Post-Print Physical Vetting of 3D Prints with Minimal Embedded Nano-Material Assessor

Saman Aliari Zonouz (Inventor), Mehdi Javanmard (Inventor), Abdul Beyah (Inventor), Luis Garcia (Inventor), Tuan-Anh Le (Inventor), Christopher Bayens (Inventor)

Research output: Innovation

Abstract

Overview of the Model System


Invention Summary:

3D printing has become highly desirable in industrial manufacturing and biomedical applications. Outsourcing the manufacturing to a 3D printing facility leaves the user without access to the printers and a lack of physical verification to determine whether small defects, invisible to the naked eye, have been inserted. Like other automated systems, there is a need for safety parameters and end-user verification.

Researchers at Rutgers University and Georgia Tech developed a method for physical verification of 3D printed structures. This method allows for real-time detection and post-production verification of erroneous prints using three methods 1) spectroscopic validation via a user-defined nano-material based barcoded filament, 2) acoustic validation via recorded audio generated by the printer and compared to a reference, and 3) gyroscopic replication via a recorded trajectory followed by the printer head and compared to a reference. The techniques verify the position and notify the user if a cyber-physical attack has occurred.


Market Applications:

  • Medical devices and implants
  • Additive manufacturing industry
  • Industrial cyber-physical security
  • Automobile manufacturing

Advantages:

  • Reliably identifies intrusions
  • End-user verification
  • Real-time controllers

Intellectual Property & Development Status:

Patent pending. Available for licensing and/or research collaboration.

Academic Publication:

Bayens, C. et al . See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Pattern Detection in Additive Manufacturing .  In Proceedings of the 26th USENIX Conference on Security Symposium (SEC'17), Engin Kirda and Thomas Ristenpart (Eds.). USENIX Association, Berkeley, CA, USA, 1181-1198.

Original languageEnglish (US)
StatePublished - Feb 2019

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3D printers
Printing
Intellectual property
Outsourcing
Patents and inventions
Medical applications
Automobiles
Acoustics
Trajectories
Defects
Controllers
Industry

Cite this

Zonouz, Saman Aliari (Inventor) ; Javanmard, Mehdi (Inventor) ; Beyah, Abdul (Inventor) ; Garcia, Luis (Inventor) ; Le, Tuan-Anh (Inventor) ; Bayens, Christopher (Inventor). / Post-Print Physical Vetting of 3D Prints with Minimal Embedded Nano-Material Assessor.
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abstract = "Overview of the Model System Invention Summary: 3D printing has become highly desirable in industrial manufacturing and biomedical applications. Outsourcing the manufacturing to a 3D printing facility leaves the user without access to the printers and a lack of physical verification to determine whether small defects, invisible to the naked eye, have been inserted. Like other automated systems, there is a need for safety parameters and end-user verification. Researchers at Rutgers University and Georgia Tech developed a method for physical verification of 3D printed structures. This method allows for real-time detection and post-production verification of erroneous prints using three methods 1) spectroscopic validation via a user-defined nano-material based barcoded filament, 2) acoustic validation via recorded audio generated by the printer and compared to a reference, and 3) gyroscopic replication via a recorded trajectory followed by the printer head and compared to a reference. The techniques verify the position and notify the user if a cyber-physical attack has occurred. Market Applications: Medical devices and implants Additive manufacturing industry Industrial cyber-physical security Automobile manufacturing Advantages: Reliably identifies intrusions End-user verification Real-time controllers Intellectual Property & Development Status: Patent pending. Available for licensing and/or research collaboration. Academic Publication: Bayens, C. et al . See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Pattern Detection in Additive Manufacturing .  In Proceedings of the 26th USENIX Conference on Security Symposium (SEC'17), Engin Kirda and Thomas Ristenpart (Eds.). USENIX Association, Berkeley, CA, USA, 1181-1198.",
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Post-Print Physical Vetting of 3D Prints with Minimal Embedded Nano-Material Assessor. / Zonouz, Saman Aliari (Inventor); Javanmard, Mehdi (Inventor); Beyah, Abdul (Inventor); Garcia, Luis (Inventor); Le, Tuan-Anh (Inventor); Bayens, Christopher (Inventor).

Research output: Innovation

TY - PAT

T1 - Post-Print Physical Vetting of 3D Prints with Minimal Embedded Nano-Material Assessor

AU - Zonouz, Saman Aliari

AU - Javanmard, Mehdi

AU - Beyah, Abdul

AU - Garcia, Luis

AU - Le, Tuan-Anh

AU - Bayens, Christopher

PY - 2019/2

Y1 - 2019/2

N2 - Overview of the Model System Invention Summary: 3D printing has become highly desirable in industrial manufacturing and biomedical applications. Outsourcing the manufacturing to a 3D printing facility leaves the user without access to the printers and a lack of physical verification to determine whether small defects, invisible to the naked eye, have been inserted. Like other automated systems, there is a need for safety parameters and end-user verification. Researchers at Rutgers University and Georgia Tech developed a method for physical verification of 3D printed structures. This method allows for real-time detection and post-production verification of erroneous prints using three methods 1) spectroscopic validation via a user-defined nano-material based barcoded filament, 2) acoustic validation via recorded audio generated by the printer and compared to a reference, and 3) gyroscopic replication via a recorded trajectory followed by the printer head and compared to a reference. The techniques verify the position and notify the user if a cyber-physical attack has occurred. Market Applications: Medical devices and implants Additive manufacturing industry Industrial cyber-physical security Automobile manufacturing Advantages: Reliably identifies intrusions End-user verification Real-time controllers Intellectual Property & Development Status: Patent pending. Available for licensing and/or research collaboration. Academic Publication: Bayens, C. et al . See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Pattern Detection in Additive Manufacturing .  In Proceedings of the 26th USENIX Conference on Security Symposium (SEC'17), Engin Kirda and Thomas Ristenpart (Eds.). USENIX Association, Berkeley, CA, USA, 1181-1198.

AB - Overview of the Model System Invention Summary: 3D printing has become highly desirable in industrial manufacturing and biomedical applications. Outsourcing the manufacturing to a 3D printing facility leaves the user without access to the printers and a lack of physical verification to determine whether small defects, invisible to the naked eye, have been inserted. Like other automated systems, there is a need for safety parameters and end-user verification. Researchers at Rutgers University and Georgia Tech developed a method for physical verification of 3D printed structures. This method allows for real-time detection and post-production verification of erroneous prints using three methods 1) spectroscopic validation via a user-defined nano-material based barcoded filament, 2) acoustic validation via recorded audio generated by the printer and compared to a reference, and 3) gyroscopic replication via a recorded trajectory followed by the printer head and compared to a reference. The techniques verify the position and notify the user if a cyber-physical attack has occurred. Market Applications: Medical devices and implants Additive manufacturing industry Industrial cyber-physical security Automobile manufacturing Advantages: Reliably identifies intrusions End-user verification Real-time controllers Intellectual Property & Development Status: Patent pending. Available for licensing and/or research collaboration. Academic Publication: Bayens, C. et al . See No Evil, Hear No Evil, Feel No Evil, Print No Evil? Malicious Fill Pattern Detection in Additive Manufacturing .  In Proceedings of the 26th USENIX Conference on Security Symposium (SEC'17), Engin Kirda and Thomas Ristenpart (Eds.). USENIX Association, Berkeley, CA, USA, 1181-1198.

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