Evidence map›Paper›PMID 38894479›Full record

ArticleSensors (Basel, Switzerland)2024

Evaluation of Depth Size Based on Layered Magnetization by Double-Sided Scanning for Internal Defects.

Zhiyang Deng, Dingkun Qian, Haifei Hong, Xiaochun Song, Yihua Kang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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.

Zhiyang DengKey Lab of Modern Manufacture Quality Engineering, Hubei University of Technology, Wuhan 430068, China.ORCID 0000-0002-1299-6314
Dingkun QianKey Lab of Modern Manufacture Quality Engineering, Hubei University of Technology, Wuhan 430068, China.
Haifei HongKey Lab of Modern Manufacture Quality Engineering, Hubei University of Technology, Wuhan 430068, China.
Xiaochun SongKey Lab of Modern Manufacture Quality Engineering, Hubei University of Technology, Wuhan 430068, China.ORCID 0000-0002-8339-4441
Yihua KangHuazhong University of Science and Technology, Wuhan 430074, China.ORCID 0000-0003-2622-4769

Funding

National Key Research and Development Program of China under Grant ,the National Natural Science Foundation of China (NSFC) under Grant,the Scientific Research Foundation of Hubei University of Technology under Grant 2022YFF0605600, 52105550,GCC2024001.
6 · The paper itself

Abstract

The quantitative evaluation of defects is extremely important, as it can avoid harm caused by underevaluation or losses caused by overestimation, especially for internal defects. The magnetic permeability perturbation testing (MPPT) method performs well for thick-walled steel pipes, but the burial depth of the defect is difficult to access directly from a single time-domain signal, which is not conducive to the evaluation of defects. In this paper, the phenomenon of layering of magnetization that occurs in ferromagnetic materials under an unsaturated magnetizing field is described. Different magnetization depths are achieved by applying step magnetization. The relationship curves between the magnetization characteristic currents and the magnetization depths are established by finite element simulations. The spatial properties of each layering can be detected by different magnetization layering. The upper and back boundaries of the defect are then localized by a double-sided scan to finally arrive at the depth size of the defect. Defects with depth size of 2 mm are evaluated experimentally. The maximum relative error is 5%.

Indexed as

defect size evaluationferromagnetic materialmagnetic permeability perturbationstepped magnetization

Identifiers

PMID38894479
PMCPMC11175300

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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.