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。     

Development of chatter detection in milling processes

The aim of this research is to develop an in-process detection of the chatter for the actual milling processes regardless of any cutting condition within the small data processing time by utilizing the dynamic cutting forces obtained during cutting. The proposed method introduces three parameters, which are calculated and obtained by taking the ratio of the average variances of the dynamic cutting forces of three force components, to identify the chatter. The algorithm was developed and implemented on five-axis computer numerical control machining center to detect the chatter in ball-end milling and end milling processes. The chatter and the nonchatter can be simply detected during the in-process cutting by mapping the obtained values of three parameters in the reference feature spaces regarding the determined threshold values. The experimental results showed that the proposed method can be effectively used to detect the chatter during cutting even though the cutting conditions are changed.     

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。     

高速铣削时颤振的诊断和稳定加工区域的预报

给出一种通过测量加工过程中的噪声来诊断高速铣削时颤振的方法.先测量环境噪声,然后测量加工噪声.理论分析和试验结果表明,如果加工噪声的主谐振频率接近其中一个环境噪声主谐振频率或者是齿频的整数倍,那么系统无颤振,否则有颤振.建立系统结构和铣削过程动力学特征参数的数学模型.根据测出的颤振频率,通过所建模型可解出系统的固有频率、阻尼比和过程参数,并计算出稳定极限曲线.试验证明,该方法能较好地预报高速铣削时的稳定加工区域.     

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.     

Chatter stability prediction in milling using time-varying uncertainties

The chatter stability in milling severely affects productivity and quality of machining. Tool wear causes both the cutting coefficient and the process damping coefficient, but also other parameters to change with cutting time. This variation greatly reduces the accuracy of chatter prediction using conventional methods. To solve this problem, we consider the cutting coefficients of the milling system to be both random and time-varying variables and we use the gamma process to predict cutting coefficients for different cutting times. In this paper, a time-varying reliability analysis is introduced to predict chatter stability and chatter reliability in milling. The relationship between stability and reliability is investigated for given depths and spindle speeds in the milling process. We also study the time-varying chatter stability and time-varying chatter reliability methods theoretically and with experiments. The results of this study show that the proposed method can be used to predict chatter with high accuracy for different cutting times.     

基于奇异值分解的铣削力信号处理与铣床状态信息分离

利用连续截断信号构造矩阵,通过奇异值分解可以将信号表示为一系列分量信号的简单线性叠加,证明了各分量之间是两两正交的,且具有零相位偏移特性.根据分量信号的信息量可以确定合理的矩阵结构.对铣削力信号的处理实例表明,奇异值分解方法分离出机床主轴旋转基频近乎完整的时域波形,分辨出两个频率很接近的信号分量,发现信号中隐藏的调幅现象,证实机床的爬行并确定爬行频率.最后与小波变换的结果进行比较,表明这一方法对铣削力信号的分离效果优于小波变换.     

Chatter stability of micro end milling by considering process nonlinearities and process damping

The micro end milling uses the miniature tools to fabricate complexity microstructures at high rotational speeds. The regenerative chatter, which causes tool wear and poor machining quality, is one of the challenges needed to be solved in the micro end milling process. In order to predict the chatter stability of micro end milling, this paper proposes a cutting forces model taking into account the process nonlinearities caused by tool run-out, trajectory of tool tip and intermittency of chip formation, and the process damping effect in the ploughing-dominant and shearing-dominant regimes. Since the elasto-plastic deformation of micro end milling leads to large process damping which will affect the process stability, the process damping is also included in the cutting forces model. The micro end milling process is modeled as a two degrees of freedom system with the dynamic parameters of tool-machine system obtained by the receptance coupling method. According to the calculated cutting forces, the time-domain simulation method is extended to predict the chatter stability lobes diagrams. Finally, the micro end milling experiments of cutting forces and machined surface quality have been investigated to validate the accuracy of the proposed model.     

基于连续小波和多类球支持向量机的颤振预报

研究了一种应用连续小波特征和多类球支持向量机进行铣削系统颤振预报的方法,该方法基于连续小波变换提取铣削振动信号的特征,利用多类球支持向量机对正常铣削状态、颤振孕育状态和颤振爆发状态的振动信号进行三分类识别,通过识别颤振孕育状态预测颤振爆发。试验结果表明,在铣削颤振识别与预测中,铣削振动信号的连续小波特征与多类球支持向量机相结合具有良好的识别颤振孕育状态和颤振爆发状态的能力,颤振孕育状态的识别正确率达95.0%,颤振爆发状态的识别正确率达97.5%。     

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.     

机床颤振的自适应多步向前预报

分析了机床颤振孕育过程的变化特征,提出了颤振的监测参数。基于人工神经网络,对监测参数提出了一种全局归纳与局部校正相结合的自适应多步向前预测计算算法,可在机床颤振孕育过程的早期对颤振进行准确、及时的预报,并在一台数控车床上进行了试验。     

Chatter reliability prediction of side milling aero-engine blisk

Reliability analysis of a dynamic structural system is applied to predict chatter of side milling system for machining blisk. Chatter reliability is defined as the probability of stability for processing. A reliability model of chatter was developed to forecast chatter vibration of side milling, where structure parameters and spindle speed are regarded as random variables and chatter frequency is considered as intermediate variable. The first-order second-moment method was used to work out the side milling system reliability model. Reliability lobe diagram (RLD) was applied to distinguish reliable regions of chatter instead of stability lobe diagram (SLD). One example is used to validate the effectiveness of the proposed method and compare with the Monte Carlo method. The results of the two approaches were consistent. Chatter reliability and RLD could be used to determine the probability of stability of side milling.

     

Chatter detection in milling processes using frequency-domain Rényi entropy

Chatter is a kind of self-excited vibration and causes negative effects in machining processes. This paper presents a practical method to identify the chatter with cutting force signals in milling processes. Since the spectrum of the chatter signal exhibits discrete spectral lines around the chatter frequencies and the Rényi entropy is an effective index to characterize the randomness of data series, the frequency-domain Rényi entropy is proposed as a chatter indicator. As the chatter severity level grows, the signal components at the chatter frequencies become more and more significant, which means a reduction of the randomness of the spectral series. As a result, the value of the Rényi entropy-based indicator decreases rapidly at the onset of the chatter. In order to eliminate the interference of the normal signal components, i.e., the spindle speed-related frequency components, the spectrum is preprocessed to filter out those components first. Various milling experiments are conducted. The results show that the value of the proposed indicator changes sharply at the onset of chatter in various milling conditions with different spindle speeds and cutting depths. Also, the proposed indicator is compared with the commonly used Shannon entropy-based indicator and verified to have a larger difference between the stable and chatter statuses and is higher sensitivity to the chatter.     

数控机床切削颤振状态监测与控制技术

针对数控机床加工过程中切削颤振的现象,通过对机床的在线监测,将采样出的加速度信号进行数据处理,提出一种θ法作为判据的方法来判别颤振是否发生,并通过监控计算机对主轴箱和进给箱进行调控,达到抑制颤振的方法。该监控系统通用性强,所用设备少,具有可行性,从而为提高加工效率提供可循的加工依据。     

Integration of Milling Process Simulation with On-Line Monitoring and Control

This paper describes the practical integration of solid modelling with milling process simulation, on-line monitoring and supervisory control. Extensions to the ACIS solid modeller parse the NC program file, determine the tool/workpiece immersion geometry for each motion, and then subtract the tool swept volume to update the part shape. The immersion geometry is then used with a mechanistic milling process model to schedule optimal feedrates, to provide on-line reference data for monitoring, and to calculate departure paths so that, if unexpected events require it, the tool can be moved smoothly away from the workpiece. The additional information is embedded into the original NC program. This information is recognised by a supervisory control computer that monitors the process, and uses the information to react intelligently to tool wear, breakage, or chatter. Experimental results are presented to demonstrate the validity of the technique.     

STABILITY PREDICTION OF TITANIUM MILLING WITH DATA DRIVEN RECONSTRUCTION OF PHASE-SPACE

Significant research effort has been carried out in the detection of chatter, which is one of the main barriers against titanium milling. State-of-the-art techniques are unable to satisfy requirements of industry in terms of in-process chatter detection. The present study reports the use of sensor-signal driven reconstructed phase space attractors combined with image correlation as a solution of chatter prediction during milling of titanium in industry. The method uses Poincaré sections of reconstructed phase space attractor as patterns to identify the onset of chatter in the apparently random behavior of vibrations in the milling process. Image correlation of Poincaré sections indicates the onset of chatter in the milling process.     

基于填充曲线刀具路径的数控铣削过程物理仿真

研究了采用一类填充曲线刀具路径进行数控铣削加工时,铣削过程的动态特性,推导了带有一般意义的瞬时名义铣削厚度算式,并通过数字物理仿真,探讨了填充曲线刀具路径对铣削过程颤振的抑制作用,结果表明,提出的截法线法能有效地解决数控铣削过程瞬时名义铣削厚度的计算问题,同时对不同刀具路径的铣削过程动态特性仿真结果表明,Hilbert填充曲线刀具路径能有效地抑制铣削过程再生颤振的发生与发展,对提高机械零部件加工效率和加工表面质量有重要作用。     

磨削过程计算机集成智能监控系统

利用神经网络建立了磨削过程计算机集成智能监控系统,该系统将磨削过程监测、故障诊断和反馈控制组成一个有机整体.通过在线提取磨削声发射(AE)信号RMS峰值、FFT峰值、信号标准偏差以及信号累积幅值增量,可以实现磨削烧伤、磨削颤振、砂轮钝化等故障的在线实时诊断.通过反馈控制系统实现磨削参数的实时调整,提高了磨削加工的性能、效率和磨削质量的稳定性.实测结果证明了该系统的有效性.