Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162399
Title: Encoding data into metal alloys using laser powder bed fusion
Authors: Sofinowski, Karl
Wittwer, Mallory
Seita, Matteo
Keywords: Engineering::Materials
Issue Date: 2022
Source: Sofinowski, 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.2022.102683
Project: NRF-NRFF2018-05 
Journal: Additive Manufacturing 
Abstract: Beyond 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.
URI: https://hdl.handle.net/10356/162399
ISSN: 2214-7810
DOI: 10.1016/j.addma.2022.102683
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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles
MSE Journal Articles
SC3DP Journal Articles

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