谈谈磨粒磨损

介绍了目前关于磨粒磨损的研究成果,对影响磨粒磨损的各种因素进行讨论,并提出一些设想、对磨损件的制作有一定的指导作用。     

热冲模零件摩擦磨损行为研究

对热冲压过程中模具零件的摩擦磨损行为进行了综述,分析了目前摩擦磨损行为研究存在的问题。实际热冲压过程中,除了分析加载应力、温度、表面涂层外,还要考虑冷焊效应、棱边应力集中、结点形成行为等因素,即结点的材料累积行为、结点材料与模具钢结合行为以及结点厚度与临界剥离应力关系等还有待研究。最后对热冲模零件摩擦磨损行为提出了2点见解,除了开发能准确模拟实际工况的试验设备外,还需将结点应力、原子扩散、结点强度等因素结合,总结多因素作用的摩擦磨损机理。     

基于HPSO优化BP神经网络的刀具磨损状态识别

针对BP神经网络容易陷入局部极值导致识别精度低的问题,文章提出了一种基于混合粒子群算法(HPSO)的BP神经网络优化算法。在刀具磨损监测实验过程中,采集刀具切削的声发射(AE)信号,利用小波包分解算法对AE信号进行滤波,并进行特征提取。将频带能量特征和切削参数分别作为主特征和辅助特征,并对其对归一化处理。采用混合粒子群优化算法(HPSO)对BP神经网络预测模型进行优化,利用优化后的模型对测试样本进行模式识别,结果表明,优化后的HPSO-BP模型能够有效地降低神经网络陷入局部极值的情况,提高刀具磨损识别精度。     

轴承磨削中砂轮磨损状态在线识别的试验研究

本文对在轴承磨削中利用磨削火花信号实现砂轮磨损状态在线辨识的可行性进行了试验研究。结果表明，磨削火花信号的各统计特征量与砂轮磨损状态的变化规律相似，利用磨削火花信号能够识别出砂轮磨损的不同状态，其中以信号为差最敏感。研究结果为在轴承磨削中实现砂轮磨损状态的在线监控，对砂轮进行视情修整提供了基础。     

钎焊金刚石磨削石材过程中磨粒磨损状态研究

通过数字视频采集系统跟踪观察钎焊金刚石砂轮磨削过程中磨粒表面形貌变化.研究了钎焊金刚石砂轮在磨削花岗石材料过程中,金刚石磨粒的出刃高度和磨损状态的变化规律.结果表明,钎焊金刚石工具在加工过程中,钎焊金刚石磨粒可分为六种磨损状态:完整晶形、微观破碎、宏观破碎、磨平、折平和脱落.磨粒磨损路径主要是以完整→微观破碎→宏观破碎→脱落的方式进行.金刚石磨粒在磨损过程中,可分为初期磨损、正常磨损和剧烈磨损三个阶段.     

基于参数策略的改进粒子群优化PNN神经网络刀具磨损研究

刀具磨损直接影响工件加工质量和尺寸精度,正确掌握刀具磨损状态及时换刀,减少机床停机时间,将直接提高加工效率。为提高刀具磨损状态识别准确率,提出一种基于参数策略的改进粒子群优化PNN(IPSO-PNN)神经网络识别刀具的磨损状态。相较于BP神经网络收敛速度慢、易陷入局部最优的缺点,IPSO-PNN神经网络结构简单、训练简洁快速。与BP神经网络和标准PNN神经网络仿真结果对比,结果表明:IPSO-PNN神经网络识别准确率高,收敛速度快,仿真耗时短,能有效提高刀具磨损识别准确率。     

新型高温磨粒磨损测试装置及方法

为研究涂层等表面工程零件的磨损性能,介绍了一种新型的高温磨粒磨损测试装置和方法,可以模拟研究高温氧化腐蚀和磨粒磨损交互作用,并研究了测试温度、测试载荷、测试时间和三种磨料对涂层磨粒磨损测试结果的影响.结果表明,这种高温磨粒磨损测试方法可以评价不同温度和载荷条件下涂层的耐磨粒磨损性能;高温磨粒磨损测试装置结构简单,运行稳定,所测试的数据准确、重现性好;当温度和载荷变化时,涂层的耐磨粒磨损性能也会变化.     

基于单颗磨粒切削的CBN铰珩工具磨粒磨损研究

采用单颗磨粒试验方法,以4Cr13不锈钢为修整材料,研究大长径比CBN铰珩工具修整过程中的磨粒磨损特性。试验结果显示:与普通切削相比,超声切削时的平均切削力降低60%～80%,但磨粒在短时间内大块破碎,磨削比严重下降。超声切削时的磨粒?工件接触比在0.6～0.8,磨粒主要处于断续切削过程,其最大切削宽度比普通切削时的增加2.7倍,且磨粒受到的最大瞬时切削力增加。根据点云信息对磨粒进行逆向建模,并对建立的单颗磨粒切削仿真模型的瞬时切削力进行定量分析。仿真结果显示:超声切削时的最大切向力比普通切削时的增加20%以上,且力的波动幅度超过80%。      

5CrMnMo钢渗硼层的高温磨损特性

本文研究了２５ＣｒＭｎＭＯ钢渗硼层高温磨损特性。结果表明,渗层磨损机制以疲劳剥落为主,兼有磨粒磨损。升高温度、增加载荷,磨损率增加。在８５０℃时,磨损失重随摩擦速度增加而减小;９５０℃时,则相反,磨损失重随摩擦速度增加而增大。     

Low stress abrasive wear behavior of a hardfaced steel

A plain carbon steel was overlayed with a wear-resistant hardfacing alloy by manual arc welding. Low stress abrasive wear tests were conducted with an ASTM rubber wheel abrasion tester using crushed silica and as the abrasive medium. The wear rate decreased with sliding distance, and there was an overall improvement in the abrasive wear resistance as a result of overlaying. The wear behavior of the samples has been discussed in terms of microstructural features while the examination of wear surface and subsurface regions provides insight into the wear mechanisms.     

奥氏体中锰钢的磨料磨损性能

在ML-100型销-盘式磨料磨损试验机上,通过不同钢种之间的对比试验,研究了奥氏体中锰钢（BTW钢）石英砂磨料及煤矸石磨料下的磨损性能,并采用SEM分析了其磨损机制。结果表明,在高硬度的石英砂磨料中,BTW钢加工硬化明显,有效硬化层深度达900 μm,耐磨性能优于其他钢种,而在质地较软的煤矸石磨料中,其耐磨性降低;BTW钢在不同磨料下的磨损机制均为犁削,但形貌差异较大,石英砂磨料下磨损表面较为均匀,犁沟深度较浅、宽度较窄,脊缘部分较薄,脊缘在反复磨损中断裂成屑的数量较多,而煤矸石磨料中,犁沟存在于整个磨损表面且变形较小,几乎没有发现切削存在。     

New starting points for the prediction of tool wear in hot forging

Formation of a sufficiently large database on tools for hot forging, which is necessary for successful prediction of wear at a given number of strokes, as well as for the prediction of the critical number of strokes when the acceptable tolerance of a forging is exceeded, is a relatively time-consuming process in the production practice. To overcome this problem, this article presents a starting point for quicker prediction of these quantities by means of conditional average estimator neural networks (CAE NN), namely by the so-called integral method and by the partial method. A comparison of the efficiency in prediction of these methods was carried out on the results of wear obtained in laboratory forging, which allowed a gradual and relatively quick tracing of wear contour progression on tools and thus the formation of a reliable database. The results presented show that in the case of a relatively small database, where, for instance, there are known data and wear parameters on at least three different tool steels, or, on differently heat treated steels, it is possible to effectively predict the wear of a fourth tool simply on the basis of the slightly perceivable wear profile of the tools. Here, the integral method gives better predictions. This conclusion is of great importance in practice: from intermediate control of gradual tool wear, we can predict its tool life.     

添加纳米氧化物颗粒对TC4合金磨损行为的影响

采用摩擦磨损试验机对TC4合金/GCr15钢进行滑动磨损试验,在摩擦界面人工添加纳米氧化物颗粒研究了氧化物种类和颗粒尺寸对TC4合金磨损行为的影响;采用XRD、SEM、EDS等方法表征了TC4合金的磨损特征并探讨了磨损机制。结果表明:氧化物颗粒的种类和尺寸对于TC4合金的磨损行为具有显著影响。TiO_2颗粒急剧促进TC4合金磨损,而Fe_2O_3显著降低磨损;Fe_2O_3粒径越小,TC4合金的磨损失重越小且几乎不随载荷变化而波动。摩擦界面添加TiO_2时,磨损机制与未添加氧化物颗粒的相似,以磨粒磨损和粘着磨损等严重磨损机制为主;当添加Fe_2O_3时,磨损机制由严重向轻微磨损转变。     

金刚石-硬质合金复合片的耐磨性测试方法研究

金刚石-硬质合金复合片的耐磨性是一个非常重要的技术指标,但迄今为止国际上尚无统一的PDC耐磨性测试标准。本文在分析我国和乌克兰评价PDC耐磨性方法优缺点的基础上进行了对比性研究,指出乌克兰超硬材料研究所按复合片在实验台上切削岩块的磨损高度和面积来评价的方法,更接近于孔底岩石破碎过程,PDC的几何磨耗量可更全面真实地反映复合片在钻探生产中的工作寿命。同时,讨论了乌克兰方法需要完善的地方。     

A cascaded neuro-computational model for spoken word recognition

In human speech recognition, words are analysed at both pre-lexical (i.e., sub-word) and lexical (word) levels. The aim of this paper is to propose a constructive neuro-computational model that incorporates both these levels as cascaded layers of pre-lexical and lexical units. The layered structure enables the system to handle the variability of real speech input. Within the model, receptive fields of the pre-lexical layer consist of radial basis functions; the lexical layer is composed of units that perform pattern matching between their internal template and a series of labels, corresponding to the winning receptive fields in the pre-lexical layer. The model adapts through self-tuning of all units, in combination with the formation of a connectivity structure through unsupervised (first layer) and supervised (higher layers) network growth. Simulation studies show that the model can achieve a level of performance in spoken word recognition similar to that of a benchmark approach using hidden Markov models, while enabling parallel access to word candidates in lexical decision making.     

基于细胞神经网络刀具磨损图像的预处理

提出了一种基于细胞神经网络的刀具磨损图像处理方法，通过设计细胞神经网络参数，运用细胞神经网络对刀具的二值图像平滑滤波，边缘提取，通过仿真证明该方法是有效的，由于细胞神经网络易于用VLSI实现并且并行处理速度快，因此该方法对刀具的磨损状态机器视觉检测中的图像处理具有实用意义。     

低合金耐磨钢板NM600组织及其磨损性能研究

对一种新型高级别低合金高强度耐磨钢NM600进行热处理实验,研究了淬火温度和回火温度对实验钢组织和力学性能的影响,并分析了最优工艺条件下实验钢的磨损性能。结果表明：当淬火温度为880 ℃,回火温度为180 ℃时,实验钢力学性能最优,其中维氏硬度、抗拉强度、伸长率和-40 ℃冲击功分别为628 HV、2 000 MPa、7.3%、27.8 J,实验钢组织为典型的板条马氏体结构,马氏体板条内部及其板条界面上分布着细小均匀的碳化物。三体冲击磨损实验结果表明：工艺优化后的实验钢的耐磨性能与瑞典SSAB公司生产的HARDOX600相近,是NM400钢的1.376倍,抗磨损性能良好。     

Study on Mn-Si-B system hardfacing electrode

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Tool wear detection and fault diagnosis based on cutting force monitoring

In metal cutting processes, an effective monitoring system, which depends on a suitably developed scheme or set of algorithms can maintain machine tools in good condition and delay the occurrence of tool wear. In this paper, an approach is developed for fault detection and diagnosis based on an observer model of an uncertain linear system. A robust observer is designed, using the derived uncertain linear model, to yield the necessary and key information from the system. Subsequently, it is used as a state (tool wear) estimator, and fault detection is carried out by using the observed variables and cutting force. The developed approach is applied to milling machine center. Several linear models are identified based on different working conditions. A dominant model plus uncertain terms is derived from these model set and used as an observer. Threshold values are proposed for detecting the fault of the milling machine. Examples taken from experimental tests shown that the developed approach is effective for the fault detection. The approach can be used for fault detection of failures arising from sensor or actuator malfunction.