was to experimentally measure orthogonal cutting force components (F_x, F_y, F_z) 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 F_x and F_y 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. 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 force and energy data are suitable for reliable predictive modelling in CNC milling of MDF.