Study on the effect of the temperature in Acoustic Emission Sensor by the Pencil Lead Break Test

Lopes, Barbara Guedes; Alexandre, Felipe Aparecido; Lopes, Wenderson Nascimento; Aguiar, Paulo Roberto de; Bianchi, Eduardo Carlos; Viera, Martin Antonio Aulestia

doi:10.1109/induscon.2018.8627213

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…For example, in He et al [13], the AE method for detecting metallic grinding burn was presented as a nondestructive detection method. Lopes et al [14] studied the influence of the temperature on the frequency response of an AE sensor. In Badger et al [15], the fundamental relationships between the AE signal and the dressing variables were proposed.…”

Section: Introductionmentioning

confidence: 99%

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Viera

Aguiar

Oliveira

et al. 2019

Sensors

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Innovative monitoring systems based on sensor signals have emerged in recent years in view of their potential for diagnosing machining process conditions. In this context, preliminary applications of fast-response and low-cost piezoelectric diaphragms (PZT) have recently emerged in the grinding monitoring field. However, there is a lack of application regarding the grinding of ceramic materials. Thus, this work presents an analysis of the feasibility of using the acoustic emission signals obtained through the PZT diaphragm, together with digital signal processing in the time–frequency domain, in the monitoring of the surface quality of ceramic components during the surface grinding process. For comparative purpose, an acoustic emission (AE) sensor, commonly used in industry, was used as a baseline. The results obtained by the PZT diaphragm were similar to the results obtained using the AE sensor. The time–frequency analysis allowed to identify irregularities throughout the monitored process.

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Section: Introductionmentioning

confidence: 99%

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Viera

Aguiar

Oliveira

et al. 2019

Sensors

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“…When the sensor is installed, the concrete surface is smoothed by grinding the bottom of the bridge, and the Vaseline glue is uniformly coated on the sensor to stabilized sensors at the bottom of the bridge. Before monitoring, the Pencil Lead Break Testing (PLBT) (Lopes et al 2018) was used to identify whether the sensor coupling was good.…”

Section: Sensor Installationmentioning

confidence: 99%

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Liu

et al. 2023

J. Perform. Constr. Facil.

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Identification Noise signal is one of the challenging problems in the health monitoring of bridge structure using acoustic emission monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common method in identifying of the signals from the defect of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focus on the AE noise signal from a bridge in operation state and other specific loading state, which is diagnosed in the hardware filtering technology, spatial identification and SOM neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50 %, and can barely filter strong noise signal. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90 % and 100 % respectively. The trained network is used to test183 sample signals, the defect signal detection accuracy reaches 76 % and 78.8 %, therefore, the noise signal filtering effect is significantly improved.

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“…The most common indices in SHM analysis are the root mean square deviation (RMSD) and the correlation coefficient deviation metric (CCDM) [33]. The RMSD is indicated to quantify the amplitude differences between two spectrums (baseline and damage) [34], while the CCDM is appropriate to measure the variations or displacement between the frequencies of the analyzed signals [35].…”

Section: Traditional Monitoring Techniques Applied To Industrial Pmentioning

confidence: 99%

A Novel Ultrasound Technique Based on Piezoelectric Diaphragms Applied to Material Removal Monitoring in the Grinding Process

Alexandre

Aguiar

Götz

et al. 2019

Sensors

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The interest of the scientific community for ultrasound techniques has increased in recent years due to its wide range of applications. A continuous effort of researchers and industries has been made in order to improve and increase the applicability of non-destructive evaluations (NDE). In this context, the monitoring of manufacturing processes, such as the grinding process, arises. This work proposes a novel technique of ultrasound monitoring (chirp-through-transmission) through low-cost piezoelectric diaphragms and digital signal processing. The proposed technique was applied to the monitoring of material removal during the grinding process. The technique is based on changes in ultrasonic waves when propagated through the material under study, with the difference that this technique does not use traditional parameters of ultrasonic techniques but digital signal processing (RMS and Counts). Furthermore, the novelty of the proposed technique is also the use of low-cost piezoelectric diaphragms in the emission and reception of ultrasonic waves, enabling the implementation of a low-cost monitoring system. The results show that the monitoring technique proposed in this work, when used in conjunction with the frequency band selection, is sensitive to the material removal in the grinding process and therefore presents an advance for monitoring the grinding processes.

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Study on the effect of the temperature in Acoustic Emission Sensor by the Pencil Lead Break Test

Cited by 12 publications

References 8 publications

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

A Time–Frequency Acoustic Emission-Based Technique to Assess Workpiece Surface Quality in Ceramic Grinding with PZT Transducer

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

A Novel Ultrasound Technique Based on Piezoelectric Diaphragms Applied to Material Removal Monitoring in the Grinding Process

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