Zgadzam się
Nasza strona zapisuje niewielkie pliki tekstowe, nazywane ciasteczkami (ang. cookies) na Twoim urządzeniu w celu lepszego dostosowania treści oraz dla celów statystycznych. Możesz wyłączyć możliwość ich zapisu, zmieniając ustawienia Twojej przeglądarki. Korzystanie z naszej strony bez zmiany ustawień oznacza zgodę na przechowywanie cookies w Twoim urządzeniu.
The project/research was financed in the framework of the project Lublin University of
Technology—Regional Excellence Initiative, funded by the Polish Ministry of Science and Higher
Education (contract no. 030/RID/2018/19).
This paper shows the surface quality results after finishing milling of AZ91D and AZ31
magnesium alloys. The study was performed for variable technological parameters: cutting speed,
feed per tooth, axial depth of cut and radial depth of cut. The tools used in the study were two
carbide cutters with a different tool cutting edge helix angle. The measurement of the research results
presented the surface roughness parameters was made on the lateral faces and the end faces of the
specimens. Statistical analysis and simulations using artificial neural networks were carried out with
the Statistica software. The normality of the distribution was examined, and the hypotheses of the
equality of mean values and variance were verified. For the AZ91D magnesium alloy on the lateral
and the end faces (Ra, Rz parameters), simulations were carried out. Two types of ANN were used:
MLP (Multi-layered perceptron) and RBF (Radial Basis Function). To increase the machining stability
and to obtain a high surface finish, the more suitable tool for finishing milling is the tool with a helix
angle of λs = 20◦. Artificial neural networks have been shown to be a good tool for predicting surface
roughness parameters of magnesium alloys after finishing milling.
