Visual high-precision detection method for tool damage based on visual feature migration and cutting edge reconstruction

Aiming at the problem that the current tool damage detection system is difficult to automatically identify the location of the tool damage and accurately measure the amount of tool damage from the collected machine tool damage images, a visual high-precision detection method for tool damage based on visual feature migration and cutting edge reconstruction is proposed. This method divides the tool damage into wear area and breakage area, and extracts tool wear image information and breakage image information based on visual feature migration and cutting edge reconstruction respectively. Furthermore, image fusion is used to combine the wear and breakage features to obtain the damaged area. Finally, the tool damage location recognition is realized, and the geometric characteristics of tool damage are accurately measured based on the identified damage information. An offline detection experiment platform was built to verify the effectiveness of the proposed method. Experiments show that the proposed tool damage visual detection method using visual feature migration and cutting edge reconstruction can solve the current difficulty in automatically identifying the tool damage location and accurately measuring the amount of tool damage from the tool damage image and has good environmental adaptability and stability. Compared with the existing local variance method, adaptive threshold method and other methods, the average accuracy of tool damage geometric feature measurement is improved by at least 20%, which has a great advantage.

Automated summary

A visual high-precision detection method for tool damage based on visual feature migration and cutting edge reconstruction is proposed to automatically identify the location of tool damage and accurately measure the amount of damage. By extracting and fusing image information, the method achieves tool damage location recognition and accurate measurement of geometric characteristics. The method outperforms existing methods in tool damage detection accuracy by at least 20%.