Effect of technological parameters on vibration acceleration in milling and vibration prediction with artificial neural networks

¹ Lublin University of Technology, Faculty of Mechanical Engineering, Department of Production Engineering, Nadbystrzycka 36, 20-618 Lublin, Poland
² Lublin University of Technology, Faculty of Management, Department of Organisation of Enterprises, Nadbystrzycka 38, 20-618 Lublin, Poland

^* Corresponding author: m.kulisz@pollub.pl

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 (v_c, f_z, a_p) affects vibration acceleration a_x, a_y and a_z. 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).