Detecting systematic and random component of surface roughness signal

Abstract

The solution of the problem of separating the initial one-dimensional signal into two components – systematic and random – has an extremely wide practical application not only in the theory of information and communication (and related disci-plines), but also in mechanical engineering disciplines. For example, mechanical engineering technology being a science discipline includes the teaching about the surface quality of the machined parts and researching the surface roughness after machining these parts by cutting and grinding. The paper shows that the theoretical and actual values of roughness parameters differ significantly (up to 20 times) due to the influence of a random component that is present in the roughness signal together with a systematic com-ponent. It is necessary to identify the share of each of these components in the specified surface quality parameters in accordance with the method proposed in the paper. This method allows detecting the systematic and random components of the signal and is based on the analysis of the signal autocorrelation function. Practical examples of this analysis are considered in detail for milled surface profilogram obtained experimentally. Both milling, which creates irregularities on the machined surface, and measurement of these irregularities are performed on modern CNC equipment: machining center 500V/5 and computer measuring station T8000, respectively. The developed and shown by examples signal separation technique is also applicable in other fields of science, technol-ogy and manufacturing. For example, when determining the signal to noise ratio in the theory of information and communication, in the field of telecommunications and telemetry, radio engineering, etc.