Evidence map›Paper›PMID 36013606›Full record

ReviewMaterials (Basel, Switzerland)2022

Research Progress and Challenges in Laser-Controlled Cleaning of Aluminum Alloy Surfaces.

Jian Deng, Guanrong Zhao, Jieheng Lei, Lin Zhong, Zeyong Lei

Abstract readReview
In one paragraph

Review in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

5 authors.

Jian DengSchool of Nuclear Science and Technology, University of South China, Hengyang 421001, China.
Guanrong ZhaoSchool of Mechanical Engineering, University of South China, Hengyang 421001, China.
Jieheng LeiSchool of Electrical Engineering, University of South China, Hengyang 421001, China.
Lin ZhongSchool of Nuclear Science and Technology, University of South China, Hengyang 421001, China.
Zeyong LeiSchool of Nuclear Science and Technology, University of South China, Hengyang 421001, China.

Funding

The National Key Research and Development Program of China;Postgraduate Scientific Research Innovation Project of Hunan Province 2019YFC1907704;QL20210227
6 · The paper itself

Abstract

Aluminum alloys have been widely utilized in automobiles, aircraft, building structures, and high-speed railways industries due to their excellent structural and mechanical properties. Surface oxide film removal prior to aluminum alloy welding and old paint removal prior to repainting aluminum alloy surfaces are critical factors in ensuring the welding quality and service life of aluminum alloy products. Because of its unique advantages, such as environmental protection and precision control, laser-controlled cleaning has great application potential as a surface cleaning technology in removing oxide films and paint layers on aluminum alloy surfaces. In this paper, the mechanism of laser cleaning of oxide films and paint layers on aluminum alloy is discussed. Furthermore, the impact of various processing parameters such as laser beam power, energy density, scanning speed, and so on is analyzed in detail. After laser cleaning, the corrosion resistance, welding performance, adhesive performance, and other properties of the aluminum alloy are optimized. This paper also discusses several real-time detection technologies for laser cleaning. A summary and the development trend are provided at the end of the paper.

Indexed as

aluminum alloycontrolled cleaninglaseroxide filmpaint layer

Identifiers

PMID36013606
PMCPMC9410451

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.