Correlating and evaluating the functionality-related properties with surface texture parameters and specific characteristics of machined components

Quanren Zeng,Yi Qin,Wenlong Chang,Xichun Luo

doi:10.1016/j.ijmecsci.2018.09.044

Quanren Zeng, Yi Qin + Show 2 more

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https://doi.org/10.1016/j.ijmecsci.2018.09.044

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Abstract

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.

Highlights

The machining-induced surface texture strongly influences the mechanical properties and functional performance of the machined components or products, such as tribologically-related properties [1,2], loadbearing capability [3,4], fatigue properties [5], optical properties [6], abrasion and corrosion resistance, as well as the aesthetic appearance desired by customers
Surface texture characterization plays a vital part in describing surface micro geometrical features and in determining surface functionality-related properties
Selecting reasonable surface texture characteristic parameters and correlating them with corresponding functionalities for specific engineering applications are critical for effective characterization and assessment of the quality of machined surfaces

Summary

Introduction

The machining-induced surface texture strongly influences the mechanical properties and functional performance of the machined components or products, such as tribologically-related properties [1,2], loadbearing capability [3,4], fatigue properties [5], optical properties [6], abrasion and corrosion resistance, as well as the aesthetic appearance desired by customers. Surface texture could be defined as the repetitive or random deviations from a nominal surface which in reality form a three-dimensional (3D) topography. Sometimes, it is used interchangeably with surface topography in the manufacturing and machining fields [7]. In order to improve the capability of accurately characterizing and evaluating the functional performance of a machined surface, it would be helpful to have well-defined 2D or 3D surface texture parameters (e.g. Ra or Sa). Once surface micro geometrical features and functionality-related characteristics are quantitatively characterized by corresponding texture parameters, the functional performance of machined surfaces could be correlated and predicted. Corresponding machining conditions could be adjusted to guide the production of surfaces with desirable geometrical features and functional performances [12,13]

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