Comprehensive Experimental Analysis of the Effect of Drilled Material on Torque Using Machine Learning Decision Trees

Abstract

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.