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Cited by 673

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Additive manufacturing in construction: A review on processes, applications, and digital planning methods

Open access
Paolini, Alexander, Kollmannsberger, Stefan, Rank, Ernst·2019·Additive Manufacturing

Abstract

The application of additive manufacturing (AM) in construction has been increasingly studied in recent years. Large robotic arm- and gantry-systems have been created to print building parts using aggregate-based materials, metals, or polymers. Significant benefits of AM are the automation of the production process, a high degree of design freedom, and the resulting potential for optimization. However, the building components and 3D-printing processes need to be modeled appropriately. In this paper, the current state of AM in construction is reviewed. AM processes and systems as well as their application in research and construction projects are presented. Moreover, digital methods for planning 3D-printed building parts and AM processes are described.

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Metadata

Authors
Paolini, A., Kollmannsberger, S., & Rank, E.
Venue
Additive Manufacturing
Publisher
Elsevier BV
Published
2019-12
Volume
30
Pages
100894
ISSN
  • 2214-7810 (ISSN-L)
  • 2214-8604
OpenAlex
W2979902195
Cited by
673
References
8
FWCI
47.00
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Additive manufacturing in construction: A review on processes, applications, and digital planning methods. https://app.cochat.ai/papers/10.1016/j.addma.2019.100894
The paper
Paolini, A., Kollmannsberger, S., & Rank, E. (2019). Additive manufacturing in construction: A review on processes, applications, and digital planning methods. Additive Manufacturing. https://doi.org/10.1016/j.addma.2019.100894

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.