发动机铁谱磨粒分析与磨粒识别研究

从发动机摩擦副及其摩擦磨损特点着手，提出了用于发动机磨损状态监测与诊断的主要10类磨粒，依据其识别特征提出了用于磨粒识别的14个磨粒形状、表面纹理与颜色特征；最后应用BP网络分层识别策略进行了磨粒识别。     

基于模糊神经网络的磨粒识别专家系统(续)

二、磨粒的分类及识别特征 铁谱分析技术将油液中的固体颗粒分为正常磨损、疲劳磨损、球状、层状、严重滑动、切削、腐蚀磨损、红色氧化物、暗金属氧化物、有色金属、非金属和晶体、污染物等12类。 领域专家根据磨粒的形态（总体形状、边缘细节和表面纹理）、颜色、粒度和厚度信息来识别铁谱磨粒,它们大都是模糊信息,一般用语义形式描述,因此需要采用模糊神经网络识别,识别结果为磨粒类型,系非模糊集合。磨粒特征见表1。     

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。     

铁谱磨粒形态分形特征参数提取方法研究

阐述了分形与分形维数的定义，给出了铁谱磨粒分形维数和无标度区间的计算原理。编制了磨粒图像处理及磨粒分维计算的计算程序，并以片状磨粒为实例，提取了该磨粒的轮廓分维特征、磨粒表面纹理特征、表面纹理方向特征与磨粒表面间隙度特征四个特征值，为有效识别磨粒提供科学依据。     

电镀CBN砂轮磨削表面粗糙度研究

为改善电镀CBN砂轮的磨削性能,利用叶序理论对磨粒排布进行设计.探讨叶序参数对磨粒间距及排布的影响,并建立相关表面粗糙度数学模型,模拟仿真叶序参数对于表面粗糙度的影响趋势.仿真结果表明,采用合理的叶序排布参数能获得较其它排布方式较小的表面粗糙度值,这为砂轮表面磨粒的有序化排布设计提供了理论依据.     

三体磨损过程理论研究

建立了三体磨损过程中摩擦副表面粗糙度的预测模型。算例表明 ,定载荷作用下 ,(1 )经受三体磨粒磨损之摩擦表面的粗糙度随着磨程的进行逐渐趋于某一平衡值 ,之后不随磨程而改变 ;(2 )平衡粗糙度的大小与磨粒粒度分布参数有关 ,对应于粒度的某均方根值 ,摩擦面的平衡粗糙度最大     

基于均匀设计法的磨粒流加工试验

根据磨粒流加工原理,以坦克发动机喷油嘴为研究对象,利用均匀试验设计方法,通过均匀分散而选出较优的磨粒流加工试验参数（磨粒粒径、磨料浓度、加工时间及研磨液的酸碱性）,再通过优化变量得到目标函数,进而获得最优磨粒流加工工艺参数。试验结果表明：采用均匀设计方案较正交设计方案可节省70%的时间;经过均匀试验后,喷油嘴工件内孔表面粗糙度减小,达到了磨粒流加工试验的目的,而回归优化后的工艺参数可以进一步减小喷嘴小孔的内表面粗糙度,所得到表面粗糙度与材料物性及加工时间的数学模型可为磨粒流的生产实际提供技术支持。     

固结磨料研磨K9玻璃表面粗糙度模型

表面粗糙度模型是研磨过程设计和工艺参数选择的重要依据,K9玻璃是应用最广泛的光学材料之一。建立研磨K9玻璃表面粗糙度模型有利于提高加工效率、节约生产成本。简化固结磨料研磨过程,基于研磨垫表面微结构,计算研磨过程中参与研磨的有效磨粒数和单颗磨粒切入工件深度,利用研磨过程中受力平衡,建立固结磨料研磨K9玻璃表面粗糙度模型。采用不同磨粒粒径和不同磨料浓度的固结磨料研磨垫以及不同压力研磨K9玻璃验证表面粗糙度模型。结果表明：固结磨料研磨K9玻璃的表面粗糙度与磨粒粒径、研磨压力1/3次方成正比,与研磨垫浓度2/9次方成反比。表面粗糙度理论值与试验值随研磨压力、磨粒粒径和研磨垫浓度的变化趋势吻合。利用该模型能够成功预测固结磨料研磨K9玻璃表面粗糙度,指导研磨过程设计及加工过程中研磨垫和工艺参数的选择,可靠性高。     

磁性磨粒光整加工钛合金板的实验研究

利用磁性磨粒光整加工的方法研磨抛光钛合金板,并测量了抛光后的工件表面粗糙度。由于钛合金是非导磁性工件,可以通过增加永磁铁来改善加工区域的磁场特性,并用仿真软件模拟了增加永久磁铁和不加永久磁铁时加工钛合金的磁感应强度的大小。最后,采用正交试验方法来评估在磁性磨粒光整加工过程中旋转速度、加工间隙、磨粒类型及加工时间对工件表面粗糙度的影响。为了获得较好的加工表面,用方差分析和SN比寻求较好的试验条件。结果表明,磁性磨粒光整加工能够有效的抛光钛合金表面,获得理想的表面粗糙度。     

钛合金薄壁曲面液态金属-磨粒流加工仿真与试验研究

针对钛合金薄壁曲面工件磨粒流抛光后表面粗糙度分布不均匀的问题,提出一种基于液态金属的磨粒流加工方法。基于SST k-ω模型、OKA冲蚀模型,流体流动颗粒追踪模型,采用COMSOL有限元软件对不同电场布置下的液态金属-磨粒流动力学特性开展深入研究。仿真结果表明,通过电场的合理布置可以控制液态金属颗粒在流场中运动;合理的电场布置可以有效提高工件表面加工均匀性,并通过仿真得出了一组冲蚀较好的试验参数。基于仿真结果开展了液态金属-磨粒流加工试验,试验结果表明：液态金属-磨粒流加工方法可有效提高工件表面加工的均匀性。在加工14 h后,不加电场的磨粒流加工表面不同区域的粗糙度分布不均,工件凹陷处粗糙度明显大于凸起处,各区域表面粗糙度极差达到66.1 nm。使用液态金属-磨粒流加工后的工件表面各区域粗糙度的均匀性明显提高,各区域表面粗糙度极差减小为20.3 nm,为液态金属-磨粒流加工的开展及其调控提供了理论和试验依据。     

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.     

On the wear debris of polyetheretherketone: fractal dimensions in relation to wear mechanisms

It has been recognized that wear debris contains extensive information about wear and friction of materials. Investigation of wear debris is important for tribological research. In order to find out an effective way that is able to diagnose and predict the wear state of polymers, the authors investigated the relationship between the wear debris morphology and the wear behaviour of the bulk material. Polyetheretherketone (PEEK) was employed as the model material. Its sliding wear and friction properties were measured by means of a pin-on-disc apparatus. At a constant sliding velocity of 1 m s⁻¹, the specific wear rate was independent of load under lower loading conditions (1–4 MPa) but increased with a rise in load under higher loading conditions (4–8 MPa). The coefficient of friction was insensitive to the variation of contact pressure. The possible mechanisms involved were analysed on the basis of the wear debris morphology as well as the wear performance. Fractal geometry, which describes non-Euclidean objects, was applied to the quantitative analysis of the boundary texture of the wear debris due to the fact that the qualitative assessment of the wear debris morphology was not effective enough to reflect the geometrical variation of the fragmental shapes. The experimental results demonstrated that the wear debris were fractals, and could be characterized with the fractal dimensions which were determined by the slit island method. In addition, it was found that the fractal dimension of the wear debris was closely related to the wear behaviour of PEEK, and can be regarded as a measure of wear rate.     

基于粗糙集和神经网络的润滑油中磨损磨粒的识别

为了更有效地对润滑油中的磨损磨粒进行识别,探讨了基于粗糙集和神经网络的磨粒识别。它首先利用粗糙集理论对磨粒特征参数进行约简,这样能够大大减少了神经网络的输入维数。然后介绍了一种径向基神经网络,并利用它对磨粒进行分类。对20个磨粒进行识别,磨粒分类分对14个,分错6个,识别率达到70.0%。     

干摩擦磨屑的三维形貌特征研究综述

磨屑是反映摩擦学系统时变特征的系统输出变量,对磨屑进行分析是确定摩擦副系统特性的主要内容之一.介绍磨屑的动态获取、识别研究现状和表面三维形貌表征参数(高度偏差Sa、均方根Sq、表面斜度Ssk、表面峭度Sku等)与摩擦学性能参数(摩擦因数和磨损量)之间的映射关系的研究现状,在此基础上提出干摩擦下磨屑的三维形貌特征研究方向.     

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.     

Surface roughness evolutions in sliding wear process

Wear debris analysis is a technique for machine condition monitoring and fault diagnosis. One key issue that affects the application of wear debris analysis for machine condition monitoring is whether the morphology of the wear particles accurately depicts their original states and the surface morphology of the components from which the particles separate. This study aimed to investigate the evolution of the surface morphology of wear debris in relation to change in the surface morphology of wear components in sliding wear process. Sliding wear tests were conducted using a ball-on-disc tester under proper lubrication and improper lubrication conditions. The study of the particle size distribution and the surfaces of both the wear debris and the tested samples in relation to the wear condition and the wear rates of the wear components were carried out in this study. The evolutions of the surface topographies of both the wear debris and the wear components as wear progressed were investigated. This study has provided insight to the progress of material degradation through the study of wear debris. The results of this research have clearly demonstrated that: (a) there is a good correlation of the surface morphology of wear debris and that of the wear components, and (b) the surface morphology of wear debris contains valuable information for machine condition monitoring.     

Intelligent identification of wear mechanism via on-line ferrograph images

Condition based maintenance（CBM） issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.     

基于Mask R-CNN的铁谱磨粒智能分割与识别

针对铁谱图像因背景复杂、尺寸分布广、颗粒重叠等导致难以精确分割与识别的问题,以相似度高的疲劳剥块、严重滑动磨粒、层状磨粒共3种异常磨粒作为研究对象,提出基于深度神经网络模型Mask R-CNN的对多目标铁谱磨粒进行智能分割与识别的方法,并对特征提取层分别选用深度不同的残差网络ResNet50和ResNet101进行对比试验。实验结果表明,基于迁移学习方法的Mask R-CNN+ResNet101模型能够在复杂背景下对多目标、多类型、多尺寸的相似磨粒进行有效分割与识别,测试集的平均精度高达76.2%,模型具有较好的泛化能力。