Sensing tool breakage in face milling with a neural network

A new approach using a neural network to process the features of the cutting force signal for the recognition of tool breakage in face milling is proposed. The cutting force signal is first compressed by averaging the cutting force signal per tooth. Then, the average cutting force signal is passed through a median filter to extract the features of the cutting force signal due to tool breakage. With the back propagation training process, the neural network memorizes the feature difference of the cutting force signal between with and without tool breakage. As a result, the neural network can be used to classify the cutting force signal with or without tool breakage. Experiments show this new approach can sense tool breakage in a wide range of face milling operations.     

基于声信号小波分析的刀具破损监测

提出了基于切削声信号的刀具破损监测方法。通过对破损声信号进行小波分析,提取出了与刀具破损具有相应关系的特征频带,去除了冲击声信号、刀具切入声信号等与刀具破损具有相似特征的声信号干扰,通过设定合适的阈值,能够较好地监测刀具的破损。这种监测方法为刀具的破损监测提供了一种新的途径。     

Multiclassification of tool wear with support vector machine by manufacturing loss consideration

Tool wear is a dynamic process, as a tool progresses from sharp to worn state and possibly to breakage. Thus the multiclassification of tool states is preferred, which can provide more timely and accurate estimation of tool states. Based on acoustic emission (AE) sensing, this paper proposes a new performance evaluation function for tool condition monitoring (TCM) by considering manufacturing loss. Firstly, two types of manufacturing loss due to misclassification (loss caused by under prediction and loss caused by over prediction) are analyzed, and both are utilized to compute corresponding weights of the proposed performance evaluation function. Then the expected loss of future misclassification is introduced to evaluate the recognition performance of TCM. Finally, a revised support vector machine (SVM) approach coupled with one-versus-one method is implemented to carry out the multiclassification of tool states. With this approach, a tool is replaced or continued not only based on the tool condition alone, but also the risk in cost incurred due to underutilized or overused tool. The experimental results show that the proposed method can reliably perform multiclassificaion of tool flank wear, and reduce the potential manufacturing loss.     

Tool breakage detection using support vector machine learning in a milling process

In this paper, an intelligent tool breakage detection system which uses a support vector machine (SVM) learning algorithm is proposed to provide the ability to recognize process abnormalities and initiate corrective action during a manufacturing process, specifically in a milling process. The system utilizes multiple sensors to record cutting forces and power consumptions. Attention is focused on training the proposed system for performance improvement and detecting tool breakage. Performance of the developed system is compared to the results from an alternative detection system based on a multiple linear regression model. It is expected that the proposed system will reduce machine downtime, which in turn will lead to reduced production costs and increased customer satisfaction.     

Micro-end-milling-II. Extending tool life with a Smart Workpiece Holder (SWH)

A new procedure is proposed to extend tool life. The new procedure uses a Smart Workpiece Holder (SWH), which monitors the cutting force and reduces the metal removal rate when it predicts tool breakage possibility. The response includes a quick move in the opposite of the feed direction by using a piezo-electric actuator and by reducing the feed rate. The proposed system was tested with mild steel and aluminum workpieces. Based on the experimental results, better than 50% of the tool breakage cases could be predicted and tool life can be increased more than 30%.     

The shape characteristic detection of tool breakage in milling operations

Detection of tool failure is very important in automated manufacturing. All previously developed tool breakage detection approaches in milling operations have adopted the strategy of parameter detection in which the detection of tool breakage was carried out according to values of specific parameters selected to reflect tool state (with or without tool breakage). In this paper the new concept of shape characteristic detection of tool breakage in milling operations is proposed. The detection of tool breakage is conducted according to the shape characteristics of discrete dyadic wavelet decomposition of cutting force. By means of the proposed method, the influence caused by the variation of cutting parameters and transients is eliminated. The proposed method is conducted in two steps. In the first step, cutting force signals are decomposed by discrete dyadic wavelet, with the shape characteristic vectors then being generated by the proposed shape characteristic vector-generating algorithm. In the second step, the shape characteristic vectors are fast classified by the ART2 neural networks. The accuracy and effectiveness of the proposed method are verified by numerous experiments.     

Micro-end-milling-III. Wear estimation and tool breakage detection using acoustic emission signals

Acoustic Emission (AE) signals have been used to monitor tool condition in conventional machining operations. In this paper, new procedures are proposed to detect tool breakage and to estimate tool condition (wear) by using AE. The proposed procedure filters the AE signals with a narrow band-width, band-pass filter and obtains the upper envelope of the harmonic signal by using analog hardware. The envelope is digitized, encoded and classified to monitor the machining operation. The characteristics of the envelope of the AE were evaluated to detect tool breakage. The encoded parameters of the envelope of the AE signals were classified by using the Adaptive Resonance Theory (ART2) and Abductory Induction Mechanism (AIM) to estimate wear. The proposed tool breakage and wear estimation techniques were tested on the experimental data. Both methods were found to be acceptable. However, the reliability of the tool breakage detection system was higher than the wear estimation method.     

Flute breakage detection during end milling using Hilbert–Huang transform and smoothed nonlinear energy operator

Real time tool condition monitoring has great significance in modern manufacturing processes. In order to prevent possible damages to the workpiece or the machine tool, reliable monitoring techniques are required to provide fast response to the unexpected tool failure. Milling is one of the most fundamental machining operations. During the milling process, the current of feed motor is weakly related to the cutter condition, the change of power consumption is not significant to identify tool condition. Thus, current of motor-based tool condition still requires some new approaches to sort out significant pattern that could be employed to indicate tool condition. In this paper, a new approach is proposed to detect end mill flute breakage via the feed-motor current signals, which implements Hilbert–Huang transform (HHT) analysis and a smoothed nonlinear energy operator (SNEO) to extract the crucial characteristics from the measured signals to indicate tool breakage. Experiments on a CNC Vertical Machining Centre are presented to show the algorithm performance. The results show that this method is feasible and can accurately and efficiently monitor the conditions of the end mill under varying cutting conditions.     

数控切削过程中的刀具破损在线监控

提出了一种数控加工过程中的刀具破损在线监控系统，并详细介绍了加工过程中用电机电流信号监测刀具破损的原理以及具体的实验方案。探讨了中断型宏指令功能在刀具破损在线监控系统中的应用。经实践证明，利用该监控系统和中断型宏指令，能够实时的识别加工过程刀具的破损，并能及时报警、自动换刀等.机床的故障停机时间大大地减少，利用率得到了很大地提高。     

Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion

In a fully automated manufacturing environment, instant detection of the cutting tool condition is essential for the improved productivity and cost effectiveness. This paper studies a tool condition monitoring system (TCM) via machine learning (ML) and machine ensemble (ME) approach to investigate the effectiveness of multisensor fusion technique when machining 4340 steel with multilayer coated and multiflute carbide end mill cutter. In this study, 135 different features are extracted from multiple sensor signals of force, vibration, acoustic emission and spindle power in the time and frequency domain by using data acquisition and signal processing module. Then, a correlation-based feature selection technique (CFS) evaluates the significance of these features along with machining parameters collected from machining experiments. Next, an optimal feature subset is computed for various assorted combinations of sensors. Finally, machine ensemble methods based on majority voting and stacked generalization are studied for the selected features to classify not only flank wear but also breakage and chipping. It has been found in this paper that the stacked generalization ensemble can ensure the highest accuracy in tool condition monitoring. In addition, it has been shown that the support vector machine (SVM) outperforms other ML algorithms in most cases tested.     

数控机床加工曲面时刀具破损的小波分析

介绍小波变换思想及特点，根据傅里叶变换原理，结合刀具破损信号，分析了快速小波变换－Mallat算法。实验表明多分辨分析的方法，对于刀具破损突变信号具有精确时－频定位和易于监测的优点，能够有效处理刀具破损监控的信号。     

基于声发射的铣刀破损信号特征的提取与优化

铣刀破损监测对实现加工自动化具有重要的意义.提出了基于小波变换的铣刀声发射破损特征提取与优化方法.首先,采用小波变换对铣刀声发射信号进行多分辨率分解,然后提取各频段子信号的能量比作为刀具破损监测的特征量.通过对正常切削、随机冲击和刀具破损这三类信号的比较分析,证明了该特征提取方法能够有效地反映刀具状态.最后,通过正交统计,分析了切削速度、进给速度和切削深度对特征量的影响,并对特征量进行优化.     

Development of a tool failure detection system using multi-sensors

Tool monitoring and machine diagnosis in real machining have been crucial to the realization of fully automated machining. Also, the on-line detection technique of the tool breakage in machining should be supported. The effect of tool breakage is usually revealed from an abrupt change in the processed measurements, which is in excess of a threshold value. Although these techniques are generally effective for a specific cutting condition, they are often not sufficiently reliable for use in production due to the inability of single measurement to reflect tool breakage under various cutting conditions. In order to enhance the reliability of tool breakage signatures obtained from a single sensor, an integrated approach based on measurements from several sensors has been put forward. In this study, the tool breakage detection method using multi-sensors is proposed and the sensor fusion algorithm is developed to integrate and make decisions from data measured through the multisensors. Also, the performances of this scheme are compared and evaluated with real cutting process.     

Detection of tool breakage in milling operations—II. The neural network approach

On-line monitoring of tool cutting conditions and tool breakage is very important for automated factories of the future. In this paper, the time series based tooth period modeling technique (TPMT) is proposed for detecting tool breakage by monitoring a cutting force or torque signal in any direction. TPMT uses the fast a posteriori error sequential technique (FAEST) for on-line modeling of cutting force or torque signals. Tool breakage is detected by evaluating variations of the characteristics of the monitored signal in each tooth period. TPMT was tested on simulated and experimental end milling data. The proposed technique detected tool breakage in all of the test cases without giving any false alarms in the transition cases.     

Study of milling cutting force pulsation applied to the detection of tool breakage

A study of the cutting force pulsation due to tool breakage is presented. Monitoring algorithms extracting the cutting force signal changes caused by tool breakage and further processing the extracted cutting force signal to recognize tool breakage are proposed. Theoretical studies and experimental results performed in milling operations have proven the feasibility of the algorithms proposed.     

Discrete wavelet transform for tool breakage monitoring

Detection of tool breakage is of vital importance in automated manufacturing. Various methods have been attempted, and it is considered that the use of discrete wavelet transform (DWT), which is much more efficient and just as accurate wavelet analysis, may provide a realistic solution to the detection of tool breakage in operation. The DWT uses an analyzing wavelet function which is localized in both time and frequency to detect a small change in the input signals. In addition, it requires less computation than Fast Fourier Transformation (FFT). This paper discusses a tool breakage monitoring system based on DWT of an acoustic emission (AE) and an electric feed current signal using an effective algorithm. The experiment results show overall 98.5% reliability and the good real-time monitoring capability of the proposed methodology for detecting tool breakage during drilling.     

Fuzzy control of feed rate in end milling operations

In order to improve productivity in end milling operations, a new adaptive control system based on fuzzy logics to maintain a constant cutting force is developed. It is shown, by experimental cutting tests, that the cutting tool travels in the air cut with fast feed rate, yet in the varying depths of cut, the tool travels with an adjustable feed rate to prevent the occurrence of tool breakage and maintain a high metal removal rate.     

Complexity measure of motor current signals for tool flute breakage detection in end milling

Automated tool condition monitoring is an important issue in the advanced machining process. Permutation entropy of a time series is a simple, robust and extremely fast complexity measure method for distinguishing the different conditions of a physical system. In this study, the permutation entropy of feed-motor current signals in end milling was applied to detect tool breakage. The detection method is composed of the estimation of permutation entropy and wavelet-based de-noising. To confirm the effectiveness and robustness of the method, typical experiments have been performed from the cutter runout and entry/exit cuts to cutting parameters variation. Results showed that the new method could successfully extract significant signature from the feed-motor current signals to effectively detect tool flute breakage during end milling. Whilst, this detection method was based on current sensors, so it possesses excellent potential for practical and real-time application at a low cost by comparison with the alternative sensors.     

数控镗刀磨损与破损的声发射监测法

通过分析声发射传感器采集的刀具磨损状态信号,提取出反映刀具磨损状态的特征向量MFCC系数及差分系数,然后利用隐马尔可夫模型进行信号处理,建立了检测镗刀刀具状态的监测系统。实验结果表明:在刀具的正常磨损阶段,该监测系统可以实现刀具大致磨损量的预报;在刀具破损或损坏情况下,能够及时监测和预报刀具损坏状态。这种监测方法可用于实时在线监测,为刀具的磨损监测提供了一条切实可行的途径。     

Residual stresses in face finish turning of high strength nickel-based superalloy

This paper investigates the residual stresses distributions introduced in a new generation nickel-based superalloy RR1000 by surface finish turning. The residual stresses introduced as a function of depth have been analysed for a series of machining trials with round and rhombic inserts, coated and uncoated inserts, new and worn tools, and chipped tool. The residual stress depth profiles obtained by X-ray diffraction, and layer removal show that the tool type, tool coating, tool wear and tool breakage influence the residual stress. The extent of plastic deformation for different cutting conditions has been inferred from the different peak width. Overall, residual stresses tend to have a tensile character at all depths in the hoop direction, but exhibit a significant compressively stressed zone in the radial direction.