INTELLIGENT CLASSIFICATION AND MEASUREMENT OF DRILL WEAR

Artificial neural networks are used for on-line classification and mea surement of drill wear. The input vector of the neural network is obta ined by processing the thrust and torque signals. Outputs are the wear states and flank wear measurements. The learning process can be perfo rmed by back propagation along with adaptive activation-function slope . The results of neural networks with and without adaptive activation- function slope, as well as various neural network architectures are co mpared. On-line classification of drill wear using neural networks has 100 percent reliability. The average flank wear estimation error usin g neural networks can be as low as 7.73 percent.