Acoustic emisson and ultrasonic wave characteristics in TIG-welded 316 stainless steel

Lee, Jin Kyung; Lee, Joon Hyun; Lee, Sang Pill; Son, In Su

doi:10.1007/s12540-014-3011-8

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Hence, an appropriate threshold voltage is set for a typically processed signal, as described in the introduction. It is common to analyze the aforementioned AE parameters based on the threshold voltage [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ].…”

Section: Sheet Metal Forming Experimentsmentioning

confidence: 99%

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A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Jeong

Hong

2021

Sensors

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In this study, an acoustic emission (AE) sensor was utilized to predict fractures that occur in a product during the sheet metal forming process. An AE activity was analyzed, presuming that AE occurs when plastic deformation and fracturing of metallic materials occur. For the analysis, a threshold voltage is set to distinguish the AE signal from the ripple voltage signal and noise. If the amplitude of the AE signal is small, it is difficult to distinguish the AE signal from the ripple voltage signal and the noise signal. Hence, there is a limitation in predicting fractures using the AE sensor. To overcome this limitation, the Kalman filter was used in this study to remove the ripple voltage signal and noise signal and then analyze the activity. However, it was difficult to filter out the ripple voltage signal using a conventional low-pass filter or Kalman filter because the ripple voltage signal is a high-frequency component governed by the switch-mode of the power supply. Therefore, a Kalman filter that has a low Kalman gain was designed to extract only the ripple voltage signal. Based on the KF-RV algorithm, the measured ripple voltage and noise signal were reduced by 97.3% on average. Subsequently, the AE signal was extracted appropriately using the difference between the measured value and the extracted ripple voltage signal. The activity of the extracted AE signal was analyzed using the ring-down count among various AE parameters to determine if there was a fracture in the test specimen.

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Section: Sheet Metal Forming Experimentsmentioning

confidence: 99%

“…When a metallic material exceeds the maximum load and is close to fracturing, AE occurs frequently. Hence, AE is widely applied for evaluating the mechanical properties of materials [ 10 , 11 , 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Jeong

Hong

2021

Sensors

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“…Generally, the number of AE events during fracture of the thermally aged material is significantly lower than in annealed ones. This effect is a result of blocking of dislocation movement by diffusion of chromium or other chemical elements [18,19]. Besides, additional AE activity is generated by increased grain boundary brittle cracking during the deformation of the aged stainless steel.…”

Section: Introductionmentioning

confidence: 99%

Application of classification neural networks for identification of damage stages of degraded low alloy steel based on acoustic emission data analysis

Krajewska-Śpiewak

Lasota

Kozub

2020

Archiv.Civ.Mech.Eng

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The paper presents the influence of low alloy steel degradation on the acoustic emission (AE) generated during static tension of notched specimen. The material was cut from a technological pipeline long-term operated in the oil refinery industry. Comparative analysis of AE activity generated by damage process of degraded and new material has been carried out. The different AE parameters were used to detect different stages of fracture process of low alloy steel under quasi-static tensile test. Neural networks with three layers were created with Broyden–Fletcher–Goldfarb–Shanno learning algorithm for a database analysis. The different AE parameters were included in the input layer. Classification neural networks were created in order to determine the stages of material degradation. The results obtained from the carried out studies will be used as the basis for new methodology development of the assessment of the structural condition of in-service equipment.

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“…The parameter vector that leads to the least mean square error (MSE) between the analytical and experimental echoes provides the most accurate parameters of the experimental echo. Many studies have been carried out in this area, each trying to enhance the ability of the model-based estimation method and to employ it in various NDE applications [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Modeling the ultrasonic testing echoes by a combination of particle swarm optimization and Levenberg–Marquardt algorithms

Gholami

Honarvar

Moghaddam

2017

Meas. Sci. Technol.

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This paper presents an accurate and easy-to-implement algorithm for estimating the parameters of the asymmetric Gaussian chirplet model (AGCM) used for modeling echoes measured in ultrasonic nondestructive testing (NDT) of materials. The proposed algorithm is a combination of particle swarm optimization (PSO) and Levenberg–Marquardt (LM) algorithms. PSO does not need an accurate initial guess and quickly converges to a reasonable output while LM needs a good initial guess in order to provide an accurate output. In the combined algorithm, PSO is run first to provide a rough estimate of the output and this result is consequently inputted to the LM algorithm for more accurate estimation of parameters. To apply the algorithm to signals with multiple echoes, the space alternating generalized expectation maximization (SAGE) is used. The proposed combined algorithm is robust and accurate. To examine the performance of the proposed algorithm, it is applied to a number of simulated echoes having various signal to noise ratios. The combined algorithm is also applied to a number of experimental ultrasonic signals. The results corroborate the accuracy and reliability of the proposed combined algorithm.

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Acoustic emisson and ultrasonic wave characteristics in TIG-welded 316 stainless steel

Cited by 5 publications

References 10 publications

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

A New Fracture Detection Algorithm of Low Amplitude Acoustic Emission Signal Based on Kalman Filter-Ripple Voltage

Application of classification neural networks for identification of damage stages of degraded low alloy steel based on acoustic emission data analysis

Modeling the ultrasonic testing echoes by a combination of particle swarm optimization and Levenberg–Marquardt algorithms

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