Tool Wear Condition Monitoring Research Articles

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

Wavelet Packet analyses of Acoustic Emission Signal for Tool Wear in High Speed Milling

Tool wear condition monitoring technology is one of the main parts of advanced manufacturing technology and is a hot research direction in recent years. A method based on the characteristics of acoustic emission signal and the advantages of wavelet packets decomposition theory in the non-stationary signal feature extraction is proposed for tool wear state monitoring with monitor the change of acoustic emission signal feature vector. In this paper, through the method, firstly, acoustic emission signal were decomposed into 4 layers with wavelet packet analysis, secondly, the frequency band energy of the have been decomposed signal were extracted, thirdly, the frequency band energy that are sensitive to tool wear were selected as feature vector, and then the corresponding relation between feature vector and tool wear was established , finally, the state of the tool wear can be distinguished according to the change of feature vector. The results show that this method can be feasibility used to monitor tool wear state in high speed milling.

Online incremental learning for tool condition classification using modified Fuzzy ARTMAP network

Condition monitoring of tool wear is paramount for guaranteeing the quality of workpiece and improving the lifetime of the cutter. To improve the training speed and the flexibility of the incremental learning, a modified Fuzzy ARTMAP classifier is developed in which the resonance layer is linked with the category node directly by many to one mapping. Therefore, the weight value and model structure can be updated simultaneously during the online incremental learning process. To testify the effectiveness of the presented method, experiments of tool condition classification in the process of end milling of Titanium alloy are carried out and two incremental learning cases are simulated. The analysis of online learning process in both cases shows that the structure and parameters of the model can be adjusted automatically without requiring access to the previous training data. At the same time, the accuracy analysis demonstrates that the presented method has strong ability to learn the new knowledge without forgetting the previous knowledge.

Force sensor based online tool wear monitoring using distributed Gaussian ARTMAP network

Tool condition monitoring is paramount for guaranteeing the quality of the workpiece and improving the life time of the cutter. Force sensor has been proven to be one of the most effective means to depict the tool wear variation during the machining process. However, because of the disturbance of noisy signal and the complexity of tool wear topology, the feature vectors usually demonstrate the non-uniformly distributed shapes and complex category boundaries, which will deteriorate the classification accuracy greatly. In this paper, a distributed Gaussian ARTMAP (dGAM) network is presented to realize the condition classification of the tool wear process. The main characteristic of this method is that the distributed Gaussian probability density, instead of the hyper rectangle match function, is used to realize the mapping between the feature vectors and the committed node. Therefore, the classifier is insensitive to the noisy data and suitable for non-uniformly distributed data. Based on the dGAM model, a monitoring system was built to realize the incremental learning and online classification of tool wear states. The analysis and comparison with Fuzzy ARTMAP show that the proposed classifier is more accurate. This method casts some new lights on the tool wear condition monitoring in real industrial environment.

Study on Feature Selection and Identification Method of Tool Wear States Based on Svm

Abstract This paper presents an on-line tool wear condition monitoring system for milling. The proposed system was developed taking the cost and performance in practice into account, in addition to a high success rate. The cutting vibration signal is obtained during the cutting process, and then extracting features using time-domain statistical and wavelet packet decomposition algorithms. It would result in two major disadvantages if creating a tool wear states identification model based on all extracted features, i.e. high computational cost and inefficient complexity of the model, which leads to overfitting. It is crucial to extract a smaller feature set by an effective feature selection algorithm. In this paper, an approach based on one-versus-one multi-class Support Vector Machine Recursive Feature Elimination (SVM-RFE) is proposed to solve the feature selection problem in tool wear condition monitoring. Moreover, in order to analyze a performance degradation process on the machine tool, Least Squares Support Vector Machines (LS-SVM) is introduced. In order to estimate the effectiveness of feature selection algorithm, the comparative analysis among Fisher Score (FS) Information Gain (IG) and SVM-RFE is exploited to real milling datasets. The identification result proves that: The selected feature set based on SVM-RFE is more effective to recognize tool wear state; LS-SVM wear identification method is superior to BP neural network, and it has higher identification accuracy; the proposed feature selection and identification method for tool wear states is efficient and feasible.

Real-Time Tool Wear Monitoring Based on Feed Motor Current in Chuck-Center Mounting Condition

On-line tool wear condition monitoring is a significant area of research in automated machining process. This study concerns the development of an on-line tool wear condition monitoring system based on a fuzzy logic scheme. The feed motor current was measured and used as the input of a fuzzy process. Previously, most studies in this field have assumed the workpiece as rigid. However, in practice, cutting force causes the elastic deflection on workpiece and changes actual cutting depth. Therefore, due to coupling between actual depth of cut and feed motor current, the measured current values vary on cutting length according to workpiece elastic deflection pattern. This oscillation in current signal which is used as the input of monitoring system, can affect output results. Thus, in this study in order to achieve more accurate results, the workpiece clamping condition has been considered and through monitoring software a correction factor has been applied on outputs. Experimental results indicate that the proposed method can be reliable for practical applications of tool monitoring systems.

Intelligent Tool Condition Monitoring In High-Speed Turning Of Titanium Ti-6Al-4V Alloy

Intelligent Tool Condition Monitoring (TCM) is an essential requirement in the drive towards automated machining operations. In this paper, a Multi-Layered Perceptron (MLP) neural net-work model has been developed for on-line condition monitoring of tool wear in high-speed turning of Titanium-based alloy (Ti-6Al-4V). Machining trials were conducted for typical rough and finish turning operations with cutting speed (90 – 120 m/min), feed rate (0.15 – 0.2 mm/rev), and depth of cut (0.5 -2.0 mm) using uncoated cemented carbide (K10 grade) inserts with Inter-national Standard Organization (ISO) designation “CNMG 120412”. The tool maximum flank wear (VBmax), cutting forces (feed force, Fx, and tangential force, Fz), and spindle motor power were measured during each machining operation. The cutting parameters (cutting speed, feed rate, and depth of cut), and cutting force and spindle power were used in isolation or in combi-nation as input dataset in training the neural network to predict wear land on cutting tool at different stages of wear propagation (light, medium and heavy). The neural network model was designed using Matlab® neural toolbox. Accuracy of model for the prediction of tool wear at dif-ferent wear stages were evaluated based on the Percentage Error (PE) for both roughing and finishing operations. Results showed that, the heavy wear stage (PE = ±5%) was predicted more accurately compared to the light (PE = +5 to -10%) and medium (PE = +25 to -30%) wear stages. The combination of the force, power signals and cutting parameters improved perform-ance of the model.Keywords: Artificial neutral network, Turning, Ti-6Al-4V alloy; Tool wear, Condition monitoring

Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection

Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing an essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or workpiece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still ongoing challenge. In this paper, force signals are used for monitoring tool-wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results found prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear.

Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system

One of the biggest problems in manufacturing is the failure of machine tools due to loss of surface material in cutting operations like drilling and milling. Carrying on the process with a dull tool may damage the workpiece material fabricated. On the other hand, it is unnecessary to change the cutting tool if it is still able to continue cutting operation. Therefore, an effective diagnosis mechanism is necessary for the automation of machining processes so that production loss and downtime can be avoided. This study concerns with the development of a tool wear condition-monitoring technique based on a two-stage fuzzy logic scheme. For this, signals acquired from various sensors were processed to make a decision about the status of the tool. In the first stage of the proposed scheme, statistical parameters derived from thrust force, machine sound (acquired via a very sensitive microphone) and vibration signals were used as inputs to fuzzy process; and the crisp output values of this process were then taken as the input parameters of the second stage. Conclusively, outputs of this stage were taken into a threshold function, the output of which is used to assess the condition of the tool.

Drill wear monitoring using cutting force signals

Monitoring of tool wear condition for the drilling process, which is one of the major cutting process in machining, is very considerable for the sake of avoiding tool failures, increasing machine utilization and decreasing production cost in an automated manufacturing environment. In the literature, many tool wear condition monitoring techniques have been investigated using a variety of sensors including dynamometers for force and torque, accelerometers for mechanical vibrations, AE sensors for acoustic emission and current probes for current/power measurement of spindle and feed motors. New techniques are proposed in this paper for real time identification of tool wear status based on cutting force and torque measurements from dynamometer during metal drilling. These techniques utilize hidden Markov models (HMM), phase plane method, transient time method and mechanistic approach.

A decision fusion algorithm for tool wear condition monitoring in drilling

Tool wear monitoring of cutting tools is important for the automation of modern manufacturing systems. In this paper, several innovative monitoring methods for on-line tool wear condition monitoring in drilling operations are presented. Drilling is one of the most widely used manufacturing operations and monitoring techniques using measurements of force signals (thrust and torque) and power signals (spindle and servo) are developed in this paper. Two methods using Hidden Markov models, as well as several other methods that directly use force and power data are used to establish the health of a drilling tool in order to avoid catastrophic failure of the drill. In order to increase the reliability of these methods, a decision fusion center algorithm (DFCA) is proposed which combines the outputs of the individual methods to make a global decision about the wear status of the drill. Experimental results demonstrate the effectiveness of the proposed monitoring methods and the DFCA.

Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)

Monitoring of tool wear condition for drilling is a very important economical consideration in automated manufacturing. Two techniques are proposed in this paper for the on-line identification of tool wear based on the measurement of cutting forces and power signals. These techniques use hidden Markov models ( HMMs), commonly used in speech recognition. In the first method, bargraph monitoring of the HMM probabilities is used to track the progress of tool wear during the drilling operation. In the second method, sensor signals that correspond to various types of wear status, e.g., sharp, workable and dull, are classified using a multiple modeling method. Experimental results demonstrate the effectiveness of the proposed methods. Although this work focuses on on-line tool wear condition monitoring for drilling operations, the HMM monitoring techniques introduced in this paper can be applied to other cutting processes.

Real-time tool wear condition monitoring in turning

A new method to monitor tool wear condition in real time using feed-motor current measured with the aid of inexpensive current sensors installed on the AC servomotor of a CNC turning centre is presented. To achieve this, the feed drive system model is analysed, the feed-motor current is measured, and the relations between feed-motor current, cutting force, and tool flank wear are addressed. The functional dependence of the feed-motor current on tool wear is then expressed in the form of a difference equation relating variation in the feedmotor current to tool flank wear rate. The computerized system automatically compares successive feed-motor current and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that this method of tool wear condition monitoring is effective and industrially applicable.

Current-sensor-based feed cutting force intelligent estimation and tool wear condition monitoring

Tool wear condition monitoring has the potential to play a critical role in ensuring the dimensional accuracy of the workpiece and prevention of damage to cutting equipment. It could also help in automating cutting processes. In this paper, the feed cutting force estimated with the aid of an inexpensive current sensor installed on the AC servomotor of a computerized numerical control tuning center is used to monitor tool wear condition. To achieve this, the feed drive system is modeled, using neuro-fuzzy techniques, to provide the framework for estimating the feed cutting force based on the feed motor current measured. Functional dependence of the feed cutting force on tool wear and cutting parameters are then expressed in the form of a difference equation relating variation in the feed cutting force to tool wear rate. The computerized system automatically compares successive feed cutting force estimates and determines the onset of accelerated tool wear in order to issue a request for tool replacement. Experimental results show that the tool wear condition monitoring is effective and industrially applicable.

Condition monitoring of tool wear in machining centres : M.A. Javed, A.D. Hope (University of Southampton, Southampton, UK). Cutting ToolNews, no 1, 1996, 8–10

Condition monitoring of tool wear in machining centres : M.A. Javed, A.D. Hope (University of Southampton, Southampton, UK). Cutting ToolNews, no 1, 1996, 8–10

A quantitative model of acoustic emissions in orthogonal cutting operations

Acoustic Emissions (AE) in cutting processes have shown a significant potential for the condition monitoring of tool wear. Attempts have been made to develop some relationship between acoustic emission and basic cutting parameters in turning operations. These models however, lack accuracy in the prediction of AE and hence are unsuitable for use in the adaptive control of machining operations. This paper presents a new model which incorporates a relationship between AE root mean square (RMS) voltage, cutting parameters, tool geometry and workpiece material in orthogonal cutting operations. A realistic seize ratio and computational statistical techniques have been employed for improving the accuracy of the developed theoretical model. A satisfactory agreement between the AE RMS values predicted by the mathematical model and those experimentally measured has been observed.

Feature-filtered fuzzy clustering for condition monitoring of tool wear

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.

On-line condition monitoring of tool wear in end milling using acoustic emission : International Journal of Production Research, Vol. 29, No. 7, pp. 1339–1353 (1991)

The monitoring of tool wear is important in maintaining the quality of workpieces produced. This paper presents a methodology to monitor on-line tool wear in end milling using acoustic emission. It is well known that the root-mean-square (RMS) value of the acoustic emission is directly proportional to the power expended in turning. A mathematical model has been developed to predict the RMS value of the acoustic-emission signal in milling. This mathematical model incorporates the machining parameters as variables. The accuracy of the model has been verified by a series of experiments. The experiments were carried out on a Bridgeport milling machine and data was collected and analysed by using an on-line computer data acquisition system. A comparison of the experimental and theoretical RMS values indicates a very good agreement between them. A control strategy similar to the moving average/moving range charts has been developed for monitoring on-line tool wear. The limits for the control charts were obtaine...

On-line condition monitoring of tool wear in end milling using acoustic emission

The monitoring of tool wear is important in maintaining the quality of workpieces produced. This paper presents a methodology to monitor on-line tool wear in end milling using acoustic emission. It is well known that the root-mean-square (RMS) value of the acoustic emission is directly proportional to the power expended in turning. A mathematical model has been developed to predict the RMS value of the acoustic-emission signal in milling. This mathematical model incorporates the machining parameters as variables. The accuracy of the model has been verified by a series of experiments. The experiments were carried out on a Bridgeport milling machine and data was collected and analysed by using an on-line computer data acquisition system. A comparison of the experimental and theoretical RMS values indicates a very good agreement between them. A control strategy similar to the moving average/moving range charts has been developed for monitoring on-line tool wear. The limits for the control charts were obtaine...