Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns.
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| Title: | Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. |
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| Authors: | Purushothaman, Srinivasan1 dr.s.purushothaman@gmail.com |
| Source: | Journal of Intelligent Manufacturing. Dec2010, Vol. 21 Issue 6, p717-730. 14p. |
| Subjects: | Machine tool monitoring, Artificial neural networks, Kalman filtering, Dimensional analysis, Vector analysis, Back propagation, Machine theory, Production engineering, Mechanical wear |
| Abstract: | The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors $${\varphi_{1 }}$$ and $${\varphi_{2}}$$. These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature 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: 55084021 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Purushothaman%2C+Srinivasan%22">Purushothaman, Srinivasan</searchLink><relatesTo>1</relatesTo><i> dr.s.purushothaman@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Dec2010, Vol. 21 Issue 6, p717-730. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+tool+monitoring%22">Machine tool monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Dimensional+analysis%22">Dimensional analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Vector+analysis%22">Vector analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Back+propagation%22">Back propagation</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+theory%22">Machine theory</searchLink><br /><searchLink fieldCode="DE" term="%22Production+engineering%22">Production engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+wear%22">Mechanical wear</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The condition of the tool in a turning operation is monitored by using artificial neural network (ANN). The recursive Kalman filter algorithm is used for weight updation of the ANN. To monitor the status of the tool, tool wear patterns are collected. The patterns are transformed from n-dimensional feature space to a lower dimensional space (two dimensions). This is done by using two discriminant vectors $${\varphi_{1 }}$$ and $${\varphi_{2}}$$. These discriminant vectors are found by optimal discriminant plane method. Thirty patterns are used for training the ANN. A comparison between the classification performances of the ANN trained without reducing the dimensions of the input patterns and with reduced dimensions of the input patterns is done. The ANN trained with transformed tool wear patterns gives better results in terms of improved classification performance in less iteration, when compared with the results of the ANN trained without transforming the dimensions of the input patterns to a lower dimension. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature 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: 10.1007/s10845-009-0249-y Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 717 Subjects: – SubjectFull: Machine tool monitoring Type: general – SubjectFull: Artificial neural networks Type: general – SubjectFull: Kalman filtering Type: general – SubjectFull: Dimensional analysis Type: general – SubjectFull: Vector analysis Type: general – SubjectFull: Back propagation Type: general – SubjectFull: Machine theory Type: general – SubjectFull: Production engineering Type: general – SubjectFull: Mechanical wear Type: general Titles: – TitleFull: Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Purushothaman, Srinivasan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2010 Type: published Y: 2010 Identifiers: – Type: issn-print Value: 09565515 Numbering: – Type: volume Value: 21 – Type: issue Value: 6 Titles: – TitleFull: Journal of Intelligent Manufacturing Type: main |
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