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.
Tool wear is a key factor that dominates the surface quality and distinctly influences the generated workpiece surface texture. In order to realize accurate evaluation of the tool wear from the generated workpiece surface after machining process, a new tool wear monitoring method is developed by fractal dimension of the acquired workpiece surface digital image. A self-made simple apparatus is employed to capture the local digital images around the region of interest. In addition, a skew correction method based on local fast Fourier transformation energy is also proposed for the surface texture direction adjustment. Furthermore, the tool wear quantitative evaluation was derived based on fractal dimension utilizing its high reliability for inherent irregularity description. The proposed tool wear monitoring method has verified its feasibility as well as its effectiveness in actual milling experiments using the material of AISI 1045 in a vertical machining center. Testing results demonstrate that the proposed method was capable of tool wear condition evaluation.
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