期刊名称：Advances in Science and Technology Research Journal
印刷版ISSN：2080-4075
电子版ISSN：2299-8624
出版年度：2017
卷号：11
期号：3
页码：261-269
DOI：10.12913/22998624/76546
语种：English
出版社：Society of Polish Mechanical Engineers and Technicians
摘要：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).
关键词：Simulation;artificial neural networks;vibration;high-speed dry milling;magnesium alloys;chatter in milling
