Tool Breakage Monitoring Research Articles

Accurate tool wear and breakage monitoring method for milling process based on vision and laser sensor fusion

Accurate online acquisition of tool wear degradation indicators is an important prerequisite for tool wear monitoring and tool remaining life prediction. The tool degradation process is usually accompanied by the flank face of cutting tool wear and blade breakage, however, the existing degradation indicators only consider the flank face wear value, but not the tool breakage value, resulting in the lack of accuracy of degradation indicators. To this end, an online identification method of tool degradation indicators based on the fusion of image sensors and laser displacement sensors is proposed, which adopts the VGG16-UNet network to identify the wear value in the image and obtains the tool breakage value based on the time series data of the laser displacement sensor. Finally, the tool wear and breakage degradation label containing wear and breakage values is established. Compared to manual measurements, the absolute average error is within 15 µms for cutter face damage values and within 3 µms for cutter face wear values.

Real-time tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions

Tool breakage occurs randomly during machining operations, which induces more severe impacts on the quality of components compared to progressive tool wear. It is widely acknowledged that the unpredictable changes in cutting conditions will cause fluctuations in the signal amplitude and thus generate false alarms. This study introduced a novel method for tool breakage monitoring based on dimensionless indicators under time-varying cutting conditions. The amplitude ratio (AR) and the energy ratio (ER) were proposed according to the power spectrum of the spindle vibration signal, which represents the change of amplitude and the energy distribution, respectively. The AR and ER are normalized and integrated into a unified indicator for real-time breakage monitoring. The floating monitoring threshold is designed based on the Gaussian distribution. Moreover, the material removal rate (MRR) is selected as a secondary indicator to accurately identify tool breakage based on determining the amplitude fluctuation caused by cutting conditions or teeth breakage. The effectiveness of the proposed method for tool breakage monitoring has been verified under the constant, time-varying, and entry/exit cutting conditions. The results show that the proposed indicators have higher sensitivity than the traditional root mean square (RMS) features and eliminate false alarms during condition change transients. This research provides a potential solution for tool breakage monitoring under complex cutting conditions.

Real-Time milling tool breakage monitoring based on multiscale standard deviation diversity entropy

Tool breakage monitoring (TBM) during milling is vital for ensuring product quality, equipment and operator safety. This paper proposes a novel real-time TBM method based on multiscale standard deviation diversity entropy (MSDDE). Unlike existing similar works that only validate the effectiveness of the extracted features while ignoring the requirements of real-time detection, the proposed method can extract effective features from short data samples containing only two spindle rotation periods and achieve computational efficiency in milliseconds. Consequently, it is more suitable for application in the real-time detection of milling tool breakage. MSDDE quantifies the dynamic complexity of vibration signals at different time scales, which is capable of reducing the requirement for the number of spindle periods in the sliding time window while comprehensively describing the tool breakage characteristics. The proposed algorithm exhibits a time complexity of O(MN) where M depends on the scale parameter and embedding dimension. Experimental investigations show that the proposed method is able to complete the calculation of feature values within two milliseconds and realize real-time detection of milling tool breakage.

Physical model-based tool wear and breakage monitoring in milling process

Tool wear and breakage is one of the biggest obstacles for developing the unattended CNC machining. Especially when cutting the difficult-to-machining materials, the tool wear will be more rapid and the tool breakage becomes more frequent, which makes an effective tool wear and breakage monitoring very urgent. In the paper, a physical model-based tool wear and breakage monitoring method is proposed. Firstly, a physical model of milling force with the influence of cutter runout and tool wear is established. The spindle box vibration and cutting torque of spindle drive system induced by milling force have been clarified theoretically. Then, through the measurements of milling force, spindle box vibration and driving current, a tool wear monitoring method by extracting the comprehensive feature from the seven-channel specific cutting force coefficients (SCFCs) has been presented. In the method, a multi-parameter decoupling identification procedure for the cutter runout, tooth wear loss and respective SCFCs has been clarified. The force homogenization effect of multi-tooth with tool wear is found. Moreover, an efficient tool breakage monitoring method is further put forward, which incorporates the amplitude ratios of multi-channel data to form an indicator for judging the occurrence of tool damage. The generation mechanism of the sudden distortion on signal waveform resulting from tool breakage is also explained. Finally, long-time milling experiments with multiple groups of machining parameters have been carried out on the general and heavy-cutting machine tools to verify the validity of the proposed method. The verification results indicate that the tool wear and breakage monitoring method can well estimate the cutting status of cutter. The study can provide a useful research basis for the potential industrial application of tool wear and breakage monitoring technology.

Tool breakage monitoring based on sequential hypothesis test in ultrasonic vibration-assisted drilling of CFRP

Tool breakage monitoring based on sequential hypothesis test in ultrasonic vibration-assisted drilling of CFRP

Tool breakage monitoring based on the feature fusion of spindle acceleration signal

Tool breakage monitoring based on the feature fusion of spindle acceleration signal

Tool Breakage Detection of Milling Cutter Insert Based on SVM

The paper deals with a new method for the tool breakage monitoring of milling cutter insert through acoustic emission (AE) technique. An insert breakage detection method based on support vector machine (SVM) is proposed, which consists of off-line phase and on-line phase. In the off-line phase, 13 AE signal features were extracted as input and then the SVM model was established and optimized. In the on-line phase, the constructed model was used to detect insert breakage. The proposed method was applied and validated by an experiment conducted on a rotor slot milling machine in a factory. The result shows the validity and practicability of this method.

A tool breakage monitoring method for end milling based on the indirect electric data of CNC system

Automatic tool breakage monitoring (TBM) is a vital technology of the unmanned workshops and automatic production lines for CNC machining. The current solutions for TBM mostly rely on direct data retrieved from the external sensors mounted on a machine tool, e.g., force and vibration sensors, which complicates the entire TBM system and adds additional costs. In this paper, instead of relying on the external sensors, indirect data from the CNC system, e.g., the spindle power, is utilized for the TBM. As the spindle power is a comprehensive signal, which not only contains a component of the cutting force, the direct factor determining tool breakage, but also involves the Coulomb friction and viscous damping forces, which are thermally sensitive and nonlinear, as well as the other components, such as the inertial force. For tool breakage to be effectively and accurately recognized, the spindle power data are preprocessed based on a mapping relationship between the spindle power and block numbers, with the one affecting tool breakage isolated via empirical mode decomposition (EMD). From the extracted signal, a support vector machine (SVM)-based method is then proposed to identify tool breakage. With the proposed method, actual machining experiments were conducted on a CNC milling center to verify that the proposed method can successfully extract the signal associated with tool breakage from the spindle power and that tool breakage can be accurately detected whenever it happens.

CNC internal data based incremental cost-sensitive support vector machine method for tool breakage monitoring in end milling

A tool breakage monitoring (TBM) system needs to detect tool breakage promptly in an unattended automation workshop. Traditional TBM systems that employ external sensors to acquire diagnostic signals such as spindle power for making judgments are inconvenient since extra sensors should be installed. Moreover, the signals from the external sensors are independent of the computer numerical control (CNC) system, and it is difficult to label them with the corresponding machining task information so that the target data can be segmented automatically. This paper proposes an incremental cost-sensitive support vector machine (ICSSVM) tool breakage monitoring method based on CNC internal data, which is inherently machining-task labeled and can be accessed directly from the CNC system without extra sensors. To satisfy the dataset’s integrity at the initial stage of model training, a simulation method that is based on the actual tool breakage characteristics is applied to generate simulated tool breakage data. The ICSSVM method combines cost-sensitive SVM (CSSVM) and modified incremental SVM (ISVM) to solve the imbalanced classification problem, which increases the misclassification probability of the minority class, train the model incrementally from the absence of samples, and guarantee high algorithm efficiency as the size of the dataset increases. It is proved that the ICSSVM algorithm has better algorithmic efficiency compared to the batch cost-sensitive SVM (BCSSVM). It is also proved that the ICSSVM algorithm has better imbalanced classification performance than batch SVM (BSVM), as assessed by the receiver operator characteristic (ROC) curves. The industrial practicability of the proposed method is verified by actual machining with a CNC system integrated with the TBM module.

Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring

Anomaly Detection via Unsupervised Learning for Tool Breakage Monitoring

Tool breakage detection from 2D workpiece profile using vision method

In-process tool breakage monitoring can significantly save cost and prevent damages to machine tool. In this paper, a machine vision approach was employed to detect the tool fracture in commercial aluminium oxide ceramic cutting tool during turning of AISI 52100 hardened steel. The contour of the workpiece profile was captured with the aid of backlighting during turning using a high-resolution DSLR camera with a shutter speed of 1/4000 s. The surface profile of the workpiece was extracted to sub-pixel accuracy using the invariant moment method. The effect of fracture in ceramic cutting tools on the surface profile signature of the machined workpiece using autocorrelation was studied. Fracture in the aluminum oxide ceramic tool was found to cause the peaks of autocorrelation function of the workpiece profile to decrease rapidly as the lag distance increased. The envelope of the peaks of the autocorrelation function was observed to deviate significantly from one another at different workpiece angles when the tool has fractured due to the continuous fracture of ceramic cutting insert during machining.

이송모터 전류신호를 이용한 공구파손 검출

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

A Method of Tool Breakage Detection in CNC High-Speed Milling

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.

다중 센서를 이용한 CNC 선반에서의 실시간 공구파손 감시에 관한 연구

This study presents a new methodology for realtime tool breakage detection by sensor fusion concept of two hall sensor and an acoustic emission (AE) sensor. Spindle induction motor torque of CNC Lathe during machining is estimated by two hall sensor. Estimated motor torque instead of a tool dynamometer was used to measure the cutting torque and tool breakage detection. A burst of AE signal was used as a triggering signal to inspect the cutting torque. A significant drop of cutting torque was utilized to detect tool breakage. The algorithm was implemented on a NI DAQ (Data Acquisition) board for in-process tool breakage detection. The result of experiment showed an excellent monitoring capability of the proposed tool breakage detection system. This system is available tool breakage monitoring through internet also provides this system's user with current cutting torque of induction motor.

Tool Breakage Feature Extraction in PCB Micro-Hole Drilling Using Vibration Signals

Tool breakage monitoring is crucial to automation fabrication, especially for high-density hole machining, such as PCB (Printed Circuit Board). A tool breakage feature extraction method in PCB micro-hole drilling is presented in this paper. The vibration signal is analyzed by wavelet transform. The decomposed signals energy ratio at each frequency band is computed as monitoring features. The monitoring performance of different features selection is given. The vibration signals are observed to provide the capability in distinguishing micro drill breakage with proper features extraction and classifier design.

A Taguchi-Neural-Based In-Process Tool Breakage Monitoring System in End Milling Operations

The use of CNC machines is one of the successful factors in the computer integrated manufacturing (CIM). Even though the CNC machine can automatically perform the machining processes, some of the situations that may significantly influence the quality of product such as a cutting tool breakage. Therefore, to prevent the machine from damaging and ensure the quality of product, it is important to develop a system that can monitor the tool conditions. The purpose of this study is to develop a Taguchi-neural-based in-process tool breakage monitoring system in end milling operations that can monitor the tool conditions and immediately response a proper action. For an in-process tool breakage monitoring system, a neural network was applied to making decisions of monitoring. One of the disadvantages of neural network is the training processes. It is difficult to determine an optimal combination of training parameters of neural networks. Traditionally, the try-and-error method is time-consuming and without systematic base. Therefore, the optimization of training parameters for neural networks using Taguchi design was applied to training the neural network model and to enhance the accuracy of the tool breakage monitoring system.

Monitoring of Drilling Tool Condition through Spindle Current

Drill wear or breakage often damages the work piece and/or machine tool. Spindle motor current reflects the cutting process and the signal can be easily and inexpensively obtained. This paper presents a strategy for on-line drilling tool wear and breakage monitoring. It employs Wavelet Transform (WT) of the spindle current signature to perform monitoring. A moving window technique is used to extract the cutting portion of data from the entire data sequence. A low pass de-nosing filter is employed to remove noise from the current signal. Features were extracted using WT node energy and selected based on their ability to detect tool wear and chipping. The Progression of tool wear based on feature of WT detail level 4 is analyzed and pointed out status of worn or chipped tool. Experimental results validate performance of the proposed algorithm.

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 Irms 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.

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.

Tool Breakage Monitoring Using Motor Current Signals for Machine Tools With Linear Motors

In recent years, a number of machining centers have been built using linear motors. These machining centers have great potential for precision and high-speed machining. Nevertheless, a number of problems remain unsolved, such as monitoring and control. This paper presents a new tool breakage monitoring method for this type of machining center using the current signal of the linear motor. First, the relationship between the cutting force and the motor current is analyzed. Then, the new tool breakage method is presented. From a mathematical point of view, the new method uses a nonlinear energy operator to capture the abrupt changes of the motor current signal, which is directly related to the tool breakage. The experiment validation is included.