Evidence map›Paper›PMID 39367082›Full record

ArticleScientific reports2024

Simulation study of turning-ultrasonic rolling compound processing for 42CrMo steel.

Haojie Wang, Xiaoqiang Wang, Yingjian Tian, Yuanfei Ling

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

4 authors.

Haojie WangSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.
Xiaoqiang WangSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China. wang_xq2002@163.com.
Yingjian TianSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.
Yuanfei LingSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang, 471003, China.

Funding

National Key Research and Development Program of China 2018YFB2000405National Natural Science Foundation of China U1804145
6 · The paper itself

Abstract

The ultrasonic rolling processing technology has been shown to significantly reduce surface roughness and enhance the residual stress of parts, thereby improving their surface properties and extending their service life. This technology is particularly effective for the precision machining and surface strengthening of ultra-high-strength steel 42CrMo. This study aims to investigate the impact of turning pre-processing on the distribution of residual stress during ultrasonic rolling, a simulation model incorporating turning pre-processing was developed and used to conduct ultrasonic rolling simulation experiments, enabling the analysis of residual stress distribution patterns. Concurrently, ultrasonic rolling strengthening experiments were performed to validate the accuracy of the simulation model. The results confirm that with increasing rotational speed and feed rate, residual stress decreases, whereas with increasing static pressure and amplitude, residual stress increases. The residual stress variation obtained from the simulation of combined turning and ultrasonic rolling closely matched the results of experimental ultrasonic rolling tests. This consistency validates the accuracy of the simulation model. This study offers a novel approach for simulating and experimentally validating ultrasonic rolling processes, particularly for shaft-like parts that undergo turning as a pre-processing step.

Indexed as

42CrMo steelCompressive stressSimulation analysisUltrasonic rolling process

Identifiers

PMID39367082
PMCPMC11452681

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.