切削颤振信号的混沌时间序列识别方法研究

对切削颤振的在线监测识别是提高加工质量的关键技术之一。基于离散动力系统非线性混沌控制理论,以加工过程振动信号为非线性系统输入量,采用嵌入时间序列和关联积分的C-C算法,确定最佳嵌入维数m与时间延迟τ,进而对在线监测获得的离散振动信号进行相空间重构。实施铣削颤振试验,采用上述时间序列分析方法获得颤振产生前后的相图,分析表明相图表现出混沌特性,实现了切削颤振的识别。     

基于切削颤振抑制系统的BP神经网络PID控制研究

从颤振的发生机制及集中质量系统的颤振运动微分方程出发,以镗削颤振抑制问题为背景,建立了具有连续分布质量的压电智能切削颤振抑制系统动力学模型,重点研究了BP神经网络与经典PID控制相结合的智能控制策略,为切削颤振的智能控制提供了新的方法。继而在Simulink中分别利用经典PID和BP神经网络PID对切削颤振抑制系统进行控制仿真。仿真结果表明:和经典的PID控制相比,BP神经网络PID控制自适应能力更强。     

以方差和互相关系数判别切削颤振的仿真研究

文章采用仿真研究方法，对方差和互相关系数绝对值对机床切削颤振是否发生的判别能力进行了系统的研究，发现它们都不宜单独作为判别函数，提出了以方差和互相关系数绝对值作为综合判据判别切削颤振是否发生的观点。采用实验实际测得的机床切削颤振信号进行仿真验证，发现综合判据对于颤振有很好的判别能力。     

基于超磁致伸缩致动器的再生型车削颤振抑制系统实验研究

切削颤振是金属加工时刀具和工件之间的一种复杂且有害的强烈相对振动。分析了再生型颤振的机制,建立了超磁致伸缩致动器(GMA)颤振系统的动力学模型,并阐述了超磁致伸缩微致动器颤振系统的稳定性。最后通过对比切削颤振实验和颤振抑制实验的振动信号数据结果,验证了基于GMA的颤振抑制系统抑制切削颤振的可行性。     

基于GMA再生型车削颤振抑制系统实验研究

切削颤振是金属加工时刀具和工件之间的一种复杂且有害的强烈相对振动。文章从理论上分析了再生型颤振的机理,建立超磁致伸缩致动器( GMA)颤振系统动力学模型,并阐述了超磁致伸缩微致动器颤振系统的稳定性,最终通过对比切削颤振实验和颤振抑制实验的振动信号数据结果,验证了基于GMA的颤振抑制系统抑制切削颤振的可行性。     

基于特征频率组的铣削颤振识别

金属切削过程中的颤振,易降低工件加工质量和机床加工精度。为实现对加工过程中颤振的在线监测,设计颤振的在线分析和识别方法十分必要。文章基于对主轴加速度信号的频域分析,提出了用于振动识别的特征频率组的分析方法,特征频率组包含了强迫振动或颤振的典型频谱信息。该方法采用加速度传感器,获取主轴的振动信号,以特征频率组为识别特征,对铣削加工的稳定性进行识别,并通过铣削试验进行验证。试验结果表明:与时域分析的方法相比,该方法利用的频域特征,无须阈值和人为判断,适用于在线的识别与分析。     

切削颤振数学模型的研究

切削系统的稳定性是高速加工领域的一个重要研究内容,在工程实践中有着广泛的应用.对于颤振临界不稳定性运动的研究,常采用动力学模型的方式.本文作者在切削颤振数学模型的基础上,建立了连续切削和断续切削的颤振数学模型,简化了切削颤振系统的自由度.     

硬质合金车梳加工中切削颤振的研究

车梳加工是现今汽车发动机曲轴主轴颈粗、半精加工时广泛应用的切削工艺。本文介绍了车梳加工的切削机理及其应用。通过车梳拉专用机床以及自行搭建的颤振实验平台来检测工件每时每刻的振动位置,以此来比较梳型刀片与普通车刀片的切削颤振情况和加工表面质量以及针对精加工的各切削参数对车梳加工切削颤振的影响。实验结果表明:在相同切削参数和切削余量下,梳型刀片的切削颤振大于C型刀片,加工表面粗糙度略低于C型刀片,切削效率则远远高于C型刀片;背吃刀量和进给速度增大时梳型刀片切削颤振增大,主轴转速增大时切削颤振减小。     

基于支持向量回归的切削颤振状态趋势预测的研究

文章使用支持向量回归方法（SVR）成功地对切削颤振状态趋势进行了预测，同时提出了一种新的信号特征提取方法。首先逐次对切削信号进行小波包分解，然后计算各频带区间内的能量并对能量进行归一化处理，于是得到了信号在各区间的能量分布图以及各区间的能量变化曲线，从能量的变化曲线可以很清楚的看到，各区间的能量分布很好地反应了切削颤振过渡的特征。最后通过SVR算法对能量变化趋势进行回归预测，与实际曲线进行比较，预测结果基本能反应出能量的变化趋势，从而为切削颤振的预报奠定了较好的基础。     

切削加工过程中颤振预测的研究进展

切削颤振是切削加工过程中伴随的一种普遍现象,具有复杂的不确定性和不可预知性,切削颤振的预测将有助于提高切削加工表面质量。对切削颤振的产生机制和主要影响因素进行了介绍,以及对切削颤振的稳定性极限、判别方法、预测方面的研究成果进行了总结,并分析了其中的技术问题与未来的发展方向。     

高速切削稳定性研究综述

介绍国内外学者在高速切削稳定性方面的研究成果,着重总结和分析以下几方面的研究工作:颤振的发生机理及数学模型、颤振的监测及预测以及抑制颤振提高切削稳定性的方法.     

Automatic chatter detection in grinding

Two methods for automatic chatter detection in outer diameter plunge feed grinding are proposed. The methods employ entropy and coarse-grained information rate (CIR) as indicators of chatter. Entropy is calculated from a power spectrum, while CIR is calculated directly from fluctuations of a recorded signal. The methods are verified using signals of the normal grinding force and RMS acoustic emission. The results show that entropy and CIR perform equally well as chatter indicators. Based on the normal grinding force, they detect chatter in its early stage, while only cases of strong chatter are detected based on RMS acoustic emission.     

基于小波包分解和奇异谱分析方法的立铣加工颤振监测

在立铣加工过程中,颤振是加工过程失稳的一个最重要的原因。颤振将会严重影响工件表面质量和材料去除率,加剧刀具磨损和恶化工作环境。虽然大部分颤振监测系统可以监测到颤振发生,但颤振发生时已经对工件和刀具产生了严重的损伤,因此,需要提前监测到颤振特征。在颤振发生过程中,振动信号具有在时域中不断增大,在频域中能量频移的特性。考虑这两个振动信号特征,提出了一种颤振特征提取方法。提取颤振发生频带中振动信号的能量比和奇异谱熵系数作为两个颤振特征,并通过人工神经网络模型实现切削颤振的识别。文中提出的颤振监测系统包括特征提取和分类,能够精确辨识立铣加工中的稳定、过渡和颤振状态。     

基于小波包和倒频谱分析的颤振特征提取方法

立铣加工过程中的颤振会严重影响工件表面质量和材料去除率,加剧刀具磨损和恶化工作环境。虽然大部分颤振监测系统可以监测到颤振发生,但颤振发生时已经对工件和刀具产生了严重的损伤,因此,需要提前监测到颤振特征。由于加工过程的非线性导致振动信号频率成分复杂,单一的时频分析方法难于得到可靠的颤振特征。通过小波包分解确定颤振发生频段并重构该频段信号,通过颤振发生频段的倒频谱辨识稳定、过渡和颤振状态。研究结果表明,该方法可以有效识别立铣加工过程的稳定、过渡和颤振状态。     

轧机工作辊磨削振纹检测与控制

研究分析轧辊磨削振纹产生原因,通过对设备、砂轮、磨削工艺、操作等方面进行改进优化,研制了专用的辊颈振纹检测方法和分级控制规则,有效控制了磨削振纹的产生.     

A New Method for Chatter Detection in Grinding

A new method for automatic chatter detection in outer-diameter grinding is proposed which exploits significant changes in grinding dynamics caused by the onset of chatter. The method is based on monitoring of a non-linear statistic called the coarse-grained entropy rate. The entropy rate is calculated from the fluctuations of the normal grinding force. Values of the entropy rate close to zero are typical of chatter, whereas larger values are typical of chatter-free grinding. If the entropy rate is normalized, a threshold value can be set which enables automatic distinction between chatter-free grinding and chatter.     

Linear analysis of chatter vibration and stability for orthogonal cutting in turning

The productivity of high speed milling operations is limited by the onset of self-excited vibrations known as chatter. Unless avoided, chatter vibrations may cause large dynamic loads damaging the machine spindle, cutting tool, or workpiece and leave behind a poor surface finish. The cutting force magnitude is proportional to the thickness of the chip removed from the workpiece. Many researchers focused on the development of analytical and numerical methods for the prediction of chatter. However, the applicability of these methods in industrial conditions is limited, since they require accurate modelling of machining system dynamics and of cutting forces. In this study, chatter prediction was investigated for orthogonal cutting in turning operations. Therefore, the linear analysis of the single degree of freedom (SDOF) model was performed by applying oriented transfer function (OTF) and \tau decomposition form to Nyquist criteria. Machine chatter frequency predictions obtained from both forms were compared with modal analysis and cutting tests.     

On stability prediction for milling

Stability of 2-dof milling is investigated. Stability boundaries are predicted by the zeroth order approximation (ZOA) and the semi-discretization (SD) methods. While similar for high radial immersions, predictions of the two methods grow considerably different as radial immersion is decreased. The most prominent difference is an additional type of instability causing periodic chatter which is predicted only by the SD method. Experiments confirm predictions of the SD method, revealing three principal types of tool motion: periodic chatter-free, quasi-periodic chatter and periodic chatter, as well as some special chatter cases. Tool deflections recorded during each of these motion types are studied in detail.     

Tool wear and chatter detection using the coherence function of two crossed accelerations

Tool wear and chatter have been found to be the main causes of rejects in the machining of super alloys. A novel detection technique to identify both tool wear and chatter in turning a nickel-based super alloy is introduced. It uses the coherence function between two crossed accelerations from the bending vibration of the tool shank. The value of the coherence function at the chatter frequency reaches unity at the onset of chatter. Its values at the first natural frequencies of the tool shank approach unity in the severe tool wear stage. The results are interpreted using the analysis of the coherence function for a single input-two output model. The advantage of using this method is that the thresholds for detecting severe tool wear and chatter can be easily set, since values of coherence function are normalized to a range of between zero and unity, and are also not so susceptible to changing cutting conditions, because the value of the coherence function is close to unity at the onset of chatter and severe tool wear.     

On-line chatter detection and identification based on wavelet and support vector machine

Chatter is very harmful to precision machining process. To avoid cutting chatter effectively, a method based on wavelet and support vector machine is presented for chatter identification before it has fully developed. Wavelet transform, which can image the information in both the time and frequency domain, is applied as an amplification for the chatter premonition. Each wavelet packet's energy regularly changes during the development of the chatter, which has a time advantage for the identification. Therefore, a two-dimensional feature vector is constructed for chatter detection based on the standard deviation of wavelet transform and the wavelet packet energy ratio in the chatter-emerging frequency band. A support vector machine (SVM) is designed for pattern classification based on the feature vector. The intelligent recognition system, composed of the feature extraction and the SVM, has an accuracy rate of 95% for the identification of stable, transition and chatter state after being trained by the experiment data. The method can be applied in different machining processes.