Evidence map›Paper›PMID 37346194›Full record

Article3D printing and additive manufacturing2023

Three-Dimensional Printed Subsurface Defect Detection by Active Thermography Data-Processing Algorithm.

Ézio Carvalho de Santana, Wellington Francisco da Silva, Marcella Grosso Lima, Gabriela Ribeiro Pereira, Douglas Bressan Riffel

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In one paragraph

Article in 3D printing and additive manufacturing, 2023. 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.

Ézio Carvalho de SantanaMechanical Engineering Department, Federal University of Sergipe, Sergipe, Brazil.
Wellington Francisco da SilvaMechanical Engineering Department, Federal University of Sergipe, Sergipe, Brazil.
Marcella Grosso LimaNon-Destructive Testing, Corrosion and Welding Laboratory, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Gabriela Ribeiro PereiraNon-Destructive Testing, Corrosion and Welding Laboratory, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.
Douglas Bressan RiffelMechanical Engineering Department, Federal University of Sergipe, Sergipe, Brazil.ORCID https://orcid.org/0000-0003-2840-216X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article evaluates an active thermography algorithm to detect subsurface defects in materials made by additive manufacturing (AM). It is based on the techniques of thermographic signal reconstruction (TSR), thermal contrast, and the physical principles of heat transfer. The subsurface defects have different infill, depth, and size. The results obtained from this algorithm are compared with state-of-the-art TSR technique and show the high performance of the proposed algorithm even for subsurface defects done by 3D AM. The resulting images are better shown using the absolute difference in the place of variance. The proposed algorithm has higher contrast, better sensitivity to the defect depths, and lower noise than the TSR. The resultant image is quite clean and gives no doubt where the subsurface defects are.

Indexed as

active thermographyadditive manufacturingalgorithmsimage processingsubsurfacethermal analysis

Identifiers

PMID37346194
PMCPMC10280207

What OpenQuestion holds

Textmetadata
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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.