聚氨酯弹性体摩擦衬垫材料的摩擦特性研究

本文研究了聚氨酯弹性体摩擦衬垫材料的摩擦特性,发现聚氨酯弹性体材料的动摩擦系数是和滑动速度有关的,在0．0l-0．4mm／s的速度范围内,速度提高摩擦系数上升,在更高的速度下摩擦系数还会下降。聚氨酯弹性体材料的静摩擦系数是比较难以确定的,本文建议把滑动量较小而滑动速度又很低的这一时期的摩擦称为“初期摩擦”,它是界于静摩擦与动摩擦之间的一种特殊的摩擦状态。文中还分析了粘着摩擦机理,提出了接点化学粘接机理。并根据这种机理对实验结果进行了分析和讨论。     

机械密封摩擦特性影响因素分析

机械密封的摩擦状态决定了密封性能的好坏.本文综合分析了摩擦系数及摩擦特性准数等因素对摩擦特性的影响,为机械密封的设计和摩擦状态分析提供了参考依据.     

人工神经网络在铁谱技术磨粒识别中的应用

铁谱技术在机械设备状态监测中得到了广泛的应用，磨粒识别是铁谱分析的一个关键环节，本文提出了一种基于神经网络的磨粒识别方法，利用前馈型神经网络模型对七种典型磨损磨粒进行了实例分析识别，取得了令人满意的结果。     

石墨—金属摩擦副的静摩擦系数

对三种石墨材料与金属Ｃｕ１７Ｎｉ２配对摩擦副的静摩擦系数进行了测试。,测定了不同温度,载荷,润滑方式下的静摩擦系数。试验发现：温度对静摩擦系数的影响较小;油润滑条件下的静摩擦系数最小。     

人工智能在二维零件图特征识别中的应用

二维零件图特征识别是一个集经验、知识和抽象思维于一体的活动 ,正适合用人工智能来解决这类问题 ,本文给出了一种基于规则的专家系统进行二维零件图特征识别的总体框架 ,阐述了基于规则的特征识别专家系统进行特征识别的算法和步骤 ,讨论了该系统的实现策略。最后 ,利用基于神经网络的知识求精方法对知识求精专家系统的初始知识库进行求精 ,提高了专家系统的运行性能。     

BP神经网络的摩擦力补偿算法研究

针对伺服系统中非线性摩擦力的影响,提出了一种基于BP神经网络的摩擦力补偿.通过神经网络的学习,可以改变PID的参数,从而达到对摩擦力的补偿.并通过MATLAB对系统进行了仿真,证明了该方法的有效性.     

钢铜摩擦副摩擦磨损特性的试验研究

本文试验分析了ＺＱＡ１９－４和ＺＱＺｎ６－６－３两种铜合金材料在不同的表面粗糙度下对摩擦系数和出口区油温的影响，以及改变载荷和相对滑动速度时，摩擦系数的变化状况。结果表明；铜合金成分不同时具有不同的硬度。     

塑料滑道的摩擦系数方程

根据摩擦的粘着变形理论和粘弹性力学,导出了塑料滑道摩擦系数方程。对ＮＬ１５０塑料,油尼龙及ＧＳ－２Ｂ滑道实验研究表明,该方程具有较高的准确性。     

机械无级变速器油摩擦系数的测定

用2种试验机对比试验了国外典型机械无级变速器油和ub系列机械无级变速器油的摩擦系数。     

ZL-50A型装载机故障监测机理的研究

利用人工神经网络，对ZL-50A型装载机工作时的振动信号进行采集、处理并提取特征参数，以实现对装载机系统工作状态故障的监测和诊断。     

液晶润滑添加剂5CB的减摩性能研究

液晶是一类介于固体和液体之间的物态,液晶态分子排列一维或二维长程有序,在垂直于表面的方向,液晶表现固体特性,阻止表面间的直接接触,而在没动剪切方向,液晶表现为低粘度液体,可有效地降低摩擦系数。本文对向列型液晶４－正戊基－４’－氰基联苯在正十六烷和季戊四醇酯中的减摩作用及机理进行了考察。     

智能故障诊断系统模式研究

分析了故障诊断步骤及体系结构，讨论了故障诊断力祛集。对基于各种算法与人工神经网络集成构造的智能故障诊断系统进行了分析比较，提出了系统中存在的问题。提出了基于多算法与人工神经网络高度集成智能化的故障诊断系统模型框架。     

变薄拉深摩擦因子的测定及在上限分析中的应用

介绍了变薄拉深摩擦因子的两种试验测试方法,并给出了相应的计算公式。实验证明,以此方法确定的摩擦因子应用于变薄拉深的上限分析,理论解有满足工程要求的足够精度。     

研究拉深用润滑剂的摩擦特性

拉深用润滑剂在使用中的摩擦特性与拉深工艺之间的关系目前研究的还比较少,本文针对这一内容进行研究.实验采用拉深工艺模拟试验机作为实验手段,研究油基和水基润滑剂在给定不同压边力值条件下的润滑性能、以及坯料直径变化对润滑性能的影响.实验结果表明:(1)在拉深过程中,水基润滑剂存在一个最佳压边力范围,在这一范围内水基润滑剂具有比油基润滑剂摩擦系数小、拉深性能好的特点.当超过这一范围时,摩擦系数明显增大,拉深性能变差.(2)油基润滑剂基本不受压边力的影响.(3)拉深坯料直径的变化对油基和水基润滑剂的摩擦性能都基本无关系.(4)建立了水基润滑剂的摩擦系数与拉深压边力之间关系的实验方程.     

Addressing Practical Challenges of Low Friction Coefficient Measurements

A friction coefficient is defined as the ratio of the friction force to the applied normal force. Despite the disarming simplicity of its calculation, there are practical challenges that make low values of friction coefficient difficult to accurately quantify. The connections of imperfect parts in friction measurement devices (called tribometers) produce small misalignments between the transducer and counterface axes. According to Schmitz et al. (J Tribol Trans ASME 127:673–678, 2005), “…the measurement of friction coefficient is extremely sensitive to misalignments” and “for materials with friction coefficients below 0.05 the alignment becomes hopelessly difficult if the goal is to have uncertainties below 1%.” This method article reviews the challenges of low friction measurements and presents a robust reversal technique that eliminates misalignment bias. Experiments with controlled misalignment angles demonstrate the bias sensitivity and validate its elimination using a low uncertainty tribometer in conjunction with the described reversal technique.     

粗糙表面弹流润滑的摩擦系数的分析计算

本文在弹性流体动力润滑情况下对摩擦系数的计算方法进行了分析,考虑了流体的非牛顿流变特性和微凸体接触压力的影响。以平均型条纹粗糙表面为计算模型,在各种工况下对摩擦系数进行了计算对比。     

Analysis of Measurement Inaccuracy in Superlubricity Tests

Although investigations on superlubricity are increasing, the corresponding measurement errors, which have a great effect on the measured friction coefficient, have not been widely discussed. The present article analyzes the origin of friction measurement errors in a rotating ball-on-flat apparatus and shows that these errors play an important role in the relationship between the measured and real friction coefficient. Based on the analysis, two methods were proposed to eliminate the main measurement error. One is obtaining the same friction coefficients in two reverse sliding directions by adjusting the level of the substrate (rotating plane) and the other is averaging the measured friction coefficients in two reverse sliding directions. The designed experiments proved the effectiveness of the two methods in eliminating the measurement error. Such methods are also effective for investigations on superlubricity.     

离合器摩擦片温升分析

本文利用能量原理和热力学方法,给出了摩擦离合器圆盘摩擦片温升的理论公式,从一定程度上满足了计算机仿真的需要。     

Applications of Artificial Intelligence to Grinding Operations via Neural Networks

In recent years, artificial intelligence played an important role in machine tool automation. Artificial neural networks, as one of the artificial intelligence algorithms, has superiority in representing the relation between the inputs and outputs of the multi-variable system. Hence, it can be applied to sophisticated operations such as grinding operation. The aim of this research is to use artificial neural networks as the brain of grinding machine controller. The target of this controller was to achieve the desired workpiece surface roughness under grinding wheel surface topography variations. The core of the system consists of two multi-layers feed forward artificial neural networks based on back error propagation learning algorithm. The first one was used for process design to achieve the desired surface roughness. It extracts suitable process variables such as grinding wheel speed and feed rate. The second one monitors the cutting operation using sensors' readings. It extracts the different controlling decisions; these are accept the process, redesign the process or start dressing operation under automatic control. According to these decisions, a PC master control program generates the appropriate control codes and sends them to the machine controllers to take the required actions.     

Applications of Artificial Intelligence to Grinding Operations via Neural Networks

In recent years, artificial intelligence played an important role in machine tool automation. Artificial neural networks, as one of the artificial intelligence algorithms, has superiority in representing the relation between the inputs and outputs of the multi‐variable system. Hence, it can be applied to sophisticated operations such as grinding operation. The aim of this research is to use artificial neural networks as the brain of grinding machine controller. The target of this controller was to achieve the desired workpiece surface roughness under grinding wheel surface topography variations. The core of the system consists of two multi‐layers feed forward artificial neural networks based on back error propagation learning algorithm. The first one was used for process design to achieve the desired surface roughness. It extracts suitable process variables such as grinding wheel speed and feed rate. The second one monitors the cutting operation using sensors' readings. It extracts the different controlling decisions; these are accept the process, redesign the process or start dressing operation under automatic control. According to these decisions, a PC master control program generates the appropriate control codes and sends them to the machine controllers to take the required actions.