Machine tool condition monitoring using workpiece surface texture analysis
Tool wear affects the surface roughness dramatically. There is a very close correspondence between the geometrical features imposed on the tool by wear and microfracture and the geometry imparted by the tool on to the workpiece surface. Since a machined surface is the negative replica of the shape of the cutting tool, and reflects the volumetric changes in cutting-edge shape, it is more suitable to analyze the machined surface than look at a certain portion of the cutting tool. This paper discusses our work that analyzes images of workpiece surfaces that have been subjected to machining operations and investigates the correlation between tool wear and quantities characterizing machined surfaces. Our results clearly indicate that tool condition monitoring (the distinction between a sharp, semi-dull, or a dull tool) can be successfully accomplished by analyzing surface image data.
tool condition monitoring surface texture analysis image processing computer vision