Wavelet based sensor fusion for tool condition monitoring of hard to machine materials

Tool condition monitoring in modern manufacturing systems is gaining more attention due to the fact that excessive tool damage can cause workpiece surface deterioration and increase idle time. Therefore, monitoring tool condition from the initial to final stages of tool life is a task that is critical yet difficult, especially in hard-to-machine materials. In this work, Wavelet Packet Decomposition is used for extracting statistical features in the time-frequency domain of two low cost sensing technologies, i.e. vibration and power, in addition to Principal Component Analysis to reduce the dimensionality of feature vectors. A Recurrent Neural Network is then trained with Bayesian regularization backpropagation method and the estimated tool wear is compared to the actual measured wear. Results show a maximum of 13% relative error in estimating tool wear which proves the effectiveness of implemented sensory data fusion method to be used in automated control of manufacturing processes.