Machine learning can replicate toolpaths in 3D printed fiber reinforced parts – IAM Network

A research team from the NYU Tandon School of Engineering has published a study that uncovers vulnerabilities in the production of carbon fiber reinforced 3D printed parts. The vulnerability is not related to the strength of the parts, but rather in protecting their toolpaths and preventing counterfeit parts. The ability to 3D print carbon fiber reinforced polymers is creating numerous exciting applications across the aerospace and industrial sectors, among others. The materials are advantageous for many reasons, but their strength-to-weight ratios and durability are most notable. However, the process of 3D printing these materials, and specifically the extrusion-based process, can actually reveal the construction of the part and its design.