Reverse Engineering of 3D-Printed Parts by Machine Learning

A new caution regarding how machine learning can lead to extraction of IP.

Reverse Engineering of 3D-Printed Parts by Machine Learning Reveals Security Vulnerabilities
NYU Tandon School of Engineering
July 1, 2020

Researchers at the New York University (NYU) Tandon School of Engineering have reverse-engineered three-dimensional (3D)-printing toolpaths with machine learning (ML) tools applied to the microstructures of a printed component obtained via computed tomography (CT). The toolpaths are a series of coordinated locations that a tool will follow in computer-aided design file instructions. The researchers captured the printing direction used during 3D-printing from the printed part's fiber orientation through micro-CT scans; as fiber orientation is difficult to spot with the naked eye, the team used ML algorithms trained over thousands of micro CT scan images to anticipate the orientation on any fiber-reinforced 3D-printed model. NYU's Nikhil Gupta said, "Machine learning methods ... used in the design of complex parts ... can be a double-edged sword, making reverse engineering also easier."