SENSORLESS DETECTION OF TOOL BREAKAGE IN MILLING

One of the most important research topics in the area of Intelligent Manufacture Systems (IMS) is the automatic detection of tool breakage, wear of chipping during the cutting process. Sensor-based techniques are available for cutting force measurements, but there are drawbacks in this approach in cost and idle times. This work proposes a sensorless monitoring system for tool monitoring in order to detect breakage and chipping by exploiting the wavelet transform and a neural network. Previous works have made use of these tools for monitoring several machining parameters, but we propose an integrated low-cost approach to detect quickly the changes in the tool integrity for monitoring. The system output produces an accurate detection of the tool integrity that enables the system to prevent damage due to tool breakage. This approach allows for an industrial solution to be developed.     

一个应用于自动化加工系统的综合刀具监测系统

对于自动化加工系统、刀具破损和异常磨损的有效实时监测是一个亟待解决的问题。本文用声发射信号监测加工中心上各种刀具的破损、折损,针对多种工序、多咱切削条件的复杂情况,进行了可变参数的模式识别算法的研究。基于这个算法,开发了一个综合刀具破损监测系统。这个系统针对自动化加工基本单元——加工中心的车、镗、铣多种工序,使得自动化加工系统的综合监测成为可能。实验验证表明,识别成功率大于90%。     

The design of a single-chip tool monitoring system for on-line turning operation

Tool condition monitoring systems play an important role in a FMS system. By changing the worn tool before or just at the time it fails, the loss caused by defect product can be reduced greatly and thus product quality and reliability is improved. To achieve this, an on-line tool condition monitoring system using a single-chip microcomputer for detecting tool breakage during cutting process is discussed in this paper. Conventionally, PC-based monitoring systems are used in most research works. The major shortcoming of PC-based monitoring systems is the incurred cost. To reduce costs, the tool condition monitoring system was built with an Intel 8051 single-chip microprocessor and the design is described in this paper. The 8051 tool monitoring system uses a strain gauge for measuring cutting force; according to the force feature, the tool monitoring system can easily recognize the breakage of the cutting tool with its tool breakage algorithm. The experimental results show that the low-cost 8051 tool monitoring board can detect tool breakage in three successive products successfully.     

A microcontroller-based end milling cutter monitoring and management system

The monitoring of end milling cutting operations for tool breakage is achieved using a low-cost microcontroller-based system. The system is based upon acquiring and analysing machine tool-based signals for characteristic responses to tool breakage. Spindle speed and load signals are shown to be responsive to tool condition and thus capable of supporting the deployed approach. The resulting system operates in real time with tool breakage detection consistently diagnosed within two revolutions. The monitoring function is extended to consider tool wear using analysis methods applied in the time and frequency domains. Decisions about tool condition are made by integrating all relevant information into a rule base. Higher-level tool management functions supported by the deployed system are identified.     

Tool breakage detection in CNC high-speed milling based in feed-motor current signals

Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition. The current consumption changes when the tool is broken as compared to when the tool is in normal cutting condition. This difference can be obtained from the waveform variances between the signals in order to ascertain the tool condition. The algorithms of this research project consist of obtaining compressed signals from the I _rms feed-motor current signals applying the DWT. Then from these compressed signals, we detect the asymmetries between them. The arithmetic mean value is applied to asymmetries of consecutive machining lengths to reduce noise in the data having a mean value of a series of asymmetries; also, a normal cutting threshold is set up in order to make decisions regarding the tool conditions so as to detect tool breakage. Therefore, this research project shows a low-cost monitoring system that is simple to implement.     

Detection of chipping in ceramic cutting inserts from workpiece profile during turning using fast Fourier transform (FFT) and continuous wavelet transform (CWT)

Ceramic cutting tool inserts are prone to premature failure by chipping instead of gradual wear due to their low impact toughness. Thus, in-process detection of failure of ceramic tools is important to prevent workpiece surface deterioration. The objective of this study is to develop a method of detection of the onset of chipping in ceramic cutting tool inserts during dry finish turning from the workpiece profile signature. The profile of the workpiece surface opposite the cutting side was captured using an 18-MP DSLR camera at a shutter speed of 0.25 ms during the turning of AISI01 oil-hardening tool steel. The edge profile was extracted to sub-pixel accuracy from the 2-D image of the workpiece surface using the invariant moment method. The effect of chipping in the ceramic insert on the surface profile signature of the workpiece was investigated using the fast Fourier transform (FFT) and continuous wavelet transform (CWT). The results show that the stochastic behavior of the cutting process after tool chipping manifest as sharp increase in the amplitude of spatial frequencies below the fundamental feed frequency. The proposed sub-window FFT method is effective in resolving the time resolution by detecting tool chipping at cutting time duration of around 17.13 s. Compared to the sub-window FFT method the CWT method is able to detect the exact onset of chipping in the cutting tool insert.     

Measurement of transient cutting force by means of a Fourier analyser

Measurements of transient cutting force are often required for analysing transient phenomena in cutting or detecting tool chipping. With most existing tool dynamometers which detect cutting force through strain, however, accurate measurements of transient cutting force cannot be expected because of inadequate frequency characteristics or large time lag. This paper proposes a method of measuring the transient cutting force. In this method, cutting force is calculated by means of a digital Fourier analyser from the output of a tool dynamometer and the transfer function, which has been identified in advance under the same set-up as used for the cutting test. The assessment tests have revealed that the cutting force calculated in this way is extremely close to the real value, regardless of the dynamic rigidity of the tool dynamometer. This method is also applicable for accurate detection of acceleration of a simple system.     

Real‐Time Monitoring of Tool Fracture in Turning Using Sensor Fusion

The sensor fusion method using both an acoustic emission (AE) sensor and a built-in force sensor is introduced for on-line tool condition monitoring during turning. The cutting force was measured by a built-in piezoelectric force sensor, which was inserted in the tool turret housing of an NC lathe. FEM analysis was carried out to locate the most sensitive position for the sensor. A burst of AE signal was used as a triggering signal to inspect the cutting force. A significant drop in cutting force indicated tool breakage. The algorithm was implemented in a DSP board and the monitoring system was installed on a CNC lathe in an FMS line for in-process tool-breakage detection. The proposed system showed an excellent monitoring capability.     

Real-time monitoring and diagnosing in wire-electro discharge machining

This paper presents the design and development of a real-time monitoring and diagnostic system for diagnosing the degraded behaviour in wire-electro discharge machining. The detection in advance of the degraded behaviour is crucial since this can lead to the breakage of the cutting tool (the wire), reducing the process productivity and the required accuracy (Ho et al., Tools Manuf 44:1247–1259, 2004). This work presents the design and development of a real-time monitoring system that alerts the degraded behaviour. It can detect different types of degraded behaviours that have been previously identified during the analysis phase. Unlike other works found in the literature review, which are focussed on proprietary hardware, the present paper proposes a flexible real-time platform based on a commercial data acquisition board that can be easily configured for different purposes. It has been applied to develop a real-time monitoring and diagnostic system that uses virtual sensors to diagnose the degradation of the process. The results of this work show a satisfactory performance of the presented approach.     

In-process tool-failure detection by means of AR models

The present paper proposes a cutting tool breaking and chipping detection system for continuous and interrupted cutting, based on the analysis of the cutting force componentsF _x andF _y. A multifactorial experimental design has been carried out, to take account of the variability of the force signal. An adaptive signal processing algorithm is proposed, which detects catastrophic failure when at least one component deviates outside an estimated oscillation band for a time duration longer than a prefixed interval. The algorithm has been implemented on a four-microprocessor transputer board. Several tests confirmed the validity of the approach for detecting breaking and chipping phenomena in a few milliseconds, both in turning and in milling operations.     

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.     

声发射预测刀具破损的研究

介绍一种用声发射（ＡＥ）进行刀具破损预测的方法。试验结果证明，刀具破损前会出现预兆性的ＡＥ信号，并可把该信号从背景噪声中检测出来。该方法使用的检测系统具有成本低、能耗低和结构简单的特点。     

An intelligent sensor fusion system for tool monitoring on a machining centre

The objective of this paper is to construct an intelligent sensor fusion monitoring system for tool breakage on a machining centre. Since none of the sensing and diagnosis techniques have proved to be completely reliable in practice, an intelligent tool-monitoring system consisting of a neural-network-based algorithm and a sensor fusion system is proposed. The dual sensing signals of cutting force and acoustic emission are used simultaneously in the proposed system owing to good correlation existing between them, and, a self-learning neural-network algorithm is used to integrate multiple sensing information to make a proper decision about tool condition. The results show good performance in tool-breakage detection by the proposed monitoring system, especially where there is high interference.     

A new approach to the analysis of high-speed milling in the case of chipped inserts

High-speed milling permits to machine materials with increased productivity. For a reliable application of this strategy, it is crucial to avoid tool chipping during the machining; in practice, this implies underestimating tool life. The formation of chipping induces vibrations, high temperature on tool nose, and poor surface quality. This is a problem when it represents the last operation. A better comprehension of how a damaged insert works is useful to design a reliable monitoring system. More researches use several sensors to monitor the health state of the milling tool, like force signal, current signal, acceleration, acoustic emission, etc., and complex elaboration systems have been considered. The objective of this paper is the proposal of a model to analyze the chipping of multi-tooth milling tool. First milling tests are carried out on a milling center and cutting forces are measured. The experimental data were elaborated and a suitable model is set up. The finite element method (FEM) is considered and simulations were performed using SFTC Deform 2D^TM, a Lagrangian implicit solutor. The material model and the friction law are calibrated following an appropriate procedure. In this work, face milling using multi-tooth milling tool is considered. Face milling are very common operations in several industrial sectors. The results show the FEM model is able to simulate the standard deviation of feed force during the chipping. A regression model between chipping scar dimension and feed cutting force is presented in order to summarize the obtained results.     

Prediction of tool breakage in face milling using support vector machine

A new approach is proposed using a support vector machine (SVM) to classify the feature of the cutting force signal for the prediction of tool breakage in face milling. The cutting force signal is compressed by averaging the cutting force signals per tooth to extract the feature of the cutting force signal due to tool breakage. With the SVM learning process, the output of SVM’s decision function can be utilized to identify a milling cutter with or without tool breakage. Experimental results are presented to verify the feasibility of this tool breakage prediction system in milling operations.     

Cutting tool damage in interrupted cutting

The shaping and turning of a workpiece with key ways, milling and the high speed turning of small workpieces, in which the risk of tool chipping and breakage decreases in the order given, exhibit three danger areas: tool entry, tool exit and cyclic heating and cooling. The relative importance of these depends on the dimensions of the cut, the cutting speed, the heating time/cooling time, the workpiece and tool materials, the shape of the tool and the geometry of the entry and exit. A better understanding of the phenomena occurring during tool entry is required, and therefore the cutting forces during entry and exit have been studied. The chipping caused by the exit phenomena has been explained using the results of a finite element study of the workpiece and the tool. The effect of rounding or chamfering the edges of carbide cutting tools for interrupted cutting, e.g. milling, is discussed and the work required before this can be optimized is outlined.     

细晶粒硬质合金刀具铣削钛合金损坏机理的研究

基于用Y330细晶粒硬质合金刀具高速铣削Ti6Al4V钛合金的试验,分析了刀具的损坏形态和失效机理。结果表明,在给定的切削条件下,刀具的损坏形态以崩刃和灼烧为主,同时伴有表面材料扩散。据此提出了延长刀具寿命、提高加工效率的途径。     

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.     

频段能量法及其在切削状态实时监测中的应用

本文提出一种新的切削状态实时监测的信号处理方法——频段能量法,它能有效地提取切削状态的信号特征,解决传统数字式谱分析中精度与实时性之间的矛盾,具有迅速、准确、抗干扰、易设定相对阈值和适应面广等优点。所建立的微机监测系统已初步实现了对刀具磨损阶段、刀具破损和切削颤振的实时监测。     

Real-time detection of the breakage of small diameter drills with wavelet transform

This paper presents a real-time tool breakage detection method for small diameter drills using acoustic emission (AE) and current signals. Using the transmitted properties of the AE signal, apparatus for detecting the AE signal for tool breakage monitoring was developed for a machine centre. The features of tool breakage were obtained from the AE signal using typical signal processing methods. The continuous wavelet transform (CWT) and the discrete wavelet transform (DWT) were used to decompose the spindle current signal and the feed current signal, respectively. The tool breakage features were extracted from the decomposed signals. Experimental results show that the proposed monitoring system possessed an excellent real-time capability and a high success rate for the detection of the breakage of small diameter drills using combined AE and current signals.