The efficacy of back propagation neural network with delta bar delta learning in predicting the wear of carbide inserts in face milling

Authors: Dutta, R.; Paul, S.; Chattopadhyay, A.

Source: The International Journal of Advanced Manufacturing Technology, Volume 31, Numbers 5-6, December 2006 , pp. 434-442(9)

Publisher: Springer

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Abstract:

Face milling is a process predominantly affected by dynamic variation of cutting forces, thermo-mechanical shocks and vibration leading to catastrophic tool failure along with gradual wear of the inserts. Keeping in view the industrial importance of this process, it is necessary to devise suitable methods to predict in advance the onset of tool failure without grossly impairing the machining set-up and the job. Hence, the applicability of back propagation neural network with delta bar delta learning rule for faster convergence has been studied with the above objective. The multi sensor based tool condition monitoring strategy shows that the learning rate adaptation scheme combined with the selection of suitable process parameters drastically reduces the training time of the artificial neural network without dispensing with the prediction accuracy.

Keywords: Face milling; Tool condition monitoring (TCM); Artificial neural network (ANN); Multi-layer perceptron (MLP); Learning rate (LR); Momentum parameter (MP); Back propagation neural network (BPNN); Modified BPNN (MBPNN); Fuzzy controlled BPNN (FBPNN); Modified BPNN with delta bar delta learning (MBPNN

Document Type: Research article

DOI: http://dx.doi.org/10.1007/s00170-005-0230-7

Affiliations: 1: Email: ranjit_kumar_dutta@yahoo.com

Publication date: 2006-12-01