Monitoring of Grinding Burn by Acoustic Emission

Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos; Canarim, Rubens Chinali

doi:10.5772/31339

Cited by 14 publications

(12 citation statements)

References 24 publications

(24 reference statements)

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“…2) Root Mean Square (RMS): RMS is one of the most important criteria to evaluating the signal acquired by the AE sensor [36]. This approach can be considered as a physical quantity of sound intensity and it is directly related to the load applied to the sensor, which makes it a very interesting value to be monitored.…”

Section: ) Energy Criterionmentioning

confidence: 99%

Assessment of Macro Fiber Composite Sensors for Measurement of Acoustic Partial Discharge Signals in Power Transformers

et al. 2017

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This paper presents a performance assessment of macro fiber composite (MFC) sensors for measuring acoustic emission (AE) signals from partial discharges (PD) in power transformers filled with mineral oil. MFC sensors are low-profile and flexible, allowing them to be attached to uneven surfaces, such as a transformer wall. Two types of MFC sensors were assessed: P1 (d33 effect) and P2 (d31 effect), which are optimized for different deformations in the structure, such as elongation and contraction, respectively. In addition, a conventional AE sensor, R15I-AST model from Physical Acoustics South America, was also used as a reference for comparative analysis. Four metrics were applied to the signals: root mean square, energy criterion, Akaike criterion, power spectral density, and correlation. The experimental results indicate a high similarity between the MFC sensors and the conventional AE sensor, which expands the research field in acoustic PD measurement in power transformers by using low-cost and flexible sensors.

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Section: ) Energy Criterionmentioning

confidence: 99%

Assessment of Macro Fiber Composite Sensors for Measurement of Acoustic Partial Discharge Signals in Power Transformers

et al. 2017

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“…Various monitoring methods basing on vibration [6], electric current [7], force [8], piezoelectric diaphragms [9], acoustic emission (AE) [10], Barkhausen [11], eddy current [12], and so on [13][14][15] are utilized to detect the occurrence of grinding burn. Among all these on line detection methods, AE is one of the most effective technology for grinding burn detection [7]. Besides, AE senor is easy to install and sensitive to the grinding signals [16].…”

Section: Introductionmentioning

confidence: 99%

“…Jemielniak et al [17] studied the relationship between wear of tool and grinding burn by examining the tool surface and AE signals [17]. Aguiar et al [7] did the grinding experiment, during which AE signals and parameters were calculated to indicate the grinding burn [7]. Bell et al [18] developed the grinding temperature prediction diagrams in high deep grinding process [18].…”

Section: Introductionmentioning

confidence: 99%

“…Parameters of AE signals such as root mean square (RMS), mean-value deviance (MVD), constant false alarm rate (CFAR) etc. have been calculated and compared [7]. Then time-frequency methods such as Short Time Fourier Transform (STFT) [22], Wavelet Packet Transform (WPT) [20], Hilbert Huang Transform (HHT) [24] and Ensemble Empirical Mode Decomposition (EEMD) [21] have been introduced to analyze grinding burn signals.…”

Section: Introductionmentioning

confidence: 99%

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Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

Gao

Lin

Wang

et al. 2019

Chin. J. Mech. Eng.

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Grinding burn monitoring is of great importance to guarantee the surface integrity of the workpiece. Existing methods monitor overall signal variation. However, the signals produced by metal burn are always weak. Therefore, the detection rate of grinding burn still needs to be improved. The paper presents a novel grinding burn detection method basing on acoustic emission (AE) signals. It is achieved by establishing the coherence relationship of pure metal burn and grinding burn signals. Firstly, laser and grinding experiments were carried out to produce pure metal burn signals and grinding burn signals. No-burn and burn surfaces were generated and AE signals were captured separately. Then, the cross wavelet transform (XWT) and wavelet coherence (WTC) were applied to reveal the coherence relationship of the pure metal burn signal and grinding burn signal. The methods can reduce unwanted AE sources and background noise. Novel parameters based on XWT and WTC are proposed to quantify the degree of coherence and monitor the grinding burn. The grinding burn signals were recognized successfully by the proposed indexes under same grinding condition. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…The damage that may occur during the grinding process cause huge losses to the productive chain since the workpiece has high added value from the previous manufacturing processes. The most common types of grinding damage are burn, crack and residual stresses [8]. Burning occurs during the contact of the cutting surface of the grinding wheel with the workpiece, when the amount of energy generated in the contact zone increases the temperature substantially, causing the change of phase in the material [9].…”

Section: Introductionmentioning

confidence: 99%

Damage detection in grinding of steel workpieces through ultrasonic waves

et al. 2018

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The quality control of the workpieces through reliable tests is of utmost importance in the manufacturing of a precision component. In this context, the monitoring of damages towards automation of the grinding process is essential for the manufacturing industry and of great interest for researchers of the area. This work proposes a technique for monitoring damages in steel parts in the grinding process, using low-cost piezoelectric diaphragms in the emitter and receiver configuration. The tests were performed in a surface grinding machine with aluminum oxide grinding wheel and SAE 4340 steel workpiece. The hardness measurements of the workpieces were carried out to identify the changes occurred from the grinding process. The signals from the transducers were sampled at a rate of 2 MHz. A spectrum analysis of the obtained signals was performed with the aim at characterizing the frequency ranges that were most related to the workpiece surface condition. The results demonstrated that the proposed method is effective to detect surface damage in steel parts in the grinding process.

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Monitoring of Grinding Burn by Acoustic Emission

Cited by 14 publications

References 24 publications

Assessment of Macro Fiber Composite Sensors for Measurement of Acoustic Partial Discharge Signals in Power Transformers

Assessment of Macro Fiber Composite Sensors for Measurement of Acoustic Partial Discharge Signals in Power Transformers

Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

Damage detection in grinding of steel workpieces through ultrasonic waves

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