nach oben

Erschienen in:

08.09.2020 | Computation & theory

Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning

verfasst von: Aly Badran, David Marshall, Zacharie Legault, Ruslana Makovetsky, Benjamin Provencher, Nicolas Piché, Mike Marsh

Erschienen in: Journal of Materials Science | Ausgabe 34/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A deep learning procedure has been examined for automatic segmentation of 3D tomography images from fiber-reinforced ceramic composites consisting of fibers and matrix of the same material (SiC), and thus identical image intensities. The analysis uses a neural network to distinguish phases from shape and edge information rather than intensity differences. It was used successfully to segment phases in a unidirectional composite that also had a coating with similar image intensity. It was also used to segment matrix cracks generated during in situ tensile loading of the composite and thereby demonstrate the influence of nonuniform fiber distribution on the nature of matrix cracking. By avoiding the need for manual segmentation of thousands of image slices, the procedure overcomes a major impediment to the extraction of quantitative information from such images. The analysis was performed using recently developed software that provides a general framework for executing both training and inference.

Graphic abstract

Vorheriger Artikel Enhanced photocathodic antifouling/antibacterial properties of polyaniline–Ag–N-doped TiO2 coatings

Nächster Artikel Conduction properties of acceptor-doped BaTiO3–Bi(Zn1/2Ti1/2)O3-based ceramics

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The variational method for segmentation at the fiber tow scale begins with a prior geometric model of the weave topology that is iteratively matched to the μCT image via an optimization process [47].

Object Research Systems, Montreal, Canada (free of charge for non-commercial use) [23].

The test specimen was supplied by Prof. G Morscher: further details of the fabrication method are given in [48].

Bale HA, Blacklock M, Begley MR et al (2012) Characterizing three-dimensional textile ceramic composites using synchrotron X-ray micro-computed-tomography. J Am Ceram Soc 95:392–402. https://doi.org/10.1111/j.1551-2916.2011.04802.xCrossRef

Bale HA, Haboub A, Macdowell AA et al (2013) Real-time quantitative imaging of failure events in materials under load at temperatures above 1,600 C. Nat Mater 12:40–46. https://doi.org/10.1038/nmat3497CrossRef

Chateau C, Gélébart L, Bornert M et al (2011) In situ X-ray microtomography characterization of damage in SiCf/SiC minicomposites. Compos Sci Technol 71:916–924. https://doi.org/10.1016/j.compscitech.2011.02.008CrossRef

Wright P, Fu Z, Sinclair I, Spearing SM (2008) Ultra high resolution computed tomography of damage in notched carbon fiber-epoxy composites. J Compos Mater 42:1993–2002. https://doi.org/10.1177/0021998308092211CrossRef

Moffat AJ, Wright P, Buffière JY et al (2008) Micromechanisms of damage in 0 splits in a [90/0] s composite material using synchrotron radiation computed tomography. Scr Mater 59:1043–1046. https://doi.org/10.1016/j.scriptamat.2008.07.034CrossRef

Mazars V, Caty O, Couégnat G et al (2017) Damage investigation and modeling of 3D woven ceramic matrix composites from X-ray tomography in situ tensile tests. Acta Mater 140:130–139. https://doi.org/10.1016/j.actamat.2017.08.034CrossRef

Cox BN, Bale HA, Begley MR et al (2014) Stochastic virtual tests for high-temperature ceramic matrix composites. Annu Rev Mater Res 44:479–529. https://doi.org/10.1146/annurev-matsci-122013-025024CrossRef

Saucedo-Mora L, Zou C, Lowe T, Marrow TJ (2017) Three-dimensional measurement and cohesive element modelling of deformation and damage in a 2.5-dimensional woven ceramic matrix composite. Fatigue Fract Eng Mater Struct 40:683–695. https://doi.org/10.1111/ffe.12537CrossRef

Barnard HS, MacDowell AA, Parkinson DY et al (2017) Synchrotron X-ray micro-tomography at the advanced light source: developments in high-temperature in situ mechanical testing. J Phys: Conf Ser 849:012043. https://doi.org/10.1088/1742-6596/849/1/012043CrossRef

10.

Larson NM, Cuellar C, Zok FW (2019) X-ray computed tomography of microstructure evolution during matrix impregnation and curing in unidirectional fiber beds. Compos Part A Appl Sci Manuf 117:243–259. https://doi.org/10.1016/j.compositesa.2018.11.021CrossRef

11.

Marshall DB, Cox BN (2008) Integral textile ceramic structures. Annu Rev Mater Res 38:425–443. https://doi.org/10.1146/annurev.matsci.38.060407.130214CrossRef

12.

Saucedo-Mora L, Lowe T, Zhao S et al (2016) In situ observation of mechanical damage within a SiC–SiC ceramic matrix composite. J Nucl Mater 481:13–23. https://doi.org/10.1016/j.jnucmat.2016.09.007CrossRef

13.

Perciano T, Ushizima DM, Krishnan H et al (2017) Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics. J Synchrotron Radiat 24:1065–1077. https://doi.org/10.1107/S1600577517010955CrossRef

14.

Straumit I, Lomov SV, Wevers M (2015) Quantification of the internal structure and automatic generation of voxel models of textile composites from X-ray computed tomography data. Compos Part A Appl Sci Manuf 69:150–158. https://doi.org/10.1016/j.compositesa.2014.11.016CrossRef

15.

Czabaj MW, Riccio ML, Whitacre WW (2014) Three-dimensional imaging and numerical reconstruction of graphite/epoxy composite microstructure based on ultra-high resolution X-ray computed tomography. In: Proceedings of the American Society for Composites—29th technical conference ASC 2014; 16th US-Japan conference on composite materials ASTM-D30 Meeting, vol 105, pp 174–182

16.

Haberl MG, Churas C, Tindall L et al (2018) CDeep3M—Plug-and-Play cloud-based deep learning for image segmentation. Nat Methods 15:677–680. https://doi.org/10.1038/s41592-018-0106-zCrossRef

17.

Sinchuk Y, Kibleur P, Aelterman J et al (2020) Variational and deep learning segmentation of very-low-contrast X-ray computed tomography images of carbon/epoxy woven composites. Materials (Basel) 13:936. https://doi.org/10.3390/ma13040936CrossRef

18.

Haboub A, Bale HA, Nasiatka JR et al (2014) Tensile testing of materials at high temperatures above 1700 C with in situ synchrotron X-ray micro-tomography. Rev Sci Instrum 85:1–13. https://doi.org/10.1063/1.4892437CrossRef

19.

Pandolfi RJ, Allan DB, Arenholz E et al (2018) Xi-cam: a versatile interface for data visualization and analysis. J Synchrotron Radiat 25:1261–1270. https://doi.org/10.1107/S1600577518005787CrossRef

20.

Greenspan H, Van Ginneken B, Summers RM (2016) Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. IEEE Trans Med Imaging 35:1153–1159CrossRef

21.

Farfade SS, Saberian M, Li LJ (2015) Multi-view face detection using Deep convolutional neural networks. In: ICMR 2015—proceedings of the 2015 ACM international conference on multimedia retrieval. Association for Computing Machinery, Inc, New York, New York, USA, pp 643–650

22.

Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39:2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615CrossRef

23.

Dragonfly|3D visualization and analysis solutions for scientific and industrial data|ORS. https://www.theobjects.com/dragonfly/index.html. Accessed 21 Mar 2020

24.

Hamwood J, Alonso-Caneiro D, Read SA et al (2018) Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers. Biomed Opt Express 9:3049. https://doi.org/10.1364/boe.9.003049CrossRef

25.

Sudre CH, Li W, Vercauteren T et al (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso MJ, Arbel T, Carneiro G et al (eds) Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, Cham, pp 240–248CrossRef

26.

Losses - Keras Documentation. https://keras.io/losses/. Accessed 21 Mar 2020

27.

Geron A (2019) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: concepts, tools, and techniques to build intelligent systems, 2nd edn. O’Rilly, Sebastopol

28.

Optimizers - Keras Documentation. https://keras.io/optimizers/. Accessed 21 Mar 2020

29.

Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings—2016 4th international conference on 3D vision, 3DV 2016. Institute of Electrical and Electronics Engineers Inc., pp 565–571

30.

Jegou S, Drozdzal M, Vazquez D et al (2017) The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: IEEE computer society conference on computer vision and pattern recognition workshops 2017 July, pp 1175–1183. https://doi.org/10.1109/CVPRW.2017.156

31.

Arhatari BD, Zonneveldt M, Thornton J, Abbey B (2017) Local structural damage evaluation of a C/C-SiC ceramic matrix composite. Microsc Microanal 23:518–526. https://doi.org/10.1017/S1431927617000459CrossRef

32.

Spearing SM, Zok FW, Evans AG (1994) Stress corrosion cracking in a unidirectional ceramic-matrix composite. J Am Ceram Soc 77:562–570. https://doi.org/10.1111/j.1151-2916.1994.tb07030.xCrossRef

33.

Marshall DB (1984) An indentation method for measuring matrix-fiber frictional stresses in ceramic composites. J Am Ceram Soc 67:C259–C260. https://doi.org/10.1111/j.1151-2916.1984.tb19690.xCrossRef

34.

Marshall DB, Cox BN, Evans AG (1985) The mechanics of matrix cracking in brittle-matrix fiber composites. Acta Metall 33:2013–2021. https://doi.org/10.1016/0001-6160(85)90124-5CrossRef

35.

Marshall DB, Evans AG (1985) Failure mechanisms in ceramic-fiber/ceramic-matrix composites. J Am Ceram Soc 68:225–231. https://doi.org/10.1111/j.1151-2916.1985.tb15313.xCrossRef

36.

Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, pp 234–241

37.

Kamnitsas K, Ledig C, Newcombe VFJ et al (2017) Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 36:61–78. https://doi.org/10.1016/j.media.2016.10.004CrossRef

38.

Prasoon A, Petersen K, Igel C et al (2013) Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, Berlin, pp 246–253

39.

Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A (eds) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer, Berlin, pp 234–241

40.

Garcia-Garcia A, Orts-Escolano S, Oprea S et al (2017) A review on deep learning techniques applied to semantic segmentation. arXiv:1704.06857

41.

Aloysius N, Geetha M (2018) A review on deep convolutional neural networks. In: Proceedings of the 2017 IEEE international conference on communication and signal processing, ICCSP 2017. Institute of Electrical and Electronics Engineers Inc., pp 588–592

42.

Dettmers T (2015) Deep learning in a nutshell: core concepts|NVIDIA Developer Blog. In: Nvidia Dev. https://developer.nvidia.com/blog/deep-learning-nutshell-core-concepts/. Accessed 30 Jun 2020

43.

Badran A, Marshall DB, Legault Z et al (2020) XCT dataset and deep learning models for automated segmentation of computed tomography images of fiber-reinforced composites. Mater Data Facil Open. https://doi.org/10.18126/SAIM-CV6CCrossRef

44.

Arganda-Carreras I, Turaga SC, Berger DR et al (2015) Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat 9:1–13. https://doi.org/10.3389/fnana.2015.00142CrossRef

45.

Cardona A, Saalfeld S, Preibisch S et al (2010) An integrated micro-and macroarchitectural analysis of the drosophila brain by computer-assisted serial section electron microscopy. PLoS Biol 8:1000502. https://doi.org/10.1371/journal.pbio.1000502CrossRef

46.

Cheng D, Meng G, Xiang S, Pan C (2017) FusionNet: edge aware deep convolutional networks for semantic segmentation of remote sensing harbor images. IEEE J Sel Top Appl Earth Obs Remote Sens 10:5769–5783. https://doi.org/10.1109/JSTARS.2017.2747599CrossRef

47.

Bénézech J, Couégnat G (2019) Variational segmentation of textile composite preforms from X-ray computed tomography. Compos Struct 230:111496. https://doi.org/10.1016/j.compstruct.2019.111496CrossRef

48.

Zhou J, Almansour AS, Chase GG, Morscher GN (2017) Enhanced oxidation resistance of SiC/SiC minicomposites via slurry infiltration of oxide layers. J Eur Ceram Soc 37:3241–3253. https://doi.org/10.1016/j.jeurceramsoc.2017.03.065CrossRef

Titel: Automated segmentation of computed tomography images of fiber-reinforced composites by deep learning
verfasst von: Aly Badran
David Marshall
Zacharie Legault
Ruslana Makovetsky
Benjamin Provencher
Nicolas Piché
Mike Marsh
Publikationsdatum: 08.09.2020
Verlag: Springer US
Erschienen in: Journal of Materials Science / Ausgabe 34/2020
Print ISSN: 0022-2461
Elektronische ISSN: 1573-4803
DOI: https://doi.org/10.1007/s10853-020-05148-7

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Graphic abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 34/2020

Effect of the heat treatment temperature on mechanical and electrochemical properties of polyimide separator for lithium ion batteries

Effect of crystallographic orientation on the friction of copper and graphenized copper

Microwave-assisted synthesis of MoS2/graphene composites for supercapacitors

1000 at 1000: Particulate-reinforced metal matrix composites

Sol–Gel SiO2 on electrospun polyacrylonitrile nanofiber for efficient oil-in-water emulsion separation

Structure design of MoS2@Mo2C on nitrogen-doped carbon for enhanced alkaline hydrogen evolution reaction

Premium Partner

Marktübersichten