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.     

变速切削抑制颤振的控制系统及实验研究

为解决切削颤振给加工过程带来的不利影响,设计了基于DSP的适用于变速切削的电动机控制系统,编制了控制程序,并应用改造后的铣床进行了变速切削抑振试验.根据检测所得的加速度信号和声音信号,分析了变速幅值和变速频率对切刘振动的抑制效果,对变速铣削进行了较为深入的分析和研究.     

变速切削抑制颤振的控制系统及实验研究

为解决切削颤振给加工过程带来的不利影响,设计了基于DSP的适用于变速切削的电动机控制系统,编制了控制程序,并应用改造后的铣床进行了变速切削抑振试验。根据检测所得的加速度信号和声音信号,分析了变速幅值和变速频率对切削振动的抑制效果,对变速铣削进行了较为深入的分析和研究。     

基于小波包能谱熵的铣削颤振监测方法

针对切削加工过程中容易出现颤振,导致零件表面加工质量降低,本文以铣削加工为研究对象,提出了基于小波包能谱熵的铣削颤振监测方法。信号的采集是一个连续的过程,为避免刀具不参与切削对这种方法的影响,本文提出基于铣削力幅值平方和的方法以识别刀具是否参与切削。试验结果表明,无论对于刀具是否参与切削的识别或者是否出现铣削颤振的监测都能有效的监测。     

基于ESPRIT频谱估计和隐马尔可夫模型的铣削颤振辨识系统建模

为有效辨识铣削过程中对工件表面质量影响较大的颤振并确定其具体发生时刻,建立了基于旋转不变技术估计信号参数(ESPRIT)和隐马尔可夫模型(HMM)的铣削颤振辨识模型。建模过程中先利用ESPRIT算法分别对以固定周期采集的切削力信号进行频谱估计。根据频谱特征将经过主轴旋转周期滤波后的切削力信号进行分类,利用具有类标签的力信号训练HMM参数,获得颤振辨识模型的参数。为实现铣削颤振辨识及切削状态监测,开发了基于"PC+FIFO高速数据采集"的切削力信号实时采集与处理功能,建立了该功能与开放式模块化结构数控系统的数据传输机制。实验结果表明,辨识系统可根据建立的颤振辨识模型准确判断加工过程中各时刻的切削状态,实现了铣削颤振的有效辨识。     

基于数值模型的数控加工中切削力与稳定域仿真

针对数控铣削加工工艺参数选择存在的问题,以球头铣刀高速铣削过程为研究对象,建立了考虑机床-刀具-工件系统振动的非线性动力学模型,分析了铣削力中的动态分量对切削颤振的影响,在考虑再生颤振的基础上建立非线性动力学模型.基于动态铣削力建模和颤振稳定域分析计算,提出了机床切削系统稳定性极限预测方法,并对其进行仿真分析,为铣削加...     

平面端铣切削振动试验

介绍了端铣振动试验方法。通过观察分析工件表面的微观不平度、切削表面的高频波纹和在机床切削点测得的振动信号,跟踪分析了铣削强迫振动和颤振现象。强迫振动随切削速度、进给量、切削深度增大而加剧,颤振随切削速度、进给量增大及切削深度减小不易发生,结论与理论分析、他人试验结果相符,为选择改变切削用量、减小铣削振动指出了方向     

基于时域仿真法的断续铣削颤振预测

颤振是影响铣削加工表面质量和限制切削效率的重要原因,准确获取稳定性叶瓣图是避免颤振的有效途径.精加工铣削常使用小径向切深/刀具直径比,产生过小的实时切削厚度,刀具容易脱离工件,造成显著非线性因素;过小的径向切深/刀具直径比也导致铣削加工高度断续.因此,常用的圆弧切削厚度已不能近似实际切削厚度,进而影响断续铣削加工颤振预测.采用考虑实际切削厚度的时域仿真法预测断续铣削加工颤振稳定性.该方法使用刀具实际运动轨迹计算切削厚度,并综合考虑了铣削过程中刀具和工件的动力学特性对切削厚度的影响.提出基于相关系数的无量纲颤振判定准则,并用于铣削仿真结果加工状态的判定.通过钛合金Ti6Al4V铣削验证试验结果,所提出的时域仿真法能准确预测小径向切深/刀具直径比所致的断续铣削加工稳定性叶瓣图,为高断续铣削加工无颤振加工参数选择提供了一种有效方法.     

基于电流信号的铣削颤振识别技术研究

以铣削为对象,建立了2自由度的铣削加工颤振动力学模型,分析了铣削颤振的产生机理;在此基础上,提出了基于电流信号的铣削颤振检测识别模型,该模型利用主轴电流信号的高频成分和低频成分的相对变化,很好地识别出颤振的发生,同时,该方法有效地消除了切削条件变化对检测结果的影响.此外,该方法算法简单,计算量小,可以实现实时检测.     

基于刀尖模态的Al-7075铣削颤振稳定性预测研究

针对切削加工中的颤振问题,以铣削加工Al-7075为例,通过刀尖模态分析、模态参数识别,获得了铣削加工Al-7075的颤振稳定性叶瓣图(Lobes图)。通过设计信号采集实验,对颤振电压信号和铣削力信号进行采集并对颤振进行实验表征,进而验证稳定域预测的准确性。研究表明,切削加工Al-7075过程中,颤振易发生在低转速区,提高主轴转速及合理加大轴向切深可提高生产率和加工质量,从而验证了Lobes图中稳定域预测的有效性。     

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。     

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.     

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.

     

Milling force vibration analysis in high-speed-milling titanium alloy using variable pitch angle mill

Chatter may cause fast wear of tools and poor surface quality of the workpieces at high cutting speed and it will happen on different process parameters; how do we select the suitable cutting speed to suppress the chatter? In this paper, a signal analysis method for milling force and acceleration is adopted to identify chatter, which can obtain the results not only in frequency of chatter but also in the contribution for milling force at different frequencies. Through the milling experiment, the machining vibration behaviors of milling Ti–6Al–4V with variable pitch end mill were investigated. Milling force and acceleration signals obtained from experiment were analyzed and compared at stable and unstable milling processes. The experimental results show that when the chatter occurs, milling forces were found to increase dramatically by 61.9–66.8% compared with that of at stable cutting; machining surface quality became poor and machined surface roughness increases by 34.2–40.5% compared with that of at stable cutting.     

Identifying bifurcation behavior during machining process for an irregular milling tool geometry

High-productivity machining processes cause tool and material defects and even damages in machine spindles. The onset of self-excited vibration, known as chatter, limits this high material removal rate. This chatter vibration refers to machining instability during cutting processes, which results in bifurcation behavior or nonlinear effect wherein the tool and the workpiece are not engaged with each other. In particular, bifurcation for low-radial immersion conditions can be easily promoted and identified. In this study, an experiment on an irregular milling tool as a variable helix and variable pitch geometry was conducted under a flexible workpiece condition. The bifurcation behavior from regenerative chatter was identified and quantified from displacement sensor and inductive sensor measurements. A series of cutting tests was used to measure the vibration signals, which were then analyzed based on the frequency spectrum, the one-per-revolution effect, and the Poincaré section. According to results, Hopf bifurcation and period-one bifurcation instabilities apparently occurred to validate chatter stability prediction through a semi-discretization method. However, period-doubling bifurcation was only determined during the unstable cutting of a uniform tool that was not in variable helix/pitch or an irregular milling tool. An irregular tool geometry caused the modulation of the regenerative effect to suppress chatter, and period-doubling instability could not be exhibited during cutting as a regular tool behavior. This period-one chatter instability of an irregular milling tool should be identified and avoided by practitioners to achieve high productivity in machining using the aforementioned irregular milling tools.     

Estimation of the chatter zones of drilled holes by using s-transformation

S-transformation based on diagnostic of machining operations from displacement, velocity, torque, and force data is proposed. Damping ratio was calculated from the time-frequency-amplitude plots of the s-transformation. The values were called damping index by considering the influence of the window functions on the calculations. Variance of the damping index in a small band around the first natural frequency of the system was found an effective chatter detection tool. The s-transformation of all the signals considered had similar characteristics and the sharp drop of the variance of the damping index indicated chatter development.     

Mirror milling chatter identification using Q-factor and SVM

Mirror milling is an effective approach to improve large-scale monolithic thin-walled parts machining quality through ensuring the mirror relations of cutter and supporting head. However, the introduction of supporting head influences the dynamic characteristics of the tool-workpiece system. Essentially, the measured raw signal contains more coupled components and shows more oscillatory and aperiodic behaviors. Therefore, it is difficult to identify the mirror milling chatter using the monitoring signals. Comparing with traditional indicators, Q-factor can be used to describe the machining state in the view of signal oscillatory behavior, which is suitable for chatter-related component extraction in thin-walled part machining. In this paper, chatter-related signal component identification and diagnosis of thin-walled parts based on signal Q-factor and support vector machine (SVM) is proposed. The frequency band with maximal variation of Q-factors is taken as the chatter-related signal component. Using the feature vector constructed by Q-factors and power spectrum values of the determined frequency band, the SVM is used for milling state diagnosis. The prediction accuracy is much higher than the other frequency band and traditional indicators. It indicates the effectiveness of the proposed mirror machining chatter identification method.     

非线性颤振对切削加工过程影响初探

研究了非线性颤振对机械加工过程的影响规律,通过实验得到了颤振稳定性图,分析了切削用量、切削条件与非线性颤振及机械加工质量的关系,提出了避免切削颤振的发生与提高加工质量的工艺措施。     

A novel deep groove machining method utilizing variable-pitch end mill with feed-directional thin support

A novel method for deep groove machining is developed which utilizes a long-shank variable-pitch end mill with a feed-directional thin support in this research. Recently, thin and tall ribs are required for many parts to reduce their weight and material consumption without sacrificing their stiffness and strength. It leads to necessity of deep and narrow grooves in dies and molds for their mass production. However, such deep groove machining is difficult, since long flexible end mills cause severe chatter vibrations induced by regeneration and mode-coupling. There have been many studies on the regenerative chatter vibration, while there have been few studies on the mode-coupling chatter vibration. Both chatter vibrations need to be suppressed in the deep groove machining. In this study, the regenerative chatter vibration is suppressed by employing one of the conventional methods, i.e. a variable-pitch end mill. On the other hand, there is not a good method to suppress the mode-coupling chatter vibration in the deep groove machining. Therefore, a new suppression method is proposed in which the long-shank variable-pitch end mill is supported with a thin plate in the feed direction. The support device and the long-shank variable-pitch end mill are developed and applied to machining of hardened die steel. Validity of the proposed method is verified both analytically and experimentally in this study.