Evidence map›Paper›PMID 40732815›Full record

ArticlePolymers2025

Bending-Induced Progressive Damage of 3D-Printed Sandwich-Structured Composites by Non-Destructive Testing.

Lianhua Ma, Heng Sun, Xu Dong, Zhenyue Liu, Biao Wang

Abstract read
In one paragraph

Article in Polymers, 2025. 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

5 authors.

Lianhua MaResearch Institute of Interdisciplinary Science & School of Materials Science and Engineering, Dongguan University of Technology, Dongguan 523808, China.ORCID 0000-0002-7013-7868
Heng SunSchool of Quality and Technical Supervision, Hebei University, Baoding 071002, China.
Xu DongSchool of Quality and Technical Supervision, Hebei University, Baoding 071002, China.
Zhenyue LiuSchool of Quality and Technical Supervision, Hebei University, Baoding 071002, China.
Biao WangResearch Institute of Interdisciplinary Science & School of Materials Science and Engineering, Dongguan University of Technology, Dongguan 523808, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the extensive application of 3D-printed composites across multiple industries, the investigation into their structural reliability under complex loading conditions has become a critical research focus. This study comprehensively employs acoustic emission (AE) monitoring, digital image correlation (DIC) measurement, and micro-computed tomography (Micro-CT) visualization techniques to explore the progressive damage behavior of 3D-printed sandwich-structured composites reinforced with continuous carbon fiber sheets under three-point bending. Mechanical tests show that increasing the fiber content of face sheets from 10% to 20% enhances average bending strength by 56%, while low fiber content compromises stiffness and load-bearing capacity. AE analysis categorizes damage modes into matrix cracking (<50 kHz), debonding/delamination (50-150 kHz), and fiber breakage (>150 kHz) using k-means clustering algorithms. DIC measurement reveals significant structural deformation processes during damage progression. The AE-DIC-Micro-CT combination demonstrates an initial undamaged state, followed by damage initiation and propagation in the subsequent stages. This integrated approach provides an effective method for damage assessment, guiding the design and reliability improvement of 3D-printed composites.

Indexed as

acoustic emissionclustering analysisfiber reinforced polymermicro-CTsandwich structures

Identifiers

PMID40732815
PMCPMC12300237

What OpenQuestion holds

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Registered trials

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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.