Identification of tool wear states with fuzzy classification.
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| Title: | Identification of tool wear states with fuzzy classification. |
|---|---|
| Authors: | Xiaoli, Li, Xinping, Guan, Hongrui, Wang |
| Source: | International Journal of Computer Integrated Manufacturing. Nov99, Vol. 12 Issue 6, p503-509. 7p. 2 Diagrams, 6 Charts, 4 Graphs. |
| Subjects: | Machine tool monitoring, Mechanical wear, Computer simulation |
| Abstract: | A new on-line tool wear states detecting method, with spindle and feed current signal in boring, is presented. By analyzing the effects of tool wear, as well as the cutting parameters on the current signals, the models of the relationship between the current signals and the cutting parameters are established under different tool wear states with partial experimental design and regression analysis. Fuzzy classification method is then used to obtain the membership degree of each tool wear classification with measured spindle and feed current values. Finally, the membership results of the spindle current and feed current are fused by the fuzzy inference method, and the tool wear state may be detected effectively. The validity and reliability of the method are verified by experimental results. The method can be effectively employed in practice. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Integrated Manufacturing 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.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 3838255 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Identification of tool wear states with fuzzy classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Xiaoli%2C+Li%22">Xiaoli, Li</searchLink><br /><searchLink fieldCode="AR" term="%22Xinping%2C+Guan%22">Xinping, Guan</searchLink><br /><searchLink fieldCode="AR" term="%22Hongrui%2C+Wang%22">Hongrui, Wang</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Integrated+Manufacturing%22">International Journal of Computer Integrated Manufacturing</searchLink>. Nov99, Vol. 12 Issue 6, p503-509. 7p. 2 Diagrams, 6 Charts, 4 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+tool+monitoring%22">Machine tool monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+wear%22">Mechanical wear</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A new on-line tool wear states detecting method, with spindle and feed current signal in boring, is presented. By analyzing the effects of tool wear, as well as the cutting parameters on the current signals, the models of the relationship between the current signals and the cutting parameters are established under different tool wear states with partial experimental design and regression analysis. Fuzzy classification method is then used to obtain the membership degree of each tool wear classification with measured spindle and feed current values. Finally, the membership results of the spindle current and feed current are fused by the fuzzy inference method, and the tool wear state may be detected effectively. The validity and reliability of the method are verified by experimental results. The method can be effectively employed in practice. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Integrated Manufacturing 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=egs&AN=3838255 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/095119299130083 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 503 Subjects: – SubjectFull: Machine tool monitoring Type: general – SubjectFull: Mechanical wear Type: general – SubjectFull: Computer simulation Type: general Titles: – TitleFull: Identification of tool wear states with fuzzy classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Xiaoli, Li – PersonEntity: Name: NameFull: Xinping, Guan – PersonEntity: Name: NameFull: Hongrui, Wang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov99 Type: published Y: 1999 Identifiers: – Type: issn-print Value: 0951192X Numbering: – Type: volume Value: 12 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Computer Integrated Manufacturing Type: main |
| ResultId | 1 |
