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Improving Quality Control of Mechatronic Systems Using KPI-Based Statistical Process Control

 
: Wohlers, B.; Dziwok, S.; Schmelter, D.; Lorenz, W.

:

Karwowski, W.:
Advances in Manufacturing, Production Management and Process Control : Joint proceedings of the AHFE 2018 International Conference on Advanced Production Management and Process Control, the AHFE International Conference on Human Aspects of Advanced Manufacturing, and the AHFE International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, July 21-25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA
Cham: Springer International Publishing, 2019 (Advances in Intelligent Systems and Computing 793)
ISBN: 978-3-319-94195-0 (Print)
ISBN: 978-3-319-94196-7 (Online)
ISBN: 3-319-94195-X
S.398-410
International Conference on Advanced Production Management and Process Control <2018, Orlando/Fla.>
International Conference on Human Aspects of Advanced Manufacturing <2018, Orlando/Fla.>
International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping <2018, Orlando/Fla.>
International Conference on Applied Human Factors and Ergonomics (AHFE) <9, 2018, Orlando/Fla.>
Englisch
Konferenzbeitrag
Fraunhofer IEM ()

Abstract
Statistical Process Control (SPC) is a quality control instrument for the manufacturing of mechatronic systems that enables to detect minor deviations within the manufactured products to prevent serious quality issues and financial loss. A significant hindrance for applying SPC is that current literature does not provide a process that supports the selection of data that shall be monitored, the gathering, and analysis of the data, and the visualization of the results all in one. In this paper, we provide a process that contains all relevant steps to establish a fully automatic SPC. Our SPC concept is based on Key Performance Indicators (KPIs) for mechatronic systems that statistically measure the product’s core functionalities based on its sensor data during product control. By reusing these KPIs, we obtain an efficient process for applying a lightweight SPC. We implement and evaluate our concepts at Diebold Nixdorf (DN) – a leading manufacturer of ATMs.

: http://publica.fraunhofer.de/dokumente/N-629133.html