Identification and authentication of additively manufactured components using their microstructural fingerprint
Year: 2025
Type: article
Abstract: • This work statistically proves that voids in AM parts are unique part identifiers. • Spherical pores with d = 100–200 μm are well suited for identification. • Using between 10 and 16 pores is sufficient for unique identification. • The pore pair distance distribution is used as a similarity measure of two parts. • The microstructural fingerprint distinguishes otherwise identically printed parts. In the field of additive manufacturing, the ability to uniquely identify and authenticate parts is crucial for certification, logistics, and anti-counterfeiting efforts. This study introduces a novel methodology that leverages the intrinsic microstructural features of additively manufactured components for their identification, authentication, and traceability. Unlike traditional tagging methods, such as embedding QR codes on the surface or within the volume of parts, this approach requires no alteration to the printing process, as it utilizes naturally occurring microstructural characteristics. The proposed workflow involves the analysis of 3D micro-computed tomography data to identify specific voids that meet predefined identification criteria. This method is demonstrated on a batch of 24 parts manufactured with identical process parameters, proving capable of achieving unambiguous identification and authentication. By establishing a tamper-proof link between the physical part and its digital counterpart, this methodology effectively bridges the physical and digital realms. This not only enhances the traceability of additively manufactured parts but also provides a robust tool for integrating digital materials, parts databases, and product passports with their physical counterparts.
Source: Materials & Design
Authors: Kanhaiya Gupta, Konstantin Poka, Alexander Ulbricht, Anja Waske
Institution: Federal Institute For Materials Research and Testing
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,650
OpenAlex ID: W4409662418