Part Authentication Through Encrypted Geometric-Magnetic Fingerprint Fusion in Cold Spray Additive Manufacturing

Abstract Cold spray additive manufacturing (CSAM) has emerged as a promising cost-effective technology for producing high-performance metal parts, particularly in aerospace applications. However, the widespread adoption of cold spray AM in critical industrial sectors has raised concerns regarding intellectual property protection and supply chain integrity. This paper presents a dual-fingerprint authentication framework for cold spray AM parts that combines inherent geometric features with engineered magnetic patterns. Our system exploits the stochastic nature of the cold spray process, where process variations and particle deposition dynamics generate unique, physically unclonable geometric patterns on each part’s surface. These geometric fingerprints are captured through a laser-based 3D scanning system. Additionally, we introduce a magnetic fingerprinting methodology by strategically embedding nickel deposits within copper main deposits during manufacturing, creating distinct magnetic signatures that can be detected non-destructively when the part is magnetized. The dual-fingerprint feature can be leveraged by various encryption algorithms to preserve fingerprint confidentiality throughout the supply chain while enabling secure authentication. To demonstrate its effectiveness, we implement a learning-with-error (LWE) encryption scheme where the magnetic fingerprint serves as an encryption key for the geometric fingerprint data. The system authenticates parts by comparing newly scanned geometric fingerprints with decrypted reference data using the magnetic key. This dual-factor authentication approach provides a robust defense against counterfeiting while protecting valuable manufacturing intellectual property, thereby securing the entire supply chain for cold spray AM parts. Experimental validation demonstrates the system’s effectiveness in authenticating parts non-destructively while maintaining stringent security standards, making it particularly suitable for aerospace and other critical applications where part authenticity and supply chain integrity are paramount.

Authors

Publication Details

Published
2025-06-23
DOI
https://doi.org/10.1115/msec2025-155551
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Part Authentication Through Encrypted Geometric-Magnetic Fingerprint Fusion in Cold Spray Additive Manufacturing

Samuel Boese, Xiaoning Jin, Ahmad Nourian, Sinan Müftü et al.
Additive Manufacturing and 3D Printing Technologies
article

Part Authentication Through Encrypted Geometric-Magnetic Fingerprint Fusion in Cold Spray Additive Manufacturing

Samuel Boese, Xiaoning Jin, Ahmad Nourian, Sinan Müftü, Sagar Kamarthi, Zifeng Wang, Aidan Sevinsky, Mrudul Satbhai
article en

Abstract

Abstract Cold spray additive manufacturing (CSAM) has emerged as a promising cost-effective technology for producing high-performance metal parts, particularly in aerospace applications. However, the widespread adoption of cold spray AM in critical industrial sectors has raised concerns regarding intellectual property protection and supply chain integrity. This paper presents a dual-fingerprint authentication framework for cold spray AM parts that combines inherent geometric features with engineered magnetic patterns. Our system exploits the stochastic nature of the cold spray process, where process variations and particle deposition dynamics generate unique, physically unclonable geometric patterns on each part’s surface. These geometric fingerprints are captured through a laser-based 3D scanning system. Additionally, we introduce a magnetic fingerprinting methodology by strategically embedding nickel deposits within copper main deposits during manufacturing, creating distinct magnetic signatures that can be detected non-destructively when the part is magnetized. The dual-fingerprint feature can be leveraged by various encryption algorithms to preserve fingerprint confidentiality throughout the supply chain while enabling secure authentication. To demonstrate its effectiveness, we implement a learning-with-error (LWE) encryption scheme where the magnetic fingerprint serves as an encryption key for the geometric fingerprint data. The system authenticates parts by comparing newly scanned geometric fingerprints with decrypted reference data using the magnetic key. This dual-factor authentication approach provides a robust defense against counterfeiting while protecting valuable manufacturing intellectual property, thereby securing the entire supply chain for cold spray AM parts. Experimental validation demonstrates the system’s effectiveness in authenticating parts non-destructively while maintaining stringent security standards, making it particularly suitable for aerospace and other critical applications where part authenticity and supply chain integrity are paramount.

Army Research Laboratory
Openalex Percentile: Top 71%
Additive Manufacturing and 3D Printing Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.