Online tool wear monitoring in turning using time-delay neural networks

Wear monitoring systems often use neural networks for a sensor fusion with multiple input patterns. Systems for a continuous online supervision of wear have to process pattern sequences. Therefore recurrent neural networks have been investigated in the past. However, in most cases where only noisy input or even noisy output patterns are available for a supervised learning, success is not forthcoming. That is, recurrent networks don´t perform noticeably better than non-recurrent networks processing only the current input pattern like multilayer perceptrons. This paper demonstrates on the basis of an application example (online tool wear monitoring in turning) that results can be improved significantly with special non-recurrent feedforward networks. The approach uses time-delay neural networks which consider the position of a single pattern in a pattern sequence by means of delay elements at the synapses. In the mentioned application example, the average error in the estimation of a characteristic wear parameter could be reduced by about 24.2% compared with multilayer perceptrons