Title: Tool vibration prediction and optimisation in face milling of Al 7075 and St 52 by using neural networks and genetic algorithm
Authors: Amir Mahyar Khorasani; Pooneh Saadatkia; Alex Kootsookos
Addresses: Faculty of High Technology and Engineering, Iran University of Industries and Mines, 578-Hafez Street, Karimkhan Blv., Tehran, Iran. ' Department of Physics, Alzahra University, Vanak, Tehran, 1993891176, Iran. ' School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, G.P.O. Box 2476V, Melbourne 3001, Australia
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%.
Keywords: face milling; tool vibration; vibration prediction; artificial neural networks; ANNs; optimisation; genetic algorithms; GAs; aluminium machining; steel machining.
International Journal of Machining and Machinability of Materials, 2012 Vol.12 No.1/2, pp.142 - 153
Available online: 16 Aug 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article