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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23210%2F25%3A43975848" target="_blank" >RIV/49777513:23210/25:43975848 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1996-1944/18/13/3145" target="_blank" >https://www.mdpi.com/1996-1944/18/13/3145</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/ma18133145" target="_blank" >10.3390/ma18133145</a>

Result language

angličtina

Original language name

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

Original language description

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 (fn, vc) and tool-related factors (D, εr , α0, ωr) on variations in torque (Mc) 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 (fn) contributes 36.263%, while the tool’s point angle (εr) and helix angle (ωr) influence torque by 1.189% and 0.310%, respectively. No significant effect of cutting speed (vc) on torque variation was observed. However, subsequent machine learning analysis revealed the complexity of interdependencies between the input factors and the resulting torque.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20501 - Materials engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Materials

ISSN

1996-1944

e-ISSN

1996-1944

Volume of the periodical

18

Issue of the periodical within the volume

13

Country of publishing house

CH - SWITZERLAND

Number of pages

22

Pages from-to

nestránkováno

UT code for WoS article

001526404500001

EID of the result in the Scopus database

2-s2.0-105010344982