基于摩擦磨损的KDP晶体固结磨料抛光垫优化

本文通过固结磨料球与KDP晶体对磨的单因素试验探究固结磨料球中反应物种类、磨粒浓度、反应物浓度、基体硬度对摩擦系数、磨痕截面积和磨痕处粗糙度的影响,试验结果表明:KHCO₃固结磨料球对磨后磨痕对称性好,磨痕处的粗糙度值低;磨痕截面积随磨粒和反应物浓度的增加而增大,随基体硬度的增大而降低;磨痕处粗糙度随磨粒和反应物浓度的增加先降低后上升,随基体硬度的增大先上升后降低;摩擦系数受磨粒和反应物浓度影响不明显,随基体硬度的增大而降低。选择KHCO₃作为反应物,Ⅰ基体,磨粒浓度为基体质量的100%,反应物浓度为15%制备固结磨料球与KDP晶体对磨后的磨痕轮廓对称度好且磨痕处粗糙度值低,以该组分制备固结磨料垫干式抛光KDP晶体,可实现晶体表面粗糙度Sa值为18.50 nm,材料去除率为130 nm/min的高效精密加工。     

基于神经网络的间隙机构运动副磨损预测

应用BP神经网络建立了磨损率与接触应力、滑动速度和材料硬度之间的非线性关系模型,并对该网络模型进行了验证和测试,结果表明,训练良好的神经网络模型能够准确反映样本所蕴含的内在磨损规律,且具有较好的预测效果。基于非线性弹簧阻尼模型和修正的Coulomb摩擦力模型对含间隙曲柄滑块机构进行数值仿真分析,获得间隙机构运动副的接触应力和相对滑动速度,利用训练好的神经网络磨损模型对轴套的磨损进行迭代磨损预测分析,发现随着曲柄转数的增加,轴套表面一些特定位置处的磨损越来越严重,最终导致轴套表面出现非均匀磨损现象,其原因是间隙机构运转过程在一些特定位置处产生了较大接触应力和碰撞力。     

石墨烯环氧涂层的耐磨耐蚀性能研究

将石墨烯水分散液添加到双组份水性环氧树脂中制备石墨烯固体润滑涂层,采用交流阻抗谱和动电位极化曲线研究了涂层在模拟海水(3.5%Na Cl溶液)中的电化学腐蚀行为和失效过程;采用UMT-3摩擦磨损试验机评价了三种石墨烯基环氧涂层在干摩擦和海水环境条件下的滑动摩擦磨损行为,并分析了其磨痕形貌和磨损机理.结果表明:石墨烯可以明显提高水性环氧的涂层电阻和电荷转移电阻,并降低环氧涂层在干燥条件与海水环境的摩擦系数和磨损率;石墨烯环氧涂层的摩擦系数和磨损率在海水环境中均比干摩擦低.     

Inhibition of cold-welding and adhesive wear occurring on surface of the 6061 aluminum alloy by graphene oxide/polyethylene glycol composite water-based lubricant

In this paper, graphene oxide/polyethylene glycol (GO/PEG) composite water-based lubricant was prepared by an ultrasonic dispersion method, and characterized and analyzed by Fourier transform infrared (FT-IR), Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and thermogravimetric analysis (TGA). The suspension performance of GO/PEG composite water-based lubricant in water was verified by static sedimentation and centrifugation, and then, the prepared GO/PEG composite water-based lubricant was added into 304 stainless steel and 6061 aluminum alloy, and the coefficient of friction (COF) curve, average COF value, average wear rate, corresponding photomicrographs of balls and disks after wear, and energy-dispersive spectrometer (EDS) elemental analysis were used to illustrate the lubrication effect and lubrication mechanism. The results show that the GO/PEG composite water-based lubricant possesses excellent suspension ability in water, and the average COF value and wear rate of GO/PEG composite water-based lubricant are reduced by 78.8% and 88.8%, respectively, compared with water lubrication. The excellent lubrication effect of GO/PEG composite water-based lubricant can effectively reduce the cold-welding and adhesive wear phenomenon, mainly because GO/PEG composite water-based lubricant first fills the uneven surface of friction mating to form a high-quality lubricating film and then because of the special space structure of GO and the low shear between GO layers and the synergistic lubrication effect of GO/PEG.     

球形WC增强铁基复合等离子堆焊层的组织与摩擦学性能

为提高304不锈钢的摩擦学性能,将质量分数为30%和60%的球形WC添加到铁基复合粉末,采用等离子堆焊技术在其表面制备了WC增强铁基复合涂层.分析其显微组织结构、物相和显微硬度,在恒定载荷(50 N)和滑动速度(20 mm/s)下进行干摩擦磨损试验,研究其干滑动摩擦学性能.结果表明:富含Cr的固溶强化奥氏体、高硬度的Cr7C3和WC增强相的存在,提高了WC增强铁基堆焊层的硬度,30%WC和60%WC涂层的显微硬度达到HV0.2665和HV0.2724,比铁基涂层提高了21.1%和31.9%,是304基体的3.7和4倍;30%WC和60%WC涂层的摩擦系数和磨损率分别为0.59和2.639×10~(–6) mm~3·N~(–1)·m~(–1),0.42和1.111×10~(–6) mm~3·N~(–1)·m~(–1).30%WC和60%WC涂层均表现出优异的耐磨性能,其磨损机理分别为黏着磨损和二体磨粒磨损的混合机制,和三体磨粒磨损.     

AISI 4340钢干滑动摩擦磨损特性研究

室温条件下,采用CFT-Ⅰ型多功能材料表面综合性能测试仪对AISI 4340钢进行了往复干摩擦磨损试验,研究了其摩擦磨损特性.采用扫描电子显微镜(SEM)和能谱仪(EDS)对磨痕和亚表层进行了观察和检测,分析了磨损表面微观形貌、亚表层塑性变形和碳氧元素含量演化.结果表明:磨损形式以磨粒磨损为主,并伴随轻微黏着磨损和剥落磨损;磨损表面分布着长度、宽度、深度不一的划痕以及鳞片状和颗粒状磨屑;磨损表面出现轻微氧化,磨屑呈现不同程度的碳富集和氧化;亚表层出现明显摩擦影响层,距表层越近,塑性变形越明显,晶界角逐渐汇聚于表面,在表面处趋于平行;往复运动导致塑性变形方向不一致,磨痕中部,塑性变形向某位置聚集.     

甲烷浓度对碳化硅基底金刚石薄膜摩擦性能影响

利用热丝化学气相沉积法(HFCVD)在碳化硅基底上制备金刚石薄膜,采用场发射扫描电子显微镜、拉曼光谱仪、原子力显微镜研究了在不同甲烷浓度条件下制备的金刚石薄膜表面形貌及物相组成,在干摩擦条件下通过往复式摩擦磨损实验测试并计算了已制备金刚石薄膜的摩擦系数和磨损率,结合物相分析及摩擦磨损实验结果分析了甲烷浓度的改变对金刚石薄膜摩擦磨损性能的影响。结果表明,由于甲烷气体含量的升高,金刚石薄膜结晶质量下降,薄膜由微米晶向纳米晶转变。摩擦磨损实验结果显示:3%甲烷浓度条件下制备的金刚石薄膜耐磨性较好,磨损率为2.2×10^-7 mm³/mN;5%甲烷浓度条件下制备的金刚石薄膜摩擦系数最低(0.032),磨损率为5.7×10^-7 mm³/mN,制备的金刚石薄膜的耐磨损性能相比于碳化硅基底(磨损率为9.89×10^-5 mm³/mN)提升了两个数量级,显著提高了碳化硅基底的耐磨性。     

Prediction and simulation of wear response of Linz–Donawitz (LD) slag filled glass–epoxy composites using neural computation

This article reports on the implementation of a soft computing technique based on artificial neural networks (ANNs) in analyzing the wear performance of a new class of hybrid composites filled with Linz–Donawitz slag (LDS). LDS is a major solid waste generated in huge quantities during steel making. It comes from slag formers such as burned lime/dolomite and from oxidizing of silica, iron etc. while refining the iron into steel in the LD furnace. In this work, hybrid composites consisting of short glass fiber (SGF) reinforced epoxy filled with different LDS content (0, 7.5, 15 and 22.5 wt%) are prepared by simple hand lay‐up technique. Solid particle erosion trials, as per ASTM G 76 test standards, are conducted on the composite samples following a well‐planned experimental schedule based on Taguchi design of experiments. Significant process parameters predominantly influencing the rate of erosion are identified. The study reveals that the LDS content is the most significant among various factors influencing the wear rate of these composites. Further, a model based on ANN for the prediction of erosion performance of these composites is implemented. The ANN prediction profiles for the characteristic wear properties exhibit very good agreement with the measured results demonstrating that a well‐trained network has been created. The simulated results explaining the effect of significant process variables on the wear rate indicate that the trained neural network possesses enough generalization capability of predicting wear rate even beyond the experimental range. Copyright © 2014 John Wiley & Sons, Ltd.     

Effect of Y2O3 on microstructural characteristics and wear resistance of cobalt‐based composite coatings produced on TA15 titanium alloy surface by laser cladding

The effects of Y₂O₃ on the microstructure, phase composition of the coatings, microhardness and wear resistance of cobalt‐based composite coatings prepared by laser cladding were investigated. The TA15 titanium alloy was selected as substrate which the cobalt‐based composite powder with different content of Y₂O₃ was cladded on. The microstructure of the coatings was observed by scanning electron microscope (SEM) and metallurgical microscope. The phase structure of the coatings was determined by X‐ray diffraction (XRD), and the microhardness and wear resistance of the coatings were measured by hardness tester and wear testing machine. The results show that the rare earth oxide Y₂O₃ can refine and purify the microstructure of the coatings, reduce the porosities and cracks and improve compactness of the coatings. Moreover the addition of Y₂O₃ improves the microhardness of the coatings and reduces the friction coefficient, thus improving the wear property of the coatings. And the wear resistance of the coating with Y₂O₃ has improved about 50 times; the highest value of microhardness in the coating is HV1181.1. And 0.8 wt% content of Y₂O₃ in the coating is the best choice for improving the microhardness and wear resistance of the coating. It is feasible to improve the microstructure and tribological properties of laser cladding coatings by adding of Y₂O₃. Copyright © 2014 John Wiley & Sons, Ltd.     

Enhancement of water barrier properties and tribological performance of hybrid glass fiber/epoxy composites with inclusions of carbon and silica nanoparticles

Water barrier properties and tribological performance (hardness and wear behavior) of new hybrid nanocomposites under dry and wet conditions were investigated. The new fabricated hybrid nanocomposite laminates consist of epoxy reinforced with woven and nonwoven tissue glass fibers and two different types of nanoparticles, silica (SiO₂) and carbon black nanoparticles (C). These nanoparticles were incorporated into epoxy resin as a single nanoparticle (either SiO₂ or C) or combining SiO₂ and C nanoparticles simultaneously with different weight fractions. The results showed that addition of carbon nanoparticles with 0.5 and 1 wt% resulted in maximum reduction in water uptake by 28.55% and 21.66%, respectively, as compared with neat glass fiber reinforced epoxy composites. Addition of all studied types and contents of nanoparticles improves hardness in dry and wet conditions over unfilled fiber composites. Under dry conditions, maximum reduction of 47.26% in weight loss was obtained with specimens containing 1 wt% carbon nanoparticles; however, in wet conditions, weight loss was reduced by 17.525% for specimens containing 0.5 wt% carbon nanoparticles as compared with unfilled fiber composites. Diffusion coefficients for different types of the hybrid nanocomposites were computed using Fickian and Langmuir models of diffusion. Copyright © 2017 John Wiley & Sons, Ltd.