基于声发射技术的超精密切削状态监测试验研究

针对超精密加工过程中的切削状态监测问题,利用声发射技术进行了接触检测和识别的试验研究。首先,将谐振频率为1 MHz的锆钛酸铅压电陶瓷传感器安装在靠近切削点的位置,并且为了降低背景噪声,利用声发射分析单元放大和100 kHz高通滤波器对信号进行了处理;然后,对切削刃与工件的接触极限进行了研究,实现了对切削刃位置的准确检测;最后,对切削状态变化引起的声发射信号变化进行了监测,对其切削状态进行了识别。研究结果表明:(1)当切削速度为60 m/min时,在切削深度为10 nm的情况下可以检测到切削刃与工件的接触;(2)声发射信号波形可以识别表面加工过程中发生的微小变化,且声发射平均值与切削速度呈高度正相关,声发射总能量与接触弧长成正比;这些结果证实了声发射技术用于切削状态监测的可行性。     

基于声发射信号监测的高速准干切削试验研究

声发射信号可以及时地反映出高速切削加工过程中工件和刀具材料的内部缺陷变化和扩展情况.为了对其进行监测和分析,建立了一套基于虚拟仪器的信号采集系统,采集高速切削铝合金加工过程中的声发射信号.对采集到的声发射信号进行小波变换并重构后,发现铝合金切削过程中的声发射信号与切削速度密切相关.随着切削速度的增大,声发射也随着增大.     

声发射监测切屑状态的可行性研究

金属切削过程中产生丰富的声发射。用声发射技术监测切屑状态是一个很有潜力的新方法。本文通过实验研究了车削时切屑状态和声发射信号间的相互关系,结果表明：1.声发射DC包络信号脉冲能准确反映断屑情况;2.声发射加权计数率波形和包络信号均值能反映切屑形状和异常切屑状态;3.不同类型切屑的形成过程具有不同的声发射信号波形。     

基于声发射技术的微切削加工表面形貌监测

采用声发射技术对工件材料为A16061-T6的微切削表面轮廓进行了实时测量.采集监控微切削加工表面时产生的声发射均方根信号,并与表面轮廓仪测得的结果进行对比.研究表明,声发射均方根信号与微切削表面形貌很好的相对应,因此,声发射技术适于微切削表面形貌的监测.研究了切削用量(每齿进给量和主轴转速)与表面形貌之间的关系,微切削的每齿进给量对表面粗糙度影响较大.     

刀具磨破损声发射信号传播特性的理论探讨

以声波导传播理论为基础，讨论了不同类型的刀具磨破破损声发射信号在流体声发射传感器中的传播特性，并对液体喷射速度和角度等因素对其传播特性的影响进行了理论分析，为进一步研究和有效利用此类液体声发射传感器奠定理论基础。     

运用声发射技术监测金属塑性成型过程中润滑状态的研究

为监测金属塑性成型过程中的润滑状态,采用运用声发射技术,通过对无润滑和有润滑时金属塑性变形过程和摩擦过程的监测及对比,分别研究了金属摩擦声发射信号和塑性变形声发射信号.结果表明,不同材料摩擦产生的声发射信号数值上大小不同,同种材料摩擦声发射信号数值上小于塑性变形声发射信号;采用声发射技术,基于实时波形和声发射信号参数的平均值都能监测金属塑性成型时的润滑状态.     

基于声发射信号的金属铣削过程研究

为了对铣削过程进行状态监测,避免在铣削工作过程中产生突发损伤,从铣削加工中的声发射现象入手,采集整个铣削过程中产生的声发射信号,利用声发射计数进行数据分析,得到铣削过程中不同阶段所对应的声发射计数的变化规律,从而进一步对整个铣削过程的稳定性进行分析研究。结果表明,主轴转速设置在1600 r/min～3200 r/min时,随着主轴转速增大,主轴转速与声发射计数呈正相关关系,随着主轴转速设置的增大,相应的各个时刻所对应的声发射计数值也发生增大,系统越不稳定。声发射计数对主轴转速这一铣削参数的变化非常敏感,能够很好地反映金属铣削过程的稳定性,很好地描述整个铣削过程。在每次铣削试验中,铣削初期阶段,声发射计数值呈阶跃性增大,然后逐渐保持平稳的特征;而当铣削进行到试件中段时,声发射计数继续增大,达到峰值。因此,铣削过程中,铣削的加工试件中间位置时需要重点关注,此时铣削状态最不平稳,最容易发生失效。在实际铣削过程中,重点监控好声发射计数,能避免刀具等加工件发生损伤破坏,提高加工质量及加工效率。     

基于传感器增强的测力仪实验平台扩展方法

针对现有测力平台无法满足大尺寸特征实验、微细切削实验以及缺乏多信号融合等问题,提出了基于传感器增强的测力仪实验平台扩展方法。以Kistler 9257B压电测力仪为基础,对测力平台进行扩展。通过增加大尺寸测力板满足大尺寸特征的切削实验要求。同时增加加速度传感器,通过测量加工过程中工件的加速度,对切削力进行惯性力补偿,提高切削力的测量精度,并可用于微细切削实验。此外通过将声发射传感器监测加工过程中的声发射信号同切削力和振动信号融合,为加工过程中刀具状态的辨识提供依据。验证表明,经过扩展的测力平台可以达到预期的效果。     

基于独立分量分析的切削声发射源信号分离

针对切削声发射(Acoustic Emission,AE)信号的多目标状态源并行分离问题和同频干扰源分离问题,引入独立分量分析(Independent Component Analysis,ICA)技术作为研究工具,用刀具破损、切屑折断和环境噪声三个AE源的线性混合模拟切削AE信号,尝试用FastICA算法分离目标状态...     

基于小波神经网络的刀具故障监测系统

用声发射传感器采集刀具的切削状态信号，用小波分析和神经网络技术对信号进行特征提取和仿真训练，并建立了基于小波神经网络的刀具故障监测系统。系统对已测得的刀具切削状态信号进行仿真试验，结果表明：系统对刀具故障的预报正确率为93．3％，可有效地应用到工程实践中。     

涂层刀具高速铣削淬硬钢的声发射信号特征

通过测量不同涂层铣刀高速铣削不同硬度淬硬钢材料时的声发射信号和切屑形态,得到了电压-时间声发射信号以及声发射信号RMS值与切削工艺参数之间的关系。研究结果表明：声发射信号与淬硬钢材料硬度、刀具涂层类型及工艺参数有关;声发射信号可用来评价淬硬钢材料硬度的变化,随着淬硬钢材料硬度的增大,采集的声发射信号电压值呈逐渐增大的趋势;TiAlN涂层产生的锯齿形切屑的剪切带长度最小,切屑易于折断,从而导致其产生过程中的声发射RMS值偏小;随着切削速度和每齿进给量的增大,TiSiN、TiAlN、AlCrN和CrSiN四种涂层铣刀的声发射信号均快速增大,而随着轴向和径向铣削深度的增大,4种涂层铣刀的声发射信号变化不明显;在同一种切削参数条件下,可根据淬硬钢切屑变形特征的变化来间接评价刀具涂层的切削性能;声发射信号波形图的峰值大小可较好地反映锯齿形切屑的生成状态,进而可用来监控淬硬钢加工过程切削稳定性。     

Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

This paper presents an online prediction of tool wear using acoustic emission (AE) in turning titanium (grade 5) with PVD-coated carbide tools. In the present work, the root mean square value of AE at the chip–tool contact was used to detect the progression of flank wear in carbide tools. In particular, the effect of cutting speed, feed, and depth of cut on tool wear has been investigated. The flank surface of the cutting tools used for machining tests was analyzed using energy-dispersive X-ray spectroscopy technique to determine the nature of wear. A mathematical model for the prediction of AE signal was developed using process parameters such as speed, feed, and depth of cut along with the progressive flank wear. A confirmation test was also conducted in order to verify the correctness of the model. Experimental results have shown that the AE signal in turning titanium alloy can be predicted with a reasonable accuracy within the range of process parameters considered in this study.     

The relationship between acoustic emission signals and cutting phenomena in turning process

The development of intelligent manufacturing by using machine tools is advancing in leaps and bounds. To maintain accuracy in machining and in the interests of fail-safe operation, monitoring of the cutting state or the final machining is very important. Acoustic emissions (AE) comprise elastic stress waves produced as a result of the deformation and fracture of materials. By measuring the AE generated during a turning process, it is possible to estimate the state of the machining operation. The correlation between cutting phenomena and AE in a turning process was examined experimentally by using a steel workpiece and a cermet tool in a numerically controlled turning process. The process of formation of chips, the types of chip, and the shear angle all markedly affected the AE signals. There was a strong negative correlation between the shear angle and the AE signal level. Similar results were obtained for various feed rates and for workpieces of various degrees of hardness. Correlations related to surface roughness and to tool wear are also described that permit the evaluation of the state of the turning process.     

A probabilistic neural network applied in monitoring tool wear in the end milling operation via acoustic emission and cutting power signals

Tool condition monitoring, which is very important in machining, has improved over the past 20 years. Several process variables that are active in the cutting region, such as cutting forces, vibrations, acoustic emission (AE), noise, temperature, and surface finish, are influenced by the state of the cutting tool and the conditions of the material removal process. However, controlling these process variables to ensure adequate responses, particularly on an individual basis, is a highly complex task. The combination of AE and cutting power signals serves to indicate the improved response. In this study, a new parameter based on AE signal energy (frequency range between 100 and 300 kHz) was introduced to improve response. Tool wear in end milling was measured in each step, based on cutting power and AE signals. The wear conditions were then classified as good or bad, the signal parameters were extracted, and the probabilistic neural network was applied. The mean and skewness of cutting power and the root mean square of the power spectral density of AE showed sensitivity and were applied with about 91% accuracy. The combination of cutting power and AE with the signal energy parameter can definitely be applied in a tool wear-monitoring system.     

A study of acoustic emission generated during orthogonal metal cutting—1: Energy analysis

The application of acoustic emission (AE) sensing in metal cutting process monitoring requires a knowledge of the signal dependence on the variables encountered in the process and an understanding of the source mechanisms responsible for AE generation. In this paper, we study the dependence of the AE signal energy on orthogonal machining variables such as cutting velocity, uncut chip thickness and the chip-tool contact length. Controlled contact length tools were used in orthogonal machining of tubular 6061-T6 aluminum, at varying cutting velocities and feed rates (the feed rate in this case is equal to the uncut chip thickness). The root mean square (RMS) value of the AE signal was found to be linearly proportional to the cutting velocity. Based on this observation, the damping of dislocation motions is proposed as a possible AE source mechanism at the high strain rates encountered in metal cutting. The validity of the dislocation damping based model for AE generation is supported by experimental results and observations.     

Flank Wear Estimation in Face Milling Based on Radial Basis Function Neural Networks

This paper presents an estimation of flank wear in face milling operations using radial basis function (RBF) networks. Various signals such as acoustic emission (AE), surface roughness, and cutting conditions (cutting speed and feed) have been used to estimate the flank wear. The hidden layer RBF units have been fixed randomly from the input data and using batch fuzzy C means algorithm, and a comparative study has been carried out. The results obtained from a fixed RBF network have been compared with those from a resource allocation network (RAN).     

Acoustic emission method for tool condition monitoring based on wavelet analysis

It is believed that the acoustic emission (AE) signals contain potentially valuable information for tool wear and breakage monitoring and detection. However, AE stress waves produced in the cutting zone are distorted by the transmission path and the measurement systems and it is difficult to obtain an effective result by these raw acoustic emission data. In this article, a technique based on AE signal wavelet analysis is proposed for tool condition monitoring. The local characterize of frequency band, which contains the main energy of AE signals, is depicted by the wavelet multi-resolution analysis, and the singularity of the signal is represented by wavelet resolution coefficient norm. The feasibility for tool condition monitoring is demonstrated by the various cutting conditions in turning experiments.     

Detecting transition to chatter mode in peakless tool turning by monitoring vibration and acoustic emission signals

Stability of a peakless tool turning on slender shafts was studied under conditions of low- and high-magnitude vibrations by registering and short-time Fourier transformation (STFT) processing of acoustic emission (AE) and vibration acceleration (VA) signals. Both VA and AE signals have been registered in three positions of the cutting tool on the workpiece and for different shaft diameters. Both amplitude- and frequency-dependent AE and VA characteristics were obtained and analyzed for overall process signal length as well as for single frames. It was shown that power spectrum characteristic could be used for monitoring the fast-occurring changes in the cutting process stability. A criterion of the cutting process stability based on the power spectrum has been offered.