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Title: Flank wear prediction in drilling using back propagation neural network and radial basis function network
Authors: Panda, S S
Charkraborty, D
Pal, S K
Keywords: Neuron
Centre Vector
Euclidian distance
Sensor signal
Flank wear
Issue Date: 2007
Publisher: Elsevier
Citation: Applied Soft Computing, (Accepted Post-print)
Abstract: In the present work, two different types of artificial neural network (ANN) architectures viz. back propagation neural network (BPNN) and radial basis function network (RBFN) have been used in an attempt to predict flank wear in drills. Flank wear in drill depends upon speed, feed rate, drill diameter and hence these parameters along with other derived parameters such as thrust force, torque and vibration have been used to predict flank wear using ANN. Effect of using increasing number of sensors in the efficacy of predicting drill wear by using ANN has been studied. It has been observed that inclusion of vibration signal along with thrust force and torque leads to better prediction of drill wear. The results obtained from the two different ANN architectures have been compared and some useful conclusions have been made.
Description: Copyright for the published version belongs to Elsevier
Appears in Collections:Journal Articles

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