Online tool wear prediction in drilling operations using selective artificial neural network ensemble model.
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| Title: | Online tool wear prediction in drilling operations using selective artificial neural network ensemble model. |
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| Authors: | Yu, Jianbo jianboyu@shu.edu.cn |
| Source: | Neural Computing & Applications. May2011, Vol. 20 Issue 4, p473-485. 13p. |
| Subjects: | Artificial neural networks, Failure Analysis System (Computer system), Prediction theory, Mathematical models, Mechanical wear, Drilling & boring equipment, Tools, Automation, Industrial productivity, Product Quality (Book), Product quality |
| Abstract: | Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Neural Computing & Applications 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 |
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| Items | – Name: Title Label: Title Group: Ti Data: Online tool wear prediction in drilling operations using selective artificial neural network ensemble model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Yu%2C+Jianbo%22">Yu, Jianbo</searchLink><i> jianboyu@shu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Neural+Computing+%26+Applications%22">Neural Computing & Applications</searchLink>. May2011, Vol. 20 Issue 4, p473-485. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Failure+Analysis+System+%28Computer+system%29%22">Failure Analysis System (Computer system)</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+theory%22">Prediction theory</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+wear%22">Mechanical wear</searchLink><br /><searchLink fieldCode="DE" term="%22Drilling+%26+boring+equipment%22">Drilling & boring equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Tools%22">Tools</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+productivity%22">Industrial productivity</searchLink><br /><searchLink fieldCode="DE" term="%22Product+Quality+%28Book%29%22">Product Quality (Book)</searchLink><br /><searchLink fieldCode="DE" term="%22Product+quality%22">Product quality</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Online tool wear prediction plays a key role in industry automation for higher productivity and product quality. In recent past, several artificial neural network (ANN) models using multiple sensor signals as inputs for prediction as well as classification of tool wear have been proposed. However, a single ANN used in these models is often tries, which could limit their wide applications due to the complicated procedure of constructing a single ANN model. This study proposed a selective ANN ensemble approach DPSOEN, where several selected component ANNs are jointly used to online predict flank wear in drilling operation. DPSOEN provides more simple training and better generalization performance than using single ANN and hence is easier to be used by operators who often are not good at ANN techniques. Two benchmark cases were used to evaluate the performance of DPSOEN in predicting flank wear. It shows improved generalization performance that outperforms those of single ANN and Ensemble ALL approach. The investigation proposed a heuristic approach for applying the DPSOEN-based model as an effective and useful tool to predict tool wear online with potential applications for tool condition monitoring in general. Analysis from this study provides guidelines in developing ANN ensemble-based tool wear prediction systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Neural Computing & Applications 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/s00521-011-0539-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 473 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Failure Analysis System (Computer system) Type: general – SubjectFull: Prediction theory Type: general – SubjectFull: Mathematical models Type: general – SubjectFull: Mechanical wear Type: general – SubjectFull: Drilling & boring equipment Type: general – SubjectFull: Tools Type: general – SubjectFull: Automation Type: general – SubjectFull: Industrial productivity Type: general – SubjectFull: Product Quality (Book) Type: general – SubjectFull: Product quality Type: general Titles: – TitleFull: Online tool wear prediction in drilling operations using selective artificial neural network ensemble model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Yu, Jianbo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2011 Type: published Y: 2011 Identifiers: – Type: issn-print Value: 09410643 Numbering: – Type: volume Value: 20 – Type: issue Value: 4 Titles: – TitleFull: Neural Computing & Applications Type: main |
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