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Research, Modelling and Prediction of the Influence of Technological Parameters on the Selected 3D Roughness Parameters, as Well as Temperature, Shape and Geometry of Chips in Milling AZ91D Alloy
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).
The main purpose of the study was to define the machining conditions that ensure the best
quality of the machined surface, low chip temperature in the cutting zone and favourable geometric
features of chips when using monolithic two-teeth cutters made of HSS Co steel by PRECITOOL.
As the subject of the research, samples with a predetermined geometry, made of AZ91D alloy, were
selected. The rough milling process was performed on a DMU 65 MonoBlock vertical milling centre.
The machinability of AZ91D magnesium alloy was analysed by determining machinability indices
such as: 3D roughness parameters, chip temperature, chip shape and geometry. An increase in
the feed per tooth fz and depth of cut ap parameters in most cases resulted in an increase in the
values of the 3D surface roughness parameters. Increasing the analysed machining parameters
did not significantly increase the instantaneous chip temperature. Chip ignition was not observed
for the current cutting conditions. The conducted research proved that for the adopted conditions
of machining, the chip temperature did not exceed the auto-ignition temperature. Modelling of
cause-and-effect relationships between the variable technological parameters of machining fz and
ap and the temperature in the cutting zone T, the spatial geometric structure of the 3D surface “Sa”
and kurtosis “Sku” was performed with the use of artificial neural network modelling. During the
simulation, MLP and RBF networks, various functions of neuron activation and various learning
algorithms were used. The analysis of the obtained modelling results and the selection of the most
appropriate network were performed on the basis of the quality of the learning and validation, as
well as learning and validation error indices. It was shown that in the case of the analysed 3D
roughness parameters (Sa and Sku), a better result was obtained for the MLP network, and in the
case of maximum temperature, for the RBF network.
