Optimization of Silicone 3D Printing with Hierarchical Machine Learning

Abstract

Abstract

Additive manufacturing of soft materials requires optimization of printable inks, formulations of these feedstocks, and complex printing processes that must balance a large number of disparate but highly correlated variables. Here, hierarchical machine learning (HML) is applied to 3D printing of silicone elastomer via freeform reversible embedding (FRE), which is challenging because it involves depositing a Newtonian prepolymer liquid phase within a Bingham plastic support bath. The advantage of the HML algorithm is that it can predict the behavior of complex physical systems using sparse data sets through integration of physical modeling in a framework of statistical learning. Here, it is shown that this algorithm can be used to simultaneously optimize material, formulation, and processing variables. The FRE method for 3D printing silicone parts was optimized based on a training set with 38 trial runs. Compared with the previous results from iterative optimization approaches using design-of-experiment and steepest-ascent methods, HML increased printing speed by up to 2.5 × while retaining print fidelity and also identified a unique silicone formulation and printing parameters that had not been found previously through trial-and-error approaches. These results indicate that HML is an effective tool with the potential for broad application for planning and optimizing in additive manufacturing of soft materials via the FRE method.