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Method of enhancing interlayer bond strength in construction scale 3D printing with mortar by effective bond area amplification

Open access
Marchment, Taylor, Sanjayan, Jay, Xia, Ming·2019·Materials & Design

Abstract

3D Construction Printing (3DCP) is a new and emerging technology that is set to revolutionise construction. Currently the technology is in its early stages of development and many hurdles are yet to overcome. Particularly in the field of extrusion 3DCP with cementitious materials, one of the primary hurdles is the weak interface bonds. The weakness in bonds is hypothesised to be present due to the layered fabrication of a stiff cementitious mix that creates a localised void structure. Due to the time gap intervals between depositions, the bond strength decreases due to lack of intermixing. This paper seeks to explore a method to strengthen the interlayer bonds, by applying a cementitious paste at the interface, to minimise voids and promote bond area. Using a flatbed scanner and a unique method of image processing on printed colour layers, a quantitative evaluation of the contact area, “effective bond” area, was determined. The application of these experimental and analytical methods shows that an increase in bond strength is achieved, which is proportional to the effective bond area.

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Metadata

Authors
Marchment, T., Sanjayan, J., & Xia, M.
Venue
Materials & Design
Publisher
Elsevier BV
Published
2019-05
Volume
169
Pages
107684
ISSN
0264-1275 (ISSN-L)
OpenAlex
W2920275820
Cited by
281
References
1
FWCI
17.09
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Method of enhancing interlayer bond strength in construction scale 3D printing with mortar by effective bond area amplification. https://app.cochat.ai/papers/10.1016/j.matdes.2019.107684
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Marchment, T., Sanjayan, J., & Xia, M. (2019). Method of enhancing interlayer bond strength in construction scale 3D printing with mortar by effective bond area amplification. Materials & Design. https://doi.org/10.1016/j.matdes.2019.107684

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

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