In view of the strong nonlinearity of the signals in the gestation period of milling chatter, and the problem that the traditional time-frequency analysis methods cannot reveal the weak characteristics of the gestation period of chatter well, a chaotic characteristic analysis method of milling vibration information is proposed. The milling force signals of stable milling, chatter gestation and chatter outbreak states are collected through variable working condition milling force measurement experiments, and the chaotic phase space reconstruction method is used to obtain the attractor images of milling force signals in different vibration states. The experiments show that the attractor features in the chatter gestation period are more significant than the traditional time-frequency features, and the chaotic attractor images can better reveal the weak features in the chatter gestation period.

切削颤振孕育期介于稳定切削与颤振爆发之间,该阶段切削力信号中颤振特征具有典型微弱信息特性.采用基于总体经验模态分解(ensemble empirical mode decomposition,简称EEMD)与奇异值分解(singular value decomposition,简称SVD)相结合的方法对颤振孕育期信号进行降噪时,大多存在噪声剔除不充分或微弱目标特征信息失真等问题.首先,通过引入功率谱密度(power spectral density,简称PSD)与常相干函数(common coherency function,简称CCF)对EEMD降噪机制进行改进,使微弱目标特征所在本征模态函数(intrinsic mode function,简称IMF)分量得到有效提取;其次,借助池化原理(pooling principle,简称PP)降低IMF分量复杂度,并联合SVD对其实施分块降噪,以实现对微弱目标特征中所含噪声进行有效消减;最后,耦合上述改进并重构信号,可面向微弱目标特征信号形成基于改进EEMD-SVD(improved EEMD-SVD,简称IES)的降噪方法.分别利用IES与EEMD-SVD对Rossler混沌信号进行降噪处理,并通过比较信噪比、均方误差及平滑度等降噪评价指标,对所提方法在降噪有效性及信息保真度方面的优势进行量化验证.在此基础上,再次借助所提IES方法对变轴向切深铣削实验中颤振孕育期铣削力信号进行降噪分析.结果表明,该方法能显著抑制颤振孕育期信号噪声,并能有效避免微弱颤振特征信号失真问题.

There exist various kinds of spatial frequency errors on the ultra-precision machined surfaces, which seriously influence their performances. According to different performances of workpieces, it is necessary to use an effective decomposition method to extract the topography containing the spatial frequency errors at specific frequency bands. The traditional spatial frequency error decomposition method has the serious problem of modal aliasing. In order to solve this problem, an adaptive bidimensional variational mode decomposition (BVMD) algorithm is proposed to decompose a three-dimensional surface topography. First, image continuation and self-convolution Hanning window arc introduced to preprocess the truncation errors when collecting 31) topographic data. Then, the particle swarm annealing optimization algorithm is used to optimize the penalty coefficient and the number of decomposition layers in the BVMD algorithm. Among them, the fitness function of the optimization algorithm is constructed by taking KI, divergence among modal components as aliasing indicators, introducing the minimum risk Bayesian decision theory, and combining KI, divergence with reconstruction errors. Finally, the measured topography of the ultra-precision machined surface is analyzed and compared with those by the discrete wavelet decomposition method and the bidimensional empirical mode decomposition methods. The results show that the KI, divergence by the proposed method is several hundred, much higher than those by the other two methods. The proposed method has a good inhibition ability for frequency error modal aliasing, and can effectively decompose the spatial frequency errors of an ultra-precision machined surface.

针对现有缺陷管道的磁记忆信号降噪效果不佳及信号完整性鲜有考虑等问题,提出了基于总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)和分层阈值的磁记忆信号降噪方法.首先,设计了以STM32F407为控制核心的金属磁记忆检测系统,用于采集缺陷管道的磁信号;然后,对磁信号进行EEMD预处理,得到其本征模函数(intrinsic mode function,IMF)分量,并根据频谱分析和相似度计算选择最佳分解层数;最后,利用分层阈值降噪算法重构在最佳分解层数下的IMF分量,得到降噪后的信号.通过仿真分析和实验测试,对EEMD分层阈值降噪方法进行定量评价.结果 表明:该方法适用于信噪比较小的含噪信号;与小波阈值降噪方法相比,其降噪后信号的信噪比和平滑度较高,均方根误差较小,缺陷特征信号完整,可更直观地显示缺陷位置.研究结果为金属管道磁信号降噪提供了一种切实可行的方法,为管道缺陷的在线检测奠定了基础.

在超精密飞切加工中,加工表面的三维形貌特征主要包括粗糙度、波纹度和面形,不同的三维形貌特征受到不同表面质量因素的影响.为了研究不同因素对表面质量的影响情况,首先对超精密飞切加工过程中的影响因素进行了分析;然后,基于刀刃复印的原则,建立了一种超精密飞切加工表面三维形貌的仿真模型;最后,基于该模型,对工艺参数及刀具-工件的相对振动等主要因素的影响规律进行了分析,获得了加工表面主要空间频率与相对振动频率之间的关系.