The abrasion of milling cutters is an important factor that affects the accuracy of a workpiece. The intervals between cutter changes is based on the burr condition of the edges on the finished products as well as their dimensional precision. Delayed replacement of cutters will result in a degradation of workpiece quality and it is important that the wear of cutters be monitored in a timely manner. In this study the actual vibration signals generated in a milling process were measured using an Automatic Intelligent Diagnosis Mechanism (AIDM) to determine cutter wear. The AIDM included two feature extraction approaches and three classification methods. The first approach used the Finite Impulse Response Filter (FIR) with Approximate Entropy (ApEn) for feature extraction. The second approach was nonlinear feature mapping using a fractional order Chen-Lee chaotic system. This used chaotic dynamic error centroids and chaotic dynamic error mapping for status identification. After feature extraction the results were substituted into a Back Propagation Neural Network (BPNN), Support Vector Machine (SVM), and a Convolutional Neural Network (CNN) for identification. The results of the experiments showed that a Chaotic Dynamic Error Map of the fractional order Chen-Lee chaotic system in the AIDM had an identification rate of 96.33% using a convolutional neural network. In addition, it was shown that the AIDM model could automatically select the most suitable feature extraction and classification model from the input signal and could determine the wear level milling cutters.
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