Digitalization platform for data-driven quality management in multi-stage manufacturing systems

Digital transformation is driving the current technological trends in manufacturing. An integral constituent is a communication between machines, between machines and humans, or between machines and products. This extensive communication involves large volumes of data. Many manufacturers apply data...

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Veröffentlicht in:	Journal of intelligent manufacturing Jg. 35; H. 6; S. 2699 - 2718
Hauptverfasser:	Filz, Marc-André, Bosse, Jan Philipp, Herrmann, Christoph
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY Springer US 01.08.2024 Springer Nature B.V
Schlagworte:	Advanced manufacturing technologies Applications programs Business and Management Control Data analytics Digital platform Digital technology Digitization Machine learning Machines Manufacturing Manufacturing system Mechatronics Processes Production Quality management Robotics Data analytics Manufacturing system Machine learning Quality management Digital platform
ISSN:	1572-8145, 0956-5515, 1572-8145
Online-Zugang:	Volltext
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Abstract	Digital transformation is driving the current technological trends in manufacturing. An integral constituent is a communication between machines, between machines and humans, or between machines and products. This extensive communication involves large volumes of data. Many manufacturers apply data analytics (e.g., for quality management or improvement purposes) to translate the data into a business value. However, isolated, rigid, and area-specific IT solutions often carry this out. Today's complex manufacturing requires quality management approaches that constitute a holistic view of and understanding of process–product interactions along the process chain instead of focusing solely on single processes. A novel platform approach to support quality management in manufacturing systems is proposed in this paper to overcome this deficit. It integrates state-of-the-art concepts of IT with modeling approaches for planning and operation of quality management. A conceptual framework and the technical architecture for implementing a digitalization platform are presented in this regard. Moreover, the approach is validated and implemented within a web application based on a use case of data-driven quality management in electronics production.
AbstractList	Digital transformation is driving the current technological trends in manufacturing. An integral constituent is a communication between machines, between machines and humans, or between machines and products. This extensive communication involves large volumes of data. Many manufacturers apply data analytics (e.g., for quality management or improvement purposes) to translate the data into a business value. However, isolated, rigid, and area-specific IT solutions often carry this out. Today’s complex manufacturing requires quality management approaches that constitute a holistic view of and understanding of process–product interactions along the process chain instead of focusing solely on single processes. A novel platform approach to support quality management in manufacturing systems is proposed in this paper to overcome this deficit. It integrates state-of-the-art concepts of IT with modeling approaches for planning and operation of quality management. A conceptual framework and the technical architecture for implementing a digitalization platform are presented in this regard. Moreover, the approach is validated and implemented within a web application based on a use case of data-driven quality management in electronics production.
Author	Filz, Marc-André Herrmann, Christoph Bosse, Jan Philipp
Author_xml	– sequence: 1 givenname: Marc-André surname: Filz fullname: Filz, Marc-André – sequence: 2 givenname: Jan Philipp surname: Bosse fullname: Bosse, Jan Philipp – sequence: 3 givenname: Christoph surname: Herrmann fullname: Herrmann, Christoph
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Copyright	The Author(s) 2023 The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2023 – notice: The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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