Open AccessSurface quality simulation with statistical analysis after milling AZ91D magnesium alloy using PCD tool
Author(s) -
Monika Kulisz,
Ireneusz Zagórski,
Jarosław Korpysa
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1736/1/012034
Subject(s) - surface roughness , machining , magnesium alloy , artificial neural network , surface finish , materials science , multilayer perceptron , radial basis function , surface (topology) , perceptron , mechanical engineering , computer science , metallurgy , engineering , mathematics , alloy , artificial intelligence , composite material , geometry
Machined surface quality is one of the key indicators of a correctly conducted milling process. This paper reports on the results from numerical and statistical analysis of the condition of AZ91D Magnesium Alloy after milling using the PCD Tool. Three surface roughness indicators were of interest - average roughness (Ra), maximum height of profile (Rz) and mean width of profile elements (RSm). The surface quality, described by Ra/Rz, shows negligible deterioration at higher speeds v c . Two artificial neural networks, MLP (Multilayer Perceptron) and RBF (Radial Basis Function), modelled with Statistica package, were employed to simulate the effects that individual process variables have on the 2D surface roughness parameters. The statistical significance of the results was assessed using the one-way ANOVA technique. Given the successful validation of the numerical and empirical data (R 2 > 0.85), it may be inferred that our ANNs are an accurate predicting tool that models milling parameters ensuring that the surface is of suitable quality. The surface roughness indicators are generated from the corresponding technological parameters. Simulations save time, effort and costs that would be incurred by additional machining tests.