Title: Modelling cutting power and tool wear in turning of aluminium matrix composites using Artificial Neural Networks
Authors: Liujie Xu, J. Paulo Davim
Addresses: Henan Engineering Research Center for Wear of Materials, Henan University of Science and Technology, Luoyang 471003, China. ' Department of Mechanical Engineering, University of Aveiro, Campus Santiago, 3810-193 Aveiro, Portugal
Abstract: Aluminium matrix composites have been investigated since 1970s because of the high performance of these materials for aerospace, aircraft and automotive industries. This paper builds Artificial Neural Network (ANN) machining models of aluminium matrix composites according to cutting parameters. Feedforward ANN is created and trained using comprehensive data sets tested by the authors, and good performances of networks are achieved. The prediction results show the tool wear and the machining power are highly influenced by the cutting velocity. The increase in the feed leads to moderate decrease in the tool wear and moderate increase in the machining power.
Keywords: aluminium matrix composites; metal matrix composites; MMC; machining composites; turning; ANN; neural networks; cutting force; tool wear; cutting parameters; cutting velocity.
International Journal of Materials and Product Technology, 2008 Vol.32 No.2/3, pp.333 - 342
Available online: 27 Jun 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article