Volume monitoring of the milling tool tip wear and breakage based on multi-focus image three-dimensional reconstruction

Abstract

Abstract In precision machining, the milling tool’ geometry has a great influence on the milled surface quality. The research on milling tool state monitoring was mainly based on one-dimensional signals and two-dimensional images, which could indirectly obtain the tool state and wear area, but it could not provide the volume of milling tool wear and breakage area, thereby making it difficult to achieve quantitative analysis tool wear. This paper proposed a three-dimensional (3D) reconstruction method of the milling tool tip, it could build a 3D model of the milling tool tip, and then the volume of the wear and breakage region of the milling tool tip was extracted by the 3D model. Firstly, the focusing degree of image sequence’s pixels was calculated based on the non-subsampled discrete shearlet transform (NSST) and Laplace algorithm, and the 3D reconstruction of the milling tool tip was completed according to the shape-from-focus (SFF) principle; secondly, the depth values were optimized by fitting the focusing degree curve of pixels in the image sequence with Gaussian function; finally, the volume of the 3D point cloud of the milling tool tip was calculated by the Simpson double numerical integration method, and the material loss in the damaged region could be obtained. In the 3D reconstruction experiment of the milling tool tip, comparing the different focus degree evalution operators of SFF, the 3D point cloud obtained by this paper's proposed 3D reconstruction method has the least noise and the best performance in the root-mean-square error, correlation, and smoothness indexes. In addition, compared with Genmagic software, the 3D point cloud volume calculation method adopted in this paper could accurately calculate the 3D point cloud volume of the milling tool tip, and the percentage error was less than 1%.