Citation: INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE Volume: 35 Issue: 8 Pages: 1137-1147
Abstract: Detection of tool failure is very important in automated manufacturing. In this study, tool failure detection was conducted in two steps by using Wavelet Transformations and Neural Networks (WT-NN). In the first step, data were compressed by using wavelet transformations and unnecessary details were eliminated. In the second step, the estimated parameters of the wavelet transformations were classified by using Adaptive Resonance Theory (ART2)-type self-learning neural networks. Wavelet transformations represent transitionary data and complex patterns in a more compact form than time-series methods (frequency and time-domain) by using a family of the most suitable wave forms. Wavelet transformations can also be implemented on parallel processors and require less computations than Fast Fourier Transformation (FFT). The training of ART2-type neural networks is faster than backpropagation-type neural networks and ART2 is capable of updating its experience with the help of an operator while it is monitoring the sensory signals. The proposed approach was tested in over 171 cases and all the presented cases were accurately classified. The proposed system can be easily trained to inspect data during transition and/or any complex cutting conditions. The system will indicate failure instantaneously by creating a new category, thus alerting the operator.