Embedded product authentication codes in additive manufactured parts : imaging and image processing for improved scan ability

Chen, Fei and Zabalza, Jaime and Murray, Paul and Marshall, Stephen and Yu, Jian and Gupta, Nikhil (2020) Embedded product authentication codes in additive manufactured parts : imaging and image processing for improved scan ability. Additive Manufacturing. ISSN 2214-8604 (In Press)

[img] Text (Chen-etal-AM-2020-Embedded-product-authentication-codes-in-additive-manufactured-parts)
Chen_etal_AM_2020_Embedded_product_authentication_codes_in_additive_manufactured_parts.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 7 May 2021.

Download (988kB) | Request a copy from the Strathclyde author

    Abstract

    The layer-by-layer printing process of additive manufacturing methods provides new opportunities to embed identification codes inside parts during manufacture. These embedded codes can be used for product authentication and identification of counterfeits. The availability of reverse engineering tools has increased the risk of counterfeit part production and new authentication technologies such as the one proposed in this paper are required for many applications including aerospace components and medical implants and devices. The embedded codes are read by imaging techniques such as micro-Computed Tomography (micro-CT) scanners or radiography. The work presented in this paper is focused on developing methods that can improve the quality of the recovered micro-CT scanned code images such that they can be interpreted by standard code reader technology. Inherent low contrast and the presence of imaging artifacts are the main challenges that need to be addressed. Image processing methods are developed to address these challenges using titanium and aluminum alloy specimens containing embedded quick response (QR) codes. The proposed techniques for recovering the embedded codes are based on a combination of Mathematical Morphology and an innovative de-noising algorithm based on optimal image filtering techniques. The results show that the proposed methods are successful in making the codes scannable using readily available smartphone apps.