Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations.
Saved in:
| Title: | Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations. |
|---|---|
| Authors: | Farooqui, Ashfaq1 (AUTHOR) ashfaqf@chalmers.se, Bengtsson, Kristofer1 (AUTHOR), Falkman, Petter1 (AUTHOR), Fabian, Martin1 (AUTHOR) |
| Source: | International Journal of Production Research. Aug2020, Vol. 58 Issue 16, p4947-4963. 17p. 5 Diagrams, 2 Graphs. |
| Subjects: | Legacy systems, Industry 4.0 |
| Abstract: | The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner – a tool for modelling and analysing production systems – and is currently implemented at an automotive company as a pilot project. [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 |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 144871573 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Farooqui%2C+Ashfaq%22">Farooqui, Ashfaq</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ashfaqf@chalmers.se</i><br /><searchLink fieldCode="AR" term="%22Bengtsson%2C+Kristofer%22">Bengtsson, Kristofer</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Falkman%2C+Petter%22">Falkman, Petter</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Fabian%2C+Martin%22">Fabian, Martin</searchLink><relatesTo>1</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>. Aug2020, Vol. 58 Issue 16, p4947-4963. 17p. 5 Diagrams, 2 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Legacy+systems%22">Legacy systems</searchLink><br /><searchLink fieldCode="DE" term="%22Industry+4%2E0%22">Industry 4.0</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The technological advancements of recent years have increased the complexity of manufacturing systems, and the ongoing transformation to Industry 4.0 will further aggravate the situation. This is leading to a point where existing systems on the factory floor get outdated, increasing the gap between existing technologies and state-of-the-art systems, making them incompatible. This paper presents an event-based data pipeline architecture, that can be applied to legacy systems as well as new state-of-the-art systems, to collect data from the factory floor. In the presented architecture, actions executed by the resources are converted to event streams, which are then transformed into an abstraction called operations. These operations correspond to the tasks performed in the manufacturing station. A sequence of these operations recount the task performed by the station. We demonstrate the usability of the collected data by using conformance analysis to detect when the manufacturing system has deviated from its defined model. The described architecture is developed in Sequence Planner – a tool for modelling and analysing production systems – and is currently implemented at an automotive company as a pilot project. [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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=144871573 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/00207543.2020.1735660 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 4947 Subjects: – SubjectFull: Legacy systems Type: general – SubjectFull: Industry 4.0 Type: general Titles: – TitleFull: Towards data-driven approaches in manufacturing: an architecture to collect sequences of operations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Farooqui, Ashfaq – PersonEntity: Name: NameFull: Bengtsson, Kristofer – PersonEntity: Name: NameFull: Falkman, Petter – PersonEntity: Name: NameFull: Fabian, Martin IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 08 Text: Aug2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 00207543 Numbering: – Type: volume Value: 58 – Type: issue Value: 16 Titles: – TitleFull: International Journal of Production Research Type: main |
| ResultId | 1 |
