# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
###### Abstract  
Obtaining the complete wear state of the milling cutter during processing can help predict tool life and avoid the impact of tool breakage. A cylindrical model of tool collection is proposed, which uses the collected partial pictures of the side edge to construct a panoramic picture of tool wear. After evaluating the splicing accuracy, the fully convolutional neural network (FCN) segmentation algorithm of the VGG16 structure is used to segment the panorama of the side edge of the end mill after splicing. The FCN model is built using Tensorflow to complete the image segmentation training and testing of the side edge wear area. Experimental results show that the FCN model can segment the side wear image and effectively solve the illumination change problem and different tool wear differences. Compared with the Otsu threshold adaptive segmentation algorithm and K-means clustering algorithm, the error of the extracted wear value is 1.34% to 8.93%, and the average error rate is 5.23%. This method can obtain a more intuitive panorama of the cutter side edge wear of the end milling and provide technical support for improving tool utilization rate, machining quality, and tool selection and optimization. convolutional neural networks; image stitching; tool wear; acquisition of cylindrical models +
Footnote †: journal: Applied  
power sensors and studied nonparametric control charts based on binomial statistics for analyzing the relationship between these signals. It can be seen that the use of the indirect method requires analysis and feature extraction of the identified tool wear signal. However, in the face of a complex processing environment and a large amount of monitoring data, it seems difficult to accurately extract the signal features related to wear. Signals such as cutting force [9], vibration [10], acoustic emission [11], and motor current [12] are collected by sensors [13,14], and the noise of the signals or the interference between the signals can easily lead to misjudgment of the processing status [15]. Furthermore, if the signal changes are small, it is difficult to determine whether the cutting condition parameters or tool wear are responsible for the signal changes. In addition, factors such as materials, parts, and machine tools used for processing have a significant impact on the results. Although a large amount of data training can obtain better test results, it is not suitable for production monitoring of small-batch parts. Direct methods measure tool wear using optical, resistive proximity sensors, or vision systems. The direct method has the advantage of capturing the actual geometric variation of tool wear and is independent of machining equipment, tool material, and machining parameters. Researchers usually use image processing methods to extract features of wear images to obtain tool wear status. [PERSON] et al. [16] collected tool wear images at different times and used image processing methods to identify and classify tool wear levels to determine tool wear levels. [PERSON] et al. [17] proposed a tool wear degree estimation method based on a statistical learning system, which can evaluate the tool replacement time. [PERSON] et al. [18] established an intermittent measurement system for tool rear surface wear during milling. By reducing the spindle speed to match the high-speed camera, the acquired image blur is minimized, and the wear of the flank is detected through the image sequence. [PERSON] [19] developed a new automatic online calibration method to obtain tool wear parameters such as average tool wear width, tool wear area, and tool wear circumference. [PERSON] et al. [20] built an online machine vision system that uses computer vision methods to automatically detect and judge blade breakage without comparing it to a reference image. In recent years, artificial intelligence technology has been continuously developed. Its advantage is that it does not need to design specific execution codes, which can reduce the computational burden and improve performance [21,22].