Advancing the Prediction of 3D Printability for Polymer Nanocomposites

The development of new thermoplastic-based nanocomposites for, as well as using, 3D printing requires extensive experimental testing. One typically goes through many failed, or otherwise sub-optimal, iterations before finding acceptable solutions (e.g. compositions, 3D printing parameters). It is desirable to reduce the number of such iterations as well as exclude failed experiments that often require laborious disassembly and cleaning of the 3D printer. This issue could be addressed if we could understand, and ultimately predict ahead of experiments, if a given material can be 3D printed successfully. Herein, we report on our investigations into forecasting the printing and resultant properties of polymer nanocomposites while encompassing both material properties and printing parameters. To do so, nanocomposites of two different commercially available bio-based PLAs with varying concentrations of nanoclay (NC) and graphene nanoplatelets (GNP) were prepared. The thermal and rheological properties of the nanocomposites were analyzed. These materials were printed at varying temperature and flow using a pellet printer. Each time, three identical cylindrical-shaped samples were printed, and to assess the printing quality, the variation in weight and geometrical factors were determined. The interactions between material properties and printing parameters are complex but can be captured effectively by a machine learning model. Specifically, we demonstrate such a predictive model to forecast print quality utilizing a Random Forest algorithm.