Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks

Hübner, Henrique Butzlaff; Duarte, Marcus Antônio Viana; Silva, Rosemar Batista da

doi:10.1007/s00170-020-05902-w

Cited by 19 publications

(3 citation statements)

References 61 publications

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“…Wavelet analysis is a suitable method for locally analyzing both the frequency and time domains [13][14][15][16]. The time and frequency features of signals can be used as reference parameters of a support vector machine (SVM) to perform binary classification that helps judging certain conditions, such as end point detection [17], tool breakage [18], grinding burns [19][20][21] and surface roughness monitoring. [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the optical quartz lens centering process based on acoustic emission signal processing and the support vector machine

Liu

Shiu

2022

Int J Adv Manuf Technol

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A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission-sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of <2 μm, a lens circularity error of <0.01 mm, a crack length of

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Section: Introductionmentioning

confidence: 99%

Analysis of the optical quartz lens centering process based on acoustic emission signal processing and the support vector machine

Liu

Shiu

2022

Int J Adv Manuf Technol

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“…Mechanical vibration is a common phenomenon in modern industrial production. Strong vibration will have an adverse impact on the normal operation of equipment, lead to component loss, greatly shorten the service life of mechanical equipment, and may have more serious accidents, and even endanger the life safety of workers [1]. Any structure or mechanical equipment will produce certain vibration under dynamic conditions.…”

Section: Introductionmentioning

confidence: 99%

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

Zhu

Samori

2022

The Journal of Engineering

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The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time-frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time-frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the timefrequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time-frequency characteristic quantity in real time.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Optical Quartz Lens Centering Process Based on Acoustic Emission Signal Processing and the Support Vector Machine

Liu

Shiu

2021

Preprint

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A method was proposed for analyzing the optical glass lens centering process, and experiments on biplane quartz lenses were performed to determine the material removal rate (MRR) for the hard, brittle material. This study used acoustic emission–sensing technology to monitor the MRR and reconstruct the original shape of the lens. The MRR was evaluated, and an error of 17.87% was obtained. A Taguchi experiment was combined with signal analysis to optimize the process parameters, and a support-vector machine was trained to classify the quality of the grinding wheel; the model had accuracy 98.8%. By using the proposed analysis method, workpiece quality was controlled to an edge surface roughness of <2 μm, a lens circularity error of <0.01 mm, a crack length of <E0.1, and an optical axis error of <150 μrad.

show abstract

Automatic grinding burn recognition based on time-frequency analysis and convolutional neural networks

Cited by 19 publications

References 61 publications

Analysis of the optical quartz lens centering process based on acoustic emission signal processing and the support vector machine

Analysis of the optical quartz lens centering process based on acoustic emission signal processing and the support vector machine

Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

Analysis of the Optical Quartz Lens Centering Process Based on Acoustic Emission Signal Processing and the Support Vector Machine

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