Surface roughness profile separation using singular spectrum analysis

Surface roughness is a critical parameter used to describe the microscopic geometric deviations of a part, and serves as an essential indicator for assessing the quality of surface processing in various mechanical components. This study evaluates Singular Spectrum Analysis (SSA) for surface roughness profile separation, comparing its effectiveness with the ISO standard Gaussian filter. Using NIST roughness measurement data, this study investigates how SSA's window length and grouping method affect roughness parameters. The findings indicate that with an appropriately chosen window length, the SSA technique can effectively separate roughness signals and yield roughness parameter values comparable to those obtained using the Gaussian filter, such as the arithmetical mean deviation of the assessed profile (Ra), the root mean square deviation of the assessed profile (Rq), and the kurtosis of the assessed profile (Rku). These findings establish SSA as a viable alternative for surface roughness profile separation, with broad applications in surface metrology.