Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network

Cutting forces and acoustic emission measures as a function of tool wear are presented for different cutting parameters and their applicability for tool condition monitoring is evaluated. The best of them, together with cutting parameters, were chosen as inputs to a feedforward, back propagation (FFBP) neural network; some training techniques were applied and their effectiveness is also evaluated. Conventional training of FFBP neural networks very soon leads to overtraining, hence to deterioration in the net response. Training of these nets depends very much on the initial weight values. A good way of finding satisfactory results is to introduce random distortions to the weight system, which efficiently push the net out of a local minimum of testing errors. An even more effective method may be to employ temporary shifts in the weights, alternately negative and positive. This has two advantages: (1) it brings the net to balance between training and testing errors and (2) it enables a great reduction in the number of hidden nodes.