Artificial Neural Network Modelling of Vibration in the Milling of AZ91D Alloy
1
Department of Production Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
2
Department of Organisation of Enterprises, Faculty of Management, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
3
Student of Production Engineering, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland
Publication date: 2017-09-03
Corresponding author
Monika Kulisz
Department of Organisation of Enterprises, Faculty of Management, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
Department of Organisation of Enterprises, Faculty of Management, Lublin University of Technology, Nadbystrzycka 38, 20-618 Lublin, Poland
Adv. Sci. Technol. Res. J. 2017; 11(3):261-269
KEYWORDS
simulationartificial neural networksvibrationhigh-speed dry millingmagnesium alloyschatter in milling
TOPICS
ABSTRACT
The paper reports the results of artificial neural network modelling of vibration in. a milling process of magnesium alloy AZ91D by a TiAlN-coated carbide tool. Vibrations in machining processes are regarded as an additional, absolute machinability index. The modelling was performed using the so-called “black box” model. The best fit was determined for the input and output data obtained from the machining process. The simulations were performed by the Statistica software using two types of neural networks: RBF (Radial Basis Function) and MLP (Multi-Layered Perceptron).
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