Abstract
Tool wear dramatically affects the texture of the machined surface. Analyzing the texture of machined surfaces has been shown to be promising for tool wear monitoring. However, most methods have their limitations when applied to real environments, where the geometric features of machined surface depend on the machining operation, and where image quality is affected by illumination and other factors. Problems of non-uniform illumination and image noise can be reduced by applying image segmentation and image enhancement techniques. This paper discusses our work on statistical and structural approaches for analyzing machined surfaces and investigates the correlation between tool wear and quantities characterizing machined surfaces. The column projection method can be used for machined surfaces with highly repetitive and regular textures while the connectivity oriented fast Hough transform based method is able to characterize surfaces produced by various machining processes and conditions. Our results clearly indicate that tool condition monitoring which is defined as the ability to distinguish between a sharp, a semi-dull, or a dull tool can be successfully accomplished by analysis of statistical and structural information extracted from the machined surface.