Tool vibration prediction and optimisation in face milling of Al 7075 and St 52 by using neural networks and genetic algorithm Online publication date: Thu, 16-Aug-2012
by Amir Mahyar Khorasani; Pooneh Saadatkia; Alex Kootsookos
International Journal of Machining and Machinability of Materials (IJMMM), Vol. 12, No. 1/2, 2012
Abstract: Tool vibration generated under unsuitable cutting conditions is an extremely serious problem during face milling as it causes excessive tool wear, noise, tool breakage, and deterioration of the surface quality. In the current study, an artificial neural network (ANN) was used to predict tool vibration stability during face milling for different materials: Al 7075 and St 52. The testing of the ANN after training had a correlation of 99.206% with experimentally determined results. A generic algorithm (GA) was then used to minimise the vibration experienced during face milling and machining was performed using the GA recommended parameters. Measurement of the vibration during machining showed that the GA had a calculated error of 0.124%.
Online publication date: Thu, 16-Aug-2012
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