Tomography of wall-thinning defect in plate structure based on guided wave signal acquisition by numerical simulations
Authors: 
Beomjin Kim, Azamatjon Kakhramon Ugli Malikov, Young H. Kim, Younho ChoAuthors Info & Claims
Beomjin Kim, Azamatjon Kakhramon Ugli Malikov, Young H. Kim, Younho ChoAuthors Info & ClaimsPages 353 - 366
Abstract
The integrity of plate structures in numerous facilities and vehicles is essential for ensuring safety. Guided wave testing is a prominent non-destructive testing (NDT) technique, especially for wide plate or long pipe structures. It can be related to tomography techniques to visualize defect information. One way to obtain data for tomography is through experimentation. However, a numerical approach, such as a computational simulation, could also be a feasible option because it can efficiently handle various defect cases. In this study, a dynamic analysis was performed to acquire the guided wave signal on a plate containing a wall-thinning defect, for which previous studies were insufficient. Acquired signals are compared to each other, and studies have demonstrated that wall-thinning defects can be visualized. This approach to signal data acquisition is expected to enhance the efficiency of data collection in several fields, such as machine learning implementation in NDT.
Graphic abstract
[graphic not available: see fulltext]
References
[1]
Alkassar Y et al. Simulation of Lamb wave modes conversions in a thin plate for damage detection Procedia Eng 2017 173 948-955
[2]
Barashok K et al. Deep learning algorithms for defect detection using phased array ultrasonic testing data J Korean Soc Nondestruct Test 2023 43 34-43
[3]
Chen X and Lei Y Excitation current waveform for eddy current testing on the thickness of ferromagnetic plates NDT&E Int 2014 66 28-33
[4]
Deng W et al. Defect image recognition and classification for eddy current testing of titanium plate based on convolutional neural network Complexity 2020
[5]
Drozdz M et al. Modeling bulk and guided waves in unbounded elastic media using absorbing layers in commercial finite element packages AIP Conf Proc 2007 894 87-94
[6]
Ewald V et al. DeepSHM: a deep learning approach for structural health monitoring based on guided Lamb wave technique Proc SPIE Smart Struct Nondestruct Eval 2019 10970 84-99
[7]
Han S et al. Finite-element analysis of Lamb wave propagation in a thin aluminum plate J Aerosp Eng 2009 22 185-197
[8]
Hu B et al. Magnetic non-destructive testing method for thin-plate aluminum alloys NDT&E Int 2012 47 66-69
[9]
Ismail N et al. Simulation of Lamb wave interactions with defects in a thin plate J Phys Conf Ser 2019
[10]
Jiang Y et al. Acoustic emission tomography based on simultaneous algebraic reconstruction technique to visualize the damage source location in Q235B steel plate Mech Syst Signal Process 2015 64–65 452-464
[11]
Jiang M et al. Modified reconstruction algorithm for probabilistic inspection of damage based on damaged virtual sensing paths Measurement 2023
[12]
Lee Y et al. Study of concrete de-bonding assessment technique for containment liner plates in nuclear power plants using ultrasonic guided wave approach Nucl Eng Technol 2022 54 1221-1229
[13]
Lee Y and Cho Y Defect imaging enhancement through optimized shape factors of the RAPID algorithm based on guided wave beam pattern analysis Sensors 2021
[14]
Lee B and Staszewski WJ Modeling of Lamb waves for damage detection in metallic structures: Part II. Wave interactions with damage Smart Mater Struct 2003
[15]
Lee S et al. Sizing-based flaw acceptability in weldments using phased array ultrasonic testing and neural networks Appl Sci 2023
[16]
Li W and Cho Y Combination of nonlinear ultrasonics and guided wave tomography for imaging the micro-defects Ultrasonics 2016 65 87-95
[17]
Li W et al. Cumulative second harmonic generation of ultrasonic guided waves propagation in tube-like structure J Comput Acoust 2016
[18]
Liu G and Jerry S A non-reflecting boundary for analyzing wave propagation using the finite element method Finite Elem Anal Des 2003 39 403-417
[19]
Liu Z et al. Delamination detection in composite plates by synthesizing time-reversed Lamb waves and a modified damage imaging algorithm based on RAPID Struct Control Health Monit 2016
[20]
Livadiotis S et al. An algebraic reconstruction imaging approach for corrosion damage monitoring of pipelines Smart Mater Struct 2019 28
[21]
Malikov A et al. Ultrasonic tomography imaging enhancement approach based on deep convolutional neural networks J vis 2023 26 1067-1083
[22]
Markovic N et al. Numerical modeling of ultrasonic wave propagation: by using of explicit FEM in ABAQUS Facta Univ Ser Archit Civ Eng 2018 16 135-147
[23]
Mohseni E et al. A study of the automated eddy current detection of cracks in steel plates J Nondestruct Eval 2020
[24]
Park J and Cho Y A study on guided wave tomographic imaging for defects on a curved structure J vis 2019 22 1081-1092
[25]
Park D et al. Detection of the subsurface cracks in a stainless steel plate using pulsed eddy current J Nondestr Eval 2013 32 350-353
[26]
Park J et al. A study on guided wave propagation in a long distance curved pipe J Mech Sci Technol 2019 33 4111-4117
[27]
Rajagopal P et al. On the used of absorbing layers to simulate the propagation of elastic waves in unbounded isotropic media using commercially available finite element packages NDT&E Int 2012 51 30-40
[28]
Rose JL Ultrasonic guided waves in solid media 2014 Cambridge Cambridge University Press
[29]
Sambath S et al. Automatic defect classification in ultrasonic NDT using artificial intelligence J Nondestr Eval 2011 30 20-28
[30]
Schmidt C et al. Artificial intelligence for non-destructive testing of CFRP prepreg materials Prod Eng Res Devel 2019 13 617-626
[31]
Shen Y and Giurgiutiu V Combined analytical FEM approach for efficient simulation of Lamb wave damage detection Ultrasonics 2016 69 116-128
[32]
Virkkunen I et al. Augmented ultrasonic data for machine learning J Nondestruct Eval 2021
[33]
Wang G et al. Optimal frequency of AC magnetic flux leakage testing for detecting defect size and orientation in thick steel plates IEEE Trans Magn 2021
[34]
Zhang H et al. Boundary-enhanced reconstruction algorithm for probabilistic inspection of composite plate defects with air-coupled Lamb waves Smart Mater Struct 2020
Recommendations
Time-domain Signal Simulation of Anti-symmetrical Mode Ultrasonic Lamb Wave
ICSPS '09: Proceedings of the 2009 International Conference on Signal Processing SystemsMulti-modes and dispersion characteristic of ultrasonic Lamb wave make it very complicated in exciting, propagating, receiving and signal processing and difficult to be used in industry ultrasonic non-destructive evaluation and testing. In this paper, ...
Study of Lamb Wave Propagation in Plate for UNDE by 2-D FEM Model
ICMTMA '10: Proceedings of the 2010 International Conference on Measuring Technology and Mechatronics Automation - Volume 03Ultrasonic nondestructive evaluation (UNDE) of notch defect by Lamb wave is investigated theoretically and experimentally in this paper. It is hard to predict and explain the received Lamb signal of UNDE due to its dispersive nature. The width of the ...
Lamb Wave Defection Based on Wavelet Analysis for the Complex Thin-walled Structure
EBIMCS '18: Proceedings of the 2018 International Conference on E-Business, Information Management and Computer ScienceThis paper reports a study of damage detection method using lamb wave with fiber Bragg grating(FBG) sensors for the complex thin-walled Structure, focusing on the response of FBG. The transmission characteristics will change because of the dispersion ...
Comments
Information & Contributors
Information
Published In
© The Visualization Society of Japan 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 29 March 2024
Accepted: 14 February 2024
Revision received: 30 January 2024
Received: 04 September 2023
Author Tags
Qualifiers
- Research-article
Funding Sources
- Korea National Research Foundation
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Feb 2025