An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes fromheterogeneous sensor data.
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| Title: | An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes fromheterogeneous sensor data. |
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| Authors: | Bastani, Kaveh1 (AUTHOR), Rao, Prahalad K.2 (AUTHOR), Kong, Zhenyu (James)1 (AUTHOR) zkong@vt.edu |
| Source: | IIE Transactions. 2016, Vol. 48 Issue 7, p579-598. 20p. 1 Black and White Photograph, 1 Diagram, 6 Charts, 3 Graphs. |
| Subjects: | Manufacturing processes, Advanced planning & scheduling, Sparse approximations, Fibers -- Design & construction, Machine learning, Discriminant analysis |
| Abstract: | The objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC). The novelty of the OSEC approach is in representing data from sensor signals as an underdetermined linear system of equations and subsequently solving the underdetermined linear system using a newly developed greedy Bayesian estimation method. We apply the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical-mechanical planarization process. Using the proposed OSEC approach, process drifts are detected and classifiedwith higher accuracy comparedwith popular machine learning techniques. Process drifts were detected and classified with a fidelity approaching 90% (F-score) using OSEC. In comparison, conventional signal analysis techniques--e.g., neural networks, support vector machines, quadratic discriminant analysis, naïve Bayes--were evaluated with F-score in the range of 40% to 70%. [ABSTRACT FROM AUTHOR] |
| Copyright of IIE Transactions 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 117875022 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes fromheterogeneous sensor data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bastani%2C+Kaveh%22">Bastani, Kaveh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rao%2C+Prahalad+K%2E%22">Rao, Prahalad K.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kong%2C+Zhenyu+%28James%29%22">Kong, Zhenyu (James)</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zkong@vt.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IIE+Transactions%22">IIE Transactions</searchLink>. 2016, Vol. 48 Issue 7, p579-598. 20p. 1 Black and White Photograph, 1 Diagram, 6 Charts, 3 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Manufacturing+processes%22">Manufacturing processes</searchLink><br /><searchLink fieldCode="DE" term="%22Advanced+planning+%26+scheduling%22">Advanced planning & scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Sparse+approximations%22">Sparse approximations</searchLink><br /><searchLink fieldCode="DE" term="%22Fibers+--+Design+%26+construction%22">Fibers -- Design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Discriminant+analysis%22">Discriminant analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The objective of this work is to realize real-time monitoring of process conditions in advanced manufacturing using multiple heterogeneous sensor signals. To achieve this objective we propose an approach invoking the concept of sparse estimation called online sparse estimation-based classification (OSEC). The novelty of the OSEC approach is in representing data from sensor signals as an underdetermined linear system of equations and subsequently solving the underdetermined linear system using a newly developed greedy Bayesian estimation method. We apply the OSEC approach to two advanced manufacturing scenarios, namely, a fused filament fabrication additive manufacturing process and an ultraprecision semiconductor chemical-mechanical planarization process. Using the proposed OSEC approach, process drifts are detected and classifiedwith higher accuracy comparedwith popular machine learning techniques. Process drifts were detected and classified with a fidelity approaching 90% (F-score) using OSEC. In comparison, conventional signal analysis techniques--e.g., neural networks, support vector machines, quadratic discriminant analysis, naïve Bayes--were evaluated with F-score in the range of 40% to 70%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IIE Transactions 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=117875022 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/0740817X.2015.1122254 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 579 Subjects: – SubjectFull: Manufacturing processes Type: general – SubjectFull: Advanced planning & scheduling Type: general – SubjectFull: Sparse approximations Type: general – SubjectFull: Fibers -- Design & construction Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Discriminant analysis Type: general Titles: – TitleFull: An online sparse estimation-based classification approach for real-time monitoring in advanced manufacturing processes fromheterogeneous sensor data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bastani, Kaveh – PersonEntity: Name: NameFull: Rao, Prahalad K. – PersonEntity: Name: NameFull: Kong, Zhenyu (James) IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 0740817X Numbering: – Type: volume Value: 48 – Type: issue Value: 7 Titles: – TitleFull: IIE Transactions Type: main |
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