In-process tool wear monitoring through time series modelling and pattern recognition.
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| Title: | In-process tool wear monitoring through time series modelling and pattern recognition. |
|---|---|
| Authors: | Kumar, S. A.1, Ravindra, H. V.1, Srinivasa, Y. G.1 |
| Source: | International Journal of Production Research. Mar1997, Vol. 35 Issue 3, p739-751. 13p. |
| Subjects: | Machining, Machine tool monitoring, Machinery, Cutting (Materials), Vibration (Mechanics) |
| Abstract: | This paper describes an indirect in-process technique for monitoring the cutting tool condition in a turning process. Here an attempt has been made to extract maximum information from force/vibration signal acquired during machining. Statistical methods like time series modelling technique are used to extract parameters called features which represent the state of the cutting process. Autoregressive (AR) model parameters and AR residual signals are investigated and found to show effective and consistent trend towards tool wear. Other parameters such as static cutting force and power of the dynamic signal (force/vibration) are also studied here as features. Once the features are extracted through preliminary processing of the signal, tool state is decided through a pattern recognition technique. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Production Research 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: 6484092 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: In-process tool wear monitoring through time series modelling and pattern recognition. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+S%2E+A%2E%22">Kumar, S. A.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ravindra%2C+H%2E+V%2E%22">Ravindra, H. V.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Srinivasa%2C+Y%2E+G%2E%22">Srinivasa, Y. G.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Mar1997, Vol. 35 Issue 3, p739-751. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+tool+monitoring%22">Machine tool monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Machinery%22">Machinery</searchLink><br /><searchLink fieldCode="DE" term="%22Cutting+%28Materials%29%22">Cutting (Materials)</searchLink><br /><searchLink fieldCode="DE" term="%22Vibration+%28Mechanics%29%22">Vibration (Mechanics)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper describes an indirect in-process technique for monitoring the cutting tool condition in a turning process. Here an attempt has been made to extract maximum information from force/vibration signal acquired during machining. Statistical methods like time series modelling technique are used to extract parameters called features which represent the state of the cutting process. Autoregressive (AR) model parameters and AR residual signals are investigated and found to show effective and consistent trend towards tool wear. Other parameters such as static cutting force and power of the dynamic signal (force/vibration) are also studied here as features. Once the features are extracted through preliminary processing of the signal, tool state is decided through a pattern recognition technique. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Production Research 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/002075497195687 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 739 Subjects: – SubjectFull: Machining Type: general – SubjectFull: Machine tool monitoring Type: general – SubjectFull: Machinery Type: general – SubjectFull: Cutting (Materials) Type: general – SubjectFull: Vibration (Mechanics) Type: general Titles: – TitleFull: In-process tool wear monitoring through time series modelling and pattern recognition. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, S. A. – PersonEntity: Name: NameFull: Ravindra, H. V. – PersonEntity: Name: NameFull: Srinivasa, Y. G. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar1997 Type: published Y: 1997 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 35 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
