Acoustic emission monitoring of the bending under tension test

Moghadam, Marcel; Suleiman, Mohd Hafis; Christiansen, Peter Leth; Bay, Niels

doi:10.1016/j.proeng.2017.10.907

Cited by 9 publications

(10 citation statements)

References 8 publications

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“…Graphs represented that the amplitude of acoustic signal at 0.5 and 1.5 bar were null at high frequency while acoustic signals with leaky hole were not null. The leaky hole with smaller diameter cause to have more frequency, while amplitude at the same frequency has increased at higher air pressure, this is because of Flow Rate Formula as in (9) Q = Av (9) When the flow rate (Q) is stable, the speed (v) of fluid inverses to cross-section area (A). Wavelength Formula is shown in equation 10˭ t (10) When the flow rate ( ) is stable, the speed (v) of fluid relate to frequency (t).…”

Section: A Results Of Frequencymentioning

confidence: 99%

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Leak Detection on Air reservoir via Acoustic Models with TensorFlow Based

Pairin¹,

Kesvarakul²

2020

IJMLC

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Fluid, both gas and liquid, is a widely used substance which can be used in pneumatic and hydraulic system. However, the pneumatic system with compressed air system integrated has a flaw that leakage air during power transmission cost a lot of loss both resources and performance. Leak detection is one of the main solution to plug the flaw. In this research, we use acoustic signal to detect the leakage by using it as an input for model. Artificial Neural Network (ANN) is used in our model to achieve deep learning property via Tensorflow. Acoustic signal is recorded in different situation and is used as a model input. So, our model can be trained with leak data and predict the leakage in pneumatic system. We evaluate model using test data and shows the leakage prediction in probability distribution.  Index Terms-Artificial neural network, leak detection, Tensorflow.

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Section: A Results Of Frequencymentioning

confidence: 99%

“…This can be used to develop a realtime monitoring system. The original acoustic signal is recorded via microphone or sensor then is extracted by frequency range of each sound using mathematical process called Fast Fourier Transform (FFT) [7]- [9]. In terms of classification, data is classified using Decision tree and Neural network.…”

Section: Introductionmentioning

confidence: 99%

Leak Detection on Air reservoir via Acoustic Models with TensorFlow Based

Pairin¹,

Kesvarakul²

2020

IJMLC

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“…Only the AE signals were recorded in this experiment. If a phenomenon does not cause a large tool vibration such as galling or wear, (6)(7)(8) it may be easy to find a problem with the AE and cumulative counts, and so forth. However, when the tool fractures immediately after the processed material fractures, the AE signal caused by the tool fracture is superimposed on that caused by the material fracture.…”

Section: Repetitive Punching Testmentioning

confidence: 99%

“…(5) The frictional condition of the curved part of a die was monitored during bending under tension. (6) The onset of galling in strip reduction was investigated. (7) A technique for process monitoring in the deep drawing of a steel sheet was presented.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tool Fracture in Punching of Thick Steel Plate

Yamashita¹,

Murase²,

Nikawa³

2020

Sensors and Materials

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The sensing of acoustic emission (AE) is efficiently used for the detection of the crack initiation of brittle materials and defects in plastic forming and other manufacturing processes. We carried out circular hole punching tests using carbon steel S45C plates of 5 or 6 mm thickness. The objective was to capture the occurrence of cracks as a predictive phenomenon of punch fracture and their propagation during punching by using an AE sensor. The impulsive stress acting on the punch due to the sudden drop in punch force at the fracture of the plate was also measured using strain gages. This stress induced fracture at the stepped portion of the punch. The maximum tensile stress of the punch depended on the fillet radius at the stepped portion. A reciprocal punching test using both plates alternately was performed to evaluate the detection of crack initiation using an AE waveform. Features in the AE waveform due to crack initiation and the frictional behavior of the crack were captured.

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“…Tribological conditions in deep drawing using BUT at elevated temperature were investigated numerically by Ceron et al [14]. Moghadam et al [15] applied the acoustic emission technique in BUT testing for assessment of tribological conditions during sheet forming with an objective of determining the onset of galling. Andreasen et al [16] developed a special BUT transducer in which friction around the tool radius can be directly measured when drawing a sheet strip around cylindrical pin under constant back tension.…”

Section: Introductionmentioning

confidence: 99%

Determination of Frictional Resistances of Deep Drawing Quality Steel Sheets in Bending Under Tension Test

Trzepieciński¹,

Lemu²

2019

AMS

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Among many friction tests, the bending under tension (BUT) test simulates the frictional conditions on rounded edge of punch in deep drawing operations. The experimental evaluation of coefficient of friction value has been carried out using a special tribological simulator. Two friction conditions i.e. dry friction and lubrication were analysed. As a test material the deep drawing quality (DDQ) steel sheet commonly used in automotive industry was selected. The mechanical properties of the sheets were selected in the uniaxial tensile test. The experimental results show that the friction coefficient value is very stable after reaching a certain value of sheet deformation. It was found that an increase of rolls surface led to an increase of the sample elongation value at which exists the maximum effectiveness of lubricant. It was also observed that an increase of the amount of sample elongation decreases the sheet surface roughness and friction behaviour changes as a result of high contact pressure and plastic deformation of surface asperities.

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Acoustic emission monitoring of the bending under tension test

Cited by 9 publications

References 8 publications

Leak Detection on Air reservoir via Acoustic Models with TensorFlow Based

Leak Detection on Air reservoir via Acoustic Models with TensorFlow Based

Prediction of Tool Fracture in Punching of Thick Steel Plate

Determination of Frictional Resistances of Deep Drawing Quality Steel Sheets in Bending Under Tension Test

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