Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 325

The DOI matches its Crossref record; 149 of the 149 references it cites were found in a registry or an index.

Advances, limitations and prospects of nondestructive testing and evaluation of thick composites and sandwich structures: A state-of-the-art review

Nsengiyumva, Walter, Zhong, Shuncong, Lin, Jiewen, Zhang, Qiukun, Zhong, Jianfeng, Huang, Yuexin·2021·Composite Structures
DOI 10.1016/j.compstruct.2020.112951
Open this paper in CoChat — free account

Abstract

Abstract In recent years, composite materials have gained popularity in numerous high-tech and engineering applications, owing to their outstanding physico-mechanical properties. They were initially used as fairings/reinforcements for different structures, but their application has recently shifted from general-purpose structures to primary and secondary load-bearing structures, where structural failures would result in catastrophic safety repercussions. This increased scope of application has prompted the introduction of composite structures featuring significant thickness and complexity. As such, the application of nondestructive testing and evaluation (NDT&E) to localize and characterize flaws in these materials at their incipient initiations could save resources, eliminate unplanned breakdown, and provide a timely window for repair-maintenance activities. Therefore, this paper critically reviews the recent advances in NDT&E as applied to the inspection of thick composite parts and sandwich structures (composites with a thickness ≥15 mm) and determines possible research prospects to address the limitations of the current technologies. A brief overview of defects/damage occurring in composite structures is provided followed by the main NDT&E techniques used to detect these flaws. Since there are many NDT&E techniques available, this work limits its scope on techniques that focus on the detection, localization, and characterization of flaws in thick composites and sandwich structures.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.compstruct.2020.112951

Cites

149 of 149 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Cited by

See all 325

Metadata

Authors
Nsengiyumva, W., Zhong, S., Lin, J., Zhang, Q., Zhong, J., & Huang, Y.
Venue
Composite Structures
Publisher
Elsevier BV
Published
2021-01
Volume
256
Pages
112951
ISSN
0263-8223 (ISSN-L)
DOI
10.1016/j.compstruct.2020.112951
OpenAlex
W3085140663
Cited by
325
References
149
FWCI
18.34
Type
journal article
Language
en
Open access
closed

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

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.