The DOI matches its Crossref record.
X-ray computed tomography of polymer composites
Open accessAbstract
The use of X-ray computed tomography (CT), exploiting both synchrotron and laboratory sources, has grown significantly over the last decade, driven primarily by improvements in spatial resolution, reduction in acquisition time and the increasing availability of laboratory X-ray CT systems. It is now able to provide highly accurate three-dimensional (3D) inspections of fibre architectures, manufacturing defects and in-service damage accumulation non-destructively, allowing the user to examine cross-sections that would previously have required laborious and skilled mechanical sectioning with the potential for inducing damage or loss of material. Further, by repeated acquisition of 3D images, it has opened new opportunities for time-lapse studies. This feature article reviews the technical aspects relating to the X-ray CT imaging of composites such as obtaining sufficient contrast, examination of thin panels, sample size/resolution issues, quantification of damage and defects, and image-based modelling. The capability of X-ray CT to provide important information is considered across applications ranging from manufacturing processes, through tensile and compression loading to fatigue and impact damage. The complementary advantages of laboratory and synchrotron X-ray CT are examined with a view to identify new opportunities and challenges.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Nsengiyumva, W., Zhong, S., Lin, J., Zhang, Q., Zhong, J., & Huang, Y. (2021) · Composite Structures · doi:10.1016/j.compstruct.2020.112951 · Cited by 325
Yanamandra, K., Chen, G. L., Xu, X., Mac, G., & Gupta, N. (2020) · Composites Science and Technology · doi:10.1016/j.compscitech.2020.108318 · Cited by 72
Metadata
- Authors
- Garcea, S. C., Wang, Y., & Withers, P. J.
- Venue
- Composites Science and Technology
- Publisher
- Elsevier BV
- Published
- 2018-03
- Volume
- 156
- Pages
- 305-319
- ISSN
- 0266-3538 (ISSN-L)
- OpenAlex
- W2765287370
- Cited by
- 719
- References
- 0
- FWCI
- 44.67
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: X-ray computed tomography of polymer composites. https://app.cochat.ai/papers/10.1016/j.compscitech.2017.10.023
- The paper
- Garcea, S. C., Wang, Y., & Withers, P. J. (2018). X-ray computed tomography of polymer composites. Composites Science and Technology. https://doi.org/10.1016/j.compscitech.2017.10.023
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.