Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing

TL;DR

This paper introduces a machine learning-driven approach to optimize foam 3D printing via Viscous Thread Printing, enabling programmable mechanical properties and complex structures with predictable process-property relationships.

Contribution

It demonstrates a self-driving lab that combines automated experimentation and machine learning to identify process parameters for programmable foam properties using VTP.

Findings

Self-stabilizing layer thickness characteristic of VTP

Successful creation of complex foam structures like gradient slabs and living hinges

Predictive models validated for TPU and PLA filaments

Abstract

Foams are versatile by nature and ubiquitous in a wide range of applications, including padding, insulation, and acoustic dampening. Previous work established that foams 3D printed via Viscous Thread Printing (VTP) can in principle combine the flexibility of 3D printing with the mechanical properties of conventional foams. However, the generality of prior work is limited due to the lack of predictable process-property relationships. In this work, we utilize a self-driving lab that combines automated experimentation with machine learning to identify a processing subspace in which dimensionally consistent materials are produced using VTP with spatially programmable mechanical properties. In carrying out this process, we discover an underlying self-stabilizing characteristic of VTP layer thickness, an important feature for its extension to new materials and systems. Several complex exemplars are constructed to illustrate the newly enabled capabilities of foams produced via VTP, including 1D gradient rectangular slabs, 2D localized stiffness zones on an insole orthotic and living hinges, and programmed 3D deformation via a cable driven humanoid hand. Predictive mapping models are developed and validated for both thermoplastic polyurethane (TPU) and polylactic acid (PLA) filaments, suggesting the ability to train a model for any material suitable for material extrusion (ME) 3D printing.