Evidence map›Paper›PMID 38258216›Full record

ArticleMicromachines2024

Research on an Online Monitoring Device for the Powder Laying Process of Laser Powder Bed Fusion.

Bin Wei, Jiaqi Liu, Jie Li, Zigeng Zhao, Yang Liu, Guang Yang, Lijian Liu, Hongjie Chang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2024. 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

8 authors.

Bin WeiCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Jiaqi LiuCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Jie LiShijiazhuang Information Engineering Vocational College, Shijiazhuang 050000, China.
Zigeng ZhaoCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Yang LiuCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Guang YangCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Lijian LiuCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.
Hongjie ChangCollege of Mechanical Engineering, Hebei University of Science and Technology, Shijiazhuang 050000, China.

Funding

Hebei Provincial Department of Human Resources and Social Security, Hebei Province three three talent project funding project B20221004Natural Science Foundation of Hebei Province, Beijing-Tianjin-Hebei Basic research cooperation project H2022208073
6 · The paper itself

Abstract

Improving the quality of metal additive manufacturing parts requires online monitoring of the powder bed laying procedure during laser powder bed fusion production. In this article, a visual online monitoring tool for flaws in the powder laying process is examined, and machine vision technology is applied to LPBF manufacture. A multiscale improvement and model channel pruning optimization method based on convolutional neural networks is proposed, which makes up for the deficiencies of the defect recognition method of small-scale powder laying, reduces the redundant parameters of the model, and enhances the processing speed of the model under the premise of guaranteeing the accuracy of the model. Finally, we developed an LPBF manufacturing process laying powder defect recognition algorithm. Test experiments show the performance of the method: the minimum size of the detected defects is 0.54 mm, the accuracy rate of the feedback results is 98.63%, and the single-layer laying powder detection time is 3.516 s, which can realize the effective detection and control of common laying powder defects in the additive manufacturing process, avoids the breakage of the scraper, and ensures the safe operation of the LPBF equipment.

Indexed as

convolutional neural networkslaser powder bed fusionmodel channel pruningpowder laying monitoring

Identifiers

PMID38258216
PMCPMC10820394

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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.