齿轮损伤磁记忆检测的数值模拟及试验研究

发布时间：2018-03-29 00:32

本文选题：金属磁记忆　切入点：有限元　出处：《国防科学技术大学》2012年硕士论文

【摘要】：齿轮机构具有传动比稳定、承载能力大、机械效率高、使用寿命长等特点，使它成为机械中应用最广泛的一种传动装置。而单齿缺陷特别是蠕变损伤引起的疲劳裂纹是齿轮传动故障的重要原因。目前在齿轮故障检测与诊断中，最常见的是振动检测法，由于振动信号只能检测宏观缺陷，难以对由应力集中导致的疲劳损伤区域进行检测。而基于磁记忆原理的无损检测方法，能有效对应力集中区进行检测，从而达到定位损伤区域，进行故障预测的目的。本文从磁记忆检测机理出发，分别建立了带磁偶极子模型和力磁耦合模型，并结合这两种模型实现了从简单平板结构的模型到复杂齿轮模型的磁记忆现象仿真，并通过齿轮弯曲疲劳试验，对齿轮在弯曲疲劳载荷作用下的失效过程和磁记忆现象进行分析。主要内容如下：（1）概述了磁记忆现象产生的理论基础，从物质的磁性出发，建立了带磁偶极子和力磁耦合两种不同的理论模型，并提出磁记忆特征的产生是两种模型共同作用的结果，但起主导作用的模型因缺陷类型的不同而有所差别，为有限元仿真和试验方案的设计提供重要的理论依据。（2）分别建立了裂纹和早期疲劳损伤两种不同类型缺陷的钢平板模型，，利用有限元的方法，并分别结合带磁偶极子模型和力磁耦合模型对构件的磁记忆特征进行分析，得到两种不同缺陷类型下的磁记忆特征信号仿真，分析了不同缺陷参数下漏磁信号的变化规律。（3）利用Solidworks软件建立了齿轮的三维有限元模型，采用有限元方法对齿轮在弯曲应力作用下和齿轮啮合状态过程中的应力进行了分析，并基于力磁耦合模型，对齿轮周围的漏磁信号进行了数值仿真，得到了齿轮损伤时漏磁信号的磁记忆特征。（4）构建齿轮弯曲疲劳试验平台，研究齿轮在弯曲疲劳载荷下的失效过程和磁记忆信号变化特征。并对试验数据进行分析，提取了齿轮弯曲疲劳失效的磁记忆信号评价准则，提出将齿顶法向漏磁场的均值和梯度最大值作为特征参数进行失效判别。以上研究结果对磁记忆技术在齿轮疲劳损伤早期检测中的应用提供了有益的参考。
[Abstract]:The gear mechanism has the characteristics of stable transmission ratio, large bearing capacity, high mechanical efficiency, long service life, etc. The fatigue crack caused by single tooth defect, especially creep damage, is an important reason for gear transmission failure. At present, vibration detection is the most common method in gear fault detection and diagnosis. Because the vibration signal can only detect macroscopic defects, it is difficult to detect the fatigue damage area caused by stress concentration, while the nondestructive testing method based on magnetic memory principle can effectively detect the stress concentration zone. In order to locate the damage area and predict the fault, starting from the mechanism of magnetic memory detection, the magnetic dipole model and the magneto-magnetic coupling model are established in this paper. Combined with these two models, the magnetic memory phenomena from simple plate structure model to complex gear model are simulated, and the gear bending fatigue test is carried out. The failure process and magnetic memory of gear under bending fatigue load are analyzed. The main contents are as follows:. In this paper, the theoretical basis of magnetic memory phenomenon is summarized, and two different theoretical models of magnetic dipole and mechanomagnetic coupling are established from the point of view of the magnetic properties of matter, and it is proposed that the generation of magnetic memory characteristics is the result of the joint action of the two models. However, the dominant models differ from each other according to the types of defects, which provide an important theoretical basis for the design of finite element simulation and test schemes. In this paper, two kinds of steel plate models of crack and early fatigue damage are established, and the magnetic memory characteristics of the members are analyzed by using the finite element method, combining the magnetic dipole model with magnetic dipole model and the magneto-magnetic coupling model, respectively. The simulation of magnetic memory characteristic signals with two different defect types is obtained, and the variation of magnetic flux leakage signals with different defect parameters is analyzed. The three-dimensional finite element model of gear is established by using Solidworks software. The stress of gear under bending stress and gear meshing state is analyzed by finite element method, and based on the coupling model of force and magnetic field. The magnetic memory characteristics of magnetic leakage signals are obtained by numerical simulation of magnetic leakage signals around gears. In this paper, a gear bending fatigue test platform is constructed to study the failure process and magnetic memory signal change characteristics of gear under bending fatigue load. The test data are analyzed, and the evaluation criteria of magnetic memory signal for gear bending fatigue failure are extracted. The mean value and the maximum gradient of the normal leakage magnetic field at the top of the tooth are used as the characteristic parameters to distinguish the failure. The above results provide a useful reference for the application of magnetic memory technology in the early detection of gear fatigue damage.
【学位授予单位】：国防科学技术大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：TH132.41;TG115.284

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