Selected Problems of Effectiveness and Quality in the Dry Rough Milling of Magnesium Alloy AZ91D Using End Mills with Different Helix Angles
Artykuł w czasopiśmie
MNiSW
70
Lista 2024
| Status: | |
| Autorzy: | Zagórski Ireneusz, Korpysa Jarosław, Kulisz Monika, Skoczylas Agnieszka |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2025 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 3 |
| Wolumen/Tom: | 16 |
| Strony: | 1 - 21 |
| Impact Factor: | 0,9 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | FD-20/IM-5/138, FD-20/IM-5/061, FD-20/IM-5/144, FD-20/IM-5/107 |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 27 lipca 2025 |
| Abstrakty: | angielski |
| From the point of view of production and manufacturing processes, issues related to surface quality and machining efficiency are very important. This paper presents the results of a study investigating selected problems of quality and efficiency in dry rough milling. Roughness parameters 2D and 3D were analysed. Additionally, 3D surface topography maps and Abbott– Firestone curves were generated. Carbide end mills with different helix angles were used in the study. Experiments were conducted on AZ91D magnesium alloy specimens. The machining process was conducted using high-speed machining parameters. The results showed that the surface roughness of the AZ91D alloy depended to a great extent on the tool geometry and applied machining parameters. Moreover, ANOVA statistical analysis and post-hoc tests (Tukey) were performed to assess the differences between individual groups of the specimens. Additionally, an artificial neural network (ANN) model was developed to predict the Ra parameter, and the results demonstrated its high predictive accuracy (R = 0.966). |
