Evidence map›Paper›PMID 41828138›Full record

ArticleMaterials (Basel, Switzerland)2026

Printed Crack Detection Sensors for SHM Based on Direct Ink Write Additive Manufacturing.

Artur Kurnyta, Klaudia Wrąbel, Marta Baran, Andrzej Leski

Abstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Artur KurnytaAirworthiness Division, Air Force Institute of Technology, ul. Ksiecia Boleslawa 6, 01-494 Warsaw, Poland.ORCID 0000-0002-0975-9780
Klaudia WrąbelAirworthiness Division, Air Force Institute of Technology, ul. Ksiecia Boleslawa 6, 01-494 Warsaw, Poland.
Marta BaranBelgian Nuclear Research Centre (SCK CEN), 2400 Mol, Belgium.
Andrzej LeskiInstitute of Aviation-Łukasiewicz Research Network, al. Krakowska 110/114, 02-256 Warsaw, Poland.ORCID 0000-0003-3006-8877

Funding

National Center for Research and Development, Poland LIDER/56/0290/L-12/20/NCBR/2021
6 · The paper itself

Abstract

The following paper aims to provide the results of an innovative structural crack detection technique using printed adaptive sensors. They were manufactured using conductive ink with silver microparticles and polymer insulators. The technique leveraged the unique properties of Direct Ink Write additive manufacturing combined with domain knowledge in the field of technical condition monitoring. The goal was to achieve high sensitivity and precision in detecting fatigue-crack-induced changes in structural components. The sensors' fabrication repeatability, output stability, and crack detection capabilities were investigated. Based on preliminary measurements of the sensors' output characteristics, the analyzed data showed that a tolerance in the range of 5% can be obtained for batch production. Damage size estimation using this new crack gauge during a fatigue crack growth test was high compared to the reference, with less than 1 mm precision over 30 mm of crack length. Throughout the fatigue test of up to 1.5 million cycles, all CCPSs remained fully functional, with no failure-related changes in their output signal patterns. The proposed sensor has proven its reliability for the detection of fatigue cracks and propagation monitoring and is a good alternative to other SHM technologies for this purpose.

Indexed as

additive manufacturingcrack gaugedirect ink write (DIW)fatigue crack detectionfatigue crack growth (FCG)printed sensorstructural health monitoring (SHM)structural integrity

Identifiers

PMID41828138
PMCPMC12986473

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.