Drill wear prediction using different neural network architectures

In the present work, an attempt has been made to use different artificial neural network (ANN) architectures to achieve more accurate prediction of drill wear. Large numbers of drilling operations, using mild steel as the work-piece and high speed steel (HSS) as the drill, have been performed and drill flank wear has been measured intermittently. Experimental results show a strong dependency of direct and indirect process parameters with drill wear. Experimentally obtained data have been used to train different ANN architectures using different combinations of important process parameters as input and measured flank wear as the output of the network. Relative performances of different ANN based drill wear prediction schemes in regard to prediction of drill wear have been compared. From the present work it has been observed that inclusions of more sensor signals as input to the network results a better-trained network, which can predict wear more accurately. It has also been observed from the present work that standard back propagation neural network (BPNN) predicts wear more accurately compared to fuzzy back propagation network (FBPN) and self-organizing method (SOM), through BPNN is slow in convergence.