Chatter detection algorithm based on machine vision

Production of precise high-value mechanical elements requires a hundred percent on-site control. Chatter may occur due to random events. Although an unaided human eye can also easily identify the presence of chatter marks, it is economically ineffective. Therefore, an algorithm based on machine vision signals was proposed for surface inspection. The algorithm was designed to build an error map of the examined surface and highlight the regions of probable imperfections. The algorithm is based on local gradient estimation, applied in respect to milling parameters. Estimated local gradient directions are used to calculate the ridge/valley orientations of the machined surface. The local ridge orientation is used for the purpose of computing the surface error map. An experiment was made to test the algorithm. Milled surfaces in both chatter-free and chatter-rich conditions have been analysed with the presented method.