Bretones Cassoli, Beatriz ; Jourdan, Nicolas ; Nguyen, Phu H. ; Sen, Sagar ; Garcia-Ceja, Enrique ; Metternich, Joachim (2022)
Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review.
In: Procedia CIRP, 112
doi: 10.1016/j.procir.2022.09.062
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS have been developed to structure the relationship between the digital and the physical worlds. However, there is no systematic review of the existing frameworks related to quality management in manufacturing CPS. Thus, our study aims at determining and comparing the existing frameworks. The systematic review yielded 38 frameworks analyzed regarding their characteristics, use of data science and Machine Learning (ML), and shortcomings and open research issues. The identified issues mainly relate to limitations in cross-industry/cross-process applicability, the use of ML, big data handling, and data security.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Bretones Cassoli, Beatriz ; Jourdan, Nicolas ; Nguyen, Phu H. ; Sen, Sagar ; Garcia-Ceja, Enrique ; Metternich, Joachim |
| Art des Eintrags: | Bibliographie |
| Titel: | Frameworks for data-driven quality management in cyber-physical systems for manufacturing: A systematic review |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Elsevier B.V. |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Procedia CIRP |
| Jahrgang/Volume einer Zeitschrift: | 112 |
| DOI: | 10.1016/j.procir.2022.09.062 |
| Kurzbeschreibung (Abstract): | Recent advances in the manufacturing industry have enabled the deployment of Cyber-Physical Systems (CPS) at scale. By utilizing advanced analytics, data from production can be analyzed and used to monitor and improve the process and product quality. Many frameworks for implementing CPS have been developed to structure the relationship between the digital and the physical worlds. However, there is no systematic review of the existing frameworks related to quality management in manufacturing CPS. Thus, our study aims at determining and comparing the existing frameworks. The systematic review yielded 38 frameworks analyzed regarding their characteristics, use of data science and Machine Learning (ML), and shortcomings and open research issues. The identified issues mainly relate to limitations in cross-industry/cross-process applicability, the use of ML, big data handling, and data security. |
| Freie Schlagworte: | Artificial Intelligence (AI), Cyber-Physical Systems (CPS), Framework, Quality Management, Systematic Literature Review |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > CiP Center für industrielle Produktivität 16 Fachbereich Maschinenbau > Institut für Produktionsmanagement und Werkzeugmaschinen (PTW) > Management industrieller Produktion |
| Hinterlegungsdatum: | 17 Apr 2023 06:56 |
| Letzte Änderung: | 17 Apr 2023 08:21 |
| PPN: | 507014243 |
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