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。     

声发射识别切屑形状系统的研究

声发射信号的包络线能准确地反映切屑形态的变化。本文通过对其间关系的分析,研究了应用声发射技术识别切屑形状的智能化问题,提出一种智能系统,经联机调试,证明该方案是可行的。     

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.     

12MnNiVR钢拉伸过程声发射信号特征分析

对大型常压储罐材料12MnNiVR钢拉伸过程中声发射信号的幅值、振铃计数、撞击计数和能量等常规特征参数进行了分析。在此基础上,对拉伸过程不同阶段典型声发射信号的时域波形、频谱、希尔伯特时频分布等进行了波形分析。结果表明,声发射信号的特征参数和波形分析能反映不同拉伸过程变形特征,可用于拉伸过程的表征。     

基于声发射技术的拉深过程中润滑状态分析

采用发射技术监测了拉深过程中的润滑状态.在干摩擦和润滑脂润滑下,进行了拉深成形实验,并分析了不同润滑状态下拉深过程中声发射信号的能量、振铃计数、信号幅度等参数的变化规律.结果表明,拉深中产生声发射信号对润滑状态非常灵敏,随着润滑状态改善,拉深过程中产生的声发射信号能量、振铃计数、信号幅度参数等均呈下降趋势;通过对声发射信号分析可以判断拉深件的摩擦状况,即将声发射技术引入金属板材拉深中的摩擦状态的监测是可行的.     

利用切屑图计算钻进时截齿的切屑厚度

已有的截齿切屑厚度的计算均是在理想状态下计算的,根据掘进机在钻进工作时,截齿不同工作状态,截割时切屑下煤壁后形成的形状不同,提出利用切屑图来计算切屑厚度的方法,此方法计算出的截齿切屑厚度更接近实际的切屑厚度.     

基于声发射波形流信号的滑动轴承故障特征双谱分析

采用非接触式声发射波形流测试技术采集滑动轴承正常、不平衡、碰摩和不对中故障的声发射波形流信号,分析不同类型故障特征与声发射波形流信号间的关系,并运用双谱分析方法对声发射波形流信号进行分析,根据双谱分析三维图及等高线图表现出的不同特征,实现对滑动轴承不同故障的判别。     

滚动轴承故障声发射信号的小波包络谱分析

利用声发射信号的高频特性采集滚动轴承故障信息,采用小波分析技术把信号分解在不同频带,对信号进行重构,从而消除背景噪声,再采用小波包络谱分析方法识别故障信息.试验结果证明,基于声发射信号的小波包络谱分析可以有效地检测滚动轴承故障.     

基于排列熵的电磁声发射信号到达时间识别

排列熵算法简单高效,能够反映出信号的微小变化,是一种有效的信号突变检测工具。通过引入信号包络排列熵分析方法,对声发射信号到达时间识别问题进行求解。排列熵对信号幅值的大小不敏感,但对信号幅值的变化敏感,通过记录信号包络排列熵的变化,根据包络排列熵第一个极小值时刻来识别信号到达时间。根据裂纹电磁声发射实测信号识别结果可知,包络排列熵方法对声发射信号到达时间具有较高的识别精度。     

机械密封端面接触状态的声发射监测方法

针对机械密封运行过程中反映密封端面接触状态的工作参数(端面开启时间、膜厚等)测量困难的问题,提出基于声发射信号的机械密封端面接触状态监测方法。根据密封端面产生的声发射信号具有时变非线性且突发性强的特点,采用经验模态分解(EMD)法对原始信号进行分离提取。EMD法能够将信号分解为不同时间尺度和不同频带的一系列固有模态函数,然后根据能量分布特征对伪分量进行剔除,得到"近源"声发射信号,抽取其信号特征运用Laplace小波相关系数法实现对密封端面接触状态的准确识别。通过机械密封测试试验证明,声发射监测技术能准确地识别机械密封装置动静环之间的接触状态和摩擦形式,能够在工业现场推广使用。     

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.     

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.     

Development of a capacity analysis and planning simulation model for semiconductor fabrication

Micro-machining has gained increased application to produce miniaturized parts in various industries. However, the uncut chip thickness in micro-machining is comparable to cutting edge radius. The relationship between the cutting edge radius and uncut chip thickness has been a subject matter of increasing interest. The acoustic emission (AE) signal can reflect the stress wave caused by the sudden release of the energy of the deformed materials. To improve the precision of machining system, determination of the minimum uncut chip thickness was investigated in this paper. The AE signal generated during micro-cutting experiments was used to analyze the chip formation in micro-end milling of Inconel 718. The finite element method (FEM) simulation was used to analyze the results of the experiments. The results showed that the cutting tool geometry and material properties affected the minimum uncut chip thickness. The estimation of the minimum uncut chip thickness based on AE signals can produce quite satisfactory results. The research on the minimum uncut chip thickness can provide theoretical basis for analysis of surface quality and optimal choice of cutting parameters.     

Practical applications of the “energy–triaxiality” state relationship in metal cutting

Most of the energy spent on metal cutting is due to the unavoidable plastic deformation of the layer being removed during its transformation into the chip. Based on the new principle of metal cutting being a purposeful fracture process, the dominant parameter that controls this process in orthogonal metal cutting (OMC) is the triaxiality state. Therefore, the chip triaxiality state in the deformation zone can be correlated to the energy of the unwanted plastic deformation for a particular cutting configuration. This article investigates this type of correlation by changing the cutting tool geometry. A series of finite element (FE) simulations were performed for various tool rake angles shows a strong relationship between the stress triaxiality state parameter in the deformation zone and the required cutting force components.     

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.     

金属切削中流屑角突变的实验研究

对金属切削中的一类非线性问题－切屑流屑角的突变进行了检测实验研究,得出了流屑角突变时刻的力,振动和声发射信号。多次实验表明,这3种信号中对切削过程的非线性特征以切削力信号最为灵敏。根据传感器突变信号的产生时间,可以计算出突变时的切削层厚度aW和切削层图形系数gs。     

Monitoring tool wear during wood machining with acoustic emission

The purpose of this study was to determine the degree of change in acoustic emission (AE) during cutting as a cutter tool was worn. AE is defined as the stress or pressure waves generated during dynamic processes in materials and is generated during fracture, delamination, deformation and distortion of wood during cutting. Previous work has shown that AE is sensitive to changes in the chip formation process and therefore could be used to monitor continuously the state of the cutting process. For this study a single-point cutting tool was worn by turning a green-white fir (Abies concolor (Gord. and Glend.) Lindl.) log on an engine lathe equipped with an automatic feed. The relationship between the AE output and the amount of wood cut was close to linear in the initial stages of the blade wear. As the blade became severely worn, the AE levels dropped dramatically and an asymptotic relationship between the two variables became evident.     

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 new approach to investigate tool condition using dummy tool holder and sensor setup

The industrial demand for automated machining systems to enhance process productivity and quality in machining aerospace components requires investigation of tool condition monitoring. The formation of chip and its removal have a remarkable effect on the state of the cutting tool during turning. This work presents a new technique using acoustic emission (AE) to monitor the tool condition by separating the chip formation frequencies from the rest of the signal which comes mostly from tool wear and plastic deformation of the work material. A dummy tool holder and sensor setup have been designed and integrated with the conventional tool holder system to capture the time-domain chip formation signals independently during turning. Several dry turning tests have been conducted at the speed ranging from 120 to 180?m/min, feed rate from 0.20 to 0.50?mm/rev, and depth of cut from 1 to 1.5?mm. The tool insert used was TiN-coated carbide while the work material was high-carbon steel. The signals from the dummy setup clearly differ from the AE signals of the conventional setup. It has been observed that time-domain signal and corresponding frequency response can predict the tool conditions. The rate of tool wear was found to decrease with chip breakage even at higher feed rate. The tool wear and plastic deformation were viewed to decrease with the increased radius of chip curvature and thinner chip thickness even at the highest cutting speed, and these have been verified by measuring tool wear. The chip formation frequency has been found to be within 97.7 to 640?kHz.