A visual analytical method for evaluating tool flank wear volumes of micro-milling cutters with AKAZE features matching: A preliminary study

Abstract

Tool wear is one of the most important factors restricting the development and application of micro-milling technology. In order to accurately evaluate the tool wear features in the micro-milling process, a visual analytical approach is proposed with multiple advanced image processing methods. Firstly, A theoretical model of tool wear volume considering the end concave angle is proposed, which is more descriptive of practical situations than the traditional one-dimensional parameters, the tool flank wear length. Secondly, the tool flank wear areas are accurately obtained of image segmentation with the Canny gradient operator, extraction with corner feature detection, and registration with Accelerated-KAZE (AKAZE) feature matching. Finally, the features of the tool flank wear are evaluated of correcting the integration areas with the Hough transformation, determining the parameters with scale conversion, and verifying the areas and volumes of the tool flank wear with experimental data and key quality metrics, Mean-Squared Error (MSE) and Multi-Scale Structural Similarity (MS-SSIM). The results demonstrate that the method presented effectively characterises the tool flank wear, giving a suitable approach for computer vision and image feature processing of tool wear monitoring during machining.