Power signal separation in milling process based on wavelet transform and independent component analysis

Among many machining condition monitoring systems, a spindle motor power monitoring system is considered as one of the most popular systems for plant floor applications. However, in practice, power signals are mixed with many signal sources relevant to cutting tool, cutting conditions as well as components of a machine tool, which contaminate with each other in feature extraction processes and decrease the monitoring reliability. In this paper, modified blind sources separation (BSS) technique is used to separate those source signals in milling process. A single-channel BSS method based on wavelet transform and independent component analysis (ICA) is developed, and source signals related to a milling cutter and spindle are separated from a single-channel power signal. The experiments with different tool conditions illustrate that the separation strategy is robust and promising for cutting process monitoring.     

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.     

Progressive cutting tool wear detection from machined surface images using Voronoi tessellation method

Tool condition monitoring by machine vision approach has been gaining popularity day by day since it is a low cost and flexible method. In this paper, a tool condition monitoring technique by analysing turned surface images has been presented. The aim of this work is to apply an image texture analysis technique on turned surface images for quantitative assessment of cutting tool flank wear, progressively. A novel method by the concept of Voronoi tessellation has been applied in this study to analyse the surface texture of machined surface after the creation of Voronoi diagram. Two texture features, namely, number of polygons with zero cross moment and total void area of Voronoi diagram of machined surface images have been extracted. A correlation study between measured flank wear and extracted texture features has been done for depicting the tool flank wear. It has been found that number of polygons with zero cross moment has better linear relationship with tool flank wear than that of total void area.     

Non-stationary signal analysis and transient machining process condition monitoring

Non-stationary machine condition monitoring is very important in modern automated manufacturing processes. In this research, an innovative non-stationary (transient) signal analysis approach has been developed for non-stationary machine condition monitoring. It is based on time–frequency distribution analysis and a singular value decomposition approach. The singular value decomposition method is used to extract features from the time–frequency distribution data. These features will serve as machine condition indices and can be easily incorporated for on-line machine condition monitoring and diagnosis. Satisfactory results have been obtained through simulation and experimental data. Experimental studies have demonstrated the effectiveness of the proposed method for transient machine and process condition monitoring.     

Development of an on-line tool-life monitoring system using acoustic emission signals in gear shaping

Sensing techniques for monitoring machining processes have been one of the focuses of research on process automation. This paper presents the development of on-line tool-life monitoring system for gear shaping that uses acoustic emission (AE). Characteristics of the AE signals are related to the cutting condition, tool material and tool geometry in the cutting of metals. The relationship between AE signal and tool wear was investigated experimentally. Experiments were carried out on the gear shaping of SCM 420 material with a pinion cutter having 44 teeth. Root-mean-square (RMS) AE voltages increase regularly according to tool wear. It is suggested that the maximum value of RMS AE voltage is an effective parameter to monitor tool life. In this study, not only the acquisition method of AE signals for rotating objects but also the signal-processing technique were developed in order to realize the in-process monitoring system for gear shaping. The on-line tool-life monitoring system developed has been successfully applied to gear machining processes.     

Application of statistical filtering for optical detection of tool wear

The application of automated tool condition monitoring systems is very important for unmanned machining systems. Tool wear monitoring is a key factor for optimization of the cutting processes. Basically, tool wear monitoring systems can be subdivided into two classes: direct and indirect. Currently direct tool wear monitoring systems are most frequently based on machine vision by camera. Several approaches have been studied for tool wear detection by means of tool images, and an innovative statistical filter proved to be very efficient for worn area detection. A new approach has been implemented and tested in order to develop an automatic system for tool wear measurement. This new approach is described in this paper and the main topics related to tool wear monitoring using wear images have been discussed.     

A brief review: acoustic emission method for tool wear monitoring during turning

Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. This paper reviews briefly the research on AE sensing of tool wear condition in turning. The main contents included are:

1. The AE generation in metal cutting processes, AE signal classification, and AE signal correction.

2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc.

3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and sensor and data fusion.

A review of AE-based tool wear monitoring in turning is an important step for improving and developing new tool wear monitoring methodology.     

基于Internet的数控机床远程控制

远程控制技术已逐步成为当代数控机床发展的主要趋势之一。文中给出了基于Internet的数控机床系统的硬件结构,并分析了软件实现方法。该数控机床可通过高速通信网络及时地向远程监控点提供当前加工状态信息并接收远程监控命令,从而实现数控系统的远程控制功能,具有很强的实用性。     

Sweeping filters and tooth rotation energy estimation (TREE) techniques for machine tool condition monitoring

This paper presents a method for the condition monitoring of the milling cutting process based upon a combination of two techniques; sweeping filters and tooth rotation energy estimation (TREE). Existing spindle speed and spindle load signals from the machine are used thus avoiding the need for any additional sensors. The sweeping filter technique determines the frequency components of the spindle signal using low cost hardware. The filter's cut off frequency is swept across a range of frequencies and its output is acquired and analysed in real time. The variations of individual tooth energies estimated by the TREE technique in the time domain are used to verify the results. The hybrid approach created is based on the verification of any indicated faults before making a final conclusion about the health of the cutting tool. This provides a robust and reliable tool monitoring system that is able to identify tool breakage in real time during machining operations.     

Wavelet analysis of sensor signals for tool condition monitoring: A review and some new results

This paper reviews the state-of-the-art of wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According to the multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in five categories in TCM: time–frequency analysis of machining signal, signal denoising, feature extraction, singularity analysis for tool state estimation, and density estimation for tool wear classification. This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area.     

Development of monitoring system on the diamond tool wear

Recently, ultra-precision machining using a single crystal diamond tool has been developing very rapidly, especially in the fields of production processes for optical or magnetic parts such as magnetic discs, laser mirrors, polygon mirrors and copier drums. As a result, it has been successfully extended to machine various soft materials, generating mirror-like surfaces to sub-micron geometric accuracy with the ultra-precision CNC machine and the single crystal diamond tool. With the real cutting operation, the geometric accuracy and the surface finish attainable in machined surfaces are mainly determined by both of the sharpness of a cutting tool and stability of the machine vibration. In this study, for monitoring the progress of machining state for assuring the machining accuracy and the surface quality, a new monitoring method of machining states in face-cutting with diamond tool is proposed, using the frequency response of multi-sensors signal, which includes wear state of tool in terms of the energy within the specific frequency band. A magnetic disc is machined on the ultra-precision lathe.     

Expert system-supported fuzzy diagnosis of finish-turning process states

Developing an effective method for on-line machining condition monitoring has been of great interest with the advent of automated machining systems. By effective it is meant that reliable and timely diagnosis of machining process states, such as tool breakage, severe wear and chip hazard, should be provided under various work conditions in a practical workshop environment. This is difficult as the machining process, especially finish-turning process, is complex, random and uncertain in nature, and influenced by numerous process parameters. In an attempt to tackle the problem, a new approach based on fuzzy state diagnosis is presented in this paper by introducing a series of fuzzy feature-state relationship matrices to quantify the strength between each key signal feature identified from cutting force-tool vibration data and various actual machining process states. The knowledge-intensive fuzzy feature-state relationship matrices are off-line developed with the support of a knowledge-based expert system that is constructed by a well-established machining reference database, expert intelligence on logic reasoning and decision-making, and experimental results of signal characteristics under various work conditions. These matrices, once established, can be on-line implemented to generate an integrated fuzzy feature-state matrix (ten features and nine states in this work) which is the essence for a fast and reliable diagnosis of machining process states. Finally, a detailed case study is worked out to demonstrate the work principle of the methodology presented in this paper.     

End milling tool breakage detection using lifting scheme and Mahalanobis distance

In this paper, a novel method based on lifting scheme and Mahalanobis distance (MD) is proposed for detection of tool breakage via acoustic emission (AE) signals generated in end milling process. The method consists of three stages. First, by investigating the specialty of AE signals, a biorthogonal wavelet with impact property is constructed using lifting scheme, and wavelet transform is carried out to separate AE components from the original signals. Second, Hilbert transform is adopted to demodulate signal envelope on wavelet coefficients and salient features indicating the tool state (i.e., normal conditions, slight breakage, and serious breakage) are extracted. Finally, tool conditions are identified directly through the recognition of these features by means of MD. Practical application results on a CNC vertical milling machine tool show that the proposed method is accurate for feature extraction and efficient for condition monitoring of cutting tools in end milling process.     

Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis

Dynamic properties of the whole machine tool structure including tool, spindle, and machine tool frame contribute greatly to the reliability of the machine tool in service and machining quality. However, they will change during operation compared with the results from static frequency response function measurements of classic experimental modal analysis. Therefore, an accurate estimation of the dynamic modal parameters of the whole structure is of great value in real time monitoring, active maintenance, and precise prediction of a stability lobes diagram.Operational modal analysis (OMA) developed from civil engineering works quite efficiently in modal parameters estimation of structure in operation under an intrinsic assumption of white noise excitation. This paper proposes a new methodology for applying this technique in the case of computer numerically controlled (CNC) machine tools during machining operations. A novel random excitation technique based on cutting is presented to meet the white noise excitation requirement. This technique is realized by interrupted cutting of a narrow workpiece step while spindle rotating randomly. The spindle rotation speed is automatically controlled by G-code part program, which contains a series of random speed values produced by MATLAB software following uniform distribution. The resulting cutting produces random pulses and excites the structure in all three directions. The effect of cutting parameters on the excitation frequency and energy was analyzed and simulated. The proposed technique was experimentally validated with two different OMA methods: the Stochastic Subspace Identification (SSI) method and the poly-reference least square complex frequency domain (pLSCF or PolyMAX) method, both of which came up with similar results. It was shown that the proposed excitation technique combined successfully with OMA methods to extract dynamic modal parameters of the machine tool structure.     

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.     

Using neural network for tool condition monitoring based on wavelet decomposition

This paper presents a neural network application for on-line tool condition monitoring in a turning operation. A wavelet technique was used to decompose dynamic cutting force signal into different frequency bands in time domain. Two features were extracted from the decomposed signal for each frequency band. The two extracted features were mean values and variances of the local maxima of the absolute value of the composed signal. In addition, coherence coefficient in low frequency band was also selected as a signal feature. After scaling, these features were fed to a back-propagation neural network for the diagnostic purposes. The effect on tool condition monitoring due to the presence of chip breaking was studied. The different numbers of training samples were used to train the neural network and the results were discussed. The experimental results show that the features extracted by wavelet technique had a low sensitivity to changes of the cutting conditions and the neural network has high diagnosis success rate in a wide range of cutting conditions.     

A novel approach to machining condition monitoring of deep hole boring

In the optimization of deep hole boring processes, machining condition monitoring (MCM) plays an important role for efficient tool change policies, product quality control and lower tool costs. This paper proposes a novel approach to the MCM of deep hole boring on the basis of the pseudo non-dyadic second generation wavelet transform (PNSGWT). This approach is developed via constructing a valuable indicator, i.e., the wavelet energy ratio around the natural frequency of boring bar. Self-excited vibration occurs at the frequency of the most dominant mode of the machine tool structure. Via modeling dynamic cutting process and performing its simulation analysis during deep hole boring, it is found that the vibration amplitudes at the nature frequency of the machine tool rise with the tool wear. The PNSGWT that has relative adjustable dyadic time-frequency partition grids, good time-frequency localizability and exact shift-invariance is used to extract the wavelet energy in the specified frequency band. Accordingly, the MCM of deep hole boring can be implemented by means of normalizing the wavelet energy. Finally, a field experiment on deep hole boring machine tool is conducted, and the result shows that the proposed method is effective in the process of monitoring tool wear and surface finish quality for deep hole boring.     

In-process measurement and assessment of dynamic characteristics of machine tool structures

In this paper, an in-process measurement procedure for a machine tool structure is illustrated. An effective technique for eliminating the effect of inteference signals is presented. The dynamic parameters of the machine tool structure estimated from the in-process measurements under various machining conditions are shown. The influence of the machining conditions on the operative receptance and the modal parameters is studied. A criterion for choosing the machining conditions of in-process measurement is proposed.     

基于内置传感器的数控机床动态加工误差测量方法

以HJ044双转台型五轴联动数控机床为例,以机床内置传感器信息和多体系统理论为基础,建立了刀具相对工件的运动学模型,提出了一种基于内置传感器信息的动态加工误差测量方法。该方法利用机床编码器或光栅尺等机床内置传感器信息获取机床各轴运动位移,并结合机床运动学模型,测量由机床的动态特性引起的加工误差。并通过实验表明该方法是一种简单有效的数控机床动态加工误差测量方法。     

A new method for monitoring micro-electric discharge machining processes

Micro-electric discharge machining (μ-EDM) is a very complex phenomenon in terms of its material removal characteristics since it is affected by many complications such as adhesion, short-circuiting and cavitations. This paper presents a new method for monitoring μ-EDM processes by counting discharge pulses and it presents a fundamental study of a prognosis approach for calculating the total energy of discharge pulses. For different machining types (shape-up and flat-head) and machining conditions (mandrel rotation and tool electrode vibration), the results obtained using this new monitoring method with the prognosis approach show good agreement between the discharge pulses number and the total energy of discharge pulses to the material removal and tool electrode wear characteristic in μ-EDM processes. On applying tool electrode vibration, the machining time becomes shorter, because it removes adhesion. The effect of tool electrode vibration in order to remove adhesion can be monitored with good results. In order to achieve high accuracy, the tool wear compensation factor has been successfully calculated, since the amount of tool electrode wear is different in each machining type and condition. Consequently, a deeper understanding of the μ-EDM process has been achieved.