Fractal dimension (FD), as an important parameter for the surface morphology of mechanical machining, can be used to analyze the friction characteristics of contacting surfaces. The accurate and rapid measurement of the FD is of significant importance. To this end, this paper presents a novel approach based on a convolutional neural network (3D-CNN) for the recognition of three-dimensional FDs on machined surfaces. We construct a dataset of anisotropic rough surfaces with different FDs using the Weierstrass-Mandelbrot (WM) fractal function and use a one-factor experimental design to analyze the influence of network parameters (network depth, filter size, filter quantity) on the accuracy of FD identification, finding the optimal combination of neural network parameters. By comparing our 3D-CNN method with three other methods (differential box-counting (DBC), triangular prism surface area (TPSA), and fractal Brownian motion (FBM)), we validated the effectiveness of our proposed method. The experimental results show that the average absolute percentage error of FD calculated by the 3D-CNN method can be controlled within 2%, and the method exhibits small errors throughout the full dynamic range of FDs. We also apply the proposed method to calculate the FD of the vertical milling surface and compare the results with the TPSA and FBM methods. The results show that the FD obtained by our method is close to the other two methods, and can be used to calculate the FD of three-dimensional surface profiles, thereby providing a new approach for the dynamic modeling and parameter identification of contact surfaces.
Nanoparticles have as characteristics super sliding, extreme pressure, self-healing, etc., which can improve the friction reduction and anti-wear performance of sliding components, when used as lubricating oil additives. Nano-copper particles have a good synergistic effect with other antifriction agents, anti-wear agents, antioxidants and grease additives because of their low shear strength and grain boundary slip effect, showing a better anti-friction and anti-wear effect. However, nanoparticles are prone to conglomerate, and this causes a bottleneck in the application of dispersant for nano-copper in a lubricating oil system. The regulation of nanosized effect and surface properties has great engineering significance in compensating for the precision in manufacturing accuracy. This paper comprehensively reviews the tribological research progress of nano-copper as a lubricant additive, which provides a reference to the application of nano-copper particles as lubricating oil additives on engineering tribology.
为探究内燃机中缸套-活塞环摩擦副在复杂工况下的摩擦因数变化规律,根据实际工况下的温度和压力,使用SRV高温摩擦试验机模拟内燃机系统缸套-活塞环之间的往复式摩擦运动,获得不同温度和压力下的摩擦因数,并测试不同温度下润滑油的黏度.以摩擦接触面温度、载荷和润滑油黏度为输入,摩擦因数为输出,构建BP神经网络预测模型.结果表明,BP神经网络具有较高的预测精度,测试集误差值稳定在1％以内,满足工程精度要求.
利用ABAQUS软件的Map Solution(MS)功能对2K型斯特封进行有限元仿真分析,该功能通过重启动分析和网格重构技术,解决了由网格畸变引起计算中断的问题,得到超高压工况下的收敛解.根据流体压力14、28、35、42、56 MPa下斯特封内、外行程的应力应变云图和密封面接触压力分布曲线,研究流体压力对密封件主应力、主应变和接触压力分布的影响规律;同时对比未使用MS功能的高压工况(35 MPa)下仿真结果,验证了 MS功能分析网格大变形问题的可靠性.结果表明:在超高压工况下,斯特封的最大主应力集中分布于空气侧PTFE阶梯形环与活塞沟槽、缸筒的接触区域,最大主应变出现在PTFE阶梯形密封环的卸荷角处;随着油液压力的增加,接触压力分布曲线趋势发生明显变化,空气侧的接触压力梯度减小;在相同流体压力下,外行程时密封面接触宽度大于内行程的接触宽度.
为改善摩擦组元SiO2颗粒与铜基体间界面结合性能,通过表面金属包覆处理的方式制备铜包覆SiO2.分别以铜包覆SiO2和普通SiO2颗粒作为摩擦组元之一,利用SPS烧结技术制备2种铜基粉末冶金摩擦材料.分析2种摩擦材料的微观组织、力学及物理性能,在MM1000-Ⅱ型惯性制动试验台上测试2种摩擦材料的制动摩擦磨损性能,并对2种摩擦材料的摩擦表面及其三维形貌特征、磨屑特征和摩擦表面物相进行微观分析,研究SiO2表面金属包覆处理对制动条件下铜基粉末冶金材料摩擦磨损性能的影响.结果表明:经铜包覆处理的SiO2与Cu基体间的界面结合性能明显改善,提高了材料的硬度、致密度及导热系数,特别是导热系数得到了显著提升;随着制动初速度的升高,2种摩擦材料的平均摩擦因数均呈现先上升后下降的趋势;在相同的制动条件下,与含普通SiO2材料相比,除了制动压力为0.8 MPa、制动初速度为100 km/h外,在其他各制动条件下含铜包覆SiO2材料具有较高的平均摩擦因数和较低的磨损率,且其摩擦表面更为平整;提高制动初速度均能够促进2种摩擦材料表面形成以氧化膜为主的摩擦膜,且含铜包覆SiO2材料的摩擦膜更均匀,因而保护作用更好,即磨损率低.
