journal article·2026-02-11

Physically Unclonable Surfaces Enabled by Cold Spray Deposition

Jaehun Jeon, Semih Akin

Publisher page

Abstract

As counterfeit components become increasingly prevalent, encoded surfaces, particularly physically unclonable functions (PUFs), have emerged as powerful tools for secure part authentication and reliable traceability. However, significant challenges remain in fabricating unclonable surface structures in a high-throughput, scalable, and cost-effective manner while also ensuring robust encryption and secure authentication. This work aims to address the existence gaps by introducing an innovative approach to PUF manufacturing utilizing the cold spray (CS) particle deposition technique, complemented by algorithmic feature extraction and cryptographic surface encoding. In our approach, a mixture of metal and fluorescent microparticles is deposited onto an aluminum (Al 5052) substrate by leveraging the process-specific two-phase (gas-solid) turbulent flow characteristic of the CS process. The inherent stochasticity of the CS flow leads to a random distribution of fluorescent particles, generating unique, physically unclonable luminescent patterns on the target surface. The spatial distribution of the optical fluorescent particles is then captured under UV light (365 nm) exposure and subsequently processed through image binarization. Features are extracted from this distribution by using Voronoi analysis. The extracted features are then encrypted using the SHA-256 cryptographic algorithm to generate a secure "certification key" for part authentication. Experimental results demonstrate the effectiveness of the proposed manufacturing approach for high-throughput, scalable PUF production, confirming its suitability for robust part authentication and its reliability under environmental stressors (e.g., thermal cycling, chemical exposure). The developed method shows strong potential for enabling tamper-evident part authentication solutions to address the growing threat of counterfeiting in critical sectors, such as aerospace, defense, and advanced manufacturing.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Physical Unclonable Functions (PUFs) and Hardware Security · Digital Media Forensic Detection · Fluid Dynamics and Heat Transfer

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Mechanical, Aerospace and Nuclear Engineering

Rensselaer Polytechnic Institute

Center for Smart Convergent Manufacturing Systems (CSCMS)

Funding

Rensselaer Polytechnic Institute

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