Evidence map›Paper›PMID 33919070›Full record

ArticleMicromachines2021

A New Method for Precision Measurement of Wall-Thickness of Thin-Walled Spherical Shell Parts.

Jiang Guo, Yongbo Xu, Bo Pan, Juntao Zhang, Renke Kang, Wen Huang, Dongxing Du

Abstract read
In one paragraph

Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
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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

7 authors.

Jiang GuoKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Yongbo XuKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Bo PanKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Juntao ZhangKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Renke KangKey Laboratory for Precision and Non-Traditional Machining Technology of Ministry of Education, Dalian University of Technology, Dalian 116024, China.
Wen HuangInstitute of Mechanical Manufacturing Technology, China Academy of Engineering Physics, Mianyang 621999, China.
Dongxing DuInstitute of Mechanical Manufacturing Technology, China Academy of Engineering Physics, Mianyang 621999, China.

Funding

Science Challenge Project No. TZ2016006
6 · The paper itself

Abstract

Thin-walled parts are widely used in shock wave and detonation physics experiments, which require high surface accuracy and equal thickness. In order to obtain the wall thickness of thin-walled spherical shell parts accurately, a new measurement method is proposed. The trajectories, including meridian and concentric trajectories, are employed to measure the thickness of thin-walled spherical shell parts. The measurement data of the inner and outer surfaces are unified in the same coordinate system, and the thickness is obtained based on a reconstruction model. The meridian and concentric circles' trajectories are used for measuring a spherical shell with an outer diameter of Φ210.6 mm and an inner diameter of Φ206.4 mm. Without the data in the top area, the surface errors of the outer and inner surfaces are about 5 μm and 6 μm, respectively, and the wall-thickness error is about 8 μm with the meridian trajectory.

Indexed as

benchmark coincidencedata processingspherical shellthin-walled partwall-thickness

Identifiers

PMID33919070
PMCPMC8143123

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.