Zobrazit v EDS

Modeling and reliability analysis of the micro-milling process considering stochastic tool wear with surrogate models.

Uloženo v:
Podrobná bibliografie
Název: Modeling and reliability analysis of the micro-milling process considering stochastic tool wear with surrogate models.
Autoři: Ding, Pengfei, Huang, Xianzhen, Rong, Zhiming, Li, Shangjie, Gao, Wei
Zdroj: Mechanics Based Design of Structures & Machines; 2025, Vol. 53 Issue 1, p611-640, 30p
Témata: DISTRIBUTION (Probability theory), MECHANICAL models, CUTTING force, SYSTEM failures, STOCHASTIC processes, MACHINING, SOFTWARE reliability
Abstrakt: Micro-milling technology is widely employed in manufacturing micro-precision parts due to its flexible processing and high accuracy. Compared to the macro-milling process, micro-milling has a smaller machining size, which leads to tool wear, tool runout, and other factors being more sensitive to the impact of machining quality. This work proposes a reliability evaluation framework for machining systems based on the micro-milling mechanical model and its surrogate method. Establish a cutting force model under shear and plow conditions with periodic variations in instantaneous uncut thickness. Subsequently, the probability distribution of the cutting parameters is obtained based on Bayesian updates. An improved buffer failure probability method is proposed to quickly get the micro-milling system failure probability. Developing new ensemble and adaptive sampling methods and introducing stochastic configuration network (SCN), an adaptive stochastic configuration network ensemble (ASCNE) model is established to alleviate the time-consuming problem of repeated calculations of mechanical models in reliability analysis. Experimental results indicate that the mechanical model can achieve good predictive performance, and the constructed ASCNE model can achieve high-precision surrogate effects. Additionally, the reliability analysis results of the system can guide tool replacement during the machining process, ensuring safe and efficient execution of the micro-milling. [ABSTRACT FROM AUTHOR]
Copyright of Mechanics Based Design of Structures & Machines 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.)
Databáze: Complementary Index
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://resolver.ebscohost.com/openurl?sid=EBSCO:edb&genre=article&issn=15397734&ISBN=&volume=53&issue=1&date=20250101&spage=611&pages=611-640&title=Mechanics Based Design of Structures & Machines&atitle=Modeling%20and%20reliability%20analysis%20of%20the%20micro-milling%20process%20considering%20stochastic%20tool%20wear%20with%20surrogate%20models.&aulast=Ding%2C%20Pengfei&id=DOI:10.1080/15397734.2024.2373263
    Name: Full Text Finder
    Category: fullText
    Text: Full Text Finder
    Icon: https://imageserver.ebscohost.com/branding/images/FTF.gif
    MouseOverText: Full Text Finder
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Ding%20P
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edb
DbLabel: Complementary Index
An: 181862211
RelevancyScore: 1007
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1007.31671142578
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Modeling and reliability analysis of the micro-milling process considering stochastic tool wear with surrogate models.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Pengfei%22">Ding, Pengfei</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Xianzhen%22">Huang, Xianzhen</searchLink><br /><searchLink fieldCode="AR" term="%22Rong%2C+Zhiming%22">Rong, Zhiming</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Shangjie%22">Li, Shangjie</searchLink><br /><searchLink fieldCode="AR" term="%22Gao%2C+Wei%22">Gao, Wei</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Mechanics Based Design of Structures & Machines; 2025, Vol. 53 Issue 1, p611-640, 30p
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22DISTRIBUTION+%28Probability+theory%29%22">DISTRIBUTION (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22MECHANICAL+models%22">MECHANICAL models</searchLink><br /><searchLink fieldCode="DE" term="%22CUTTING+force%22">CUTTING force</searchLink><br /><searchLink fieldCode="DE" term="%22SYSTEM+failures%22">SYSTEM failures</searchLink><br /><searchLink fieldCode="DE" term="%22STOCHASTIC+processes%22">STOCHASTIC processes</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINING%22">MACHINING</searchLink><br /><searchLink fieldCode="DE" term="%22SOFTWARE+reliability%22">SOFTWARE reliability</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Micro-milling technology is widely employed in manufacturing micro-precision parts due to its flexible processing and high accuracy. Compared to the macro-milling process, micro-milling has a smaller machining size, which leads to tool wear, tool runout, and other factors being more sensitive to the impact of machining quality. This work proposes a reliability evaluation framework for machining systems based on the micro-milling mechanical model and its surrogate method. Establish a cutting force model under shear and plow conditions with periodic variations in instantaneous uncut thickness. Subsequently, the probability distribution of the cutting parameters is obtained based on Bayesian updates. An improved buffer failure probability method is proposed to quickly get the micro-milling system failure probability. Developing new ensemble and adaptive sampling methods and introducing stochastic configuration network (SCN), an adaptive stochastic configuration network ensemble (ASCNE) model is established to alleviate the time-consuming problem of repeated calculations of mechanical models in reliability analysis. Experimental results indicate that the mechanical model can achieve good predictive performance, and the constructed ASCNE model can achieve high-precision surrogate effects. Additionally, the reliability analysis results of the system can guide tool replacement during the machining process, ensuring safe and efficient execution of the micro-milling. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Mechanics Based Design of Structures & Machines 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://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=181862211
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/15397734.2024.2373263
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 30
        StartPage: 611
    Subjects:
      – SubjectFull: DISTRIBUTION (Probability theory)
        Type: general
      – SubjectFull: MECHANICAL models
        Type: general
      – SubjectFull: CUTTING force
        Type: general
      – SubjectFull: SYSTEM failures
        Type: general
      – SubjectFull: STOCHASTIC processes
        Type: general
      – SubjectFull: MACHINING
        Type: general
      – SubjectFull: SOFTWARE reliability
        Type: general
    Titles:
      – TitleFull: Modeling and reliability analysis of the micro-milling process considering stochastic tool wear with surrogate models.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Ding, Pengfei
      – PersonEntity:
          Name:
            NameFull: Huang, Xianzhen
      – PersonEntity:
          Name:
            NameFull: Rong, Zhiming
      – PersonEntity:
          Name:
            NameFull: Li, Shangjie
      – PersonEntity:
          Name:
            NameFull: Gao, Wei
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Text: 2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 15397734
          Numbering:
            – Type: volume
              Value: 53
            – Type: issue
              Value: 1
          Titles:
            – TitleFull: Mechanics Based Design of Structures & Machines
              Type: main
ResultId 1