MIT breakthrough means there's no material too weird for 3D printing

Thanks to sensors and math, machines can 'learn' to adapt to new mediums

Eggheads at MIT say they have developed a method for 3D printing, which they claim greatly reduces the time taken to adapt machines to using different materials.

One of the drawbacks to 3D printing, which has been hyped as a revolutionary technology for more than a decade, is that changing the printed medium can require lengthy adjustments to the machinery, in something of a trial and error process.

A collaboration between MIT's Center for Bits and Atoms (CBA), the US National Institute of Standards and Technology (NIST), and the National Center for Scientific Research in Greece (Demokritos) has attempted to address that problem.

The team used a 3D printer they had already developed to capture data and provide feedback as it operates. They added three instruments to the machine's extruder – the part that pushes the printing medium along, melts it, and squirts it on the item being printed. The additional instruments were designed to take measurements as the printer did its thing, which are used to calculate parameters.

The setup was designed to measure the pressure being exerted on the print material, the thickness of the feed, and the actual rate at which it is being fed through the printer.

Using a mathematical function to interpret the data – in this case, with parameters determined using the Levenberg-Marquardt algorithm – the researchers proved that the system could automatically identify the parameters of new printing material.

The technique essentially allows 3D printers to automatically optimize their fused filament fabrication (FFF) printing process when using a new material.

In a paper published in the journal Integrating Materials and Manufacturing Innovation, the researchers said: "Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before.

"Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters."

In an interview with MIT News, senior author Neil Gershenfeld, who leads CBA, said: "In this paper, we demonstrate a method that can take all these interesting materials that are bio-based and made from various sustainable sources and show that the printer can figure out by itself how to print those materials. The goal is to make 3D printing more sustainable." ®