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BIM-Based Concrete Printing
Publikationstyp
Conference Paper
Date Issued
2020-08
Sprache
English
Institut
TORE-URI
First published in
Number in series
98 LNCE
Start Page
992
End Page
1002
Citation
International Conference on Computing in Civil and Building Engineering (ICCCBE 2020)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Springer
Extrusion-based additive manufacturing (AM), or three-dimensional (3D) printing, has matured into a set of advanced methods to automate the construction of large-scale concrete structures, while minimizing cost and material waste. However, current AM data models are inadequate for 3D concrete printing due to insufficient incorporation of information on the relationships between process, material, and geometry, which may cause redundancy, information loss, and inconsistencies. Aiming at improving AM data modeling for concrete printing, this paper proposes a metamodeling approach for AM of concrete structures, referred to as “printing information modeling”, which takes advantage of building information modeling (BIM). As will be shown in this paper, the BIM-based printing information model, serving as a metamodel, incorporates the digital data triplet of process, material, and geometry parameters to generate computer numerical control (CNC) commands that may readily be used for concrete printing. A validation test is performed, which instantiates the printing information model, using a BIM model, for generating CNC commands, enabling optimal digital data exchange from BIM models to concrete printers. As a result of this study, it is demonstrated that printing information modeling adequately defines the information required for AM of concrete structures using a BIM-based approach, showing promising potential to improve current AM data modeling efforts.
Subjects
Additive manufacturing
Building information modeling (BIM)
Concrete printing
Metamodeling
Printing information modeling (PIM)
DDC Class
000: Allgemeines, Wissenschaft
Funding Organisations
More Funding Information
Financial support of the German Research Foundation (DFG) through grant SM 281/7-1 is gratefully acknowledged. Major parts of this work have been conducted in the ?Structural Health Monitoring Laboratory?, sponsored by the European Union through the European Fund for Regional Development (EFRD) and the Thuringian Ministry for Economic Affairs, Science and Digital Society (TMWWDG) under grant 2016 FGI 0009. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of DFG, EFRD, or TMWWDG.