Academic Journal
Roughness Prediction Method for Precision Magnetic Abrasive Finishing of the Inner Surface of Aeroengine Bent Tubes Based on the Improved Particle Swarm Algorithm Optimized Support Vector Machine (IPSO-SVM) Model.
| Τίτλος: | Roughness Prediction Method for Precision Magnetic Abrasive Finishing of the Inner Surface of Aeroengine Bent Tubes Based on the Improved Particle Swarm Algorithm Optimized Support Vector Machine (IPSO-SVM) Model. |
|---|---|
| Συγγραφείς: | Xiao, Chun Fang, Xiao, Jun Jie, Han, Bing |
| Πηγή: | Tribology Transactions; Aug-Oct2026, Vol. 69 Issue 5, p1242-1251, 10p |
| Θεματικοί όροι: | Surface roughness, Particle swarm optimization, Surface finishing, Process optimization, Finishes & finishing, Process control systems, Support vector machines, Tube bending |
| Περίληψη: | Addressing the issues of insufficient parameter coupling characterization, susceptibility to local optima, and slow convergence speed in the existing optimization model for magnetic abrasive finishing (MAF) process parameters of bent pipes, this paper proposes a prediction method for the internal surface roughness of aeroengine bent pipes based on the combination of improved particle swarm optimization (IPSO) and support vector machine (SVM). By deeply analyzing the nonlinear mapping relationship between the multiparameter coupling effect of MAF process and surface quality, a key influencing factor system is established, and the morphology data before and after processing are collected. Subsequently, a normalization operator is introduced to eliminate the influence of dimensions. The optimal MAF process parameters for tube bending are obtained as follows: magnetic field rotational speed of 996.86 r/min, feed rate of 0.98 mm/s, abrasive grain size of 97.32 mesh, and processing time of 57.24 min. The constructed IPSO-SVM model integrates the robustness of SVM in small-sample regression with the efficient global optimization potential of IPSO. By dynamically iterating and optimizing the model weights and process variables, it effectively overcomes the blindness of manual trial and error methods and significantly reduces experimental costs. It provided theoretical basis and technical support for the optimization of MAF process on the inner surface of aeroengine bends. [ABSTRACT FROM AUTHOR] |
| Copyright of Tribology Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=10402004&ISBN=&volume=69&issue=5&date=20260801&spage=1242&pages=1242-1251&title=Tribology Transactions&atitle=Roughness%20Prediction%20Method%20for%20Precision%20Magnetic%20Abrasive%20Finishing%20of%20the%20Inner%20Surface%20of%20Aeroengine%20Bent%20Tubes%20Based%20on%20the%20Improved%20Particle%20Swarm%20Algorithm%20Optimized%20Support%20Vector%20Machine%20%28IPSO-SVM%29%20Model.&aulast=Xiao%2C%20Chun%20Fang&id=DOI:10.1080/10402004.2026.2684206 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 196514258 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Roughness Prediction Method for Precision Magnetic Abrasive Finishing of the Inner Surface of Aeroengine Bent Tubes Based on the Improved Particle Swarm Algorithm Optimized Support Vector Machine (IPSO-SVM) Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiao%2C+Chun+Fang%22">Xiao, Chun Fang</searchLink><br /><searchLink fieldCode="AR" term="%22Xiao%2C+Jun+Jie%22">Xiao, Jun Jie</searchLink><br /><searchLink fieldCode="AR" term="%22Han%2C+Bing%22">Han, Bing</searchLink> – Name: TitleSource Label: Source Group: Src Data: Tribology Transactions; Aug-Oct2026, Vol. 69 Issue 5, p1242-1251, 10p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Surface+roughness%22">Surface roughness</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Surface+finishing%22">Surface finishing</searchLink><br /><searchLink fieldCode="DE" term="%22Process+optimization%22">Process optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Finishes+%26+finishing%22">Finishes & finishing</searchLink><br /><searchLink fieldCode="DE" term="%22Process+control+systems%22">Process control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Tube+bending%22">Tube bending</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Addressing the issues of insufficient parameter coupling characterization, susceptibility to local optima, and slow convergence speed in the existing optimization model for magnetic abrasive finishing (MAF) process parameters of bent pipes, this paper proposes a prediction method for the internal surface roughness of aeroengine bent pipes based on the combination of improved particle swarm optimization (IPSO) and support vector machine (SVM). By deeply analyzing the nonlinear mapping relationship between the multiparameter coupling effect of MAF process and surface quality, a key influencing factor system is established, and the morphology data before and after processing are collected. Subsequently, a normalization operator is introduced to eliminate the influence of dimensions. The optimal MAF process parameters for tube bending are obtained as follows: magnetic field rotational speed of 996.86 r/min, feed rate of 0.98 mm/s, abrasive grain size of 97.32 mesh, and processing time of 57.24 min. The constructed IPSO-SVM model integrates the robustness of SVM in small-sample regression with the efficient global optimization potential of IPSO. By dynamically iterating and optimizing the model weights and process variables, it effectively overcomes the blindness of manual trial and error methods and significantly reduces experimental costs. It provided theoretical basis and technical support for the optimization of MAF process on the inner surface of aeroengine bends. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Tribology Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=196514258 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/10402004.2026.2684206 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1242 Subjects: – SubjectFull: Surface roughness Type: general – SubjectFull: Particle swarm optimization Type: general – SubjectFull: Surface finishing Type: general – SubjectFull: Process optimization Type: general – SubjectFull: Finishes & finishing Type: general – SubjectFull: Process control systems Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Tube bending Type: general Titles: – TitleFull: Roughness Prediction Method for Precision Magnetic Abrasive Finishing of the Inner Surface of Aeroengine Bent Tubes Based on the Improved Particle Swarm Algorithm Optimized Support Vector Machine (IPSO-SVM) Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiao, Chun Fang – PersonEntity: Name: NameFull: Xiao, Jun Jie – PersonEntity: Name: NameFull: Han, Bing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug-Oct2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10402004 Numbering: – Type: volume Value: 69 – Type: issue Value: 5 Titles: – TitleFull: Tribology Transactions Type: main |
| ResultId | 1 |