A threshold-based and neural network approach for multi-tooth milling cutter tool breakage monitoring

This study aims to achieve fast and accurate identification of tool breakage in multi-tooth milling cutters using methods such as thresholding, clustering, and neural networks. A multi-level thresholding strategy combining fixed thresholds and dynamic thresholds was designed to enable rapid and accurate response in tool breakage identification. The fuzzy c-means clustering algorithm (FCM) and one-dimensional convolutional Softmax classifier (1D-CNN Softmax) were employed to identify the tool breakage states, distinguishing between normal cutting, single-tooth breakage, and doubletooth breakage. Experimental results demonstrate that this method exhibits fast response and high accuracy in classifying the breakage states of the three-tooth milling cutter, achieving an accuracy rate of 98.6%. This research provides a rapid and accurate technique for tool breakage identification in the field of multi-tooth milling cutter tools.