不同摩擦副在MM3000型试验机和TM-3型台架机上的对比试验研究

在MM3000型试验机和TM-3型台架机上,在0.40-0.50MP制动压力、80-200km/h制动速度下,对比了不同摩擦副干态工况条件的平均摩擦系数与磨损量。结果表明:克诺尔铜基摩擦材料配对钢制动盘的摩擦副在MM3000试验机上的摩擦系数高于在TM-3台架机上的摩擦系数。相反,克诺尔铜基摩擦材料和金属陶瓷摩擦材料配对碳陶制动盘的摩擦副在MM3000试验机上的摩擦系数都低于在TM-3台架机上的摩擦系数。另外,不论是MM3000试验机,还是TM-3台架机,克诺尔铜基摩擦材料配对碳陶制动盘时的磨损量最大,克诺尔铜基摩擦材料配对钢制动盘时的磨损量居中,金属陶瓷摩擦材料配对碳陶制动盘时的磨损量最小。     

石墨含量对铜基滑动轴承材料摩擦特性影响的数值模拟

采用离散元软件建立了不同石墨含量铜基滑动轴承材料的数值模型,模拟了铜基复合材料与45#钢摩擦副的滑动摩擦过程,探究了石墨含量对铜基轴承材料的抗压强度和摩擦学行为的影响.结果表明:随着石墨含量的增多,材料的抗压强度持续降低;铜基石墨复合材料与45#钢滑动摩擦过程中会在45#钢表面附着转移石墨颗粒,转移石墨颗粒的形成可以有...     

两种受电靴材料与不锈钢带电接触摩擦磨损特性的试验研究

在销-盘式摩擦磨损试验机上分别进行了不锈钢／铜基烧结合金材料和不锈钢／铜石墨烧结材料接触的载流摩擦磨损行为的试验研究。在试验中记录了摩擦因数和磨损量的变化,并对磨痕形貌进行了光学显微镜观察。结果显示,电流对2种摩擦副带电接触的摩擦磨损行为有重要的影响。2种材料的摩擦因数随电流的增大而呈现截然相反的变化趋势,但两者的磨损量却随电流的增加而增大。不锈钢／铜基烧结合金材料的磨损机制主要是粘着磨损及氧化磨损。不锈钢／铜石墨烧结材料磨损机制包括磨粒、粘着磨损和电弧烧蚀,其中电弧烧蚀对磨损量的影响随电流的增大而增加。     

钢背铜基合金双金属滑动轴承的制坯工艺

对目前主要生产钢背铜基合金双金属滑动轴承的制坯工艺进行了详细阐述,主要从材料选用、钢背与铜基合金的复合工艺及表面处理方面进行了总结和分析。鉴于采用离心熔铸工艺生产且未进行表面处理的钢背铜基合金双金属圆柱形衬套在铁路机车车辆中的成功应用,恳请广大专家和学者对通过合理选材、合理的复合工艺等措施制备的圆柱形衬套进行研究和认证,以避免进行电镀等表面处理,积极响应"绿色环保"号召。     

现代滑动轴承的复合材料

介绍了几种复合材料．重点介绍了内燃机滑动轴承中广泛使用的铝基合金材料与铜基合金材料：用于自润滑轴承中的DU材料和DX材料。文中分别论述了这几种复合材料的构成、性能、特点、用途、制造和使用方法及工艺流程。     

多油楔浮动滑动轴承磨损实验研究

实验研究了三种不同结构的多油楔浮动滑动轴承,它们可用于不同载荷下的旋转机械,兼有普通径向滑动轴承和多油楔滑动轴承的优点.实验研究表明,这种轴承既可以提高运行过程中轴承的稳定性,又能够减少轴承的磨损,提高轴承的寿命.磨损试验研究发现,轴承间隙是影响轴承磨损量的重要因素之一.当外间隙一定,内间隙值在规定范围内时,多油楔浮动滑动轴承磨损量最小;内间隙值过大或过小,均会加剧轴承的磨损.实验研究工作为进一步设计研究此类轴承提供了一定的实验依据.     

石墨含量对铜基石墨自润滑复合材料摩擦过程中石墨润滑膜的影响

为研究石墨含量对铜基石墨自润滑复合材料摩擦过程中形成石墨润滑膜的影响,采用粉末冶金法制备了不同石墨含量的铜基石墨自润滑复合材料,测试了复合材料的力学性能,用自制环-块摩擦试验机测试评估了材料的耐磨性能,用光学显微镜实时原位观察了摩擦表面组织形貌的变化,用扫描电镜对磨痕进行观察和分析,通过能谱仪成分扫描分析接触面石墨润滑膜的覆盖率。结果表明:随着复合材料中石墨含量的增加,材料的力学性能逐渐降低,石墨润滑膜的覆盖率先升高后降低,磨损量先减小后增大;当石墨体积分数为14%时,石墨润滑膜的覆盖率最高,磨损量最小,耐磨性能最好。     

烧结温度对铜基粉末冶金摩擦材料组织和性能的影响

在不同烧结温度下(850~1 000℃)制备了铜基粉末冶金摩擦材料,研究了烧结温度对其组织、密度、硬度、抗压强度和摩擦磨损性能的影响,由此得到了最佳的烧结温度。结果表明:在不同烧结温度下,材料中的各组元均分布均匀,鳞片状石墨垂直于压制方向,并呈层状分布,SiO2以黑色大颗粒状镶嵌于铜基体内;随着烧结温度升高,孔隙的数量减少,尺寸减小,材料的硬度逐渐增大,密度和抗拉压度均先增大后减小,磨损量先降低后升高,磨擦因数逐渐降低;最佳的烧结温度为950℃,此时材料的密度为5.84g·cm-3,抗压强度为115MPa,摩擦因数为0.46,磨损量为0.063g。     

表面织构形状对牙轮钻头轴承摩擦学性能影响的实验研究

牙轮钻头滑动轴承作为钻头破岩过程中传递载荷的关键部件,在低速重载的钻井过程中易发生黏着磨损失效。为改善牙轮钻头滑动轴承的耐磨性能,利用纳秒激光雕刻技术,在牙轮钻头滑动轴承轴颈表面加工了面积比为10%、深度为20μm的圆形、矩形、三角形及复合织构;基于赫兹相似理论,设计近似模拟钻头轴承工况的环-块配对副单元实验方案,并在UMT摩擦试验机上开展织构形状对钻头滑动轴承摩擦副摩擦因数、磨损量、温升变化和微观形貌影响规律的实验研究。结果表明:仿生织构的形状及布置方式对减摩和耐磨效果影响极大,其中圆形、矩形织构的减摩和耐磨性能最优,其次为三角形织构,而复合织构反而增大了摩擦因数及磨损量;单一织构对试件磨损量及温升的影响不大,而复合织构在增加摩擦的同时温度有明显升高,不利于井下高温环境下延长牙轮钻头寿命。     

高承载滑动轴承启停试验评价

首先介绍一种倒置式三油叶滑动轴承,应用于航空发动机减速齿轮箱的中介齿轮上。该滑动轴承的实际工况是芯轴静止,而外圈旋转。为了更加真实地模拟实际工况,搭建了高承载滑动轴承试验台,模拟启停工况的载荷和转速条件,验证滑动轴承的启停性能。为了验证不同表面材料对滑动轴承性能的影响,分别在不同试件表面使用类金刚石涂层和铜基合金材料,观察50次启停试验后试件表面磨损情况,对滑动轴承启停试验性能进行初步的判断。     

Analysis of Sliding Wear Characteristics of BFS Filled Composites Using an Experimental Design Approach Integrated with ANN

Short fiber-reinforced polymer composites are used in numerous tribological applications. In the present work, an attempt was made to improve the wear resistance of short glass fiber (SGF)-reinforced epoxy composites by incorporation of microsized blast furnace slag (BFS) particles. The effect of various operational variables and material parameters on the sliding wear behavior of these composites was studied systematically. The design of experiments approach using Taguchi's orthogonal arrays was used. This systematic experimentation led to identification of significant variables that predominantly influence the wear rate. The Taguchi approach enabled us to determine optimal parameter settings that led to minimization of the wear rate. The morphology of worn surfaces was then examined by scanning electron microscopy and possible wear mechanisms are discussed. Further, in this article, the potential of using artificial neural networks (ANNs) for the prediction of sliding wear properties of polymer composites is explored using an experimental data set generated from a series of pin-on-disc sliding wear tests on epoxy matrix composites. The ANN prediction profiles for the characteristic tribological properties exhibited very good agreement with the measured results, demonstrating that a well-trained network was created. The simulated results explaining the effect of significant process variables on the wear rate indicated that the trained neural network possessed enough generalization capability to predict wear rate from any input data that are different from the original training data set.     

Artificial neural networks for predicting sliding friction and wear properties of polyphenylene sulfide composites

In this paper the potential of using artificial neural networks (ANNs) for the prediction of sliding friction and wear properties of polymer composites was explored using a newly measured dataset of 124 independent pin-on-disk sliding wear tests of polyphenylene sulfide (PPS) matrix composites. The ANN prediction profiles for the characteristic tribological properties exhibited very good agreement with the measured results demonstrating that a well trained network had been created. The data from an independent validation test series indicated that the trained neural network possessed enough generalization capability to predict input data that were different from the original training dataset.     

Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks

In this work, artificial neural networks (ANNs) technique was used in the prediction of abrasive wear rate of Cu–Al₂O₃ nanocomposite materials. The abrasive wear rates obtained from series of wear tests were used in the formation of the data sets of the ANN. The inputs to the network are load, sliding speed, and alumina volume fraction. Correlation coefficients between the experimental data and outputs from the ANN confirmed the feasibility of the ANNs for effectively model and predict the abrasive wear rate. The comparison between the ANNs and the multi-variable regression analysis results showed that using ANNs technique is more effective than multi-variable regression analysis for the prediction of abrasive wear rate of Cu–Al₂O₃ nanocomposite materials. Optimization of the training process of the ANN using genetic algorithm (GA) is performed and the results are compared with the ANN trained without GA. Sensitivity analysis is also done to find the relative influence of factors on the wear rate. It is observed that load and alumina volume fraction effectively influence the wear rate.     

小样本条件下BP网络对滑动轴承材料磨损系数的预测研究

利用具有高度非线性映射能力的BP神经网络解决滑动轴承磨损预测的计算问题。在论述BP算法及改进模型原理的基础上，利用它们对滑动轴承材料边界磨损系数的预测效果进行了比较，进而在小样本情况下通过Baysian正规化BP网络对滑动轴承材料边界磨损系数进行了预测，分析了影响预测效果的原因，在合理剔除奇异点后给出了对滑动轴承材料磨损预测的最佳Baysian正规化BP网络结构，为合理进行磨损试验提供了理论依据。对预测结果进行残差分析证明，该方法效果较为理想。     

Neural network prediction of brake friction materials wear

Wear of brake friction materials depends on many factors such as temperature, applied load, sliding velocity, properties of mating materials, and durability of the transfer layer. Prediction of friction materials wear versus their formulation and manufacturing conditions in synergy with brakes operating conditions can be considered as a crucial issue for further friction materials development. In this paper, the artificial neural network abilities have been used for predicting wear of the friction materials versus influence of all relevant factors. The neural model of friction materials wear has been developed taking into account: (i) complete formulation of the friction material (18 ingredients), (ii) the most important manufacturing conditions of the friction material (5 parameters), (iii) applied load and sliding velocity of the friction material both represented by work done by brake application, and (iv) brake interface temperature.     

模具钢铣削中刀具磨损的试验研究

以面铣刀刀片磨损为研究对象,结合类神经网络系统建构高速数控铣削加工的预测模型。以加工参数为模型输入条件,刀腹磨耗为输出条件。采用多因素试验方法,选择切削速度、进给速度、切削深度三个试验参数,利用直交表式的试验计划法设计试验点。依照试验点铣削工件后再测量刀具加工后的刀腹磨耗量,进而求得倒传递网络所需的36组训练范例与11组验证数据。刀腹磨耗预测模式是利用类神经网络中的倒传递网络原理,以田口法求得倒传递网络参数的最优值。试验结果显示,刀腹磨耗随着切削速度、进给速度、切削深度增加而上升。铣削模具钢后,刀具磨耗预测值的平均误差为4.72%,最大误差为11.43%,最小误差为0.31%。整体而言,类神经网络对于铣削加工可进行有效预测。     

A step towards the in-process monitoring for electrochemical microdrilling

The bandsawing as a multi-point cutting operation is the preferred method for cutting off raw materials in industry. Although cutting off with bandsaw is very old process, research efforts are very limited compared to the other cutting process. Appropriate online tool condition monitoring system is essential for sophisticated and automated machine tools to achieve better tool management. Tool wear monitoring models using artificial neural network are developed to predict the tool wear during cutting off the raw materials (American Iron and Steel Institute 1020, 1040 and 4140) by bandsaw. Based on a continuous data acquisition of cutting force signals, it is possible to estimate or to classify certain wear parameters by means of neural networks thanks to reasonably quick data-processing capability. The multi-layered feed forward artificial neural network (ANN) system of a 6?×?9?×?1 structure based on cutting forces was trained using error back-propagation training algorithm to estimate tool wear in bandsawing. The data used for the training and checking of the network were derived from the experiments according to the principles of Taguchi design of experiments planned as L ₂₇. The factors considered as input in the experiment were the feed rate, the cutting speed, the engagement length and material hardness. 3D surface plots are generated using ANN model to study the interaction effects of cutting conditions on sawblade. The analysis shows that cutting length, hardness and cutting speed have significant effect on tooth wear, respectively, while feed rate has less effect. In this study, the details of experimentation and ANN application to predict tooth wear have been presented. The system shows that there is close match between the flank wear estimated and measured directly.     

Abrasive wear behavior of sintered steels prepared with MoS2 addition

Sintered steels containing molybdenum di sulphide powders were developed for application as bearings, gears, connecting rods, etc. Abrasive wear characteristics of these materials are investigated as the machine elements also need to have a good abrasive wear resistance. Materials with base composition Fe–C–Cu and Fe–C–Cu–Ni containing varying levels of MoS₂ were developed from elemental powders. Compressive strength, hardness and density are influenced by the addition of MoS₂. Abrasive wear tests were conducted by sliding against the SiC abrasive sheet at room temperature. MoS₂ added material exhibited a high coefficient of friction and good wear resistance compared to the base composition. The artificial neural network model developed predicts the wear volumes, which are in agreement with the experimentally measured data.     

Friction and wear behaviour of stainless steel rubbing against copper-impregnated metallized carbon

A series of experimental tests were carried out using stainless steel rubbing against copper-impregnated metallized carbon under electrical current on a pin-on-disc test rig. The test parameters include the sliding speed of 60-100 km/h, normal force of 40-80 N and electrical current of 0-50 A. During testing, the friction coefficient and wear volume were recorded. The topography of worn surfaces was also observed with SEM. The cross sectional profiles of worn surfaces of stainless steel were measured with Ambios profiler. The result displays that electrical current, normal load and sliding speed have a distinct effect on the friction and wear behaviour of stainless steel rubbing against copper-impregnated metallized carbon. Without electric current, the friction coefficient is largest but the wear volume of copper-impregnated metallized carbon is lowest. With increasing electric current, the friction coefficient decreases while the wear volume of copper-impregnated metallized carbon increases. Through the whole test, it is found that the wear loss of stainless steel was light. The wear of copper-impregnated metallized carbon becomes severe when electrical current or sliding speed is high. When the electrical current or sliding speed is high, arc ablation is a dominant wear mechanism of copper-impregnated metallized carbon.     

Electrical and tribological properties of composite material with copper matrix reinforced by superelastic hard carbon

Electrical and tribological properties of a copper-based composite material reinforced by superelastic hard carbon particles are studied. Composite material specimens have been produced using the hightemperature pressing of mixtures of copper and fullerene powders. Electrical and tribological reciprocal tests carried out using the plane-on-plane arrangement have shown that the coefficient of friction of the composite material–Ni pair is lower than that of the reference L63 brass–Ni pair at similar values of contact electrical resistance. The abrasive wear resistance of the composite material is 40 times higher than that of brass. The developed copper-based composite materials reinforced by superelastic carbon particles hold promise for use in sliding electrical contacts.