Evidence map›Paper›PMID 40038462›Full record

ArticleScientific reports2025

Heat treatment control technology of high-strength steel gears based on support vector machine.

Yanzhong Wang, Libin Zhang, Yulu Su, Hai Liu, HaiLong Yang, Yanyan Chen

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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
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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

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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

6 authors.

Yanzhong Wang *School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.
Libin Zhang *School of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China. by2007112@buaa.edu.cn.
Yulu SuSchool of Mechanical Engineering and Automation, Beihang University, Beijing, 100191, China.
Hai LiuTechnology Center Process Room 2, Chongqing Tiema Transmission Co., Ltd, Chongqing, 400050, China.
HaiLong YangTechnology Center Process Room 2, Chongqing Tiema Transmission Co., Ltd, Chongqing, 400050, China.
Yanyan ChenAVIC Beijing Changcheng Aeronautical Measurement and Control Technology Research Institute, Beijing, 101111, China. besthouniao@foxmail.com.

Funding

The Innovative Achievement Transformation and Application Program 62502010204
6 · The paper itself

Abstract

In the actual production process of gears often because of the selection of heat treatment parameters is unreasonable and can not accurately achieve the small deformation, high precision, less grinding machining allowance heat treatment sample requirements, there are uneven distribution of carburized layer, surface hardness, hardness of the heart can not meet the requirements of the indicators. At the present stage, the method of multi-parameter multi-level combination test block trial production is often used, but its production cycle is long, and the waste of human and material resources is serious. In this study, with the help of machine learning, a support vector machine prediction model of gear tissue distribution is constructed based on heat treatment parameters, and the radial basis functions kernel function is selected as the kernel function of the support vector machine to improve the accuracy of model prediction by optimizing the kernel parameters. The root mean square error value of the final model is 3.16%, and the coefficient of determination is 0.993. The results show that the method of this paper can accurately and efficiently predict the heat treatment results of gears, and save the manufacturing cycle and cost. The precise control of hardness, carburization layer distribution pattern and metallographic organization of ultra-high-strength steel gears can be realized in actual production.

Indexed as

Carburizing-quenchingHigh-strength steel gearsMachine learningPrecise controlSupport vector machine

Identifiers

PMID40038462
PMCPMC11880393

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.