20.500.12556/RUL-154755 Roughness parameters with statistical analysis and modelling using artificial neural networks after finish milling of magnesium alloys with different edge helix angle tools Uporaba statistične analize in modeliranja za določitev parametrov hrapavosti po končni obdelavi magnezijevih zlitin z orodji z variabilnim kotom vijačnice The paper presents the results of a study investigating the roughness parameters Rq, Rt, Rv, and Rp of finished-milled magnesium alloys AZ91D and AZ31B. Carbide end mills with varying edge helix angles were used in the study. Statistical analysis was additionally performed for selected machining conditions. In addition, modelling of selected roughness parameters on the end face for the AZ91D alloy was carried out using artificial neural networks. Results have shown that the tool with λs = 20° is more suitable for the finish milling of magnesium alloys because its use leads to a significant reduction in surface roughness parameters with increased cutting speed. Increased feed per tooth leads to increased surface roughness parameters. Both radial and axial depth of cut has an insignificant effect on surface roughness parameters. It has been proven that finish milling is an effective finishing treatment for magnesium alloys. In addition, it was shown that artificial neural networks are a good tool for the prediction of selected surface roughness parameters after finishing milling of the magnesium alloy AZ91D. magnesium alloys finish milling roughness surface quality statistical analysis artificial neural networks magnezijeve zlitine končna obdelava hrapavost kakovost površin statistična analiza umetne nevronske mreže true false true Angleški jezik Slovenski jezik Članek v reviji 2024-02-29 08:16:43 2024-02-29 08:16:47 2024-03-01 03:57:14 0000-00-00 00:00:00 2024 0 0 Str. 27-41 no. 1/2 Vol. 70 Jan.-Feb. 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 620.1 0039-2480 10.5545/sv-jme.2023.596 187155203 Zagórski,_Kulisz,_Szczepaniak-Roughness_parameters_with_statistical_analysis_and_modelling_using_artificial_neural_networks_after_finish_milling_of_magnesium_alloys_with_different_edge_helix_angle_tools.pdf Zagórski,_Kulisz,_Szczepaniak-Roughness_parameters_with_statistical_analysis_and_modelling_using_artificial_neural_networks_after_finish_milling_of_magnesium_alloys_with_different_edge_helix_angle_tools.pdf 1 F728A12A3C31902C37F096205FCE7D8D 84e046c4f082224c7b1e017780c6022711dc17c9d97817e46fb9830556b69a0a e30a8801-d6d2-11ee-ac9d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=181320 https://www.sv-jme.eu/sl/article/roughness-parameters-with-statistical-analysis-and-modelling-using-artificial-neural-networks-after-finish-milling-of-magnesium-alloys-with-different-edge-helix-angle-tools/ 0 f1d40f52-d6d2-11ee-ac9d-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=181322 Fakulteta za strojništvo Strojniški vestnik 0 0 0