Evidence map›Paper›PMID 40946473›Full record

ArticleUltrasonics sonochemistry2025

Ultrasonic rolling of 42CrMo Steel: Simulation-Based process optimization for high-efficiency and sustainable manufacturing.

Haojie Wang, Xiaoqiang Wang, Eric Velázquez-Corral, Ramón Jerez-Mesa

Abstract read
In one paragraph

Article in Ultrasonics sonochemistry, 2025. 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

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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

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

4 authors.

Haojie WangSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; Department of Mechanical Engineering, Universitat Politècnica de Catalunya, Escola d'Enginyeria de Barcelona Est, Av. Eduard Maristany, 16, 08019 Barcelona, Spain.
Xiaoqiang WangSchool of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China. Electronic address: wang_xq2002@163.com.
Eric Velázquez-CorralDepartment of Mechanical Engineering, Universitat Politècnica de Catalunya, Escola d'Enginyeria de Barcelona Est, Av. Eduard Maristany, 16, 08019 Barcelona, Spain.
Ramón Jerez-MesaDepartment of Mechanical Engineering, Universitat Politècnica de Catalunya, Escola d'Enginyeria de Barcelona Est, Av. Eduard Maristany, 16, 08019 Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ultrasonic rolling has emerged as a highly promising surface strengthening technique due to its ability to enhance surface properties under low energy consumption conditions. In this study, a thermo-mechanically coupled simulation model was developed to investigate the ultrasonic rolling process of 42CrMo steel, aiming to achieve a balance between high surface performance and low carbon emissions through process optimization. An orthogonal array of 25 simulation experiments were designed and conducted to analyze the effects of process parameters-including workpiece rotational speed, static pressure, feed rate, and ultrasonic amplitude-on surface properties and thermal behavior. Based on the simulation data, a response surface methodology (RSM) was employed to establish predictive models for surface performance. To address the conflicting objectives among various surface performance indicators and significantly improve both surface quality and processing efficiency, a novel hybrid optimization algorithm, the Particle Swarm Simulated Annealing Optimization (PSSAO), was proposed. Multi-objective optimization was performed to determine an optimal parameter domain that enables effective strengthening in a single-pass rolling process. The optimal processing window obtained from PSSAO was experimentally validated, and the results confirmed the reliability of the simulation model and the feasibility of this sustainable manufacturing strategy. The findings demonstrate that ultrasonic rolling of 42CrMo steel, when optimized through thermo-mechanical simulations and advanced parameter design, can effectively enhance surface properties, reduce energy consumption, shorten processing time, and lower carbon emissions-providing a viable pathway toward sustainable manufacturing. The PSSAO algorithm identified the optimal processing parameter ranges as a feed rate of 0.05-0.07 mm/r, static pressure of 520-700 N, ultrasonic amplitude of 7-9 μm, and spindle speed of 50-180 r/min. Under these conditions, the maximum residual compressive stress reaches -1283 to -1296 MPa, average surface roughness (Ra) is reduced to 0.120-0.156 μm, and surface hardness increases to 60.9-61.6 HRC.

Indexed as

EfficientGreen manufacturingPerformance optimizationSimulationUltrasonics

Identifiers

PMID40946473
PMCPMC12504978

What OpenQuestion holds

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LicenceCC BY
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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.