A Novel Generalized Approach for Real-Time Tool Condition Monitoring

Hassan, Mahmoud; Sadek, A.; Attia, Helmi; Thomson, Vince

doi:10.1115/1.4037553

Cited by 19 publications

(12 citation statements)

References 23 publications

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“…Motor current sensors are considered to be more suitable for industrial manufacturing settings than cutting force sensors due to their relatively simple application and lack of installation effects on machining operations [39], [40]. Ghosh [41]. However, current sensors are less commonly employed for TCM in milling processes than the above three types of sensors.…”

Section: ) Motor Currentmentioning

confidence: 99%

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Zhou

Sun

2020

IEEE Access

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Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time − frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multidomain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors. INDEX TERMS Tool condition monitoring (TCM), milling process, current sensor, kernel extreme learning machine (KELM), angle kernel.

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Section: ) Motor Currentmentioning

confidence: 99%

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Zhou

Sun

2020

IEEE Access

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“…One of the main drawback of the available TCM systems is the extensive experimental work needed for system learning in order to build a reliable database of features corresponding to different cutting conditions, tool geometries and tool paths. This effort has been minimized by the pre-processing approach introduced in [21]. This approach significantly reduces the required experimental system learning in intermittent cutting operations by masking the effect of the cutting conditions and magnifying the effect of the tool condition.…”

Section: Tool Post-failure Detectionmentioning

confidence: 99%

“…These extracted features can be used in a pattern recognition technique to classify the tool condition. In [21], more than 40 extracted features have been ranked according to their sensitivity to the tool condition using the results of an N-way ANOVA test. The highest ranked features of the spindle motor current signal in a milling process were the signal mean (M), maximum peak of periodogram (P p ), root mean square (rms), peak to root mean square ratio (P2rms), mean frequency (F mean ), band power (BP), median frequency (F med ), maximum peak of welch power spectral energy (P w ), kurtosis (K), minimum (min) and variance (Var).…”

Section: Tool Post-failure Detectionmentioning

confidence: 99%

“…A statistical study of the AE rms and vibration signals was carried on at the tool prefailure, failure and post-failure stages to select the most indicative features to be correlated to the tool condition. The signals were pre-processed using the approach provided in [21] before feature extraction. Figure 7 shows the indicative extracted features for (a) the AE rms and (b) the vibration in the feed direction signals.…”

Section: Tool Pre-failure and Failure Detectionmentioning

confidence: 99%

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Intelligent Machining: Real-Time Tool Condition Monitoring and Intelligent Adaptive Control Systems

Hassan

Sadek

Attia

et al. 2018

Journal of Machine Engineering

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Unmanned manufacturing systems has recently gained great interest due to the ever increasing requirements of optimized machining for the realization of the fourth industrial revolution in manufacturing ‘Industry 4.0’. Real-time tool condition monitoring (TCM) and adaptive control (AC) machining system are essential technologies to achieve the required industrial competitive advantage, in terms of reducing cost, increasing productivity, improving quality, and preventing damage to the machined part. New AC systems aim at controlling the process parameters, based on estimating the effects of the sensed real-time machining load on the tool and part integrity. Such an aspect cannot be directly monitored during the machining operation in an industrial environment, which necessitates developing new intelligent model-based process controllers. The new generations of TCM systems target accurate detection of systematic tool wear growth, as well as the prediction of sudden tool failure before damage to the part takes place. This requires applying advanced signal processing techniques to multi-sensor feedback signals, in addition to using ultra-high speed controllers to facilitate robust online decision making within the very short time span (in the order of 10 ms) for high speed machining processes. The development of new generations of Intelligent AC and TCM systems involves developing robust and swift communication of such systems with the CNC machine controller. However, further research is needed to develop the industrial internet of things (IIOT) readiness of such systems, which provides a tremendous potential for increased process reliability, efficiency and sustainability.

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“…Since the indirect methods can implement Sensors 2020, 20, 6113 2 of 20 online monitoring, it has been widely adopted. The most commonly used indirect monitoring signals include cutting force signal [2][3][4], vibration signal [5][6][7], acoustic emission signal [8,9], machined surface image [10,11], and current signal [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

Yuan

Liu

Yang

et al. 2020

Sensors

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Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

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A Novel Generalized Approach for Real-Time Tool Condition Monitoring

Cited by 19 publications

References 23 publications

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Intelligent Machining: Real-Time Tool Condition Monitoring and Intelligent Adaptive Control Systems

Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

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