3D printed guns: is it now possible to track them?

A new technology developed by engineers has uncovered the unique 'fingerprints' of 3D printers, which could help trace weapons and counterfeit goods.

PinTracker could ultimately help intelligence agencies track the origin of 3D-printed guns, counterfeit products and other goods - image courtesy of University of Buffalo.
PinTracker could ultimately help intelligence agencies track the origin of 3D printed guns, counterfeit products and other goods – image courtesy of University of Buffalo.

The advancement, which the research team from the University of Buffalo calls “PrinTracker,” could potentially help police and intelligence agencies track the origin of 3D printed guns, counterfeit products and track high value IP.

While 3D printing has many positive and exciting applications; 3D printed housing, additive manufacturing could fix dying coral reefs, or even prevent corneal blindness in the future, not to mention lower emissions with smart turbine designs. It also lends itself to the production of counterfeit goods.

It has the potential to make firearms more readily available to people who are not authorised to have them.

Every 3D printed object has a ‘fingerprint’

Like an inkjet printer, 3D printers move back-and-forth while ‘printing’ an object. Instead of ink, a nozzle discharges a filament, such as plastic, in layers until a three-dimensional object forms.

Each layer of a 3D printed object contains tiny wrinkles, usually measured in submillimeters, these are called in-fill patterns.

These patterns are supposed to be uniform. However, the printer’s model type, filament, nozzle size and other factors cause microscopic imperfections in the patterns.

3D printers are built to be the same. But, there are slight variations in their hardware created during the manufacturing process that lead to unique, inevitable and unchangeable patterns in every object they print.

Testing PrinTracker

PinTracker could curb 3D printed counterfeit products.
The research team tested PinTracker and had a 99.8% success rate – image courtesy of Depositphotos.

To test PrinTracker, the research team created five door keys each from 14 different 3D printers.

With a standard scanner, the researchers created digital images of each key. From there they enhanced each image, identifying elements of the in-fill pattern.

They then developed an algorithm to align and calculate the variations of each key to verify the authenticity of the ‘fingerprint’.

Having created a ‘fingerprint’ database of the 14 3D printers, the researchers were able to match the key to its printer 99.8% of the time.

They ran a separate series of tests 10 months later to determine if additional use of the printers would affect PrinTracker’s ability to match objects to their machine of origin. The results were the same.

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