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In Situ Testing Using Synchrotron Radiation Computed Tomography in Materials Research

Published online by Cambridge University Press:  21 October 2019

Xinchen Ni ,
Nathan K. Fritz  and
Brian L. Wardle
Xinchen Ni*
Affiliation:
Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA02139, U.S.A.
Nathan K. Fritz
Affiliation:
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA02139, U.S.A.
Brian L. Wardle
Affiliation:
Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA02139, U.S.A.
*
*(Email: codyni@mit.edu)
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Abstract

High resolution (< 1 µm) computed tomography is an attractive tool in materials research due to its ability to non-destructively visualize the three-dimensional internal microstructures of the material. Recently, this technique has been further empowered by adding a fourth (temporal) dimension to study the time-lapse material response under load. Such studies are referred to as four-dimensional or in situ testing. In this snapshot review, we highlight three representative examples of in situ testing using synchrotron radiation computed tomography (SRCT) for composites failure analysis, measurement of local corrosion rate in alloys, and visualization and quantification of electrochemical reactions in lithium-ion batteries, as well as forward-looking integration of machine learning with in situ CT. Lastly, the future opportunities and challenges of in situ SRCT testing are discussed.

Keywords

in situx-ray tomography
Type
Review Article
Information
MRS Advances , Volume 4 , Issue 51-52: Snapshot reviews in Materials Advances 2019 , 2019 , pp. 2831 - 2841
Copyright
Copyright © Materials Research Society 2019 

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References

REFERENCES

Wu, S. C., Xiao, T. Q., and Withers, P. J., “The imaging of failure in structural materials by synchrotron radiation X-ray microtomography,” Eng. Fract. Mech., 2017.CrossRefGoogle Scholar
Bak, S. M., Shadike, Z., Lin, R., Yu, X., and Yang, X. Q., “In situ/operando synchrotron-based X-ray techniques for lithium-ion battery research,” NPG Asia Mater ., vol. 10, no. 7, pp. 563–580, 2018.CrossRefGoogle Scholar
Singh, S. S., Williams, J. J., Hruby, P., Xiao, X., De Carlo, F., and Chawla, N., “In situ experimental techniques to study the mechanical behavior of materials using X-ray synchrotron tomography,” Integr. Mater. Manuf. Innov., vol. 3, no. 1, pp. 109–122, Dec. 2014.CrossRefGoogle Scholar
Spearing, S. M. and Sinclair, I., “The Micro-mechanics of Strength, Durability and Damage Tolerance in Composites: New Insights from High Resolution Computed Tomography,” IOP Conf. Ser. Mater. Sci. Eng., vol. 139, no. 1, p. 012007, Jul. 2016.CrossRefGoogle Scholar
Knight, S. P., Salagaras, M., Wythe, A. M., De Carlo, F., Davenport, A. J., and Trueman, A. R., “In situ X-ray tomography of intergranular corrosion of 2024 and 7050 aluminium alloys,” Corros. Sci., vol. 52, no. 12, pp. 3855–3860, 2010.CrossRefGoogle Scholar
Clarke, A. J. et al., “X-ray Imaging and Controlled Solidification of Al-Cu Alloys Toward Microstructures by Design,” Adv. Eng. Mater., vol. 17, no. 4, pp. 454–459, Apr. 2015.CrossRefGoogle Scholar
Hsieh, J., Computed tomography: principles, design, artifacts, and recent advances, 3rd Editio. SPIE.Google Scholar
Bonse, U. and Busch, F., “X-ray computed microtomography (μCT) using synchrotron radiation (SR),” Prog. Biophys. Mol. Biol., vol. 65, no. 1–2, pp. 133–169, Jan. 1996.CrossRefGoogle Scholar
Patterson, B. M., Cordes, N. L., Henderson, K., Xiao, X., and Chawla, N., “Data Challenges of In Situ X-Ray Tomography for Materials Discovery and Characterization,” in Materials Discovery and Design: By Means of Data Science and Optimal Learning, Lookman, T., Eidenbenz, S., Alexander, F., and Barnes, C., Eds. Cham: Springer International Publishing, 2018, pp. 129–165.CrossRefGoogle Scholar
Garcea, S. C., Sinclair, I., and Spearing, S. M., “In situ synchrotron tomographic evaluation of the effect of toughening strategies on fatigue micromechanisms in carbon fibre reinforced polymers,” Compos. Sci. Technol., vol. 109, pp. 32–39, Mar. 2015.CrossRefGoogle Scholar
Patterson, B. M. et al., “In situ X-ray synchrotron tomographic imaging during the compression of hyper-elastic polymeric materials,” J. Mater. Sci., vol. 51, no. 1, pp. 171–187, Jan. 2016.CrossRefGoogle Scholar
Chapman, N. C., Silva, J., Williams, J. J., Chawla, N., and Xiao, X., “Characterisation of thermal cycling induced cavitation in particle reinforced metal matrix composites by three-dimensional (3D) X-ray synchrotron tomography,” Mater. Sci. Technol., vol. 31, no. 5, pp. 573–578, Mar. 2015.CrossRefGoogle Scholar
Haboub, A. et al., “Tensile testing of materials at high temperatures above 1700°C with in situ synchrotron X-ray micro-tomography,” Rev. Sci. Instrum., vol. 85, no. 8, p. 083702, Aug. 2014.CrossRefGoogle Scholar
Singh, S. S., Williams, J. J., Lin, M. F., Xiao, X., De Carlo, F., and Chawla, N., “In Situ Investigation of High Humidity Stress Corrosion Cracking of 7075 Aluminum Alloy by Three-Dimensional (3D) X-ray Synchrotron Tomography,” Mater. Res. Lett., vol. 2, no. 4, pp. 217–220, Oct. 2014.CrossRefGoogle Scholar
Eckermann, F. et al., “In situ monitoring of corrosion processes within the bulk of AlMgSi alloys using X-ray microtomography,” Corros. Sci., vol. 50, no. 12, pp. 3455–3466, Dec. 2008.CrossRefGoogle Scholar
Connolly, B. J. et al., “X-ray microtomography studies of localised corrosion and transitions to stress corrosion cracking,” Mater. Sci. Technol., vol. 22, no. 9, pp. 1076–1085, Sep. 2006.CrossRefGoogle Scholar
Grunwaldt, J.-D., Wagner, J. B., and Dunin-Borkowski, R. E., “Imaging Catalysts at Work: A Hierarchical Approach from the Macro- to the Meso- and Nano-scale,” ChemCatChem, vol. 5, no. 1, pp. 62–80, Jan. 2013.CrossRefGoogle Scholar
Bozzini, B. and Goldoni, A., “Will in situ synchrotron-based approaches beat the durability issues of next-generation batteries?,” J. Phys. D. Appl. Phys., vol. 51, no. 5, pp. 0–8, 2018.CrossRefGoogle Scholar
Ni, X. et al., “Static and fatigue interlaminar shear reinforcement in aligned carbon nanotube-reinforced hierarchical advanced composites,” Compos. Part A-Appl. S., vol. 120, no. October 2018, pp. 106–115, May 2019.CrossRefGoogle Scholar
Scott, A. E., Mavrogordato, M., Wright, P., Sinclair, I., and Spearing, S. M., “In situ fibre fracture measurement in carbon-epoxy laminates using high resolution computed tomography,” Compos. Sci. Technol., vol. 71, no. 12, pp. 1471–1477, Aug. 2011.CrossRefGoogle Scholar
Garcea, S. C., Wang, Y., and Withers, P. J., “X-ray Computed Tomography of Polymer Composites,” Compos. Sci. Technol., vol. 156, pp. 305–319, Mar. 2018.CrossRefGoogle Scholar
Garcea, S. C., Sinclair, I., Spearing, S. M., and Withers, P. J., “Mapping fibre failure in situ in carbon fibre reinforced polymers by fast synchrotron X-ray computed tomography,” Compos. Sci. Technol., vol. 149, pp. 81–89, Sep. 2017.CrossRefGoogle Scholar
Davis, J. R. (Joseph R. ., Corrosion of aluminum and aluminum alloys. ASM International, 1999.Google Scholar
Singh, S. S., Williams, J. J., Stannard, T. J., Xiao, X., De Carlo, F., and Chawla, N., “Measurement of localized corrosion rates at inclusion particles in AA7075 by in situ three dimensional (3D) X-ray synchrotron tomography,” Corros. Sci., vol. 104, pp. 330–335, 2016.CrossRefGoogle Scholar
van Schalkwijk, W. A. and Scrosati, B., Eds., Advances in Lithium-Ion Batteries. Springer US, 2002.CrossRefGoogle Scholar
Ebner, M., Marone, F., Stampanoni, M., and Wood, V., “Visualization and quantification of Electrochemical and Mechanical,” Science, vol. 342, no. November, pp. 716–721, 2013.CrossRefGoogle ScholarPubMed
Mohri, M., Rostamizadeh, A., and Talwalkar, A., Foundations of machine learning, 2nd Editio. MIT Press, 2018.Google Scholar
Perciano, T., Ushizima, D. M., Bethel, E. W., Mizrahi, Y. D., Parkinson, D., and Sethian, J. A., “Reduced-complexity image segmentation under parallel Markov Random Field formulation using graph partitioning,” Proc. - Int. Conf. Image Process. ICIP, vol. 2016-Augus, pp. 1259–1263, 2016.Google Scholar
Ushizima, D. M. et al., “IDEAL: Images Across Domains, Experiments, Algorithms and Learning,” Jom, vol. 68, no. 11, pp. 2963–2972, 2016.CrossRefGoogle Scholar
Pelt, D. M. and Batenburg, K. J., “Fast Tomographic Reconstruction From Limited Data Using Artificial Neural Networks,” IEEE Trans. Image Process., vol. 22, no. 12, pp. 5238–5251, Dec. 2013.CrossRefGoogle ScholarPubMed