Multi-parameterised surface texture characterisation for ultra-precision machined surfaces

Abstract

In surface metrology, the multi-parameterised characterisation of surface texture measurement is beneficial not only for surface quality evaluation but also for manufacturing process inspection. To bridge this gap for ultra-precision machined surfaces, a white light interferometer was firstly employed for measuring surface texture generated by orthogonal ultra-precision machining experiments. Next, surface texture was filtered by the zero-order Gaussian regression filter to the limited scale bandwidth. Then, twenty-one surface texture parameters were calculated based on seventy-five S-L surfaces according to the ISO 25178-2. Finally, the outlier effect of surface measurement was investigated by the 95%–99% rule and the Spearman correlation coefficient matrix was proposed to determine their statistical correlation. The results revealed that most of the height parameters (Sp, Sv, Sz, Ssk, and Sku), several function and related parameters (Vmp, Vvv, Spk, and Svk), and the spatial parameter (Str) and hybrid parameter (Sdr) presented a strong sensitivity to the outlier effect. The height parameters (Sa, Sq, Sp, Sv, and Sz), the function and related parameters (Vmp, Vmc, Vvv, Vvc, Spk, Svk, and Sk), and the spatial parameters (Sdq and Sdr) showed a strong correlation to each other, while the miscellaneous parameter Std had a weak correlation to the other parameters. This study provides a systematic multi-parameterised surface texture characterisation for ultra-precision machined surfaces to promote the advancement of nanotechnology and nanometrology.