刀具磨损声发射信号处理中小波基选取的研究

通过对小波基性质和刀具磨损声发射（AE）信号特点的研究,从理论上分析了小波变换中刀具磨损AE信号处理中小波基选取的方法。在试验验证过程中,根据小波包信号分解遵循能量守恒原理,用四种小波基对刀具磨损AE信号进行三层小波包分解;以AE信号经小波包分解后各频带上的能量为特征参数,比较四种情况下特征参数的变化,验证了理论分析的正确性。     

A Feature Extraction Method for the Wear of Milling Tools Based on the Hilbert Marginal Spectrum

Abstract

The Hilbert–Huang transform (HHT) can adaptively delineate complex non-linear, non-stationary signals when used as the Hilbert–Huang marginal spectrum through empirical mode decomposition (EMD) and the Hilbert transform, to highlight local features of signals. Characterized by high resolution, the Hilbert marginal spectrum has been widely applied in mechanical signal processing and fault diagnosis. In the research, an HHT based on the improved EMD was proposed to analyze the cutting force, vibration acceleration (AC), and acoustic emission (AE) signals during tool wear in the milling process. At first, the collected signals were subjected to range analysis, which revealed that tool wear was closely related to the signals collected during the cutting process. Then, EMD was applied to the signals, followed by variance analysis after calculating the energies of each intrinsic mode function (IMF) component. Afterwards, the IMF components significantly influenced by wear degree, while slightly influenced by the three cutting factors (cutting velocity, feed per tooth, and cutting depth), were selected as IMF sensitive to the degree of wear. The HHT was finally applied to the sensitive IMF components of signals containing major tool wear information, thus obtaining the Hilbert marginal spectra of the signals, which were able to reflect the changes in signal amplitude with frequency. On the basis of the Hilbert marginal spectrum, the method defined the feature energy function which was then used as the eigenvector for predicting tool wear in milling processes. The analysis of signals in four tool wear states indicated that the method can extract salient tool wear features.     

异形螺杆铣削过程刀具磨损建模研究

介绍了一种螺杆铣削过程刀具磨损建模的方法。该方法针对螺杆加工中变切削参数的工况，提取了振动信号和功率信号的刀具磨损特征值，并建立了信号特征值与刀具磨损量之间的映射关系，从而得到刀具磨损模型。实验证明，由此建立的刀具磨损模型。能够排除切削参数变化的干扰，可以较好地反映加工中刀具磨损状态。同时也为具有时变切削参数特性的加工过程刀具磨损状态监控提供了新的研究方法。     

Monitoring of hard turning using acoustic emission signal

Monitoring of tool wear during hard turning is essential. Many investigators have analyzed the acoustic emission (AE) signals generated during machining to understand the metal cutting process and for monitoring tool wear and failure. In the current study on hard turning, the skew and kurtosis parameters of the root mean square values of AE signal (AERMS) are used to monitor tool wear. The rubbing between the tool and the workpiece increases as the tool wear crosses a threshold, thereby shifting the mass of AERMS distribution to right, leading to a negative skew. The increased rubbing also led to a high kurtosis value in the AERMS distribution curve.     

Tool wear monitoring in ramp cuts in end milling using the wavelet transform

Tool wear identification and estimation present a fundamental problem in machining. With tool wear there is an increase in cutting forces, which leads to a deterioration in process stability, part accuracy and surface finish. In this paper, cutting force trends and tool wear effects in ramp cut machining are observed experimentally as machining progresses. In ramp cuts, the depth of cut is continuously changing. Cutting forces are compared with cutting forces obtained from a progressively worn tool as a result of machining. A wavelet transform is used for signal processing and is found to be useful for observing the resultant cutting force trends. The root mean square (RMS) value of the wavelet transformed signal and linear regression are used for tool wear estimation. Tool wear is also estimated by measuring the resulting slot thickness on a coordinate measuring machine.     

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.     

The Application of an ANFIS and Grey System Method in Turning Tool-Failure Detection

The cutting process is a major material removal process; hence, it is important to search for ways of detecting tool failure. This paper describes the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure detection in a single-point turning operation. In a turning operation, wear and failure of the tool are usually monitored by measuring cutting force, load current, vibration, acoustic emission (AE) and temperature. The AE signal and cutting force signal provide useful information concerning the tool-failure condition. Therefore, five input parameters of the combined signals (AE signal and cutting force signal) have been used in the ANFIS model to detect the tool state. In this model, we adopted three different types of membership function for analysis for ANFIS training and compared their differences regarding the accuracy rate of the tool-state detection. The result obtained for the successful classification of tool state with respect to only two classes (normal or failure) is very good. The results also indicate that a triangular MF and a generalised bell MF have a better rate of detection. We also applied grey relational analysis to determine the order of influence of the five cutting parameters on tool-state detection.     

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.     

基于PSO优化的小波神经网络在刀具磨损识别中的应用

刀具状态监控是保证自动化加工顺利进行的重要环节,本文针对切削加工中功率信号的获取,以及反映刀具状态的信号特征抽取的问题,设计了采集功率信号,利用小波包分解的方法抽取反映刀具磨损状态的特征值作为神经网络输入值,采用神经网络对特征值进行训练,然后借用粒子群算法(PSO)算法优化神经网络及其结构的方法,获得了结构简单、准确性高、实时性好的神经网络,仿真和试验表明该方法对特征信号反映灵敏,对切削参数的变化不敏感,能够准确反映刀具的磨损状态。     

金属切削中刀具月牙洼磨损模型的研究

刀具的前刀面月牙洼磨损和后刀面磨损在刀具磨损的研究中占据同等地位,但月牙洼磨损轮廓和刀屑交界面上的切削过程变量不易测量等因素,导致真正依据月牙洼磨损测试获取月牙洼磨损模型的研究非常少。基于硬质合金刀具切削低碳钢的月牙洼磨损实验,提出了一个经验磨损模型。该模型综合了黏结磨损和扩散磨损,考虑了刀屑交界面温度和压力对月牙洼磨损的影响。研究结果表明,该模型能够分析并预测相似切削条件下的月牙洼磨损轮廓。     

Failure mechanisms of a PCD tool in high-speed face milling of Ti–6Al–4V alloy

High-speed milling tests were carried out on Ti–6Al–4V titanium alloy with a polycrystalline diamond (PCD) tool. Tool wear morphologies were observed and examined with a digital microscope. The main tool failure mechanisms were discussed and analyzed utilizing scanning electron microscope, and the element distribution of the failed tool surface was detected using energy dispersive spectroscopy. Results showed that tool flank wear rate increased with the increase in cutting speed. The PCD tool is suitable for machining of Ti–6Al–4V titanium alloy with a cutting speed around 250 m/min. The PCD tool exhibited relatively serious chipping and spalling at cutting speed higher than 375 m/min, within further increasing of the cutting speed the flank wear and breakage increased greatly as a result of the enhanced thermal–mechanical impacts. In addition, the PCD tool could hardly work at cutting speed of 1,000 m/min due to the catastrophic fracture of the cutting edge and intense flank wear. There was evidence of workpiece material adhesion on the tool rake face and flank face in very close proximity to the cutting edge rather than on the chipped or flaked surface, which thereby leads to the accelerating flank wear. The failure mechanisms of PCD tool in high-speed wet milling of Ti–6Al–4V titanium alloy were mainly premature breakage and synergistic interaction among adhesive wear and abrasive wear.     

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.     

涂层刀具磨损对工件表面质量的非线性影响关系

在高速硬态切削过程中,涂层刀具高速切削失效形态分为非正常磨损（破损）与正常磨损两种。刀具非正常磨损失效发生在低速切削时,而高速切削过程中的刀具失效形式倾向于后刀面磨损、边界磨损和切削刃斜面磨损等多种一常磨损彤式的组合。本文通过涂层刀具高速车削45#钢的试验,研究了刀具磨损对工件表面质量的影响。试验表明：提高切削速度,工件已加工表面质量并未随刀具磨损情况加剧而呈线性下降,刀具磨损对已加工表面质量负面影响作用的减轻,使表面质量在一定程度上得到了改善。     

The dependence of tool overhang on surface quality and tool wear in the turning process

In the turning process, the importance of machining parameter choice is increased, as it controls the surface quality required. Tool overhang is a cutting tool parameter that has not been investigated in as much detail as some of the better known ones. It is appropriate to keep the tool overhang as short as possible; however, a longer tool overhang may be required depending on the geometry of the workpiece and when using the hole-turning process in particular. In this study, we investigate the effects of changes in the tool overhang in the external turning process on both the surface quality of the workpiece and tool wear. For this purpose, we used workpieces of AISI 1050 material with diameters of 20, 30, and 40 mm; and the surface roughness of the workpiece and tool wear were determined through experiments using constant cutting speed and feed rates with different depth of cuts (DOCs) and tool overhangs. We observed that the effect of the DOC on the surface roughness is negligible, but tool overhang is more important. The deflection of the cutting tool increases with tool overhang. Two different analytical methods were compared to determine the dependence of tool deflection on the tool overhang. Also, the real tool deflection values were determined using a comparator. We observed that the tool deflection values were quite compatible with the tool deflection results obtained using the second analytical method.     

刀具破损状态的特征提取及自动识别

文章采用机床功率法和声发射法对车削过程中的刀具破损进行监控。在试验中发现了刀具破损时机床功率信号的四种表现形式,说明了刀具破损形式的随机性。针对这种情况,首次提出了功率信号处理的延时方差法;对切削过程中发出的各种声发射（AE）信号采用时频分析进行处理并提取出反映刀具破损的特征量,最后利用神经网络ART2实现了刀具破损状态的自动识别。     

Tool State Detection by Harmonic Wavelet and Sample Entropy

It is a fact that 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,it is difficult to obtain a reliable result by these raw AE data.It is generally known that the process of tool wear belongs to detect weak singularity signals in strong noise.The objective of this paper is to combine Newland Harmonic wavelet and Richman-Moorman(2000) sample entropy for detecting weak singularity signals embedded in strong signals.First,the raw AE signal is decomposed by harmonic wavelet and transformed into the three-dimensional time-frequency mesh map of the harmonic wavelet,at the same time,the contours of the mesh map with log space is induced.Second,the profile map of the three-dimensional time-frequency mesh map is offered,which corresponds to decomposed level on harmonic wavelets.Final,by computing sample entropy in each level,the weak singularity signal can be easily extracted from strong noise.Machining test was carried out on HL-32 NC turning center.This lathe does not have a tailstock.Tungsten carbide finishing tool was used to turn free machining mild steel.The work material was chosen for ease of machining,allowing for generation of surfaces of varying quality without the use of cutting fluids.In turning experiments,the feasibility for tool condition monitoring is demonstrated by 27 kinds of cutting conditions with the sharp tool and the worn tool,54 group data are sampled by AE.The sample entropy of each level of wavelet decomposed for each one of 54 AE datum is computed,wear tool and shaper tool can be distinguished obviously by the sample entropy value at the 12th level,this is a criterion.The proposed research provides a new theoretical basis and a new engineering application on the tool condition monitoring.     

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。     

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.     

Applying the self-organization feature map (SOM) algorithm to AE-based tool wear monitoring in micro-cutting

This study applies a self-organization feature map (SOM) neural network to acoustic emission (AE) signal-based tool wear monitoring for a micro-milling process. An experiment was set up to collect the signal during cutting for the system development and performance analysis. The AE signal generated on the workpiece was first transformed to the frequency domain by Fast Fourier transformation (FFT), followed by feature extraction processing using the SOM algorithm. The performance verification in this study adopts a learning vector quantification (LVQ) network to evaluate the effects of the SOM algorithm on the classification performance for tool wear monitoring. To investigate the improvement achieved by the SOM algorithms, this study also investigates cases applying only the LVQ classifier and based on the class mean scatter feature selection (CMSFS) criterion and LVQ. Results show that accurate classification of the tool wear can be obtained by properly selecting features closely related to the tool wear based on the CMSFS and frequency resolution of spectral features. However, the SOM algorithms provide a more reliable methodology of reducing the effect on the system performance contributed by noise or variations in the cutting system.     

Model development for tool wear effect on AE signal generation in micromilling

A model for the relation between the acoustic emission signal generation and tool wear was established for cutting processes in micromilling by considering the acoustic emission (AE) generation and propagation mechanisms. In addition, the effect of tool wear on the AE signal generation in frequency and amplitude was studied. In the model development, the finite element analysis was first used to calculate the shear strain rate distribution on the shear plane based on the orthogonal cutting assumption. Conversely, the contact stress distribution of workpiece on the flank wear face was established based on the Waldorf model. Following the finite element method, the dislocation density in materials was calculated based on Orowan’s law with the calculated stress rate. Finally, the AE signal detected by the sensor was calculated by considering the Gaussian probability density function for the distribution of AE source on the shear plane and the one-dimension wave equation for AE signal propagation. Based on the developed model, the effect of tool wear on the AE signal generation was investigated and compared to the experimental results. The results obtained from these investigations indicate that the proposed model can be used to predict the effect of tool wear on the AE signal generation.