切削加工颤振智能监控技术

切削加工颤振智能监控技术是智能机床中不可或缺的一部分，是智能加工的一个重要发展方向。它对于提高零件的加工精度与效率，增加企业的运营绩效具有重要的意义。以传感器的选择、特征提取、颤振识别和颤振抑制为主线，系统的综述了切削加工过程中颤振智能监控的研究进展。分析颤振信号的选择和时域、频域、时频域以及特征自适应智能提取的特征提取方法；分析神经网络、支持向量机、隐马尔科夫模型、混合模型和在线智能进化模型在颤振识别中的应用；着重分析基于主轴转速调整的颤振智能控制方法。在此基础上，对切削加工颤振智能监控的研究难点进行了分析，并总结了目前存在的问题。最后，对切削加工颤振智能监控技术今后的发展趋势进行了展望。     

基于EMD复杂度与鉴别信息的磨削颤振预测

为避免磨削加工中出现颤振,提出一种基于经验模式分解(empiracal mode decomposition,简称EMD)复杂度与鉴别信息的颤振预测方法。采用经验模式分解对磨削加工中滤波后的振动信号进行分解,获得振动信号的本征特征函数;采用L-Z复杂度指标对本征特征函数进行复杂度分析,获得磨削颤振特征值;采用鉴别信息对复杂度变化进行量化,通过鉴别信息对磨削加工颤振进行预测。在外圆磨床实验平台对该方法的有效性进行了验证,分别采用变工件转速、变砂轮转速和变磨削厚度3种加工方式逼近颤振状态。分析结果表明,当磨削加工趋于颤振时对应的鉴别信息值增大。实验结果通过鉴别信息的大小可以对磨削加工中的颤振进行预测。     

基于自适应变分模态分解与功率谱熵差的机器人铣削加工颤振类型辨识

机器人铣削加工存在模态耦合颤振和再生颤振现象,有效地进行机器人铣削加工颤振类型的辨识是进行颤振精准抑制和保证加工质量的基础。为此,提出一种基于自适应变分模态分解与功率谱熵差的颤振类型辨识(AVMD-ΔPSE)方法。通过分析机器人铣削加工颤振特性和主导模态,将机器人铣削颤振分为机器人结构模态主导的模态耦合颤振和刀具-主轴结构模态主导的再生颤振两种类型。为了提取颤振敏感子信号,利用自适应变分模态分解方法对原始信号进行分解,根据功率谱熵和频率消除算法设计功率谱熵差颤振类型辨识指标,结合多组试验数据采用高斯混合模型自适应地确定辨识指标最佳分类阈值。颤振辨识试验表明机床铣削加工颤振辨识方法运用于机器人铣削加工中仅能识别颤振却无法区分不同的颤振类型,而AVMD-ΔPSE方法能准确有效地辨识和区分机器人铣削加工中的模态耦合颤振和再生颤振,为机器人铣削颤振的针对性抑制提供理论指导。     

基于开放式控制器的铣削颤振在线抑制

为实现在线抑制铣削颤振,对颤振领域常用的传感器监控技术,尤其是三向切削力和振动加速度传感器的各向分量在颤振监控过程中的时域和频域敏感信号特征进行试验研究。针对监控的颤振敏感信号频域特性,研究快速傅里叶变换技术对信号有效信息的在线提取技术。对自激颤振的机理进行分析,建立颤振频率与主轴转速间的关系模型,为实现变主轴转速抑制自激颤振提供理论基础。对集成在线参数采集、反馈控制的全软件型模块化铣削控制器进行设计,将在线抑制颤振的相关变主轴转速算法嵌入开放式控制器中,并设计控制参数数据流在控制器模块间的实现流程。对连续变切削深度铝合金工件进行在线颤振抑制加工试验,试验验证开放式智能铣削控制器在线抑制颤振相关技术的正确性。     

智能主轴高速铣削颤振的模糊控制方法研究

在航空叶片、整体叶盘等零件高速高效加工过程中,切削过程阻尼作用的减弱,致使颤振相比低速切削时更容易发生,严重影响了加工精度和效率的提高.而颤振监控作为智能主轴的主要功能之一,是解决高速高效加工过程中颤振难题的一种有效途径.首先,在主轴结构上集成压电作动器及位移传感器等,搭建颤振主动控制系统,并在此基础上,建立主动控制系统模型.然后,分析主轴系统动态特性,据此设计模糊控制规则,开发模糊控制器.接着,通过力锤激励试验及扫频试验辨识主轴动态系统模型,进行铣削加工及颤振控制仿真分析.最后,开展铣削颤振主动控制试验.试验结果表明,提出的颤振模糊控制方法能够有效控制铣削颤振,提升铣削加工的稳定性.     

薄壁件高速切削稳定性研究

颤振现象是薄壁零件加工过程中的普遍问题,以壁板零件为模型,通过对加工过程中颤振的原理进行分析,建立薄壁件一维铣削稳定性理论模型,绘制了频域颤振稳定性叶瓣图。基于叶瓣图,提出一种通过优选切削参数来控制颤振的方法,最终获取了避免加工颤振的理论加工参数,对减小或消除颤振提供了有益的指导。     

亚干式深孔钻削颤振试验研究

亚干式深孔加工的振动对加工质量有很大影响,主要是亚干式深孔加工的颤振.分析了亚干式深孔加工的颤振产生原理,并从理论上分析亚干式深孔加工中的颤振产生因素;然后通过试验的方法来分析和论证切削参数对亚干式深孔加工中的振动影响.最后通过对比的方法来分析加工中的颤振,来判断加工工况.     

基于小波包-BP神经网络的切削颤振监测

对切削加工状态进行实时监测尤其是颤振的监测,为提高切削加工质量有着重要意义。搭建切削颤振实验平台,采用加速度传感器获取切削加工信号,通过时频分析,将切削加工状态划分为稳定、过渡及颤振3种加工状态。利用小波包分解,计算各状态在不同频带的小波系数均方根值(RMS),并把它作为BP神经网络的输入向量。依照BP神经网络分类方法,对3种加工状态进行识别。结果表明,该监测系统可对切削颤振进行有效识别。     

基于四阶矩法车削颤振可靠性研究*

再生颤振是影响加工质量、加速刀具磨损、刀具破坏的主要原因。以车削加工为研究对象,针对具有不确定参数的车削加工颤振预测问题,研究车削加工系统结构动态特性参数具有随机特性的情况下颤振可靠性建模及求解问题。定义车削加工过程不出现颤振的概率为颤振可靠度,建立车削加工系统可靠性模型,研究四阶矩法求解可靠度的问题,提出利用颤振可靠性叶瓣图方法进行颤振预测。通过模态试验对一车床进行频响函数测试,采用四阶矩法计算获得了颤振可靠度,并与蒙特卡洛法获得的可靠度相比较。结果表明四阶矩法计算获得的可靠度与蒙特卡洛仿真结果一致性很好,但是四阶矩法计算精度高而且计算耗时远小于蒙特卡洛法。进行颤振可靠性切削试验,通过观察振纹和分析噪声功率谱识别颤振,对典型参数进行验证,试验结果与分析结果一致。     

基于EMD和SVM的缸盖加工颤振在线检测

切削载荷下加工系统的颤振现象直接影响加工过程的效率和性能。本文介绍了一种基于经验模式分解(Empirical Mode Decomposition,EMD)的机床刀具颤振分析方法。通过对机床主轴的振动信号进行综合分析,并对异常颤振信号进行EMD分解以获得本征模函数,采用Hilbert变换得到其包络信号,计算包络谱,提取噪声信号的特征频率,对特征频率进行支持向量机(Support Vector Machine,SVM)颤振判别学习,通过现场信号验证,证明该方法能有效检测加工颤振。     

Prediction of regenerative chatter in the high-speed vertical milling of thin-walled workpiece made of titanium alloy

With the wide application of high-speed cutting technology, high-speed machining approach of titanium alloy has become one of the most effective ways to improve processing efficiency and to reduce the processing cost, but the cutting chatter which often occurs in the cutting process not only affects the machining surface quality but also reduces the production efficiency. Regenerative chatter is a typical phenomenon during actual cutting, and it has the greatest impact on the cutting process. With the purpose of avoiding regenerative chatter and selecting appropriate cutting parameters to achieve a steady cutting process and a high surface quality, it is necessary to determine the critical boundary conditions where chatter occurs. Built on the work of previous theoretical researches of regenerative chatter, this paper utilized Visual C++ software to calculate the chatter stability domain during the finish machining of titanium alloy. It was shown that the border between a stable cut and an unstable cut can be visualized in terms of the axial depth of cut as a function of the spindle speed. Using the result, it can find the specific combination of machining parameters, which lead to the maximum chatter-free material removal rate. In order to verify the result, the high-speed milling experiment of an I-shaped thin-walled workpiece made of titanium alloy was conducted. It revealed that the actual machining result was consistent with the calculation prediction. This study will offer a useful guide for effective parameter selection in future CNC machining applications.     

ESPRIT- and HMM-based real-time monitoring and suppression of machining chatter in smart CNC milling system

Suppression of machining chatter during milling processes is of great significance for surface finish and tool life. In this paper, a smart CNC milling system integrating the function of signal processing, monitoring, and intelligent control is presented with the aim of real-time chatter monitoring and suppression. The algorithm of estimation of signal parameters via rotational invariance techniques (ESPRIT) is adopted to extract the frequency characteristics of acceleration signals, and then, cutting state is categorized as stable state, chatter germination state, and chatter state based on amplitude-frequency characteristics of identified acceleration signals. The model of chatter identification is acquired by training a hidden Markov model (HMM), which combines acceleration signals and labeled cutting state. To implement real-time chatter suppression, the algorithm of fuzzy control is integrated into a smart CNC kernel to determine the relationship between cutting force and spindle speed. Furthermore, spindle speed of machine tool could be adjusted timely in the presented system once the chatter is identified. Finally, the effectiveness of the proposed real-time chatter monitoring and suppression system is experimentally validated.     

Method for early detection of the regenerative instability in turning

Nowadays, approaches in chatter detection and control are based on chatter prediction, by using a machining system dynamic model, or on chatter detection by different techniques, but after chatter onset. They are not efficient because the models are complicated and specific (in the first case) respectively because of chatter unwanted consequences occurrence (in the second case). This paper presents a method for early detection of the process regenerative instability state (as a specific process current dynamical state), based on cutting force monitoring. Using the cutting force records, the process current dynamical state is assessed. Appropriate cutting force signal features are defined, based on signal statistic processing, signal chaotic modeling or signal harmonic analysis, and used on this purpose. The process dynamical state evolution is modeled aiming the features values prediction. Two types of models were used in this purpose: linear and neural. The instability regenerative mechanism is identified by using either dedicated features or input variable selection. The method was conceived and experimentally implemented in the case of turning process. The results show the method reliability and the possibility of using it in developing an intelligent system for stability control.     

Image processing for chatter identification in machining processes

Identifying chatter or intensive self-excited relative tool–workpiece vibration is one of the main challenges in the realization of automatic machining processes. Chatter is undesirable because it causes poor surface finish and machining accuracy, as well as reducing tool life. The identification of chatter is performed by evaluating the surface roughness of a turned workpiece undergoing chatter and chatter-free processes. In this paper, an image-processing approach for the identification of chatter vibration in a turning process was investigated. Chatter is identified by first establishing the correlation between the surface roughness and the level of vibration or chatter in the turning process. Images from chatter-free and chatter-rich turning processes are analyzed. Several quantification parameters are utilized to differentiate between chatter and chatter-free processes. The arithmetic average of gray level G _a is computed. Intensity histograms are constructed and then the variance, mean, and optical roughness parameter of the intensity distributions are calculated. The surface texture analysis is carried out on the images using a second-order histogram or co-occurrence matrix of the images. Analysis is performed to investigate the ability of each technique to differentiate between a chatter-rich and a chatter-free process. Finally, a machine vision system is proposed to identify the presence of chatter vibration in a turning process.     

数控车床切削加工过程的颤振稳定性研究

分析了数控车床加工过程中切削颤振的产生机理，总结了现在国内外对切削颤振抑制理论实践的研究现状。基于脉冲激振法针对CJK6140数控车床刀架系统的初步研究，提出了基于计算机仿真技术对加工过程进行动态仿真的方法，从而达到提高加工效率的目的。     

超声振动改善深孔镗削加工质量

深孔加工在航空发动机制造过程中广泛存在,由于其刚性弱,静态让刀量大,导致加工颤振和刀具磨损严重,使得其加工质量难以得到保证。超声振动切削作为一种特种切削加工手段,具有降低切削力,提高系统刚性和抑制加工颤振等优势。将超声振动应用于深孔镗削,进行了断屑条件验证,孔径误差测量,已加工表面粗糙度测量以及表面形貌观测等试验。试验结果表明,超声振动镗削能够有效缓解深孔镗削过程中的堵屑问题,减小孔径误差和表面粗糙度,抑制切削颤振,从而改善深孔镗削加工质量。     

Hybrid model- and signal-based chatter detection in the milling process

Chatter causes machining instability and reduces productivity in the metal cutting process. It has negative effects on the surface finish, dimensional accuracy, tool life and machine life. Chatter identification is therefore necessary to control, prevent, or eliminate chatter and to determine the stable machining condition. Previous studies of chatter detection used either model-based or signal-based methods, and each of them has its drawback. Model-based methods use cutting dynamics to develop stability lobe diagram to predict the occurrence of chatter, but the off-line stability estimation couldn’t detect chatter in real time. Signal-based methods apply mostly Fourier analysis to the cutting or vibration signals to identify chatter, but they are heuristic methods and do not consider the cutting dynamics. In this study, the model-based and signal-based chatter detection methods were thoroughly investigated. As a result, a hybrid model- and signal-based chatter detection method was proposed. By analyzing the residual between the force measurement and the output of the cutting force model, milling chatter could be detected and identified efficiently during the milling process.

     

基于电流变材料的切削颤振在线监控技术研究

提出可以利用电流变材料的电控流变特性,通过在线调控切削系统动态特性以提高切削稳定性。并针对镗削系统,开发出一套可根据实时采集的切削振动信号自适应地快速调整系统动态特性以避免颤振发生的颤振智能监控系统。     

Chatter prediction based on frequency domain solution in CNC pocket milling

Chatter has been a problem in CNC machining process especially during pocket milling process using an end mill with low stiffness. Since an iterative time-domain chatter solution consumes a computing time along tool paths, a fast chatter prediction algorithm for pocket milling process is required by machine shop-floor for detecting chatter prior to real machining process. This paper proposes the systematic solution based on integration of a stability law in frequency domain with geometric information of material removal for a given set of tool paths. The change of immersion angle and spindle speed determines the variation of the stable cutting depth along cornering cut path. This proposed solution transforms the milling stability theory toward the practical methodology for the stability prediction over the NC pocket milling.