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Automated identification and classification of internal defects in composite structures using computed tomography and 3D wavelet analysis

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The necessity of detection and identification of damages in structural elements intensifies the development of non-destructive testing (NDT) methods and techniques suitable for different composite materials and sensitive to particular types of damages. One of the most sensitive and accurate methods is the computed tomography (CT), which has already found a wide application in medical diagnostics and materials testing. However, there are some difficulties during testing of composite materials, whose internal structure is often quite complex. Other difficulties of application of CT are related to correct identification of diverse composite-specific defects. The following study presents the method of identification and classification of internal defects of polymeric composite structures after the water-jet cutting based on CT scanning and 3D wavelet analysis. The application of wavelet transform allows extracting meaningful information about the size of the defect, their locations and orientations and reducing the measurement noise. An applied classification procedure is based on the analysis of geometric properties of identified defects and the magnitudes of detail coefficients resulted from the wavelet transform. Proposed method could find an application both in quality control of composite components as well as their NDT during operation.
Rocznik
Strony
436--448
Opis fizyczny
Bibliogr. 37 poz., rys., wykr.
Twórcy
autor
  • Silesian University of Technology, Institute of Fundamentals of Machinery Design, 18A Konarskiego Str., 44-100 Gliwice, Poland
autor
  • Technische Universität Dresden, Institute of Lightweight Engineering and Polymer Technology, Holbeinstrasse 3, 01307 Dresden, Germany
autor
  • Technische Universität Dresden, Institute of Lightweight Engineering and Polymer Technology, Holbeinstrasse 3, 01307 Dresden, Germany
Bibliografia
  • [1] J. Kastner, B. Plank, A. Reh, D. Salaberger, C. Heinzl, Advanced X-ray tomographic methods for quantitative characterization of carbon fibre reinforced polymers, in: Proc. 4th International Symposium on NDT in Aerospace, Augsburg, (2012), pp. 1–9.
  • [2] Z. Chen, R. Ning, Breast volume denoising and noise characterization by 3D wavelet transform, Computerized Medical Imaging and Graphics 28 (5) (2004) 235–246.
  • [3] K.-S. Seo, Automatic hepatic tumor segmentation using composite hypotheses, Lecture Notes in Computer Science 3656 (2005) 922–929.
  • [4] J.L.R. Guivernau, 3D Wavelet-based Fusion Techniques for Biomedical Imaging, (PhD thesis), Universidad Politécnica de Madrid, Madrid, 2012.
  • [5] K. Hackmack, F. Paul, M. Weygandt, C. Allefeld, J.-D. Haynes, Multi-scale classification of disease using structural MRI and wavelet transform, NeuroImage 62 (1) (2012) 48–58.
  • [6] A. Kaestner, M. Schneebeli, F. Graf, Visualizing three-dimensional root networks using computed tomography, Geoderma 136 (1–2) (2006) 459–469.
  • [7] M. Lontoc-Roy, P. Dutilleul, S.O. Prasher, L. Han, T. Brouillet, D.L. Smith, Advances in the acquisition and analysis of CT scan data to isolate a crop root system from the soil medium and quantify root system complexity in 3D-space, Geoderma 137 (1–2) (2006) 231–241.
  • [8] J.S. Perret, M.E. Al-Belushi, M. Deadman, Non-destructive visualization and quantification of roots using computed tomography, Soil Biology and Biochemistry 39 (2) (2007) 391– 399.
  • [9] C.A. Carlson, Computerized tomography for non-destructive testing of materials and its efficient use, Materials and Design 15 (5) (1992) 265–268.
  • [10] R. Huang, K.-L. Ma, P. McCormick, W. Ward, Visualizing industrial CT volume data for nondestructive testing applications, in: Proc. 14th IEEE Conference Visualization VIS '03, Seattle, WA, (2003), pp. 547–554.
  • [11] C. Heinzl, J. Kastner, E. Gröller, Surface extraction from multi- material components for metrology using dual energy CT, IEEE Transactions on Visualization and Computer Graphics 13 (6) (2007) 1520–1527.
  • [12] D.J. Bull, L. Helfen, I. Sinclair, S.M. Spearing, T. Baumbach, A comparison of multi-scale 3D X-ray tomographic inspection techniques for assessing carbon fibre composite impact damage, Composites Science and Technology 75 (2013) 55–61.
  • [13] D.J. Bull, I. Sinclair, S.M. Spearing, Partial volume correction for approximating crack opening displacements in CFRP material obtained from micro-focus X-ray CT scans, Composites Science and Technology 81 (2013) 9–16.
  • [14] J.F. Barrett, N. Keat, Artifacts in CT: recognition and avoidance, Radiographics 24 (2004) 1679–1691.
  • [15] C.P. Chu, D.J. Lee, Bilevel thresholding of sliced image of sludge floc, Environmental Science and Technology 38 (4) (2004) 1161–1169.
  • [16] C.P. Chu, D.J. Lee, J.H. Tay, Bilevel thresholding of floc images, Journal of Colloids and Interface Science 273 (2) (2004) 483– 489.
  • [17] J.B.T.M. Roerdink, M.A. Westenberg, Wavelet-based Volume Visualization, Technical Report 98-9-06, University of Groningen, 1998.
  • [18] W.C. Moss, S. Haase, J.M. Lyle, D.A. Agard, J.W. Sedat, A novel 3D wavelet-based filter for visualizing features in noisy data, Journal of Microscopy 219 (2) (2005) 43–49.
  • [19] E. Douka, S. Loutridis, A. Trochidis, Crack identification of plates using wavelet analysis, Journal of Sound and Vibration 270 (1–2) (2004) 279–295.
  • [20] S. Loutridis, E. Douka, A. Trochidis, Crack identification in double-cracked beams using wavelet analysis, Journal of Sound and Vibration 277 (4–5) (2004) 1025–1039.
  • [21] A. Katunin, Identification of multiple cracks in composite beams using discrete wavelet transform, Scientific Problems of Machines Operation and Maintenance 45 (2) (2010) 41–52.
  • [22] Q. Wang, X. Deng, Damage detection with spatial wavelets, International Journal of Solids and Structures 36 (23) (1999) 3443–3468.
  • [23] C.C. Chang, L.-W. Chen, Damage detection in rectangular plate by spatial wavelet based approach, Applied Acoustics 65 (8) (2004) 819–832.
  • [24] W. Fan, P. Qiao, A 2-D continuous wavelet transform of mode shape data for damage detection of plate structures, International Journal of Solids and Structures 46 (25–26) (2009) 4379–4395.
  • [25] A. Katunin, Damage identification in composite plates using two-dimensional B-spline wavelets, Mechanical Systems and Signal Processing 25 (8) (2011) 3153–3167.
  • [26] A. Katunin, F. Holewik, Crack identification in composite elements with non-linear geometry using spatial wavelet transform, Archives of Civil and Mechanical Engineering 13 (3) (2013) 287–296.
  • [27] B. Grübner, W. Hufenbach, R. Gottwald, M. Lepper, B. Zhou, Experimental and numerical validation of an analytical calculation method for notched fibre-reinforced multilayered composites under bending and compressive loads, in: Proc. 19th International Conference on Composite Materials, Montreal, 2013.
  • [28] P.S. Addison, The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance, CRC Press, London, 2010.
  • [29] P.J. Van Fleet, Discrete Wavelet Transformations: An Elementary Approach with Applications, John Wiley & Sons, Hoboken, NJ, 2011.
  • [30] J.C. Goswami, A.K. Chan, Fundamentals of wavelets: theory, algorithms and applications, in: Wiley Series in Microwave and Optical Engineering, vol. 233, 2nd ed., Hoboken, NJ, 2011.
  • [31] S.V. Narasimhan, N. Basumallick, S. Veena, Introduction to Wavelet Transform: A Signal Processing Approach, Alpha Science International, Oxford, 2012.
  • [32] A. Katunin, Modal-based non-destructive damage assessment in composite structures using wavelet analysis: a review, International Journal of Composite Materials 3 (6B) (2013) 1–9.
  • [33] J.C. Hong, Y.Y. Kim, H.C. Lee, Y.W. Lee, Damage detection using Lipschitz exponent estimated by the wavelet transform: application to vibration modes of beam, International Journal of Solids and Structures 39 (7) (2002) 1803–1816.
  • [34] E. Douka, S. Loutridis, A. Trochidis, Crack identification in beams using wavelet analysis, International Journal of Solids and Structures 40 (13–14) (2003) 3557–3569.
  • [35] M. Rucka, K. Wilde, Application of continuous wavelet transform in vibration based damage detection method for beams and plates, Journal of Sound and Vibration 297 (3–5) (2006) 536–550.
  • [36] R. Cohen, Signal Denoising Using Wavelets, Technical Report, Technion, Israel Institute of Technology, Haifa, 2012.
  • [37] D.L. Donoho, J.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika 81 (3) (1994) 425–455.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-acf5ee37-6572-4b91-8a52-67204e283b06
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