3-D characterization, optimization, and classification of textured surfaces

Surface texturing is a new technology aiming at reducing friction in operating machinery. Different surface features of varying shape and density are artificially introduced onto the existing surfaces. The methods currently available for the 3-D characterization and description of these surfaces are inadequate. Reliable surface description is necessary for the optimization of those surfaces and quality control during production. In this paper possible ways of solving the problems associated with 3-D description and optimization of textured surfaces are outlined and discussed. The problems associated with the development of automated classification systems for textured surfaces are also presented

Directional Multiscale Analysis and Optimization for Surface Textures

Surface texturing has a potential to become a cost effective and easy way to improve the tribological performance of lubricated interfacing surfaces. Effects of surface textures on the performance of machine elements as frictional pairs have been investigated over the past two decades. However, despite this research only a limited number of analytical solutions have been proposed as the majority of studies have been experimental and results obtained have not been optimal. This is because the commonly used surface characterization methods are not able to quantify surface textures over a range of scales at different directions and the optimization methods used work only for relatively simple textures and specific constraints imposed on pressure, film thickness, sliding velocity and lubricant rheology. Previous studies have addressed these issues, to some degree, by developing directional fractal signature methods and unified computational approach for texture optimization. In this article, recent advancements in the development of fractal methods and optimization of surface textures are presented

A Unified Computational Approach to the Optimization of Surface Textures: One Dimensional Hydrodynamic Bearings

In tribological applications, surface textures are used to increase load capacity and reduce friction losses in hydrodynamic lubricated contacts. However, there is no systematic, efficient and general approach allowing for the optimization of surface texture shapes to give an optimal performance. The work conducted is, in most cases, by “trial and error”, i.e. changes are introduced and their effects studied. This is time consuming and inefficient. In this paper, a unified computational approach to the optimization of texture shapes in bearings is proposed. The approach aims at finding the optimal texture shape that supports the maximum load and/or minimizes friction losses in one dimensional hydrodynamic bearings. The texture shape optimization problem is transformed into a nonlinearly constrained mathematical programming problem with general constraints that can be solved using optimal control software. Load-carrying capacity or friction force of a bearing becomes an objective functional that is maximized or minimized, subject to: (i) any Reynolds equations given by first order ordinary differential equations, (ii) pressure boundary conditions and (iii) functions/parameters that define the surface texture shape. This newly developed approach is demonstrated on examples of parallel textured hydrodynamic bearings. The effects of non-Newtonian fluids, cavitation and viscosity varying with temperature are considered

Optimization of textured surface in 2D parallel bearings governed by the Reynolds equation including cavitation and temperature

Surface texturing has been demonstrated to improve tribological performance of hydrodynamic bearings. Because the texturing increases complexity of a surface, numerical methods are required. However, no comparison study has so far been conducted to determine which methods are most accurate with the least number of grid/mesh points. Knowing this would allow for the analysis and optimisation of bearings with complex geometries. In this work, performance of four discretisation methods (finite difference, finite element, finite volume and spectral element (SE)) in approximating the pressure function and three integration methods (Newton-Cotes formulas and Gauss quadrature) in approximating the load capacity, coefficient of friction and film height was evaluated in a systematic manner. Three slider bearing geometries were used: inclined surface without texturing and two parallel surfaces textured with trapezoidal and quadratic dimples. For the evaluation, pressure function, load capacity, coefficient of friction were calculated analytically using the Reynolds equation. Differences between the analytical values and their approximations produced by the numerical methods were calculated versus the number of grid/mesh points. The numbers of points were determined for the differences below 5, 1 and 0.1 %. Results showed that the SE method and the Gauss quadrature were most accurate regardless of the bearing geometry and used up to 28 times fewer points as compared to other methods

Analysis of AFM images of self-structured surface textures by directional fractal signature method

A new method, called augmented blanket with rotating grid (ABRG), has been proposed in our recent work on characterizing roughness and directionality of self-structured surface textures. This is the first method that calculates fractal dimensions (FDs) at individual scales and directions for the entire surface image data and does not require the data to be Brownian fractal. However, before the ABRG method can be used in real applications, effects of atomic force microscope (AFM) imaging conditions on FDs need to be evaluated first. In this paper, computer-generated AFM images with three different resolutions, 48 combinations of tip radii and cone angles, and 15 noise levels were used in the tests. The images represent isotropic self-structured surface textures with small, medium and large motif sizes, and anisotropic surfaces exhibiting two dominating directions. For isotropic surfaces, the ABRG method is not significantly affected (i.e. FDs changes <5 %) by image resolution, tip size (for surfaces with large motifs) and noise (except the level above 8 %). For anisotropic surfaces, the method exhibits large changes in FDs (up to −34 %). The results obtained show that the ABRG method can be effective in analysing the AFM images of self-structured surface textures. However, some precautions should be taken with anisotropic and isotropic surfaces with small motifs

Applications of the variance orientation transform method to the multiscale characterization of surface roughness and anisotropy

There is no generally accepted method that could provide a full description of 3D surface topography (ST). Most of the currently used methods work well with isotropic surfaces at a single scale. Recently new method, called a variance orientation transform (VOT), has been developed for ST characterization. In this work, the usefulness of the VOT and its sensitivity to minute changes in ST have been tested. Images of real engineering surfaces, surfaces of adhesive wear particles and trabecular bone were used for the tests. The results obtained show that the VOT can be useful in both engineering and medical applications

Automated classification of wear particles based on their surface texture and shape features

In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry

Effects of information loss in texture details due to the PIFS encoding on load and friction in hydrodynamic bearings

Textured surfaces can significantly improve the performance of hydrodynamic bearings. However, there is no generally accepted method for their accurate and automated 3D characterization. A promising solution to this problem is partition iterated function system (PIFS) model, which encapsulates information about 3D topography of textured surfaces. However, some loss in surface details can occur. Therefore, before PIFS could be used, effects of this information loss on load and friction need to be investigated. In this study, this issue was addressed using a textured hydrodynamic pad bearing. The results obtained showed that PIFS models might become useful in characterization of textured surfaces

Evaluation of Discretisation and Integration Methods for the Analysis of Hydrodynamic Bearings With and Without Surface Texturing

Surface texturing has been demonstrated to improve tribological performance of hydrodynamic bearings. Because the texturing increases complexity of a surface, numerical methods are required. However, no comparison study has so far been conducted to determine which methods are most accurate with the least number of grid/mesh points. Knowing this would allow for the analysis and optimisation of bearings with complex geometries. In this work, performance of four discretisation methods (finite difference, finite element, finite volume and spectral element (SE)) in approximating the pressure function and three integration methods (Newton–Cotes formulas and Gauss quadrature) in approximating the load capacity, coefficient of friction and film height was evaluated in a systematic manner. Three slider bearing geometries were used: inclined surface without texturing and two parallel surfaces textured with trapezoidal and quadratic dimples. For the evaluation, pressure function, load capacity, coefficient of friction were calculated analytically using the Reynolds equation. Differences between the analytical values and their approximations produced by the numerical methods were calculated versus the number of grid/mesh points. The numbers of points were determined for the differences below 5, 1 and 0.1 %. Results showed that the SE method and the Gauss quadrature were most accurate regardless of the bearing geometry and used up to 28 times fewer points as compared to other methods