Wavelet networks for sensor signal interpretation in flank wear assessment

摘要: It is known that the vibration sensor signals in a turning process are sensitive to the gradually increasing ank wear. Accordingly, this paper investigates a ank wear assessment technique in turning through vibration signals. To overcome some of the limitations associated with former methods based on sensor data fusion and neural networks for continuous ank wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters and the parameters for the signal interpretation (equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to ank wear classes. A novel training algorithm for such a network is developed. This research investigates for the rst time the application of wavelet networks to manufacturing process monitoring; its results can also be useful for developing signal interpretation schemes in machine tool monitoring, critical component monitoring and product quality monitoring.