Evidence map›Paper›PMID 37322105›Full record

ArticleScientific reports2023

Multiscale characterization and contact performance analysis of machining surfaces.

Ling Li, Wang Zhang, Jingjing Wang, Xiaoguang Ruan, Lixia Li, Miaoxia Xie

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Ling LiSchool of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi, China.
Wang ZhangSchool of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi, China.
Jingjing WangSchool of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi, China.
Xiaoguang RuanSchool of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi, China. liling@xauat.edu.cn.
Lixia LiSchool of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi, China.
Miaoxia XieSchool of Mechanical and Electrical Engineering, Xi'an University of Architecture and Technology, Xi'an, 710055, Shaanxi, China.

Funding

Key Research and Development Program of Shaanxi 2021GY-309National Natural Science Foundation of China 51975449
6 · The paper itself

Abstract

Accurately characterizing the surface topography of parts is crucial to improve the surface measurement accuracy and analyze the surface contact performance. A method is proposed to separate the morphological characteristics of the actual machined surface based on the layer-by-layer error reconstruction method and the signal-to-noise ratio method during the wavelet transform process, so as to evaluate the contact performance of the different joint surfaces. First, the actual machined surface morphological features are separated by using the wavelet transform method, the layer-by-layer error reconstruction method, and the signal-to-noise ratio method. Second, the reconstructed three-dimensional surface contact model is established by the reverse modeling engineering method. Third, the finite element method is used to analyze the impact of processing methods and surface roughness on contact surface parameters. The result demonstrates that the simplified and efficient three-dimensional reconstructed surface is achieved based on the real machining surface in contrast to other existing approaches. The surface roughness has a more significant influence on contact performance. The contact deformation increases with the increase of surface roughness, while the curves of average contact stress, contact stiffness, and contact area have the opposite trend.

Identifiers

PMID37322105
PMCPMC10272123

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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.