An overview of approaches to end milling tool monitoring

Prickett, Paul William; Johns, C. K.

doi:10.1016/s0890-6955(98)00020-0

Cited by 148 publications

(77 citation statements)

References 47 publications

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“…The tangential and radial components of the element cutting forces dF c are computed from the specific cutting energy, chip thickness and chip width dz, as shown in Eq. (2). Fig.…”

Section: Cutting Force Model and Experimentsmentioning

confidence: 99%

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Integration of Pre-Simulation and Sensorless Monitoring for Smart Mould Machining

Kim¹

2016

Int. j. simul. model.

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A smart mould machining system, which detects machining status and controls the CNC through presimulation and real-time data, was developed. Pre-simulation predicts cutting forces, and inserts the best feed rate and virtual load on each line of the NC data. The active feed rate reduced machining by up to 36 %, without increasing the maximum cutting forces. The actual cutting load was computed from spindle load data and a friction load compensation algorithm. Collision and tool wear were detected by comparing the actual and virtual load, with the time synchronised by using tool position data. The system machined an automotive grill mould cavity for 34 hours without the supervision of a worker, because pre-simulation had stabilized the milling process and monitoring would have stopped the process if the actual load was different to the virtual load. Pre-simulation has been verified by thousands of mould makers and integrated with sensorless monitoring in an open CNC.

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“…The tangential and radial components of the element cutting forces dF c are computed from the specific cutting energy, chip thickness and chip width dz, as shown in Eq. (2). Fig.…”

Section: Cutting Force Model and Experimentsmentioning

confidence: 99%

“…A significant amount of research has been dedicated to the task of cutting tool monitoring [2]. A direct measurement of tool wear on machine tools using laser displacement sensor is presented [3].…”

Section: Introductionmentioning

confidence: 99%

Integration of Pre-Simulation and Sensorless Monitoring for Smart Mould Machining

Kim¹

2016

Int. j. simul. model.

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“…Additional features have been extracted from the frequency domain. Since the FFT analysis showed that tool and tooth frequencies turn out to be two dominant spectral components (Prickett & Johns 1999), their amplitudes (tlAmp and thAmp, respectively) are then utilized as tool wear features. Although AE signals have been filtered during the acquisition process, additional filtrations were carried out using discrete wavelet transformation (DWT), i.e.…”

Section: Signal Processing and Extraction Of Featuresmentioning

confidence: 99%

Tool wear estimation using an analytic fuzzy classifier and support vector machines

et al. 2010

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A new type of continuous hybrid tool wear estimator is proposed in this paper. It is structured in the form of two modules for classification and estimation. The classification module is designed by using an analytic fuzzy logic concept without a rule base. Thereby, it is possible to utilize fuzzy logic decision-making without any constraints in the number of tool wear features in order to enhance the module robustness and accuracy. The final estimated tool wear parameter value is obtained from the estimation module. It is structured by using a support vector machine nonlinear regression algorithm. The proposed estimator implies the usage of a larger number and various types of features, which is in line with the concept of a closer integration between machine tools and different types of sensors for tool condition monitoring.

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“…The monitoring of machining abnormal and the surface state estimation of the machined products during cutting operation are indispensible to the stability and the automation of manufacturing process as well as the high machining accuracy (1)- (3) .…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Monitoring Using Time Series Analysis

Song

Ohara²,

Tamaki³

et al. 2009

JMMP

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A tool wear monitoring approach considering the nonlinear behavior of cutting mechanism caused by tool wear and/or localized chipping is proposed, and its effectiveness is verified through the cutting experiment and actual turning machining. Moreover, the variation in the surface roughness of the machined workpiece is also discussed using this approach. In this approach, the residual error between the actually measured vibration signal and the estimated signal obtained from the time series model corresponding to dynamic model of cutting is introduced as the feature of diagnosis. Consequently, it is found that the early tool wear state (i.e. flank wear under 40µm) can be monitored, and also the optimal tool exchange time and the tool wear state for actual turning machining can be judged by this change in the residual error. Moreover, the variation of surface roughness Pz in the range of 3 to 8µm can be estimated by the monitoring of the residual error.

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An overview of approaches to end milling tool monitoring

Cited by 148 publications

References 47 publications

Integration of Pre-Simulation and Sensorless Monitoring for Smart Mould Machining

Integration of Pre-Simulation and Sensorless Monitoring for Smart Mould Machining

Tool wear estimation using an analytic fuzzy classifier and support vector machines

Tool Wear Monitoring Using Time Series Analysis

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