Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/156164
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dc.contributor.authorHu, Erhaien_US
dc.contributor.authorSeetoh, Ianen_US
dc.contributor.authorLai, Chang Quanen_US
dc.date.accessioned2022-04-07T07:56:00Z-
dc.date.available2022-04-07T07:56:00Z-
dc.date.issued2022-
dc.identifier.citationHu, E., Seetoh, I. & Lai, C. Q. (2022). Machine learning assisted investigation of defect influence on the mechanical properties of additively manufactured architected materials. International Journal of Mechanical Sciences, 221, 107190-. https://dx.doi.org/10.1016/j.ijmecsci.2022.107190en_US
dc.identifier.issn0020-7403en_US
dc.identifier.urihttps://hdl.handle.net/10356/156164-
dc.description.abstractAdditive manufacturing techniques can introduce defects that worsen the mechanical properties of 3D printed parts. Current techniques for quantifying the detrimental effects of these defects can only provide detailed analysis for a small number of geometries. Here, we investigate the effect of each defect feature (surface roughness and void position, number density and size) on the mechanical properties of a large number of truss lattices belonging to the stretch-dominated and bending-dominated topology. This is done by reducing each truss lattice into a single-beam sub-unit cell and conducting finite element simulations on it. The generated data is subjected to machine learning algorithms to identify the most important defect and design features that determine the mechanical properties of the overall structure. Our results indicate that surface roughness, Rmax (i.e. peak-to-trough height), exceeding 10% of the beam diameter strongly reduces the specific modulus and strength of lattice structures, especially for bending-dominated geometries. Interior voids, on the other hand, adversely affect stretch-dominated geometries but improve the specific properties of bending-dominated structures by removing under-stressed material in the core of the beams and causing them to become more “tube-like”. These insights are supported by first-principles analytical modeling and experimental data of additively manufactured metal lattices in the literature.en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.relationTLIG21-02en_US
dc.relation.ispartofInternational Journal of Mechanical Sciencesen_US
dc.rights© 2022 Elsevier Ltd. All rights reserved. This paper was published in International Journal of Mechanical Sciences and is made available with permission of Elsevier Ltd.en_US
dc.subjectEngineering::Materialsen_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleMachine learning assisted investigation of defect influence on the mechanical properties of additively manufactured architected materialsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.contributor.researchTemasek Laboratories @ NTUen_US
dc.identifier.doi10.1016/j.ijmecsci.2022.107190-
dc.description.versionSubmitted/Accepted versionen_US
dc.identifier.scopus2-s2.0-85126300734-
dc.identifier.volume221en_US
dc.identifier.spage107190en_US
dc.subject.keywordsAdditive Manufacturingen_US
dc.subject.keywordsArchitected Materialsen_US
dc.description.acknowledgementThis work was partially funded by the Temasek Labs Innovation Grant (TLIG21-02) for which the authors are grateful for.en_US
item.grantfulltextembargo_20240508-
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