Abstract
This paper reports on the study of vibration acceleration in milling and vibration prediction by means of artificial neural networks. The milling process, carried out on AZ91D magnesium alloy with a PCD milling cutter, was monitored to observe the extent to which the change of selected technological parameters (vc, fz, ap) affects vibration acceleration ax, ay and az. The experimental data have shown a significant impact of technological parameters on maximum and RMS vibration acceleration. The simulation works employed the artificial neural networks modelled with Statistica Neural Network software. Two types of neural networks were employed: MLP (Multi-Layered Perceptron) and RBF (Radial Basis Function).
Highlights
Vibration is a physical phenomenon that normally accompanies milling and which is a significant factor deciding whether the machined element will exhibit high quality of finish and maintain specified parameters
This paper reports on the study of vibration acceleration in milling and vibration prediction by means of artificial neural networks
Analysis of milling parameters impact highlights the strong correlation between the selection of suitable technological parameters and vibration acceleration
Summary
Vibration is a physical phenomenon that normally accompanies milling and which is a significant factor deciding whether the machined element will exhibit high quality of finish and maintain specified parameters. The methods for the prevention and damping of self-excited vibrations are based on: adjustment of technological parameters of machining, or readjustment of the machine tool design The disadvantages of both approaches include their purely predictive rather than reactive character, which determines that any observations carried out in the process are not accounted for [16]. A properly defined optimal value of n guarantees the absence of chatter and the resulting increase of efficiency, and may be modelled on a virtual machine with the implementation of the methodology for the selection of stable spindle speeds This solution requires such data as: the number of cutter teeth in the tool and chatter frequency [18]. Neural network simulations enable initial determination of process parameters and vibration components prediction [4]
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