3D printing has become increasingly popular, but many of the plastics used in the process are not easily recyclable. New materials for 3D printing are emerging, but setting up the printers for each material can be time-consuming and difficult as the properties of renewable and recyclable materials can vary. To address this issue, researchers at MIT, NIST, and Demokritos have developed a 3D printer that can automatically identify parameters for unknown materials. By modifying the extruder, the printer can measure the forces and flow of the material, allowing it to generate printing parameters automatically.
By automating the process of setting up 3D printers for new materials, this research aims to make additive manufacturing more sustainable. The goal is to allow printers to use bio-based and sustainable materials without the need for manual parameter setting. The research team includes members from MIT’s Center for Bits and Atoms, NIST, and Demokritos, with the results published in the journal Integrating Materials and Manufacturing Innovation. The automatic parameter generation method developed by the team aims to reduce the environmental impact of 3D printing, which typically relies on nonrecyclable materials derived from fossil fuels.
Renewable and recycled materials present unique challenges when used in 3D printing due to variations in material properties. The properties of bio-based polymers and resins can vary based on the composition of plants used, while recycled materials can have differing properties based on the source. The researchers developed a 3D printer and workflow to automatically identify process parameters for unknown materials. By adding instruments to the extruder that measure pressure and flow rate, the team can calculate essential printing parameters such as flow rate and temperature.
The researchers designed a 20-minute test to capture data on temperature and pressure readings at different flow rates, which are then used to generate real parameters for the material and machine configuration. The calculated parameters can be input into 3D printing software to generate instructions for the printer. In experiments with six different materials, including bio-based materials, the method consistently produced viable parameters that resulted in successful prints of complex objects. By automating the parameter generation process, the researchers aim to streamline 3D printing with new and sustainable materials.
Moving forward, the researchers plan to integrate the automated parameter generation process with 3D printing software to eliminate manual parameter input. They also aim to enhance their workflow by incorporating a thermodynamic model of the hot end of the printer, which is responsible for melting the filament. This collaboration between MIT, NIST, and Demokritos is focused on developing computational metrology, where the output of measurements is a predictive model rather than just a parameter. The researchers hope to apply this approach in other areas of advanced manufacturing and expand access to metrology.
The research is supported by the National Institute of Standards and Technology and the Center for Bits and Atoms Consortia. By leveraging computational metrology and automated parameter generation, the research team is making significant strides in making 3D printing more sustainable and environmentally friendly. By enabling printers to adapt to a wide range of renewable and recyclable materials without manual parameter adjustments, this research contributes to the broader goal of reducing the environmental impact of additive manufacturing.
