Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148291
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dc.contributor.authorLao, Wenxinen_US
dc.contributor.authorLi, Mingyangen_US
dc.contributor.authorWong, Teck Nengen_US
dc.contributor.authorTan, Ming Jenen_US
dc.contributor.authorTjahjowidodo, Tegoehen_US
dc.date.accessioned2021-05-05T05:07:28Z-
dc.date.available2021-05-05T05:07:28Z-
dc.date.issued2020-
dc.identifier.citationLao, W., Li, M., Wong, T. N., Tan, M. J. & Tjahjowidodo, T. (2020). Improving surface finish quality in extrusion-based 3D concrete printing using machine learning-based extrudate geometry control. Virtual and Physical Prototyping, 15(2), 178-193. https://dx.doi.org/10.1080/17452759.2020.1713580en_US
dc.identifier.issn1745-2767en_US
dc.identifier.other0000-0003-1507-1509-
dc.identifier.other0000-0002-3029-2521-
dc.identifier.other0000-0002-3583-1723-
dc.identifier.other0000-0003-0074-5101-
dc.identifier.urihttps://hdl.handle.net/10356/148291-
dc.description.abstract3D Concrete Printing (3DCP) has been gaining popularity in the past few years. Due to the nature of line-by-line printing and the slump of the material deposition in each extruded line, 3D printed structures exhibit obvious lines or marks at the layer interface, which affects surface finish quality and potentially affect bonding strength between layers. This makes it necessary to control the extrudate formation in 3DCP. However, it is difficult to directly analyse the extrudate formation process because the extrudate shape depends on many parameters. In this paper, a machine learning technique is applied to correlate the formation of the extrudate to the printing parameters using an Artificial Neural Network model. The training data for the model development was obtained from extrudates printed in 3DCP experiments. The performance of the trained model was experimentally validated and the predicted extrudate geometry resulting from the developed model showed good agreement to the actual extrudate geometry. Subsequently, the developed model was used to find proper nozzle shapes to produce designated extrudate geometries. Significant improvement on the printing quality was demonstrated using nozzle shapes generated from the model on 3D printed objects consisting a vertical wall, an inclined wall and a curved part.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation.ispartofVirtual and Physical Prototypingen_US
dc.rightsThis is an Accepted Manuscript of an article published by Taylor & Francis in Virtual and Physical Prototyping on 5 Feb 2020, available online: http://www.tandfonline.com/10.1080/17452759.2020.1713580en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleImproving surface finish quality in extrusion-based 3D concrete printing using machine learning-based extrudate geometry controlen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.researchSingapore Centre for 3D Printingen_US
dc.identifier.doi10.1080/17452759.2020.1713580-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85079050530-
dc.identifier.issue2en_US
dc.identifier.volume15en_US
dc.identifier.spage178en_US
dc.identifier.epage193en_US
dc.subject.keywordsAdditive Manufacturingen_US
dc.subject.keywords3D Concrete Printingen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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