DOI: https://doi.org/10.21203/rs.3.rs-2111214/v1
In the machinery field, drilling is one of the most important machining methods. Real-time monitoring of drill wear can effectively prevent the part quality from not meeting the specifications due to drill failure. This paper proposes a tool wear assessment and life prediction model based on image processing and deep learning methods, which shows great performance for small sample datasets and for low quality images. We construct an image database of drill bits and extract the normal areas and worn areas of the drill bits using the U-Net network and traditional image processing methods, respectively. Moreover, the original dataset is classified using the migration learning technique. The wear level of a drill bit can be accurately evaluated through experimental tests. Testing results show that the proposed method is more convenient and efficient than the previous methods that used manual measurements; the results can be applied to real-time drill wear monitoring, thereby reducing part damage caused by tool wear.