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Machining-process-induced surface texture plays an indispensable role in determining surface integrity and final functional performance of the machined components. Although there are already many existing standard parameters for quantitatively characterizing the machined surface, accurately describing and effectively correlating the 3D surface texture parameters and specific characteristics with the relevant functional performances in practice, are still not well solved. The inadequacy of using 2D single-valued surface profile parameters and the non-ubiquity of using 3D areal surface texture parameters in industry are the main obstacles. The research reported in this paper addressed this issue by proposing a practical means which makes use of both 3D surface texture parameters and statistical functions for surface geometrical characterization and functional correlation and evaluation. To better investigate the influence of machining-induced surface texture and its characterization on the functionality-related performance of machined surfaces, Ni-based superalloy GH4169, a typical difficult-to-machine material widely used in aircraft industry, was selected for the machining experiment. Two kinds of mechanically-processed surfaces, one ground and the other turned, both having an identical value of 3D arithmetic mean deviation (Sa), were quantitatively characterized and analyzed using 2D and 3D surface texture parameters. Considering that the measured 3D surface texture is of random nature, the corresponding functionality-related performances were also investigated with statistical functions, e.g. power spectral density (PSD) and auto-covariance (ACV). Correlation between the 3D surface texture parameters or statistical functions with the corresponding functional performance, e.g. contact, running-in wear and lubricant retention, were then established. This study emphasized on the effectiveness and veracity of the 3D surface texture parameters and statistical functions in characterizing and evaluating machined-surface performance along with the traditional 2D parameters. It is especially suitable for machining materials whose functionality-related properties are machining-process-sensitive and surface-texture-dependent.
Zeng et al. (Wed,) studied this question.