An Improved RAPID Imaging Method of Defects in Composite Plate Based on Feature Identification by Machine Learning.

Fei Deng, Xiran Zhang, Ning Yu, Lin Zhao

Open access · goldAbstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

0.3field-weighted citation impact, top 54% of its field

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

2 citing papers in PubMed, 3 citations in OpenAlex.

Damage Localization on a Complex Composite Structure Based on NRMSD and Normal Distribution Using Ultrasonic Guided Waves.Sensors (Basel, Switzerland) · 2026
Article
Deep Learning-Assisted Porosity Assessment for Additive Manufacturing Components Using Ultrasonic Coda Waves.Sensors (Basel, Switzerland) · 2026
Article

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 at 1 institution in 1 country.

Fei DengSchool of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 200235, China.

Xiran ZhangSchool of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 200235, China.

Ning YuSchool of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 200235, China.

Lin ZhaoSchool of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai 200235, China.

Shanghai Institute of Technology · CN

Funding

Collaborative innovation fund of Shanghai Institute of Technology XTCX2018-11Shanghai Alliance Program 2019025The National Natural Science Foundation of China 11202137

6 · The paper itself

Abstract

The RAPID (reconstruction algorithm for probabilistic inspection of defect) method based on Lamb wave detection is an effective method to give the position information of a defect in composite plate. In this paper, an improved RAPID imaging method based on machine learning (ML) is proposed to precisely visualize the location and features of defects in composite plate. First, the specific feature information of the defect, such as type, size and direction, can be identified by analyzing the detection signals through multiple machine learning models. Then, according to the obtained defect features, the scaling parameter

Indexed as

composite platedefect imaginglamb wavemachine learningnondestructive testing

Identifiers

PMID36366110

DOI10.3390/s22218413

PMCPMC9655716

OpenAlexW4307861882

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

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.