切削颤振的在线监测与控制研究现状分析

切削颤振是切削过程中，工件和刀具之间自发产生的振荡，它严重影响机床加工性能。因此，多年来国内外学者针对切削颤振的机理、在线监测和在线控制进行了大量的研究。本文介绍了切削颤振在线监测与控制技术国内外研究现状，分析了现有方法的优缺点，总结了研究中存在的问题，展望了机床颤振的在线监测与控制技术的发展趋势。     

对再生型切削颤振模型的试验分析

一个专门设计的切削颤振试验揭示了颤振发生和发展的全过程.在连续增加切深的条件下,颤振会在一个特定的切深下突然发生,其特征是某一频率上的振幅突然大幅度增加;切深继续加大,振幅也继续增大但颤振频率几乎不变;最后,颤振频率向下漂移并保持颤振.随后进行的激振试验,确认了本次颤振试验中出现的两个颤振频率分别属于工件系统和刀具系统.从颤振能量补充和质量效应的角度分析,颤振应发生于两个最终执行部件之一的固有频率上.以此为基础建立了一个具有两个颤振主动体的颤振模型,新的模型可以用来合理解释本试验的现象,也更适合颤振监测和抗颤振结构改良的需要.     

Tool wear monitoring using acoustic emission in the existence of chatter

A method using an acoustic emission (AE) technique to monitor automatically a tool wear was examined. A flank wear is calculated using a statistical model which uses RMS value of AE signal and cutting speed as variables. The investigation reveals that the AE signal is influenced by tool vibration, specially during chatter. This contradicts the theory that the AE signal is not susceptible to mechanical vibration. The paper discusses the mechanism that explains the effect of the tool vibration on the AE signals.     

Acoustic emission analysis for tool wear monitoring in face milling

Monitoring the condition of the cutting tool in any machining operation is very important since it will affect the workpiece quality and an unexpected tool failure may damage the tool, workpiece and sometimes the machine tool itself. Advanced manufacturing demands an optimal machining process. Many problems that affect optimization are related to the diminished machine performance caused by worn out tools. One of the most promising tool monitoring techniques is based on the analysis of Acoustic Emission (AE) signals. The generation of the AE signals directly in the cutting zone makes them very sensitive to changes in the cutting process. Various approaches have been taken to monitor progressive tool wear, tool breakage, failure and chip segmentation while supervising these AE signals. In this paper, AE analysis is applied for tool wear monitoring in face milling operations. Experiments have been conducted on En-8 steel using uncoated carbide inserts in the cutter. The studies have been carried out with one, two and three inserts in the cutter under given cutting conditions. The AE signal analysis was carried out by considering signal parameters such as ring down count and RMS voltage. The results show that AE can be effectively used to monitor tool wear in face milling operation.     

Integrated in-process chatter monitoring and automatic suppression with adaptive pitch control in parallel turning

Simultaneous processes such as parallel turning or milling offer great opportunities for more efficient manufacturing because of their higher material removal rates. To maximize their advantages, chatter suppression technologies for simultaneous processes must be developed. In this study, we constructed an automatic chatter suppression system with optimal pitch control for sharedsurface parallel turning with rigid tools and a flexible workpiece, integrating in-process chatter monitoring based on the cutting force estimation. The pitch angle between two tools is tuned adaptively in a position control system in accordance with the chatter frequency at a certain spindle speed, in a similar manner as the design methodology for variable-pitch cutters. The cutting force is estimated without using an additional external sensor by employing a multi-encoder-based disturbance observer. In addition, the chatter frequency is measured during the process by performing a low-computational-load spectrum analysis at a certain frequency range, which makes it possible to calculate the power spectrum density in the control system of the machine tool. Thus, the constructed system for automatic chatter suppression does not require any additional equipment.The full text can be downloaded at https://link.springer.com/content/pdf/10.1007%2Fs40436-018-0222-0.pdf     

细长轴切削颤振的稳定性分析和实验研究

为了对细长轴的加工颤振进行稳定性研究,在对细长轴切削颤振机理的研究基础上,建立了刀具和细长轴耦合振动的两自由度系统的再生型颤振分析模型。利用解析法对时滞的动力学方程进行稳定性分析,得出了关于切削宽度和转速的稳定性切削极限图。从结果中可以得出两自由度系统的极限切削宽度比单自由度系统减小了28.6%。该成果可以为柔性零件的高效稳定加工提供理论切削参数。在车铣复合中心上进行了细长轴的切削颤振实验,通过与稳定性极限图的对比,发现实验结果与理论研究相吻合,并且总结出颤振发生前后刀具和细长轴的振动特性变化规律。这种振动特征的变化过程是对加工颤振进行监测和预警的重要识别指标     

Optical sectioning by holographic coherence imaging: a generalized analysis

A theory of optical sectioning by image plane holography is developed, emphasizing the use of broad-spectrum holographic methods to enhance the process. It is shown that a broad-spectrum source in a grating interferometer imitates the behavior of a monochromatic broad source.     

刀具磨损监测系统的实用化研究

为了实现刀具磨损监测系统的工业应用,根据常规的刀具磨损检测理论,通过对大量不同状态的实验数据进行分析,提出了刀具磨损监测系统的实用化模型,并将其应用于车床的刀具状态监测.实验结果与工具显微镜离线测量数据吻合,表明提出的监测模型有效地实现了多种加工方式、任意加工条件、任意工件及刀具组合的刀具状态监测.     

Real-time tool wear condition monitoring in turning

A new method to monitor tool wear condition in real time using feed-motor current measured with the aid of inexpensive current sensors installed on the AC servomotor of a CNC turning centre is presented. To achieve this, the feed drive system model is analysed, the feed-motor current is measured, and the relations between feed-motor current, cutting force, and tool flank wear are addressed. The functional dependence of the feed-motor current on tool wear is then expressed in the form of a difference equation relating variation in the feedmotor current to tool flank wear rate. The computerized system automatically compares successive feed-motor current and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that this method of tool wear condition monitoring is effective and industrially applicable.     

Identification of feature set for effective tool condition monitoring by acoustic emission sensing

In tool condition monitoring systems, various features from suitably processed acoustic emission signals are utilized by researchers. However, not all of these features are equally informative in a specific monitoring system: certain features may correspond to noise, not information; others may be correlated or not relevant for the task to be realized. This study comprehensively takes all these known signal features and aims to identify the most effective set that can give robust and reliable identification of tool condition. In this paper, the aim is investigated through feature selection, in which automatic relevance determination (ARD) under a Bayesian framework and support vector machine (SVM) are coupled together to perform this task. In tool condition monitoring, this proposed method is able to identify the worst features according to their corresponding ARD parameters and delete them. Then the effectiveness of this pruning may be evaluated by a model validation. Finally, the effective feature set in the developed tool wear recognition system is obtained. The experimental results show that the AE feature set selected through this method is more effective and efficient to recognize tool status over various cutting conditions.     

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.     

Cutting force denoising in micro-milling tool condition monitoring

An independent component analysis (ICA) algorithm for cutting force denoising was applied in micro-milling tool condition monitoring. In micro-milling, the comparatively small cutting force signal is prone to contamination by relatively large noise, and as a result it is important to denoise the force signal before further processing it. However, the traditional denoising methods, based on Gaussian noise assumption, lose here because the noise is identified as containing a high non-Gaussian component in the experiment. ICA was recently developed to deal with the blind source separation (BSS) problem. It solves the BSS problem by measuring the non-Gaussianity of the signal and it is particularly effective in the separation of non-Gaussian signals. This approach employs fixed-point ICA (FastICA), assuming the noises are sources and the force signal is an instantaneous mixture of sources and by treating the signal denoising process as a BSS. The results are illustrated both in time and frequency domains. The FastICA denoising performances are compared with the popular wavelet thresholding. The results show that FastICA performs better than wavelet. Theoretical discussion of the nature of ICA and wavelet thresholding supports the results: ICA separates both Gaussian and non-Gaussian noise sources, while wavelet only suppresses Gaussian noise.     

含干摩擦的二自由度制动系统颤振分析

以二自由度颤振系统为对象,对含有干摩擦的制动系统的颤振分岔、粘滑等复杂的非线性动力学行为进行研究。采用非线性摩擦系数、用二自由度的制动模型取代单自由度模型,建立相应的系统动力学模型;利用Runge-Kutta-Fehlberg(RK45)法数值分析与研究了系统动力学特性,得到随制动速度、支撑刚度和阻尼变化的质量块颤振振幅的变化曲线,分析发现:系统在低速下出现颤振现象,系统的支撑刚度和阻尼对其颤振的剧烈程度具有一定的影响,合理的系统参数能够减轻系统的颤振。     

In vitro studies toward noninvasive glucose monitoring with optical coherence tomography

We use optical coherence tomography (OCT) to measure glucose-induced changes in Intralipid and in mouse skin samples in vitro. Mouse skin samples are cultured in a CO2 incubator before measurements are made with different amounts of added glucose concentrations. The results show that the glucose-induced changes in the OCT slope value vary between 20% and 52%/30 mM glucose in different mouse skin samples. This change is much larger than the change in 2% Intralipid (2.1%/30 mM) and in 5% Intralipid (0.86%/30 mM). Hence the results show that OCT has potential to monitor glucose-induced changes in tissues in vitro.     

Sensor fusion for online tool condition monitoring in milling

The objective of this paper is to combine a direct sensor (vision) and an indirect sensor (force) to create an intelligent integrated tool condition monitoring (TCM) system for online monitoring of flank wear and breakage in milling, using the complementary strengths of the two types of sensors. For flank wear, images of the tool are captured and processed in-cycle using successive moving-image analysis. Two features of the cutting force, which closely indicate flank wear, are extracted in-process and appropriately pre-processed. A self-organizing map (SOM) network is trained in a batch mode after each cutting pass, using the two features derived from the cutting force, and measured wear values obtained by interpolating the vision-based measurement. The trained SOM network is applied to the succeeding machining pass to estimate the flank wear in-process. The in-cycle and in-process procedures are employed alternatively for the online monitoring of the flank wear. To detect breakage, two features in time domain derived from cutting force are used, and the thresholds for them are determined dynamically. Again, vision is used to verify any breakage identified in-process through the cutting force monitoring. Experimental results show that this sensor fusion scheme is feasible and effective for the implementation of online tool condition monitoring in milling, and is independent of the cutting conditions used.     

Passive measurement tool for monitoring mobile packet network performances

Continuous monitoring of performances is necessary to detect critical situations in the radio interface of digital mobile telephone networks. This paper presents a measurement tool that analyzes Transmission Control-Protocol (TCP) level packet traffic and detects the effects of variations in the radio-channel conditions of a wireless network. The analysis algorithm considers the wavelet coefficients of the aggregated-traffic process at a suitably selected number of time scales. Variations in the energy of wavelet coefficients are detected by a statistical test, from which a score is calculated. This indicator reveals changes in the performances of the monitored network.     

A data acquisition system for machine tool condition monitoring

A flexible, generically applicable and inexpensive data acquisition system (DAS), for machine tool condition monitoring, has been designed, constructed and installed as part of a European Union sponsored project. The DAS is more than just a data logger and an array of sensors. It also consists of a methodology for analysing data logging requirements and a relational database that supports this methodology. The database is held on a central ‘maintenance management’ computer. The monitoring to be carried out by the DAS is specified through this database, which contains detailed information about the DAS's facilities. This feature makes it simple to reconfigure the DAS to implement new monitoring requirements and to customize its operation to meet the needs of different machines. The information in the database is transformed into a look-up table that is read by the software that sets up and controls the data logging processes.     

机床切削颤振的非线性理论研究

由于机床结构结合面的动态特性常常表现为迟滞非线性特性,这种特性既包含了非线性刚度,又包含了非线性阻尼,因此,本文利用迟滞非线性建立了一个新的非线性颤振理论模型,利用等效线性化方法克服了解析分析上的困难,确定了该模型稳定性阈的近似解,获得了符合实际的三维稳定性图。通过对该稳定性图的分析,可以合理地分析及解释以往颤振理论所无法解释的种种颤振现象,例如颤振振幅稳定性现象、稳定性阈的分离现象、有限振幅不稳定性现象、微幅颤振现象以及双频颤振现象。