Real-time Tool Wear Research Articles

Tool Wear Control through Cognitive Paradigms

In the modern manufacturing systems, machining parameters are fundamental to achieve efficiency for the whole production process. The feed rate, the cutting speed and several other parameters affect significantly the machining efficiency; furthermore, the selection of an appropriate cutting tool results fundamental to set-up, in the possible best way, the other parameters. This problem is one of the most complex in machining processes and it refers directly to the quality of the finished product. The life of the selected cutting tool, under the conditions given by the other parameters, is crucial in term of efficiency and it should be estimated as accurately as possible and permanently kept under control. An optical monitoring process (video camera) can observe the tool wear development. The images give the opportunity to drive the process in achieving the target of zero defects manufacturing but there is the need to firstly elaborate and homogenize them, in order to standardize the control and predict the tool wear. By the use of a DNA Based-Computing method, the influence of user-settings on the elaboration of a set of images will be investigated.In order to supply a direction for the development of methodologies for real-time tool wear recognition and prediction in a complex and high automated environment, this paper proposes an approach for the identification of the tool wear defection. The methodology designs an artificial Neural Network (NN) for automatic tool wear recognition: a set of images are standardized in grayscale and then processed in order to extract features for NN training phase. By the use of a DNA Based Computing method (DBC), the influence of user-settings on the elaboration of a set of images will be then investigated. It will be a crucial point for the development of a new method for the real-time tool wear recognition that will be based on the information provided by the DBC.

Evaluation of correlation between vibration signal features and three-dimensional finite element simulations to predict cutting tool wear in turning operation

This article presents the correlation of vibration signal feature, that is, displacement due to vibration (microns) during the machining and three-dimensional finite element simulations in tool wear monitoring of a metal turning operation. Machining of AISI 1040 has been carried on using an uncoated inserts and online vibration signals acquired using a laser Doppler vibrometer. The measured tool wear forms correlate to features in the vibration signals in frequency domains. Analyses of the results suggested that the vibration signals’ features are effective for use in cutting tool wear monitoring and wear qualification. Present work demonstrates the three-dimensional finite element analysis to predict the workpiece displacements in feed direction and corresponding tool wear with the help of induced vibrations in face turning under dry machining conditions. Vibration-assisted turning model is developed and validated by comparing the simulation results with experimental results. In this research, three-dimensional finite element modelling and simulation issues for vibration-assisted turning are explored in detail. Machine dynamic effects are taken into account to predict the outcomes such as tool wear displacements and chip formation. The model can be implemented in an online tool wear monitoring system which predicts the actual state of tool wear in real time by correlating displacement variations during the machining. The correlation between the displacement due to vibration and flank wear has been evaluated through three-dimensional finite element modelling and simulation. Comparisons of simulations with experimental results demonstrate their predictive capability. From the results, useful conclusions may be drawn, and it can be stated that the proposed models can be used for industrial application.

Real-time tool wear monitoring in milling using a cutting condition independent method

This paper describes a new method to monitor end milling tool wear in real-time by tracking force model coefficients during the cutting process. The behavior of these coefficients are shown to be independent from the cutting conditions and correlated with the wear state of the cutting tool. The tangential and radial force model coefficients are normalized and combined into a single parameter for wear monitoring. A number of experiments with different workpiece materials are run to investigate the feasibility of tool wear monitoring using this method. We show that this method can be used in real-time to track tool wear and detect the transition point from the gradual wear region to the failure region in which the rate of wear accelerates.

Vibration-based estimation of tool major flank wear in a turning process using ARMA models

This paper investigates the correlation between vibrational features of tool/holder assembly and tool major flank wear in a turning process. During cutting, 3D tool acceleration signals are recorded by an accelerometer. Then, based on the dynamics of the tool/holder system that manifests itself in natural frequencies/modes, wear sensitive features are determined and derived from autoregressive moving average (ARMA) model of the recorded signals. After a comprehensive investigation of extracted features, dispersion ratio of natural modes, a correlation between the wear and features is found. Moreover, a metric calculated based on the system eigenvalues is introduced to find the distance between ARMA models of tool with different wear levels and the baseline model, as another wear-sensitive feature. Analysis of experimental results reveals that in the accelerating stage of wear, a change of vibration mode from second bending mode in the main cutting direction to the first bending mode in feed direction occurs, and simultaneously, the ARMA distance reaches a minimum value. This analysis provides a reliable algorithm for tool wear estimation since it directly originates from tool/holder system natural modes/frequencies and interprets the physical behaviour of the system in connection with the tool wear. Based on the results from dispersion analysis, the outputs of ARMA metric can also be used to provide reliable predictions for tool replacement policy. The method obtained here can be utilized for development of an online real-time tool wear estimation algorithm in turning.

Design of a smart turning tool with application to in-process cutting force measurement in ultraprecision and micro cutting

In modern micromachining, there is a need to measure and monitor certain machining process parameters in process so as to detect tool wear in real time, to optimize the process parameters setup, and to render the machining process some level of smartness and intelligence. This paper presents the innovative design of a smart turning tool using two pieces of piezoelectric films to measure cutting and feed force in real time. The tool was tested on its performance through the calibration and cutting trials against the commercial dynamometer. The results show the smart turning tool has achieved the performance as designed.

A New Tool Wear Monitoring Method Based on Ant Colony Algorithm

Tool wear prediction is a major contributor to the dimensional errors of a work piece in precision machining, which plays an important role in industry for higher productivity and product quality. Tool wear monitoring is an effective way to predict the tool wear loss in milling process. In this paper, a new bionic prediction model is presented based on the generation mechanism of tool wear loss. Different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, neural network method is proposed to establish the mapping relation and ant algorithm is used to train the weights of BP neural networks during tool wear modeling. Finally, a real-time tool wear loss estimator is developed based on ant colony alogrithm and experiments have been conducted for measuring tool wear based on the estimator in a milling machine. The experimental and estimated results are found to be in satisfactory agreement with average error lower than 6%.

Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989–0.995 and 2.30–5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.

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.

Online tool wear monitoring using portable digital assistant (PDA)

Real-time tool wear monitoring is an important technique need to be developed to fulfill the demand from the fast development in machining industry. This paper proposed a new method of online tool wear monitoring using portable digital assistant (PDA)-based device to monitor progression of flank wear land during turning process by on-line detecting and analyzing the machining force signals. The machining tests were carried out on turning machine, and Kistler 9255B dynamometer was used to measure the cutting force signal, which was amplified and displayed in the DasyLab software. The measured cutting force signals stored in Dasylab was then analyzed using I-kaz 3D. The analysis was successfully done using newly developed program, C++ language and Visual Basic embedded to display the result on the PDA. This new developed program was installed on PDA to monitor the tool flank wear progression by three modes of different color which include: green (good condition), yellow (transition zone) and red (end of cutting). Therefore, it gives early warning related to the damage and failure of cutting tool, consequently helps the operation of machine to be smoothly operated in order to produce an acceptable quality in manufacturing process. Key words: Real time monitoring, tool flank wear, I-kaz 3D, cutting force signal, portable digital assistant (PDA).

A New Tool Wear Modeling Method Based on Project Pursuit Regression

Tool wear is a major contributor to the dimensional errors of a workpiece in precision machining. Tool wear modeling is an effective way to predict the tool wear loss in milling process. In this paper, different milling conditions are estimated as the input variables, tool wear loss is estimated as the output variable, and projection pursuit regression (PPR) method is proposed to establish the relationship between the input and the output variables. A real-time tool wear loss estimator is developed based on the PPR model, the experimental and estimated results are found to be in satisfactory agreement with average error lower than 10%.

Online Cutting Tool Wear Monitoring Using I-Kaz Method and New Regression Model

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals using the regression model and I-kaz method. The detection of tool wear was done automatically using the in-house developed regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out on a CNC turning machine Colchester Master Tornado T4 in dry cutting condition, and Kistler 9255B dynamometer was used to measure the cutting force signals, which then stored and displayed in the DasyLab software. The progression of the cutting tool flank wear land (VB) was indicated by the amount of the cutting force generated. Later, the I-kaz was used to analyze all the cutting force signals from beginning of the cut until the rejection stage of the cutting tool. Results of the I-Kaz analysis were represented by various characteristic of I-kaz 3D coefficient and 3D graphic presentation. The I-kaz 3D coefficient number decreases as the tool wear increases. This method can be used for real time tool wear monitoring.

Electrode wear prediction in milling electrical discharge machining based on radial basis function neural network

Milling electrical discharge machining (EDM) enables the machining of complex cavities using cylindrical or tubular electrodes. To ensure acceptable machining accuracy the process requires some methods of compensating for electrode wear. Due to the complexity and random nature of the process, existing methods of compensating for such wear usually involve off-line prediction. This paper discusses an innovative model of electrode wear prediction for milling EDM based upon a radial basis function (RBF) network. Data gained from an orthogonal experiment were used to provide training samples for the RBF network. The model established was used to forecast the electrode wear, making it possible to calculate the real-time tool wear in the milling EDM process and, to lay the foundations for dynamic compensation of the electrode wear on-line. This paper demonstrates that by using this model prediction errors can be controlled within 8%.

Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys

Online monitoring and in-process control improves machining quality and efficiency in the drive towards intelligent machining. It is particularly significant in machining difficult-to-machine materials like super alloys. This paper attempts to develop a tool wear observer model for flank wear monitoring in machining nickel-based alloys. The model can be implemented in an online tool wear monitoring system which predicts the actual state of tool wear in real time by measuring the cutting force variations. The correlation between the cutting force components and the flank wear width has been established through experimental studies. It was used in an observer model, which uses control theory to reconstruct the flank wear development from the cutting force signal obtained through online measurements. The monitoring method can be implemented as an outer feedback control loop in an adaptive machining system.

A tool wear observer model for flank wear monitoring in the milling of Nickel-based alloys

Tool wear monitoring is of great industrial importance in machining Nickel-based alloys. This paper presents the research effort to develop a tool wear observer model for flank wear monitoring in the milling of Nickel-based alloys. It aims at an online tool wear monitoring system that is capable of predicting tool wear in real time by measuring the cutting force variation. The correlation between the cutting force components and the flank wear width has been established through experimental studies. An observer model is developed to estimate the flank wear growth from the measured cutting force signals.

On-line wear detection of milling tools using a displacement fiber optic sensor

A cost-effective technique for on-line wear monitoring of a milling tool is presented. The tool wear is estimated directly from a fiber optic sensor with high resolution and high bandwidth characteristics. This sensor provides a distance measurement between its probe and the tool profile. The contribution of this work is the application of this sensor to sense on-line wear of a milling tool. Since the light emitted by this sensor comes from a photodiode, it does not produce eye damage, this is safer than laser displacement sensors. This technique senses the tool wear in real time, while the tool is rotating, with an accuracy less than 1 micron. Experimental results are also presented for a four-flank cutting tool rotating at 300 rpm.

Real-time Tool Wear Compensation in Milling EDM

Accurate machining by milling EDM (i.e. CNC contouring EDM with a rotating cylindrical or tubular electrode) necessitates compensation of the tool electrode wear. Existing anticipated wear compensation is based on off-line tool wear simulation prior to machining. This can be combined with corrections based on periodical measurements of tool length during machining. Anticipated wear compensation involves an important restriction: an exact model of the blank geometry must be available in order to perform the tool wear simulation. This paper presents a new method of wear compensation. On-line estimation of tool wear is used for combining anticipated compensation with real-time compensation. This extends the scope of milling EDM to the machining of blanks of which the exact shape is not known in advance.

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.

On the Derivation and Application of a Real-Time Tool Wear Sensor in EDM

Success of EDM applications depends to some extent on the customer's demands. Technological requirements concerning a particular job can only be met if the available EDM equipment is provided therefore. Although a substantial progress in EDM manufacturing performance has been achieved during the last decade, due to the efforts trade in machine technology development, some drawbacks still remain. One of these problems is the tool wear phenomenon In EDM. In the past, attention was given on how to limit the overall tool wear but hardly any attempt was made on the real time evaluation of the tool wear itself. Indeed, this could only be done if one knew what kind of tool wear sensing parameters had to be looked for. This paper is therefore focused on the basic analysis of EDM tool wear. Based on theoretical considerations, which are confirmed by tests, an analysis is made on the basic rules that govern the tool wear. It yields the process parameters and pulse parameters that must be looked for, when real time tool wear evaluation is concerned. Afterwards, practicle tool wear parameters are derived that can be applied in a real time EDM control environment. It allows to track the actual relative wear or absolute tool wear very accurately down to values of 1% relative wear. Results of a real time optimization run are shown and discussed to illustrate the process improvements obtained.