Machine learning in manufacturing and industry 4.0 applications.
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| Title: | Machine learning in manufacturing and industry 4.0 applications. |
|---|---|
| Authors: | Rai, Rahul1 (AUTHOR) rrai@clemson.edu, Tiwari, Manoj Kumar2 (AUTHOR), Ivanov, Dmitry3 (AUTHOR), Dolgui, Alexandre4 (AUTHOR) |
| Source: | International Journal of Production Research. Aug2021, Vol. 59 Issue 16, p4773-4778. 6p. 2 Diagrams. |
| Subjects: | Machine learning, Decision support systems, Manufacturing industries, Supply chain management, Edge computing, Intelligent sensors |
| Abstract: | The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems. [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: 151932922 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning in manufacturing and industry 4.0 applications. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Rai%2C+Rahul%22">Rai, Rahul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rrai@clemson.edu</i><br /><searchLink fieldCode="AR" term="%22Tiwari%2C+Manoj+Kumar%22">Tiwari, Manoj Kumar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ivanov%2C+Dmitry%22">Ivanov, Dmitry</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dolgui%2C+Alexandre%22">Dolgui, Alexandre</searchLink><relatesTo>4</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Aug2021, Vol. 59 Issue 16, p4773-4778. 6p. 2 Diagrams. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+support+systems%22">Decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Manufacturing+industries%22">Manufacturing industries</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Intelligent+sensors%22">Intelligent sensors</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems. [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: 10.1080/00207543.2021.1956675 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 6 StartPage: 4773 Subjects: – SubjectFull: Machine learning Type: general – SubjectFull: Decision support systems Type: general – SubjectFull: Manufacturing industries Type: general – SubjectFull: Supply chain management Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Intelligent sensors Type: general Titles: – TitleFull: Machine learning in manufacturing and industry 4.0 applications. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Rai, Rahul – PersonEntity: Name: NameFull: Tiwari, Manoj Kumar – PersonEntity: Name: NameFull: Ivanov, Dmitry – PersonEntity: Name: NameFull: Dolgui, Alexandre IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 59 – Type: issue Value: 16 Titles: – TitleFull: International Journal of Production Research Type: main |
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