Chatter classification by entropy functions and morphological processing in cylindrical traverse grinding

During cylindrical traverse grinding processes, two types of regenerative chatter—workpiece and grinding wheel—may degrade the accuracy of the surface finish. To maintain productivity and quality, a closed-loop vibration control system should be provided for the grinding system. An algorithm for automated classification by type is essential in developing such a system. In cylindrical traverse grinding, the chatter vibration signals display unstable dynamic characteristics, which makes the task of chatter classification especially difficult. This paper introduces an approach that combines entropy techniques with morphological preprocessing to classify traverse grinding regenerative chatter by type based on the vibration spectrum. Experimental data analysis is used to demonstrate that the proposed method can effectively distinguish workpiece regenerative chatter from wheel regenerative chatter. Because both the entropy function and morphological processing are computationally easy, this method is not only readily understood, but also conveniently adaptable to system expansion and real-time applications.