Bibliograph. Daten | Wilhelm, Yannick; Schreier, Ulf; Reimann, Peter; Mitschang, Bernhard; Ziekow, Holger: Data Science Approaches to Quality Control in Manufacturing: A Review of Problems, Challenges and Architecture. In: Springer Proceedings Series Communications in Computer and Information Science (CCIS). Universität Stuttgart, Fakultät Informatik, Elektrotechnik und Informationstechnik. englisch. Springer, Oktober 2020. Artikel in Tagungsband (Konferenz-Beitrag).
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Körperschaft | 14th Symposium on Service-Oriented Computing |
CR-Klassif. | H.2.8 (Database Applications)
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Keywords | Data Science; Machine Learning; Quality Control; Challenges; Functional Architecture |
Kurzfassung | Manufacturing environments are characterized by non-stationary processes, constantly varying conditions, complex process interdependencies, and a high number of product variants. These and other aspects pose several challenges for common machine learning algorithms to achieve reliable and accurate predictions. This overview and vision paper provides a comprehensive list of common problems and challenges for data science approaches to quality control in manufacturing. We have derived these problems and challenges by inspecting three real-world use cases in the eld of product quality control and via a comprehensive literature study. We furthermore associate the identi ed problems and challenges to individual layers and components of a functional setup, as it can be found in manufacturing environments today. Additionally, we extend and revise this functional setup and this way propose our vision of a future functional software architecture. This functional architecture represents a visionary blueprint for solutions that are able to address all challenges for data science approaches in manufacturing quality control.
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Abteilung(en) | Universität Stuttgart, Institut für Parallele und Verteilte Systeme, Anwendersoftware
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Projekt(e) | GSaME-NFG
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Eingabedatum | 6. Mai 2020 |
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