Optimization of Magnetic Abrasive Finishing Parameters for Co-Cr Alloy Vascular Stent Tubing Using PSO-SVM.

Kai Xing, Yugang Zhao, Qilong Fan, Li Guo, Zhi Qi, Kaihao Ma, Guangzheng Chen

Abstract read

In one paragraph

Article in Micromachines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Kai XingSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Yugang ZhaoSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Qilong FanSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Li GuoSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Zhi QiSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Kaihao MaSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Guangzheng ChenSchool of Mechanical Engineering, Shandong University of Technology, No. 266 Xincun West Road, Zibo 255049, China.

Funding

General Program of the Natural Science Foundation of Shandong Province ZR2019MEE013General Program of the Natural Science Foundation of Shandong Province ZR2021ME159National Natural Science Foundation of China 51875328

6 · The paper itself

Abstract

To achieve accurate prediction of surface roughness (Ra) in magnetic abrasive finishing (MAF) of the inner wall of Co-Cr alloy vascular stent tubing, and to obtain the optimal process parameter combination for improving the inner surface quality, iron-based diamond magnetic abrasive powders (MAPs) were prepared via plasma melting, centrifugal spraying and rapid solidification. MAF experiments were conducted on Co-Cr alloy vascular stent tubing with an inner diameter of 1.6 mm and an outer diameter of 1.8 mm, and the effects of tube rotational speed, magnetic pole feed rate, abrasive particle size and working gap on surface roughness were investigated. An orthogonal experiment was designed, and a surface roughness prediction model based on particle swarm optimization (PSO) and support vector machine (SVM) was established. Simulation results indicate that the proposed PSO-SVM surface roughness prediction model achieved a coefficient of determination (R

Indexed as

magnetic abrasive finishing (MAF)orthogonal experimentparticle swarm optimization (PSO)support vector machine (SVM)surface roughness

Identifiers

PMID42653557

DOI10.3390/mi17080916

PMCPMC13515259

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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.