# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
###### Abstract
It is regarded as a \"black box\" for developing the flexibility of tool detection systems. Therefore, the application of machine learning technology in image processing has rapidly developed. The visual monitoring method of tool wear using machine learning technology can improve the detection ability and accuracy and has a wide range of adaptations. The degree of tool wear and the amount of tool wear can be identified by extracting the geometric and wear characteristics of the tool wear area during the milling process and establishing a machine learning model. In addition, the collected datasets can be used for training to build models for tool wear classification, tool status identification, and tool life prediction [23]. [PERSON] [24] proposed a Derivative Wavelet Framework (DWFs) method combined with a Convolutional Neural Network (CNN) to analyze the vibration signal of the machine tool spindle and obtain the tool state during the end milling process. Based on image data collection, [PERSON] [25] used a convolutional neural network model to identify the wear type of milling cutters automatically and combined the wear type to improve the automatic detection method of tool wear value, providing a practical tool wear detection method. Visual monitoring methods can be integrated into intelligent manufacturing systems to obtain wear values [26]. In addition, with the advancement of machine vision technology, the training and learning of tool wear will provide more effective support for tool parameter selection, failure analysis, and tool design [23]. However, the tool structure is complex, and the acquisition resolution, frame rate, and viewing angle will significantly impact the acquisition method and acquisition quality of the tool wear image. At present, for tools with complex structures, such as milling cutters and drills, it is difficult to obtain complete and accurate wear area information due to their multi-edge and helical cutting edges. Therefore, only the local information on tool wear is usually collected, and the wear state of the tool cannot be thoroughly evaluated. The FCN network [27] is an improvement of CNN. The final full connection layer of the CNN network is replaced by convolutional layers. FCN classifies the pixel-level classification of images, thereby solving the problem of semantic segmentation. Different from CNN, FCN can accept input images of arbitrary size. It uses a deconvolution layer to upsample the feature map of the last convolutional layer to restore it to the same size as the input image so that a prediction can be generated for each pixel. At the same time, the spatial information in the original input image is preserved, and finally, pixel-by-pixel classification is carried out on the upsampled feature map. In this paper, the wear condition of the end mill is taken as the research object, and the established tool acquisition cylindrical model is used to collect the wear condition of the side edge of the end mill. The collected wear images are stitched into a panorama. The stitched panorama is segmented using an FCN network, and the segmentation quality and accuracy are evaluated. It provides an effective method for obtaining tool wear information, replacing worn tools in time, and avoiding tool failures.