A Digital Exploration of The Mechanical Property Space of Self-Stabilizing Dynamic Printable Foams

Foams are versatile by nature, and are used ubiquitously in applications ranging from padding and insulation to acoustic dampening. Previous work established that foams additively manufactured via Viscous Thread Printing (VTP) are capable of enabling a greater degree of control over many of the key mechanical properties of conventional foams such as Young’s modulus, fracture characteristics, and toughness while eliminating the need for chemical foaming agents. However, the relationship between input parameters and output properties is currently only accomplished via iterative empirical testing which limits generalizability and predictive control of desired output properties. Our work addresses this by combining high-throughput automated experimentation with machine learning to identify a subspace able to predict material behavior down to the stress-strain curve level. We identify a self-stabilizing microstructure trend in the VTP process, amplifying confidence in achieving desired output properties. Evidence for this self-stabilization is demonstrated by introducing various print height perturbations during the print process and measuring layer thickness as a function of the number of layers before restitution. This predictive mapping was developed utilizing data collected from thermoplastic polyurethane (TPU) specimens before being generalized by applying assumptions inherent to filament-based additive manufacturing to VTP's core physical models. This generalization was then validated using polylactic acid (PLA) and Nylon suggesting inherent compatibility with any material suitable for filament-based 3D printing.