Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
IEEE Transactions on Neural Networks
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
Drill wear prediction using different neural network architectures
International Journal of Knowledge-based and Intelligent Engineering Systems
Modelling of the cutting tool stresses in machining of Inconel 718 using artificial neural networks
Expert Systems with Applications: An International Journal
Journal of Intelligent Manufacturing
Intelligent process modeling and optimization of die-sinking electric discharge machining
Applied Soft Computing
Prediction of microdrill breakage using rough sets
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach
Fuzzy Sets and Systems
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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.