INTELLIGENT DETECTION OF DRILL WEAR

Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The neural network consisted of three layers: input, h idden, and output. The input vector comprised drill size, feed rate, s pindle speed, and eight features obtained by processing the thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various drill sizes, feed rates and spindle speeds were carried out. The learning process was p erformed effectively by utilising backpropagation with smoothing and a n activation function slope. The on-line detection of drill wear state s using BPNs achieved 100% reliability even when the drill size, feed rate and spindle speed were changed. In other words, the developed on- line drill wear detection systems have very high robustness and hence can be used in very complex production environments, such as flexible manufacturing systems. (C) 1998 Academic Press.