Method of Controlling Cutting Tool Wear Based on Signal Analysis of Acoustic Emission for Milling

Pechenin, V. A.; Khaimovich, Alexander; Kondratiev, A. I.; Bolotov, M. A.

doi:10.1016/j.proeng.2017.02.294

Cited by 30 publications

(21 citation statements)

References 9 publications

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“…Sensors based on AE are particularly suitable for conducting TCM in milling processes because the resulting signals are not mechanically disturbed, have a superior sensitivity to the those of cutting force and vibration sensor signals, and propagate at a frequency much greater than the characteristic frequency caused by cutting, which reduces interference [32], [33]. Hassan et al demonstrated the potential of AE signals for detecting the unstable crack propagation preceding tool chipping/breakage within a time span on the order of 10 ms [34].…”

Section: ) Acoustic Emissionmentioning

confidence: 99%

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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: ) Acoustic Emissionmentioning

confidence: 99%

“…Many studies have applied ANNs and HMMs to TCM in milling processes with outstanding results [33], [51], [52]. Deep neural networks such as CNNs [53], [54] and RNNs [55], [56] have also been applied with considerable success.…”

Section: B Monitoring Modelmentioning

confidence: 99%

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Zhou

Sun

2020

IEEE Access

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“…Zhao-Atlas-Marks distribution (ZAMD) [33] reduces the cross interference comprised in multicomponent signals. ZAMD is useful in modeling of small spectral peaks and analyze non-stationary multicomponent signals [32].…”

Section: Problem Statementmentioning

confidence: 99%

“…Formant analysis [33] is used to analyze a vibroacoustic signals because these signals have multi-frequency components connected with different anomalies while cutting [35,6].…”

Section: Problem Statementmentioning

confidence: 99%

A model of milling process based on Morlet wavelets decomposition of vibroacoustic signals

Khaymovich¹,

Prokhorov²,

Stolbova³

et al. 2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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The paper considers the problem of online monitoring the condition of cutting tools to avoid its unexpected failure. To approach this problem we proposed a model of milling process based on Morlet decomposition of vibroacoustic signals. In addition, using the wavelets scalogram, we imposed a new condition that helps to improve early wear detection of the cutting tool. The findings of this research reveal the advantages of the proposed model compared to the previously reported models that rely on Haar wavelets and Short-time Fourier transform.

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“…Some people have gradually turned their attention to the study of acoustic emission (AE) signals. AE signals are well studied and applied in the classification of composite material damage mechanisms [ 18 , 19 ], tool wear analysis [ 20 , 21 ] and detection of crack damage based on ultrasonic testing [ 22 ]. However, in the aspect of identification and recognition of bone layers, the AE signals are still in a research phase.…”

Section: Introductionmentioning

confidence: 99%

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation

Guan

Sun

et al. 2018

Sensors

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Pedicle drilling is an important step in pedicle screw fixation and the most significant challenge in this operation is how to determine a key point in the transition region between cancellous and inner cortical bone. The purpose of this paper is to find a method to achieve the recognition for the key point. After acquiring acoustic emission (AE) signals during the drilling process, this paper proposed a novel frequency distribution-based algorithm (FDB) to analyze the AE signals in the frequency domain after certain processes. Then we select a specific frequency domain of the signal for standard operations and choose a fitting function to fit the obtained sequence. Characters of the fitting function are extracted as outputs for identification of different bone layers. The results, which are obtained by detecting force signal and direct measurement, are given in the paper. Compared with the results above, the results obtained by AE signals are distinguishable for different bone layers and are more accurate and precise. The results of the algorithm are trained and identified by a neural network and the recognition rate reaches 84.2%. The proposed method is proved to be efficient and can be used for bone layer identification in pedicle screw fixation.

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Method of Controlling Cutting Tool Wear Based on Signal Analysis of Acoustic Emission for Milling

Cited by 30 publications

References 9 publications

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

Tool Wear Condition Monitoring in Milling Process Based on Current Sensors

A model of milling process based on Morlet wavelets decomposition of vibroacoustic signals

State Recognition of Bone Drilling Based on Acoustic Emission in Pedicle Screw Operation

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