Literature DB >> 33816132

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

Fei Chen1, Jaime Zabalza2, Paul Murray2, Stephen Marshall2, Jian Yu3, Nikhil Gupta1.   

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.

Entities:  

Keywords:  3D printing; additive manufacturing; cyber-physical system; image processing; product authentication

Year:  2020        PMID: 33816132      PMCID: PMC8017490          DOI: 10.1016/j.addma.2020.101319

Source DB:  PubMed          Journal:  Addit Manuf        ISSN: 2214-7810


  5 in total

1.  Obfuscation of Embedded Codes in Additive Manufactured Components for Product Authentication.

Authors:  Fei Chen; Jian H Yu; Nikhil Gupta
Journal:  Adv Eng Mater       Date:  2019-04-23       Impact factor: 3.862

2.  Using 3D Printing to Create Personalized Brain Models for Neurosurgical Training and Preoperative Planning.

Authors:  Caitlin C Ploch; Chris S S A Mansi; Jayaratnam Jayamohan; Ellen Kuhl
Journal:  World Neurosurg       Date:  2016-02-24       Impact factor: 2.104

3.  A novel classification and online platform for planning and documentation of medical applications of additive manufacturing.

Authors:  Jukka Tuomi; Kaija-Stiina Paloheimo; Juho Vehviläinen; Roy Björkstrand; Mika Salmi; Eero Huotilainen; Risto Kontio; Stephen Rouse; Ian Gibson; Antti A Mäkitie
Journal:  Surg Innov       Date:  2014-03-09       Impact factor: 2.058

4.  Cranial reconstruction: 3D biomodel and custom-built implant created using additive manufacturing.

Authors:  André Luiz Jardini; Maria Aparecida Larosa; Rubens Maciel Filho; Cecília Amélia de Carvalho Zavaglia; Luis Fernando Bernardes; Carlos Salles Lambert; Davi Reis Calderoni; Paulo Kharmandayan
Journal:  J Craniomaxillofac Surg       Date:  2014-08-06       Impact factor: 2.078

5.  Designing patient-specific 3D printed craniofacial implants using a novel topology optimization method.

Authors:  Alok Sutradhar; Jaejong Park; Diana Carrau; Tam H Nguyen; Michael J Miller; Glaucio H Paulino
Journal:  Med Biol Eng Comput       Date:  2015-12-11       Impact factor: 2.602

  5 in total
  1 in total

1.  Automated Process Planning for Embossing and Functionally Grading Materials via Site-Specific Control in Large-Format Metal-Based Additive Manufacturing.

Authors:  Michael Borish; Brian T Gibson; Cameron Adkins; Paritosh Mhatre
Journal:  Materials (Basel)       Date:  2022-06-11       Impact factor: 3.748

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.