Modeling and Testing of 2D Surface Roughness Parameters After Milling AZ91D Magnesium Alloy With the HSS Tool
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Kulisz Monika, Zagórski Ireneusz, Józwik Jerzy |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 70 - 75 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | The project/research was financed in the framework of the project Lublin University ofTechnology-Regional Excellence Initiative, :funded by the Polish Ministry of Science and Higher Education (contract no. 030/RID/2018/19). |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 9th International Workshop on Metrology for AeroSpace |
| Skrócona nazwa konferencji: | MetroAeroSpace 2022 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 27 czerwca 2022 do 29 czerwca 2022 |
| Miasto konferencji: | Pisa |
| Państwo konferencji: | WŁOCHY |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| The main aim of the research was to analyze the influence of the milling process parameters on selected indicators of 2D surface roughness parameters after milling with the HSS milling cutter AZ91D magnesium alloy. The tests were carried out with variable machining parameters /z and ap. The results of the analyzed roughness parameters (Rq, Rp, Rv, Rt) were presented in bar charts. lt has been observed that it is possible to achieve an average surface quality using cheap and widely available HSS cutters. An additional aim of the work was modeling of roughness parameters (Rq, Rp, Rv, Rt) using the Statistica Artificial Neural Networks software. The results of modeling and prediction were presented on the surface charts. The obtained networks show satisfactory predictive ability (R = 0.99), andd thus it can be concluded that they can be a suitable tool for predicting the indicated (Rq, Rp, Rv, Rt) surface roughness parameters. |
