Modeling of the wood drilling process represents a new management principle that leads to high-quality wooden products [9]. Various approaches could be used to model the drilling process of wood and wood-based materials. The most applied method to model and optimize the drilling process is Response Surface Methodology (RSM). In addition, the artificial neural network (ANN) modeling technique was, also, applied to predict the various outputs of the drilling process. Nowadays, ANN models are included in various decision support systems that are developed for the forest industry [10]. [PERSON] et al. [11] studied how to control the delamination factor in the drilling of MDF by testing two types of MDF panels with different coating layers, using cemented carbide drills. They analyzed the main interaction effects of the drilling parameters on the delamination factor. Feed rate and cutting speed were used as independent variables and delamination was measured at the entry and exit of the holes. The authors used response surface methodology to design and model the experiments. ANOVA, also, was used to check the validity of the models. The article published by [PERSON] and [PERSON] [12] explores the prediction of surface roughness during the drilling of MDF using response surface methodologies with TiN-coated carbide step drill bits. The experiments, guided by [PERSON]'s design technique, involve spindle speed, feed rate, and drill diameter as factors. The study establishes a second-order model through response surface methodology, revealing that the feed rate is the most influential parameter on surface roughness, followed by the cutting speed. The developed model shows a promising predictive accuracy, validated through additional experiments. The study published by [PERSON] et al. [13] examines delamination's impact on drilling quality in MDF, specifically focusing on variable feed pressure in multi-spindle drilling machines. Second-order polynomial models are established using response surface method ology to analyze the relationship between delamination and machining parameters. The analysis of variance (ANOVA) validates these models, revealing that delamination increases with decreased drill hardness and increased feed pressure at the MDF entry and exit. At low drill hardness and high feed pressure, the delamination factor remains relatively insensitive to variations in these factors. The paper of [PERSON] et al. [14] explores how to achieve good surface quality in drilling particleboard by changing the input parameters. It measures the surface roughness (Ra) and finds that it is lowest with a high cutting speed, low feed speed, and small point angle. It uses ANOVA to show that feed speed has the most effect on Ra, followed by point angle and cutting speed. It also uses RSM and adaptive neuro-fuzzy inference system (ANFIS) to make mathematical models to predict Ra and compares their accuracy. The R\({}^{2}\) coefficient values of 98.5% (RSM) and 99.9% (ANFIS) indicates that the models are useful to predict the surface roughness of particleboards. [PERSON] et al. [15] used a parallel robot to drill MDF and examined the effect of cutting parameters on surface roughness. They used the Taguchi technique and ANOVA to find the best drilling parameters and fit a quadratic regression model. They report that the surface roughness is optimal when the point angle is 118\({}^{\circ}\), the cutting speed is 47.1 m/min, and the feed rate is 0.01 mm/rev. [PERSON] et al. [16] studied how to optimize the drilling process of wood particleboards by adjusting selected factors. They used ANN models and RSM to predict and analyze the effects of drill tip angle, tooth bite, and drill type on delamination factor, thrust force, and drilling torque. The authors found that a helical drill assures a lower delamination factor, a lower thrust force, and a lower drilling torque than a flat drill. The designed ANN models were reliable.