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The International Journal of Advanced Manufacturing Technology, January 2014, Volume 70, Issue 1-4.
Kunpeng Zhu, Birgit Vogel-Heuser.
1. Institute of Advanced Manufacturing Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Huihong Building, Changwu middle road 801#, Changzhou, 213164, China and
2. Department of Mechanical Engineering, Institute of Automation and Information Systems, Technical University of Munich, Boltzmannstr. 15, 85748 Garching, Munich, Germany.
Abstract
This paper presents a new approach for cutting force denoising in micro-milling condition monitoring. In micro-milling, the comparatively small cutting force signal is contaminated by heavy noise, and as a result, it is necessary to denoise the force signal before further processing it. The traditional denoising methods, based on Gaussian noise assumption, are not effective in this situation because the noise is found to contain high non-Gaussian component. Based on the force and noise’s sparse structures in the time–frequency domain, this approach employs a sparse decomposition approach and solves denoising as a convex optimization problem. It is shown that the proposed approach can separate the heavy non-Gaussian noise and recover useful information for condition monitoring.
