磨粒三维表面形貌的研究分析

磨粒三维表面形貌分析是针对磨粒二维分析技术的不足而提出的磨粒分析方法。分析了目前磨粒三维表面形貌获取技术特点和实用性,探讨了近年来磨粒表面形貌三维表征描述中采用的几种方法——灰度共生矩阵、分形维数法、方向测度法,并通过分析表明,方向测度法是一个比较好的磨粒表面形貌三维特征分析方法。     

柴油机出厂磨合过程中磨粒特征参数的统计分析

利用计算机图象技术,本文对铁谱技术进行了改进。用大小磨粒的真实面积和替代了光密度值。对磨粒形貌的数字特征采用了时间过程分析技术,实现了形貌描述的定量化。研究中发现拉缸前大小磨粒出现两极分化现象,其磨损不以切削为主。提出了新的磨合监测指标,采用散射度、欧拉数等特征参数对磨合过程进行了监测,在一定程度上提高了磨损监测的准确性。     

柴油机出厂摩合过程中磨粒特征参数的统计分析

利用计算机图象技术，本文对铁谱技术进行了改进。用大小磨粒的真实面积和替代了光密度值。对磨粒形貌的数字特征采用了时间过程分析技术，实现了形貌描述的定量化。研究中发现拉缸前大小磨粒出现两极分化现象，其磨损不以切削为主。提出了新的磨合监测指标，采用散射度，欧拉数等特征参数对磨合过程进行了监测，在一定程度上提高了磨损监测的准确性。     

砂轮表面轮廓的分形探索

提出了用分形几何的方法描述砂轮表面轮廓形貌。采用结构函数法计算砂轮表面周向轮廓的分维数ｄ和拓扑常量ｃ。综合应用分形参数ｄ和ｃ,能提供不依赖于尺度的砂轮轮廓形貌的描述方式,可有效地识别砂轮的磨粒大小、表面的磨损状态等。     

基于元胞自动机的冷轧工作辊表面形貌演变过程仿真与试验研究

为掌握冷轧过程中毛化工作辊三维表面形貌的变化规律,建立了基于元胞自动机理论的冷轧工作辊三维表面形貌动态演变过程仿真分析模型,模型中工作辊表面黏着磨损量和磨粒磨损量分别采用Archard磨损理论和脆性断裂机制进行计算,磨粒的粒径由Monte Carlo方法随机选取,位置通过"打靶法"随机生成。以工业生产现场实际生产数据和表面形貌测试结果为依据,完成了轧制过程工作辊表面形貌变化过程的磨损试验,并利用试验数据验证了仿真模型的有效性和准确性。利用仿真模型对冷轧过程中工作辊三维表面形貌的幅度特性、空间特性、综合特性和功能特性进行了研究,清晰地揭示了轧制过程中工作辊三维表面形貌的演变过程,为进一步研究和控制工作辊表面形貌的磨损,以在轧制过程中更好地进行带钢表面形貌的控制提供了理论基础。     

超精密磨削加工表面形貌建模与仿真方法宰

超精密磨削技术是实现微/纳米加工的主要手段.系统深入研究超精密磨削过程的机理,洞悉磨削加工表面生成的内涵,成为超精密磨削加工技术的重要研究内容之一.提出一种新型的超精密磨削加工表面生成方法.基于Jobnson变换和线性滤波技术,给出砂轮表面形貌数字生成方法.该砂轮表面数值生成方法克服了利用试验测量砂轮表面形貌所得数据而带来的误差,提高了磨削加工表面仿真分析的准确性.根据磨削运动学,建立磨粒运动轨迹方程、相互干涉条件和有效磨粒确定方法.据此,给出超精密磨削加工表面生成算法.通过数值计算生成不同统计学特征的砂轮形貌,并得到不同加工参数下磨削表面的表面形貌,仿真结果验证了所给算法的正确性和有效性.     

基于磨粒表面信息的磨损表面特征评估

建立了基于磨粒表面信息的磨损表面评估方法。首先选择合理的磨粒和磨损表面特征参数，通过识别磨粒类型，获得磨损过程中具有典型性和代表性的磨粒类型，然后选取这些具有代表性的磨粒类型，得到磨粒的表面特征向量，进而来研究磨损表面和磨粒表面的映射关系，实现基于磨粒表面信息的磨损表面特征评估。实例表明，根据磨粒表面特征评估磨损表面特征是可行的。     

不同磨损形式下的滑动轴承磨损表面及其磨粒特征

为了对其实现快速和准确的诊断,在试验机上模拟了滑动轴承的各个典型磨损过程,收集各阶段产生的磨粒信息,观察磨损表面形貌,研究了磨粒和磨损表面特征及其对应关系.结果表明通过检测润滑油中的磨粒信息可以间接获得滑动轴承的磨损表面特征,进而进行滑动轴承的状态诊断.     

基于激光共焦扫描显微镜方法的磨损表面三维数字化描述

表面形貌的精确描述在许多领域诸如材料、生物医学、摩擦学和机器状态监测等领域变得越来越重要。开发了一种基于激光共焦显微镜和图像处理技术的研究磨损表面及表面参数的新方法。首先用B io-rad Rad iance 2000激光共焦显微镜方法获得精确的三维表面形貌,然后用计算机辅助图像分析技术自动计算出表面特征参数。应用示例表明本文所研究的方法是可靠的,能对工程表面的表面粗糙度特征进行精确描述。     

基于彩色条纹投影术的三维形貌测量

物体表面三维形貌数据的获取在智能制造、航空航天、文物保护、医疗卫生、远程教育等领域有着广泛的应用。三维形貌数据的获取受限于系统硬件的性能,特别是现有数字投影系统的投影速度,无法快速测得物体面形的三维形貌。彩色成像和投影系统的出现,为并行多颜色通道三维成像系统提供了新的研究方向。详细综述了基于彩色条纹投影术的三维形貌数据测量研究的现状。具体包括彩色条纹投影术的基本原理、彩色条纹调制和解调相关技术、三维成像系统的标定、以及未来的研究方向。接着给出几个利用彩色条纹投影术获取物体表面三维形貌和彩色纹理的实例。为彩色条纹投影术测量物体表面三维形貌数据提供了详尽的综述,并指明了未来潜在的研究新方向。     

3D Surface Characterizations of Wear Particles Generated from Lubricated Regular Concave Cylinder Liners

Wear particle analysis can be developed as an effective method for assessment of the running conditions of concaved cylinder liners. The aim of this study was to numerically characterize the topographical features of wear particles generated from different surface textured cylinder liners and to investigate their changes with alternations in both rotational speeds and surface textures. To achieve this goal, cylinder liners with three different surface textures were prepared and tested in four different speeds. In addition to an untreated surface, concave cylinder liner surfaces with two different diameters (1 and 2 mm) and two different depths (200 and 300 μm) were investigated. Wear particles were extracted from the lubrication oil; three-dimensional images of the wear debris were acquired using laser confocal microscopy; and their topographical features were analyzed quantitatively. This study has revealed that running-in conditions and stable state can be detected using wear debris analysis techniques at a micrometer scale. It has also been discovered that concave B cylinder liner with a depth–diameter ratio of 0.1 always generated wear particles different to those from the other two cylinder liners on each rotational speed. It is believed that the quantitative surface topography characterization results obtained in this study provide a practical base for developing a new, non-intrusive tool for monitoring the operation conditions of cylinder liner–piston rings in diesel engines.     

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.     

Scale-invariant analysis of wear particle morphology—a preliminary study

The importance of wear particle characterization is continuously growing, as the need for prediction and monitoring of wear increases. Accurate analysis of wear particles can, however, be limited by problems associated with particle characterization, especially of the wear particles' surface morphology. Since the shape and surface topography of wear particles often exhibit a fractal nature, fractal (scale-invariant) methods are, therefore, used in their characterization. However, the methods used to date ignore the fact that all fractal objects can be described by a small set of mathematical rules; although finding those rules which describe a particular fractal image is a difficult problem. No general solution exists to date and this paper attempts to redress this problem. A new analysis method, ‘scale-invariant analysis’, which is based on a partitioned iterated function system (PIFS), is proposed for the characterization of wear particle morphology. PIFS is a collection of contractive affine transformations. Each affine transformation transforms one part of a wear particle image onto another part of the same image. PIFSs were constructed for both computer generated and SEM images of wear particles. Results obtained in this study clearly demonstrate that the morphology of wear particles can effectively be characterized using the PIFS method.     

Automatic wear‐particle classification using neural networks

Although the study of wear debris can yield much information on the wear processes operating in machinery, the method has not been widely applied in industry. The main reason is that the technique is currently time consuming and costly due to the lack of automatic wear particle analysis and identification techniques. In this paper, six common types of metallic wear particles have been investigated by studying three‐dimensional images obtained from laser scanning confocal microscopy. Using selected numerical parameters, which can characterise boundary morphology and surface topology of the wear particles, two neural network systems, i.e., a fuzzy Kohonen neural network and a multi‐layer perceptron with backpropagation learning rule, have been trained to classify the wear particles. The study has shown that neural networks have the potential for dealing with classification tasks and can perform wear‐particle classification satisfactorily. This revised version was published online in June 2006 with corrections to the Cover Date.     

Investigation of Wear Particles Generated in Human Knee Joints Using Atomic Force Microscopy

Wear particle analysis can be potentially developed as an effective method for assessment of osteoarthritis (OA). To achieve this goal, the surface morphological and mechanical properties of human wear particles extracted from the osteoarthritic synovial joints with different OA grades need to be studied. Atomic force microscopy (AFM) has been used for cartilage analysis owing to its high resolution and the capability of revealing both mechanical properties and surface topographical data in three-dimensions. Few studies have been conducted on human wear particles due to difficulties in obtaining the samples and technical challenges in preparing wear debris samples for AFM investigations in a hydrated environment. This work aimed to develop a suitable preparation technique to study the mechanical properties and surface morphology of human wear particles using AFM. Wear particles were separated from synovial fluid samples which were collected from OA patients and deposited on an aldehyde functional plasma polymer surface to immobilise wear particles. They were imaged for the first time using AFM. The nanoscaled surface topographies and nanomechanical properties of the particles were obtained in a hydrated mode. The methodology established in this study enables investigations of the surface morphology and mechanical properties of wear particles at the nanoscale for better understanding of OA and the possibility of developing a new diagnostic method based on the wear debris analysis technique.     

The influence of bone and bone cement debris on counterface roughness in sliding wear tests of ultra-high molecular weight polyethylene on stainless steel

Studies of explanted hip prostheses have shown high wear rates of ultra-high molecular weight polyethylene (UHMWPE) acetabular cups and roughening of the surface of the metallic femoral head. Bone and bone cement particles have also been found in the articulating surfaces of some joints. It has been proposed that bone or bone cement particles may cause scratching and deterioration in the surface finish of metallic femoral heads, thus producing increased wear rates and excessive amounts of wear debris. Sliding wear tests of UHMWPE pins on stainless steel have been performed with particles of different types of bone and bone cement added. Damage to the stainless steel counterface and the motion of particles through the interface have been studied. Particles of bone cement with zirconium and barium sulphate additives and particles of cortical bone scratched the stainless steel counterface. The cement particles with zirconium additive produced significantly greater surface damage. The number of particles entering the contact and embedding in the UHMWPE pin was dependent on particle size and geometry, surface roughness and contact stress. Particles are likely to cause surface roughening and increased wear rates in artificial joints.     

Surface uplift and wear implications of zirconia toughened ceramics

Zirconia ceramics have shown promising wear properties in a number of applications. However, in certain load configurations the wear performance is very poor. The reason for this is believed to be subsurface phase transformation. The surface uplift due to transformation of a circular inclusion in a half-plane and of a spherical inclusion in a half-space is analyzed. The general uniform transformation strain has significant effects on the surface topography and has ramifications for the rolling/sliding wear characteristics of the surface. The two-dimensional approximation overestimates the surface uplift by up to three times compared with the more realistic three-dimensional model. The results indicate that the occurrence of transformation strains, and in particular shear transformation strain, in the near surface area will affect the surface topography considerably.