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Sub-Surface 3D Structures for Marking and Counterfeit Protection of Additive Manufactured Parts


Title: Sub-Surface 3D Structures for Marking and Counterfeit Protection of Additive Manufactured Parts

Authors: Tobias Meyer, Sascha Hartig, Eugen Musienko, Marc Fette, Jens Wulfsberg

DOI: 10.33599/nasampe/s.21.0582

Abstract: During the last decade, additive manufacturing evolved from a mere prototyping technology into a mature industrial production method. Its potential for integrating entire assemblies into single parts while retaining the overall functionality and enabling weight reduction through complex, topologically optimized structures contributed to its spread throughout a variety of industrial sectors. Since parts that have been manufactured by additive layer manufacturing (ALM) technologies may have to be checked periodically if they are used in sensible applications, a reliable method for identification and counterfeit protection is required. Commonly used markings like serial numbers or barcodes are usually applied superficially onto the surface of a part. Since the markings are easily accessible from the outside, they are vulnerable to manipulation or accidental damage, which compromises the identifiability of the part. Therefore, this paper proposes a sub-surface approach, utilizing geometric design freedom of additive manufacturing. First, the basic principle of using part integrated and density reduced 3D ball arrays as volumetric data storage is presented. Afterwards, the conducted experiment is described in which the method of density variation has been tested for application in ALM parts using the process of selective laser melting (SLM) with 1.4404 (316L) stainless steel. Finally, the detectability of the density varied ball arrays has been validated by using computed tomography.

References: 1. Wei, C., Sun, Z., Huang, Y., Li, L.: Embedding Anti-counterfeiting Features in Metallic Components via Multiple Material Additive Manufacturing. Additive Manufacturing, Vol. 24. United Kingdom: Manchester, 2018, 1-12. DOI: 10.1016/j.addma.2018.09.003. 2. Kieser, B.: Unique device identification through high data density structural encoding. US Patent, US10105192B2, 2017. 3. Feldkamp, L., Davis, L., Kress, J.W.: Practical cone beam algorithm. In: Journal of the Optical Society of America, A1, 1984, Vol. 6, 612-619. DOI: 10.1364/JOSAA.1.000612. 4. Carmignato, S., Dewulf, W., Leach, R.: Industrial X-Ray Computed Tomography, 1st ed. Switzerland, 2018, 51-52. DOI: 10.1007/978-3-319-59573-3.

Conference: SAMPE NEXUS 2021

Publication Date: 2021/06/29

SKU: TP21-0000000582

Pages: 15

Price: FREE

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