Evidence map›Paper›PMID 41266552›Full record

ArticleScientific reports2025

Anti-loosening bolt looseness diagnosis method for flange node based on quick response code.

Jun Luo, Kaili Li, BaoYa Shi, Shiwei Chen, Ying Wu, Lei Cheng

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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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

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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

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0 citing papers in PubMed.

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4 · The record

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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

6 authors.

Jun LuoSchool of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Kaili LiSchool of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
BaoYa ShiSchool of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Shiwei ChenSchool of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China.
Ying WuChongqing Institute of Metrology and Quality Testing, No.1 Yangliu North Road, Yubei District, Chongqing, 401120, China.
Lei ChengSchool of Civil and Hydraulic Engineering, Chongqing University of Science and Technology, Chongqing, 401331, China. zrhd244@163.com.

Funding

General Project of Research Plan of Chongqing Administration for Market Regulation CQSJKJDW2024004Natural Science Foundation of Chongqing CSTB2023NSCQLZX0051
6 · The paper itself

Abstract

Flange connections are widely used in various fields such as civil engineering, aerospace, and mechanical manufacturing due to their convenient installation and reliability. However, the bolts in the flange connections may become loose, which could affect the safety of the connections. Nowadays, the vision-based detection techniques are widely used because of its ability to provide quantitative loosening angles, high efficiency, and low cost. However, the vision-based detection techniques are susceptible to the influence of perspective angle and it is hard to provide the historical information of bolt automatically. Therefore, the quick response code is introduce to record the historical information of bolt and a novel bolt image correction method is proposed based on quick response code. Furthermore, the anti-loosening bolt looseness diagnosis method is established. Firstly, the quick response code is designed with three finder patterns and pasted on the end face of screw. And then, the four intersections of quick response code edge lines are used to correct the image based on the homography-based perspective rectification method. Additionally, three finder patterns are used to rotate the image to the unified reference state to reduce the effects of camera position deviation and simultaneous rotation of screws and nuts. Finally, the anti-loosening bolt looseness diagnosis method is established by using the change in rotation angles of nut under initial status and loose status. A prototype flange node was used for experimental verification. The results show that the proposed method can effectively correct the perspective distortion of bolt image, reduce the effects of camera position deviation and simultaneous rotation of screws and nuts, and detect the loosening angle of anti-loosening bolts.

Indexed as

Anti-loosening boltsFlange nodeLooseness detectionQuick response codeVision-based technique

Identifiers

PMID41266552
PMCPMC12635294

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LicenceCC BY
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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.