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Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time-frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

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Tool wear condition monitoring (TCM) is of great significance to ensure manufacturing quality in milling processes, and the development of deep learning (DL) in recent years has led to increasing interest in DL-based TCM methods. However, most of these DL-based methods rely on large training samples to achieve good performances, which is expensive. In this paper, a new TCM method based on an edge-labeling graph neural network (EGNN) is proposed for small training datasets. First, the tool wear image is input into a convolution neural network (CNN) to extract features and obtain the features of the training samples. A fully connected graph is established based on these features, and the values of the edge labels are obtained by updating the nodes and edge features in the fully connected graph. Finally, the tool wear condition is predicted through the sample label of the support set and the predicted value of the edge connected with the query sample using a weighted voting method. The effectiveness of the proposed EGNN-based TCM method was demonstrated by its application to milling TCM experiments, and the results indicated that the proposed method outperformed three state-of-the-art methods (CNN, AlexNet, and ResNet) with small samples.