Application of wavelet packet transform based Normalised Radial Basis Function Network in a machining process Online publication date: Sat, 16-May-2009
by Karali Patra, Surjya K. Pal, Kingshook Bhattacharyya
International Journal of Materials and Product Technology (IJMPT), Vol. 35, No. 1/2, 2009
Abstract: In this work, an attempt has been made to develop a drill wear prediction system. A Hall-effect current sensor has been used for acquiring motor current signals during drilling under different cutting conditions. Wavelet packet transform has been used on the acquired current signals to extract features. A normalised Radial Basis Function (RBF) neural network model has then been developed to correlate the extracted features with drill wear. The proposed network outperforms the standard RBF neural network in terms of training error and also in terms of prediction error.
Online publication date: Sat, 16-May-2009
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Materials and Product Technology (IJMPT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com