Comprehensive Experimental Analysis of the Effect of Drilled Material on Torque Using Machine Learning Decision Trees
Na minha lista:
| Publicado no: | Materials vol. 18, no. 13 (2025), p. 3145-3167 |
|---|---|
| Autor principal: | |
| Outros Autores: | , , , , , |
| Publicado em: |
MDPI AG
|
| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3229153586 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1996-1944 | ||
| 024 | 7 | |a 10.3390/ma18133145 |2 doi | |
| 035 | |a 3229153586 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231532 |2 nlm | ||
| 100 | 1 | |a Hnátik Jan | |
| 245 | 1 | |a Comprehensive Experimental Analysis of the Effect of Drilled Material on Torque Using Machine Learning Decision Trees | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Drilling machines (tools) | ||
| 653 | |a Cemented carbides | ||
| 653 | |a Machine learning | ||
| 653 | |a Torque | ||
| 653 | |a Verification | ||
| 653 | |a Cutting speed | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Carbide tools | ||
| 653 | |a Case hardening | ||
| 653 | |a Machining | ||
| 653 | |a Structural steels | ||
| 653 | |a Statistical methods | ||
| 653 | |a Cutting tools | ||
| 653 | |a Engineering | ||
| 653 | |a Aluminum composites | ||
| 653 | |a Decision trees | ||
| 653 | |a Deformation | ||
| 653 | |a Carbon steels | ||
| 653 | |a Geometry | ||
| 653 | |a Drilling | ||
| 653 | |a Steel | ||
| 700 | 1 | |a Fulemová Jaroslava | |
| 700 | 1 | |a Sklenička Josef | |
| 700 | 1 | |a Gombár Miroslav | |
| 700 | 1 | |a Vagaská Alena | |
| 700 | 1 | |a Sýkora Jindřich | |
| 700 | 1 | |a Lukáš, Adam | |
| 773 | 0 | |t Materials |g vol. 18, no. 13 (2025), p. 3145-3167 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3229153586/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3229153586/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3229153586/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |