RECAST: An Open Source Platform for Item-Specific Capturing of Real Production Processes

The digitization of production is an essential component of Industry 4.0. The collection of data in the production process is a decisive factor and a prerequisite for the use of artificial intelligence. At machine level, groundbreaking successes have been achieved in the field of predictive maintenance using machine learning, which is directly reflected in increased productivity and cost savings. In contrast, digitization at item level, i.e. the digital tracking of all items in the process context that ultimately lead to the finished product, is much more complex. A non-structured collection of data at item level prevents the use of artificial intelligence, for example in the quality testing of products. As part of the RECAST project, the extent to which existing non-networked production facilities can be digitized with this objective was investigated. As part of the work, a framework was created using exclusively open source components, which allows a digital representation of various real existing complex production processes in the aerospace industry to be created. With this digital twin, the system data can be recorded in real time on a item-specific basis. Thanks to the generic approach to process mapping, the results can be applied to a wide range of application fields in mechanical engineering and data-intensive administrative tasks.

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Publication Details

Published
2024-08-15
DOI
https://doi.org/10.1145/3694860.3694861
Primary Topic
Manufacturing Process and Optimization
Type
article
Field-Weighted Citation Impact
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article

RECAST: An Open Source Platform for Item-Specific Capturing of Real Production Processes

Thomas Fraunholz, Alfons Schuster, Lars Christian Larsen, Tim Köhler et al.
Manufacturing Process and Optimization
article

RECAST: An Open Source Platform for Item-Specific Capturing of Real Production Processes

Thomas Fraunholz, Alfons Schuster, Lars Christian Larsen, Tim Köhler, Dennis Rall, Dominik Görick
article en

Abstract

The digitization of production is an essential component of Industry 4.0. The collection of data in the production process is a decisive factor and a prerequisite for the use of artificial intelligence. At machine level, groundbreaking successes have been achieved in the field of predictive maintenance using machine learning, which is directly reflected in increased productivity and cost savings. In contrast, digitization at item level, i.e. the digital tracking of all items in the process context that ultimately lead to the finished product, is much more complex. A non-structured collection of data at item level prevents the use of artificial intelligence, for example in the quality testing of products. As part of the RECAST project, the extent to which existing non-networked production facilities can be digitized with this objective was investigated. As part of the work, a framework was created using exclusively open source components, which allows a digital representation of various real existing complex production processes in the aerospace industry to be created. With this digital twin, the system data can be recorded in real time on a item-specific basis. Thanks to the generic approach to process mapping, the results can be applied to a wide range of application fields in mechanical engineering and data-intensive administrative tasks.

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) (DE)
Openalex Percentile: Top 71%
Manufacturing Process and Optimization
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