The development of the modified Hurst orientation transform for the characterization of surface topography of wear particles

A modified Hurst orientation transform (HOT) method for characterization of wear particle surfaces is proposed and described in this paper. The method involves the calculation of self-affine Hurst coefficients in all directions and displays the calculated coefficient values in a form of rose plot. The calculation of individual Hurst coefficients, H, is based on the rescale range (r/s) analysis (r(d)/s∼ d ^H ). The rose plot is then used to obtain three texture surface parameters, i.e.: texture aspect ratio, texture minor axis and texture direction. The effectiveness of this modified HOT and resulting surface texture parameters was evaluated. The method was first applied to computer-generated images of isotropic and anisotropic particle fractal surfaces and then to field emission scanning electron microscope images of wear particles found in synovial joints. The ability of the surface parameters to reveal surface isotropy or anisotropy, measure roughness and determine the dominant direction of surface texture was assessed. The effects of measurement conditions such as noise, gain variations and focusing on the surface parameters were also investigated. The results demonstrate that the HOT and surface texture parameters developed can successfully be used in the characterization of wear particle surface topography. This revised version was published online in August 2006 with corrections to the Cover Date.     

基于形态变换的三维表面形貌方向特性研究

针对面向功能的三维表面形貌表征存在的问题,基于灰值形态学的基本运算构造了边缘检测形态算法,有效地提取了三维形貌的形态学边缘,实现了三维形貌的纹理方向特征的提取,获得了表征纹理方向特性的表征参数.该方法不仅适用于直线纹理,而且也适用于曲线纹理,为分析表征三维形貌的各向异性特征提供了一个新的方法.把它引入到粗糙表面的润滑分析中,能够更确切地揭示出各向异性表面的纹理方向特性对润滑性能的影响规律.     

Directional Fractal Signature Analysis of Self-Structured Surface Textures

Currently available directional fractal signature (DFS) methods are not suited for self-structured surface textures since they base on the assumption of Brownian fractal or they do not use the entire image data in calculation. To address these difficulties, two new DFS methods were developed in this study, i.e., an augmented blanket with rotating grid (ABRG) method and a blanket with shearing image (BSI) method. The performance of these methods in measuring surface roughness and directionality, the capacity for quantifying multi-patterned textures, and the ability to detect differences between textures of self-structured surfaces were evaluated. The methods were compared against a blanket with rotating grid (BRG) method. Computer-generated images of self-structured surface textures with different roughness, directions and patterns, and atomic force microscope images of real self-structured surfaces were used. The computer texture images were generated using a specially developed motif-based texture generator. Results obtained showed that the ABRG method is more accurate and reliable than the BRG and BSI methods.     

Fractal characterization by frequency analysis: III. Effect of noise

The effect of noise in the fractal characterization by frequency analysis of surface images obtained by scanning tunnelling microscopy (STM), atomic force microscopy (AFM) or profilometry has been studied. The origin of noise and its relationship to the signal is discussed. A procedure to simulate noisy images is presented. From the study it is concluded that the method usually used to characterize noise in STM is not valid and it is shown that fractal characterization of surfaces when noise is present by traditional frequency analysis methods is not possible. A new method to perform both the noise characterization and the fractal characterization of surfaces when noise is present is proposed.     

Hybrid Fractal-Wavelet Method for Characterization of Tribological Surfaces—A Preliminary Study

Many types of tribological surfaces have been observed to exhibit a multiscale nature, i.e. topographical features over different scales ranging from nanometers to hundreds of micrometers. Consequently, recent developments in the characterization of tribological surfaces have concentrated on wavelet transformation and fractal-based methods. The wavelet methods are used due to their ability to decompose surface data into different scale components and to characterize surface data at each individual scale. On the other hand, fractal methods are used due to their ability to characterize surface data in a scale-invariant manner. It has been shown that these two capabilities of (i) decomposition of surface data and (ii) scale-invariant analysis are essential in the characterization of 3-D surface topography. Thus, it is apparent that a characterization method which exhibits both of these capabilities would yield the best results. Developing such a method is not an easy task. This problem in the characterization of tribological surfaces is addressed in our paper. A new method, called a hybrid fractal-wavelet method, is proposed. This method is a combination of a partition iterated function system (PIFS) and a symmetric wavelet transform (SWT). The PIFS is used to scale-invariantly characterize the surface topography over a wide range of different scales, while the SWT is used to characterize the surface topography at each individual scale. This multiscale characterization ability of the newly developed method is demonstrated on a tribological surface example.     

Classification of Tribological Surfaces Without Surface Parameters

Quantitative measures are obtained from images of tribological surfaces. Based on these data, decisions are made regarding manufacturing and maintenance processes, machine-condition monitoring and failure analysis of engineering components. These decisions are often guided by an automated pattern recognition system. Components of this system are: surface topography data acquisition, surface characterization, surface classification and performance evaluation. The characterization and classification of tribological surfaces are major challenges that make the development of a reliable pattern-recognition system difficult. The reasons are that: (i) tribological surfaces often exhibit a non-stationary and multiscale nature, while most surface characterization methods currently used work well with surface data exhibiting a stationary random process, (ii) changes in topography that might occur between the interacting surfaces usually need to be known in advance, and (iii) the selection of surface parameters that separate different classes of surfaces is usually time-consuming and cumbersome. Because of these difficulties, characterization and classification methods which do not use surface parameters have been developed. In the classification methods, a measure of dissimilarity (e.g., Euclidean distance) calculated between a surface to be classified and already classified surfaces was used, instead of surface parameters. The unclassified surface was assigned to the class (of classified surfaces) with the lowest value of dissimilarity measure. The suitability of different classifiers; such as k-nearest neighbour classifier, linear-discriminant-analysis based classifiers and different dissimilarity measures; for the classification of tribological surface topographies (without the need for surface parameters) is investigated in this paper. Recent developments in this area, i.e., a fractal measure and a hybrid fractal-wavelet measure, are also discussed. The most suitable method for the classification of tribological surfaces has been selected.     

A method for visualization of surface texture anisotropy in different scales of observation

Anisotropy of functional surfaces can in many practical cases significantly influence the surface function. Tribological contacts in sheet forming and engine applications are good examples. This article introduces and exemplifies a method for visualization of anisotropy. In a single graph, surface texture properties related to the anisotropy as a function of scale are plotted. The anisotropy graph can be used to explain anisotropy properties of a studied surface such as texture direction and texture strength at different scales of observation. Examples of milled steel surfaces and a textured steel sheet surface are presented to support the proposed methodology. Different aspects of the studied surfaces could clearly be seen at different scales. Future steps to improve filtering techniques and an introduction of length-scale analysis are discussed.     

A novel robust Gaussian filtering method for the characterization of surface generation in ultra-precision machining

A lot of research work has been focused on the study of the surface generation mechanisms in order to predict the surface topography and provide the optimal machined parameters based on the experiential understanding of relationship of machined conditions and surface features. Although the formation of novel geometrical product specification (GPS) and verification framework system promotes the relevant research work to new characterization methods and draft of international standards, relative little research work was conducted on the application of surface characterization techniques to ultra-precision machining which is very important to evaluate the surface quality. In this paper, a novel robust Gaussian filtering method (RGF) is proposed and used to characterize the surface topography of ultra-precision machined surfaces. Cubic B-spline and M-estimation are used to make the method reliable and robust. Based on the property comparisons of classical weighting functions, a novel auto-developed robust weighting function (ADRF) is defined to improve the robustness of RGF. To verify the characterization feasibility of the proposed method, computer simulation is used and then the real ultra-precision machined surfaces are analyzed. The experimental results indicate that the RGF method cannot only separate the surface components effectively on the whole measured area and but also eliminates the influence of freak outliers.     

基于多重分形去趋势波动分析算法的表面磨损形式特征研究

为了识别表面的磨损形式以研究零件表面的摩擦学特性,以不同载荷下的磨料磨损和黏着磨损2种磨损形式的表面为研究对象,应用LSTM-1型磨损表面形貌测量仪和稳健高斯滤波方法对磨损表面形貌进行数据采集和滤波处理后,使用多重分形去趋势波动分析算法(MF-DFA)计算磨损表面高频信息的广义赫斯特指数,并通过分析该指数与表面形貌磨损纹理特征之间的关系,使用主成分分析法提取用于识别2种磨损形式的特征,然后采用K-means聚类、支持向量机(SVM)和BP神经网络方法,分别对所提取的特征参数进行分类,比较不同分类器识别结果的准确率。研究结果表明：广义赫斯特指数可用于区分磨损表面犁沟类和凹坑类纹理特征的指标,作为机器学习特征对表面磨损形式识别分类。     

Fractal-wavelet based classification of tribological surfaces

Classification of the topography of freshly machined, worn and damaged surfaces (e.g. damaged by adhesion, scoring, abrasion, pitting) is still a problem in machine failure analysis. Tribological surfaces often exhibit both a multiscale nature (i.e. different length scales of surface features) and a non-stationary nature (i.e. features which are superimposed on each other and located at different positions on a surface). The most widely used approaches to surface classification are based on the Fourier transform or statistical functions and parameters. Often these approaches are inadequate and provide incorrect classification of the tribological surfaces. The main reason is that these techniques fail to simultaneously capture the multiscale nature and the non-stationary nature of the surface data. A new method, called a hybrid fractal-wavelet method, has recently been developed for the characterization of tribological surfaces in a multiscale and non-stationary manner. In contrast to other methods, this method combines both the wavelets’ inherent ability to characterize surfaces at each individual scale and the fractals’ inherent ability to characterize surfaces in a scale-invariant manner. The application of this method to the classification of artificially generated fractal and tribological surfaces (e.g. worn surfaces) is presented in this paper. The newly developed method has been further modified to better suit tribological surface data, including a new measure of differences between initial and decoded images. The accuracy of this method in the classification of surfaces was assessed.     

Two-dimensional fast Fourier transform and power spectrum for wear particle analysis

Statistical parameters, such as R_a and R_q, have been widely used to investigate the roughness of wear particle surfaces in the literature. It has been reported that wear particle analysis based only on numerical characterization is often insufficient to distinguish certain types of wear debris. In this study, two-dimensional fast Fourier transform, power spectrum and angular spectrum analyses are applied to describe wear particle surface textures in three dimensions. Laminar, fatigue chunk and severe sliding wear particles, which have previously proven difficult to identify by statistical characterization, have been studied. The results show that spectral analysis effectively identifies the surface texture pattern (e.g. isotropy or anisotropy) and can be applied to classify these three types of wear particles.     

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.     

Analysis of the active profile of grinding wheel surfaces

Tests have been carried out on simulated surfaces (produced by a signal generator in conjunction with an analogue computer) and on real surfaces to determine the effect of limited depth of stylus penetration in the characterization and specification of surface texture. This work is directed mainly to the working surface of grinding wheels.     

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.     

Surface roughness classification using image processing

Surface roughness is an important factor in determining the satisfactory functioning of the machined components. Conventionally the surface roughness measurement is done with a stylus instrument. Since this measurement process is intrusive and is of contact type, it is not suitable for online measurements. There is a growing need for a reliable, online and non-contact method for surface measurements. Over the last few years, advances in image processing techniques have provided a basis for developing image-based surface roughness measuring techniques. Based upon the vision system, novel methods used for human identification in biometrics are used in the present work for characterization of machined surfaces. The Euclidean and Hamming distances of the surface images are used for surface recognition. Using a CCD camera and polychromatic light source, low-incident-angle images of machined surfaces with different surface roughness values were captured. A signal vector was generated from image pixel intensity and was processed using MATLAB software. A database of reference images with known surface roughness values was established. The Euclidean and Hamming distances between any new test surface and the reference images in the database were used to predict the surface roughness of the test surface.