The presented publication is focused on increasing the productivity and efficiency of machine production of precision holes. Considerable attention was paid to the influence of the pre-preparation and drilling technology on the quality of the reamed hole. The material selected for machining was chromium-molybdenum steel 42CrMo4 QT. The cluster analysis is implemented to evaluate the experimental results obtained by measuring the cylindricity and surface roughness of the drilled and reamed holes. The factor of hole pre-preparation technology was selected in two quality levels, and the factor of drilling technology in four quality levels. The constant factors were carefully controlled during the experiment, i.e., other possible influences were excluded or minimised. Under constant cutting conditions, the experiment of the subject machining process was carried out. The cutting process parameters investigated were the cylindricity and the roughness of the machined surface Ra. The novelty and contribution of this research lie in confirming the assumption that drilling technology influences hole quality primarily, whereas hole pre-preparation technology has a secondary effect.

Current research studies devoted to cutting forces in drilling are oriented toward predictive model development, however, in the case of mechanistic models, the material effect on the drilling process itself is mostly not considered. This research study aims to experimentally analyze how the machined material affects the feed force (Ff) during drilling, alongside developing predictive mathematical–statistical models to understand the main effects and interactions of the considered technological and tool factors on Ff. By conducting experiments involving six factors (feed, cutting speed, drill diameter, point angle, lip relief angle, and helix angle) at five levels, the drilling process of stainless steel AISI1045 and case-hardened steel 16MnCr5 is executed to validate the numerical accuracy of the established prediction models (AdjR = 99.600% for C45 and AdjR = 97.912% for 16MnCr5). The statistical evaluation (ANOVA, RSM, and Lack of Fit) of the data proves that the drilled material affects the F_f value at the level of 17.600% (p < 0.000). The effect of feed represents 44.867% in C45 and 34.087% in 16MnCr5; the cutting speed is significant when machining C45 steel only (9.109%). When machining 16MnCr5 compared to C45 steel, the influence of the point angle (lip relief angle) is lower by 49.198% (by 22.509%). The effect of the helix angle is 163.060% higher when machining 16MnCr5.

This article deals with drilling, the most common and simultaneously most important traditional machining operation, and which is significantly influenced by the properties of the machined material itself. To fully understand this process, both from a theoretical and practical perspective, it is essential to examine the influence of technological and tool-related factors on its various parameters. Based on the evaluation of experimentally obtained data using advanced statistical methods and machine learning decision trees, we present a detailed analysis of the effects of technological factors (f_n, v_c) and tool-related factors (D, ε_r, α₀, ω_r) on variations in torque (M_c) during drilling of two types of engineering steels: carbon steel (C45) and case-hardening steel (16MnCr5). The experimental verification was conducted using CTS20D cemented carbide tools coated with a Triple Cr SHM layer. The analysis revealed a significant influence of the material on torque variation, accounting for a share of 1.430%. The experimental verification confirmed the theoretical assumption that the nominal tool diameter (D) has a key effect (53.552%) on torque variation. The revolution feed (f_n) contributes 36.263%, while the tool’s point angle (ε_r) and helix angle (ω_r) influence torque by 1.189% and 0.310%, respectively. No significant effect of cutting speed (v_c) on torque variation was observed. However, subsequent machine learning analysis revealed the complexity of interdependencies between the input factors and the resulting torque.