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...     

基于颤振预报和预测的稳定性控制理论及方法研究

针对由颤振预测控制策略指导的稳定切削控制方法实时动态调整能力差的缺点,综合颤振预报、预测理论,进行了稳定性在线寻优控制理论及方法的研究。基于刀具—工件系统铣削再生颤振动力学模型,研究了考虑系统结构参数(刀具、工件装夹刚度)和加工参数(切削加工参数优化选择)的切削稳定性评价方法,提出了以扩大稳定性区域和稳定最大材料切除率为控制目标的机床稳定性控制方法。形成了以"预报—控制—效果评估—再控制"为步骤的在线监测、智能诊断和实时控制的集成一体化策略。设计并实施了稳定性控制理论验证实验,获得了与理论分析一致的结论。     

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.     

大型蜗杆铣削颤振稳定性研究

颤振通常会降低产品质量和生产效率,缩短机床和刀具的使用寿命。针对工业现场某大型蜗杆工件铣削加工中出现的颤振问题,建立其铣削颤振数学模型,采取对时滞项进行离散化的方法,利用Floquet理论进行判稳。在不同切削加工参数下试验对比稳定与失稳状态时刀具的振动信号特征,验证稳定性理论分析的正确性,避免了铣削过程颤振的发生。     

铣削颤振在线智能控制方法研究

针对铣削加工中颤振降低加工效率的情况,研究了颤振在线自主识别与控制的方法。使用传感器拾取加工状态信息,提取振动信号时域均方差特征及频域幅度谱特征,提出了具有广泛适用性的颤振在线识别算法。以2π作为刀具前后两次铣削振纹间的最优相位差,依据颤振频率调整主轴转速,消除颤振。以SIEMENS 840D系统为平台研究了机床转速在线实时调整的方法。进行了铝合金工件的铣削实验,验证了颤振在线抑制、提高加工效率的有效性。     

细长类结构零件的高速铣削工艺参数优化研究

在铣削加工过程中,细长类结构零件易出现颤振而导致加工表面质量变差,选用工艺参数保守易导致加工效率降低,针对这些问题,以长桁零件为例,开展了高效铣削参数的优化研究。首先,根据锤击试验获取了刀具系统的动力学特性参数;其次,建立了不同工况下工件系统的有限元模型,进行了谐响应分析,并获取了零件加工过程中的动态特性;结合刀具系统和工件系统的动态特性,建立了工艺系统的颤振稳定域;最后,建立了以加工效率和刀具寿命为目标函数、切削参数为变量、以切削稳定性为主要约束条件的参数优化模型,并通过粒子群算法获得了铣削参数的全局最优解。研究结果表明:使用优化后的切削参数进行加工,可有效消除切削加工过程中的颤振现象,且加工的效率平均可提高19%;该工艺参数对避免细长结构零件在工艺准备初期发生颤振具有一定的借鉴意义。     

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。     

基于稳定性理论的机床动态优化方法的研究

基于建立的刀具-工件铣削再生颤振多自由度动力学模型,研究了考虑机床结构参数和加工参数的切削稳定性评价方法。提出了基于稳定性理论以扩大稳定性区域和最大材料切除率为优化目标的机床全生命周期稳定性动态优化方法。在设计阶段,通过对影响稳定性的工艺参数动力修改扩大稳定区域;在生产阶段,进行刀具装夹结构和切削参数的优化,进一步扩大稳定区并确保稳定切削下的最大材料切除率。     

微径球头铣刀铣削力建模与仿真

以微径球头铣刀铣削力为研究对象,分析了刀具刃线模型.将刀具沿刀轴方向离散为若干切削单元,分别依照单齿切削及两齿切削求得各切削单元的实际瞬时切削厚度.基于实体造型的方法提取了参与切削的切削刃段,并通过实验识别了瞬时切削力系数及主轴径向跳动参数,建立了综合考虑主轴径向跳动、微细铣削所特有的尺度效应的影响及可能出现的单齿切削现象的微径球头铣刀铣削力模型.实验结果验证了所提模型的有效性和可行性.     

离散隐马尔可夫模型在颤振预报中的应用研究

对于切削过程中颤振孕育的动态模式,提出了基于离散隐马尔可夫模型(DHMM)的模式识别理论预报颤振的新方法。首先对切削过程的振动信号进行FFT特征提取,然后利用自组织特征映射(SOM)神经网络对提取的特征矢量进行冗余信息压缩与预分类编码;再根据多变量DHMM建模理论,对切削颤振孕育的各种过程模式建立相应的DHMM,把矢量编码作为观测序列引入到DHMM中进行机器学习、训练;最后将观测序列引入到DHMM中进行颤振孕育的概率识别尝试。实验表明,该方法对颤振孕育过程识别是十分有效的,颤振预报正确率达93.3%。     

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.     

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.     

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.

     

CONTINUITY AND BREAK OF CHATTER VIBRATION STATUS

By turning a specifically designed conical part, complete process of metal cutting, in which the chatter occurs and expands, is recorded and analyzed. This process exposes that chatter vibration has two characters called continuity and break. The continuity character means that vibration extent enlarges continuously while chatter frequency is almost changeless as the cutting depth extends downwards continuously. The break one is that chatter frequency moves rapidly downwards to a lower level while chatter remains after the cutting depth reach another given value. It is confirmed through an exciting test that the two chatter frequencies obtained in chatter test belong to the natural frequencies of workpiece system and cutting tool system respectively. From the viewpoints of chatter energy supplying and chatter mass effect, the. chatter should occur on one of the two final executive components in its natural frequency. On this basis, a new chatter model with two chatter active bodies is proposed. This new model can better explain the above phenomenon, and adapt to chatter monitoring and improvement of component structure well.     

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.     

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.     

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。     

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。     

A novel chatter stability criterion for the modelling and simulation of the dynamic milling process in the time domain

The modelling of the dynamic processes in milling and the determination of chatter-free cutting conditions are becoming increasingly important in order to facilitate the effective planning of machining operations. In this study, a new chatter stability criterion is proposed, which can be used for a time domain milling process simulation and a model-based milling process control. A predictive time domain model is presented for the simulation and analysis of the dynamic cutting process and chatter in milling. The instantaneous undeformed chip thickness is modelled to include the dynamic modulations caused by the tool vibrations so that the dynamic regeneration effect is taken into account. The cutting force is determined by using a predictive machining theory. A numerical method is employed to solve the differential equations governing the dynamics of the milling system. The work proposes that the ratio of the predicted maximum dynamic cutting force to the predicted maximum static cutting force can be used as a criterion for the chatter stability. Comparisons between the simulation and experimental results are given to verify the new model.