油润条件下磨粒分形行为与摩擦状态相关性的研究

以45#钢-铜配副在浸油状态下进行磨损试验,采用自行构建的磨粒分析系统考察不同磨损阶段产生的磨粒形态,测量多种磨粒表征参数,并就各表征参数与摩擦力的关联性进行了对比分析.分析结果表明:磨粒群体中轮廓分形维数的分布呈正态分布;磨粒分形维数相对于其它表征参数而言,和摩擦力相关性更大.     

基于链码的金属磨粒分形参数计算

介绍了基于链码的磨粒分形参数及其计算方法，采用编写的磨粒识别程序，对不同磨损阶段测得的磨粒分布数进行分析，可以得到磨粒二重分形参数和拐点数值，发现磨粒分布分形维数的变化与磨损状态改变相对应。该方法用于磨粒分形特征与磨损状态相关性规律的研究识别，简便快捷。     

片状磨粒、块状磨粒和层状磨粒轮廓分形维数研究

选取计盒维数法作为计算磨粒轮廓分形维数方法,采用磨粒相片、实验法和现场收集3种方法收集了片状磨粒、块状磨粒和层状磨粒各600个样本,并在500和800倍放大倍数下进行了轮廓分形维数计算.结果表明,这3种磨粒轮廓分形维数分布为正态分布,在放大800倍数时具有最好的统计分形;研究结果可用于磨粒自动识别和磨损状态实时检测.     

磨粒分形识别及发展

相互作用表面间必然会产生磨粒,磨粒含有大量的有关材料摩擦磨损的信息。磨粒形态分析是确定磨损方式和磨损程度的有益手段。磨粒并非是欧氏几何体,而是展示出了分形性质。基于分形几何理论,可获得尺度不变的分形参数,用这类参数可对磨粒形态进行客观、全面的表征。本文综合评述了磨粒分形表征以及磨粒形态与磨损方式、磨损程度间的定量耦合关系等的研究进展,对将来磨粒分形研究的趋势和注意的问题进行了探讨。     

基于分形理论的磨粒磨损类型

本文在M-R接触分形模型的基础上，根据塑变磨损理论导出了基于分形参数的磨粒磨损模型，建立了磨损率与分形维数之间的关系，综合反映了材料的磨损规律和表面特性。根据该模型可知，当分形维数在某一范围时，磨损率达到最小值。当分形维数一定时，磨损率随尺度系数、磨损概率常数的增大而增大。随材料性能参数的增大而减小；当其余各影响参数保持一定时，磨损率随接触面积的增大而增大。     

基于分形理论的磨粒磨损模型

本文在M-B接触分形模型的基础上,根据塑变磨损理论导出了基于分形参数的磨粒磨损模型,建立了磨损率与分形维数之间的关系,综合反映了材料的磨损规律和表面特性。根据该模型可知,当分形维数在某一范围时,磨损率随分形维数的减小而迅速增大;而在另一范围时,磨损率随分形维数的增大而增大;当分形维数等于1.5时,磨损率达到最小值。当分形维数一定时,磨损率随尺度系数、磨损概率常数的增大而增大,随材料性能参数的增大而减小;当其余各影响参数保持一定值时,磨损率随接触面积的增大而增大。     

PEEK摩擦信号分形维数与表面形貌相关性试验研究

为研究聚醚醚酮树脂/球墨铸铁摩擦副磨合初期摩擦信号分形维数与聚醚醚酮初始表面形貌分形维数的相关性,在UMT-3MT摩擦磨损试验机上对聚醚醚酮树脂/球墨铸铁摩擦副进行了摩擦磨损试验,运用结构函数测度法对初始表面形貌、摩擦力信号、摩擦因数信号进行了分形表征,计算得到了不同载荷下的分形维数。研究结果表明,聚醚醚酮初始表面和摩擦信号均具有显著的分形特征;在相同速度、相同初始表面下,摩擦信号的分形维数随着载荷的增大而增大;在相同速度、不同载荷下,磨合初期摩擦信号的分形维数均与初始表面分形维数负相关。     

磨合表面形貌变化的分形表征

用结构函数法计算磨损表面轮廓的分形维数和尺度系数。研究表明 :分形维数或尺度系数不能实现表面的唯一性表征。因此 ,把分形维数和尺度系数相结合 ,提出一个新的分形参数———特征粗糙度 ,给出了其定义和计算表达式 ,并在推进式试验机上进行摩擦磨损试验 ,对试件表面某一位置在不同磨合阶段的形貌进行精确复位测量 ,用特征粗糙度表征形貌的变化。表征结果表明 :特征粗糙度对反映磨合表面形貌的变化不但表现出很好的灵敏性 ,而且比分形维数更具有规律性。     

RIFLE表面的变化对弹头磨损影响的分形研究

为探讨枪管RIFLE线表面的变化对弹头磨损的影响，应用分形理论对弹头磨损表面进行了分析研究，并应用结构函数法计算出RIFLE不同程度磨损下的弹头磨损面的分形维数。结果表明：随着RIFLE表面磨损程度的增大，弹头磨损面的分形维数D也将随之发生变化。RIFLE磨损程度不同，弹头磨损面的分形维数不同。因此，分形维数可以作为弹头磨损表面量化检验的参数，通过分析弹头上膛线磨损痕迹面的变化特征可以推断枪支的磨损程度。     

苜蓿草粉对金属材料磨损表面的分形维数计算

基于分形理论和扫描电镜分析,采用差分计数盒法(DBC)计算了苜蓿草粉对金属材料磨损表面的三维分形维数。结果表明:基于DBC法和磨损表面SEM照片计算所得的分形维数,可以表征苜蓿草粉对金属材料磨损表面的形貌特征;相同加工条件下的金属加工表面具有相近的分形维数,但不能通过分形维数区分材料;苜蓿草粉磨损后的磨损表面分形维数与磨损体积损失有着密切的关系,体积磨损量越大,分形维数也越大。     

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.     

Shape and texture features in the automated classification of adhesive and abrasive wear particles

In this study the automated classification system, developed previously by the authors, was used to classify wear particles. Two kinds of wear particles, adhesive and abrasive, were classified. The wear particles were generated using a pin-on-disk tribometer. Various operating conditions of load, sliding time and abrasive grit size were applied to simulate adhesive and abrasive wear of different severity. SEM images of wear particles were acquired, forming a database for further analysis. The particle images were divided into eight groups or classes, each class representing different wear test conditions. All eight particle classes were first examined visually. Next, area, perimeter and elongation parameters were determined for each class and the parameters were statistically analysed. The automated classification system, based on particle surface texture, was then applied to all particle classes. The results of the automated particle classification were compared to those based on either the visual assessment of particle morphology or numerical parameter values. It was shown 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.     

Abrasive wear resistance of textured steel rings

The fundamental aim of the present research is to study the effect of dimple shape and area density on abrasive wear in lubricated sliding. The other aims are to recommend a method of obtaining the local linear wear of a textured ring on the basis of profilometric measurement and to analyse the changes in the surface topography of this ring with selection of parameters that could monitor the “zero-wear” process.The experiments were conducted on a block-on ring tester. The stationary block made from cast iron of 50 HRC hardness was ground. The rotated ground ring was made from 42CrMO4 steel of 32 HRC hardness. The rings were modified by a burnishing technique in order to obtain surfaces with oil pockets. Oil pockets of spherical and of drop shape were tested. The pit-area ratios were in the range: 7.5–20%. The tested assembly was lubricated by oil L-AN 46. Because of the great hardness of the co-acting parts the wear resistance test was carried out under artificially increased dustiness conditions. The dust consists mainly of SiO₂ and Al₂O₃ particles. Measurement of local microscopic ring wear was made using a three-dimensional scanning instrument. The tendencies of ring surface topography changes during wear were analysed. Various methods of obtaining the local wear value during a low wear process were proposed and compared. We found that a spherical shape of dimples was superior to a drop shape with regard to wear resistance of steel rings.     

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。     

铁谱磨粒多重分形特征研究

为有效描述铁谱磨粒特征,提出用多重分形谱参数表达磨粒形态特征的新方法。选择盒计数法计算磨粒图像的多重分形谱,研究磨粒多重分形谱的有效性,分析磨粒多重分形谱参数的不变性和鲁棒性;确定磨粒图像预处理方法,并对4类典型磨粒的多重分形谱参数进行统计分析。结果表明:将多重分形谱参数应用于磨粒识别,总识别率为82.5%。磨粒具有明显的多重分形特性,可用多重分形谱参数来描述磨粒的形态特征;多重分形谱参数具有平移不变性,但对灰度变化和噪声干扰的鲁棒性较差,在提取多重分形谱参数时,需要对磨粒图像做严格的预处理。     

机械密封端面黏着磨损分形模型

在机械密封端面接触分形模型基础上,依据Archard磨损理论,通过引入分形磨损系数及求解塑性和弹塑性变形微凸体的体积,建立了机械密封端面黏着磨损分形模型。得到了机械密封软质环端面磨损率与端面轮廓分形参数、真实接触面积、材料性能参数以及工作参数之间的关系式。对B104a-70型机械密封软质环端面的磨损率进行了计算和分析。结果表明,端面磨损率随着端面比压、转速及端面特征尺度系数的增大而增大;随着端面分形维数的增大先迅速减小后逐渐增大,即存在一个使磨损率最小的最优分形维数。     

磨粒在磨粒探测器中的沉降运动研究

研究了在线铁谱仪磨粒探测器中的磨粒沉降运动规律,建立了磨粒在流道-磁极倾角方案下的运动模型,采用有限元法仿真了直流电磁装置的磁场分布情况,并研究了初始高度y0和磨粒直径D等参数对磨粒沉积位置的影响。结果表明,在油样流动方向和磁极之间存在倾角γ的条件下,磨粒在磨粒探测器中按照尺寸大小有序沉积,且沉积位置和油样入口端之间的距离随着初始高度y0增加而增加。     

纳米和微米SiO2颗粒对PPESK复合材料摩擦学性能的影响

用热压成型法制备了纳米、微米SiO2填充聚醚砜酮(PPESK)复合材料，考察了复合材料的硬度和抗弯强度，并研究了干摩擦条件下纳米、微米SiO2颗粒对复合材料摩擦磨损性能的影响，用扫描电镜观察分析了复合材料磨损表面形貌及磨损机理。结果表明：干摩擦条件下，纳米SiO2填充PPESK主要是轻微的磨粒磨损；而微米SiO2填充PPESK主要是严重的磨粒磨损。     

基于形态谱的磨粒图像特征参数提取方法

为提高磨粒识别的精度,提出一种基于形态谱磨粒图像特征参数提取新方法,给出磨粒图像的归一化形态谱的计算方法,并将磨粒的形态谱作为其特征向量,采用径向基函数神经网络对磨粒进行自动识别。结果表明:利用磨粒的形态谱实现了对球形磨粒、切削磨粒、严重滑动磨粒、疲劳剥块4种典型磨粒的分类识别,磨粒的形态谱可以作为磨粒的有效特征参数。