Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
نویسندگان
چکیده
Obtaining the complete wear state of milling cutter during processing can help predict tool life and avoid impact breakage. A cylindrical model collection is proposed, which uses collected partial pictures side edge to construct a panoramic picture wear. After evaluating splicing accuracy, fully convolutional neural network (FCN) segmentation algorithm VGG16 structure used segment panorama end mill after splicing. The FCN built using Tensorflow image training testing area. Experimental results show that effectively solve illumination change problem different differences. Compared with Otsu threshold adaptive K-means clustering algorithm, error extracted value 1.34% 8.93%, average rate 5.23%. This method obtain more intuitive provide technical support for improving utilization rate, machining quality, selection optimization.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2022
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app12168100