Modelling of tool life in end milling of Ti6Al4V alloy using artificial neural networks

Authors:
Salah Al-Zubaidi;Jaharah A. Ghani;Che Hassan Che Haron
Affiliations:
Department of Mechanical and Material Engineering, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Material Engineering, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia;Department of Mechanical and Material Engineering, University Kebangsaan Malaysia, Bangi, Selangor, Malaysia
Venue:
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
Year:
2012

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Abstract

Nowadays, artificial neural networks (ANN) are often applied in solving numerous problems in machining processes. A tool life prediction of coated and uncoated cutting tools proves to be significant. In this study, a feed forward back propagation neural network with a Levenberg-Marquard (L-M) training algorithm is used in modeling the tool life of a PVD insert cutting tool when end milling of Ti6Al4V under dry cutting conditions. The objective of this study is to apply ANN in the prediction of the tool life of PVD cutting tools using low experimental data sets. One hundred and ten (110) models were designed, trained and tested using Matlab neural network tool box. Good agreement was obtained between the ANN model and the experimental data.