Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162399
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dc.contributor.authorSofinowski, Karlen_US
dc.contributor.authorWittwer, Malloryen_US
dc.contributor.authorSeita, Matteoen_US
dc.date.accessioned2022-10-18T01:38:45Z-
dc.date.available2022-10-18T01:38:45Z-
dc.date.issued2022-
dc.identifier.citationSofinowski, K., Wittwer, M. & Seita, M. (2022). Encoding data into metal alloys using laser powder bed fusion. Additive Manufacturing, 52, 102683-. https://dx.doi.org/10.1016/j.addma.2022.102683en_US
dc.identifier.issn2214-7810en_US
dc.identifier.urihttps://hdl.handle.net/10356/162399-
dc.description.abstractBeyond near-net shape manufacturing of parts with complex geometry, additive manufacturing (AM) makes it possible to fabricate materials with distinct, site-specific microstructures. This ability is unique to AM and enables the design of architected materials that were previously unattainable. Here, we leverage this strategy to encode data into metal parts using the microstructure as the medium to store information. We use a novel laser scanning technique to control the local solidification conditions during laser powder bed fusion and embed a linear barcode and Quick Response (QR) code into stainless steel 316 L. The codes use blocks of different crystallographic texture–i.e., regions in which the crystal lattice of most grains is aligned towards a preferred orientation–as the data “bits”. The data may be retrieved by analytical techniques that are sensitive to local microstructure variations. As a demonstration, we decode the barcodes by measuring the scattering of optical light from their etched surface using a technique called directional reflectance microscopy. The resulting texture maps are readable by conventional barcode scanners, such as those found on mobile phones. The ability to embed data has significant potential in fields such as law enforcement, biomedicine, and transportation, where permanent, damage-resistant tracking is essential.en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF-NRFF2018-05en_US
dc.relation.ispartofAdditive Manufacturingen_US
dc.rights© 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.subjectEngineering::Materialsen_US
dc.titleEncoding data into metal alloys using laser powder bed fusionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.contributor.researchSingapore Centre for 3D Printingen_US
dc.identifier.doi10.1016/j.addma.2022.102683-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85124656285-
dc.identifier.volume52en_US
dc.identifier.spage102683en_US
dc.subject.keywordsAdditive Manufacturingen_US
dc.subject.keywordsLaser Powder Bed Fusionen_US
dc.description.acknowledgementThis work was supported by the National Research Foundation, Singapore (NRF), under the NRF Fellowship program (grant ID NRF-NRFF2018-05) and the NTU-CSIRO seed fund.en_US
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