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。     

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.     

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.     

基于声发射技术监测刀具磨损的研究

刀具磨损的研究方法很多,本文针对近些年发展的声发射技术(AE)在监测刀具磨损上的应用,采用理论分析和现场试验的方法进行可行性分析和验证,结果表明:在考察的几个影响声发射信号强度的因素之中,刀具主切削刃后刀面磨损量对其影响最为显著,这为利用AE技术研究刀具磨损提供了可行性依据;通过对铣刀AE信号进行时域振铃分析,清晰再现了刀具在不同时刻的磨损情况。     

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.     

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.     

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.     

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.     

基于小波多分辨率分析和RBF神经网络的刀具磨损状态识别

刀具磨损监测对于提高加工过程的精度和自动化程度具有重要意义。本文提出一种基于RBF函数神经网络的刀具磨损状态监测模式。该系统利用声发射传感器对切削过程进行监测,采用多分辨率小波分解技术从声发射信号中提取反映刀具磨损的特征向量,并输入RBF神经网络,实现了刀具磨损的自动识别。     

Nonlinear adaptive backstepping control for variable-speed wind energy conversion system-based permanent magnet synchronous generator

Tool wear degradation and working status of slotting cutter have a great effect on the surface quality of rotor slot; therefore, tool condition monitoring and its degradation estimation are needed for guaranteeing slot machining quality. This paper proposes a two-phase method based on acoustic emission (AE) signal classification and logistic regression model for slotting cutter condition monitoring and its degradation estimation. In the first phase, the failure reliability estimation models corresponding to different machining processes are established considering the variability of process system like tool regrinding times and material randomness of workpiece. In the second phase, the most appropriate estimation model corresponding to the optimum cluster is selected and used for failure reliability estimation and status determination of slotting cutter. This approach has been validated on a CNC rotor slot machine in a factory. Experimental results show that the proposed method can be effectively used for cutting tool degradation estimation and status determination of slotting cutter with high accuracy.     

Simulation and analysis of serrated chip formation in cutting process of hardened steel considering ploughing-effect

A realistic finite element model considering the ploughing effect of cutting edge fillet was developed in high speed machining. Taking the hardened tool steel AISI D2 as the object of research, the cutting force and chip morphology were reasonably analyzed and compared with the actual results of cutting experiments, which verified the correctness of the model. Then, based on the model, the formation process of single serrated tooth was analyzed, while the effects of cutting heat and temperature field, material hardness and cutting speed on chip formation were explored. The research results indicate that: (1) The ploughing-effect has a great impact on the feed force, and for hardened tool steel AISI D2, the stagnation angle of 30o is more appropriate. (2) Also, stress concentration appears and shear slipping occurs along the shear plane in the process of serrated chip formation. The strain rate on the shear slipping surface is much greater than other places and the temperature gradient perpendicular to the shear plane is relatively higher. (3) The cutting force becomes larger with increasing the hardness value of workpieces, which causes the chip to more likely to produce serrated chips. (4) The fluctuation of cutting force is more significant as the cutting speed increases, which puts forward higher requirements for the tool and machine tool.     

A study of tool wear using statistical analysis of metal-cutting acoustic emission

Many aspects of the interactions between cutting tools, workpiece material and the chips formed during machining that affect the wear and failure of the tool are not fully understood. The analysis of acoustic emission signals generated during machining has been proposed as a technique for studying both the fundamentals of the cutting process and tool wear and as a methodology for detecting tool wear and failure on line. A brief review of the theory of acoustic emission is presented. Acoustic emission data from reduced contact length machining experiments and tool flank wear tests are analyzed using distribution moments. The analysis shows that the skew and kurtosis of an assumed β distribution for the r.m.s. acoustic emission signal are sensitive to both the stick-slip transition for chip contact along the tool rake face and progressive tool wear on the flank of the cutting tool.     

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.     

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.     

基于小波能谱系数的风力机疲劳裂纹特征

提出了风力机叶片裂纹扩展声发射信号的优化小波重分配尺度谱及小波能谱系数相结合的分析法。基于Shannon熵理论计算裂纹扩展声发射信号的重分配尺度谱小波基函数带宽参数,得到最适合裂纹声发射信号的Morlet小波基函数。用优化后的小波基函数计算重分配尺度谱,获得裂纹扩展特征成分在时间尺度平面的高幅值能量分布,利用特征能谱系数表征其重分配尺度谱的特征。实验结果表明,该方法有良好的时频聚集性和抗噪能力,实现了风力机叶片裂纹扩展声发射信号的时频特征提取,得到了能谱系数作为特征向量表示信号特征。该方法可用来实现风力机叶片在复杂环境中的模式识别。     

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.     

Thermal modeling of drilling process in titanium alloy (Ti-6Al-4V)

Abstract

In drilling in titanium alloys, heat trapped in a hole adversely affects tool life, hole surface quality and integrity. Therefore, modeling temperature distribution in drilling is vital for effective heat dissipation and improving quality of drilled surfaces. The existing numerical and finite element models consider only frictional heat, whereas the effect of shear heat generation and tertiary heat generation is neglected. In the present work, a comprehensive thermal model of the drilling process is developed by considering all heat generated in shear, friction and tertiary zones. The drill cutting edges are divided into a series of independent elementary cutting tools (ECT). The calculated heat flux loads are applied on an individual ECT in the finite element model to determine the temperature distribution and the maximum temperature around the cutting edge. The temperature in the drill was also measured experimentally with the help of an Infrared (IR) camera. The results of numerical simulations lie within the error of ～8.75% when compared to the prior studies, and ～5.41% when compared to our experimental work. The thermal model gives the temperature distribution, and the maximum temperature observed at the corner of cutting edge was 604.2°C at a cutting speed of 35?m/min.     

晶须增韧陶瓷刀具材料的磨损机理

本文对SiC晶须增韧Al2O3陶瓷刀具材料的摩擦磨损特征和耐磨性能进行了试验及理论研究。用有限元法分析了SiC晶须在磨损过程中的力学特点,解释了晶须定向对不同表面的耐磨性能的影响。通过对x射线衍射极点密度的测试计算,发现基体Al2O3的晶体有择优取向的趋势,从而由晶体学角度分析并提出该现象对试件不同表面耐磨性能的差异也有一定影响。切削试验进一步验证了以上结论。本文提出应选用平行于热压轴方向的表面作为承受磨损的面。     

考虑后刀面磨损条件下的正交切削模型分析

在试验研究基础上进行了有后刀面磨损的正交切削模型分析。经过正交切削试验及理论分析,发现后刀面磨损无论是定性上还是定量上都不影响刀具基本切削或剪切过程,即不改变剪切角和摩擦角,但是在磨损区的摩擦力及整个切削力都会增加。充分利用剪切区分析理论,确定了剪切区的切削力、后刀面磨擦力和后刀面磨损量的对应关系,从而建立了在后刀面磨损情况下的切削力模型。     

Ring wear monitoring in IC engines: an acoustic emission approach

Monitoring the condition of the engine compression ring in an engine operation is very important since it affects the engine performance. One of the most promising ring wear monitoring methods is based on the analysis of acoustic emission (AE) signals, which is an extremely powerful technique that can be deployed in a wide range of applications of non-destructive testing [Vallen Systeme (2000)]. This technique is already used for monitoring tool wear almost in all machining operations, but in this study, the AE signals were applied for monitoring ring wear in internal combustion (IC) engines. The AE signals generated in the ring sliding zone are very sensitive for correlation with ring wear, which in turn affects ring performance. This study was carried out with a single compression ring mounted on the piston. The AE signals were analyzed by considering signal parameters such as ring down count and RMS voltage [Krzysztof Jemielniak (2000) J Mater Process Technol 4752:1–6]. Analysis showed that the AE signal technique is applicable for ring wear monitoring in IC engines.