Milling chatter monitoring based on sparse representation and image similarity measurement

Abstract

It is well known that chatter is one of the main bottlenecks affecting the surface quality of workpieces and production efficiency during machining. In this paper, a new chatter monitoring methodology is proposed on the basis of sparse representation and image similarity measurement to recognise chatter in the manufacturing process. Due to its non-stationary nature with regard to chatter signals, variational mode decomposition (VMD) is utilised and timefrequency entropy (TFE) based on VMD is introduced to measure the complexity. Then, the overcomplete dictionary is pre-trained using the characteristic matrix image extracted from the multi-domain features, thus facilitating the description of chatter from various perspectives. Subsequently, the visualised sparse coefficient matrix is acquired from the trained dictionary and regarded as the reference image, in which detailed information can be obtained from the visualisation of the image. Next, an image similarity measurement method is applied to assess the similarity between the tested sparse coefficient image and the reference image, thereby considering the local and global quality maps such that a comprehensive index for chatter detection can be obtained to fuse the various features. Finally, to validate the proposed methodology, experimental chatter tests are conducted under different machining conditions. The results demonstrate that the chatter can be discriminated at the early stage of chatter development, thus leaving more time to take suppression measures.