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Foams, essential for applications from car seats to thermal insulation, are limited by traditional manufacturing techniques that struggle to produce graded stiffness, a key feature for enhanced functionality. Here, we introduce a novel slicing algorithm for producing heterogeneous foams through viscous thread printing (VTP). Our slicer generates a single, global toolpath for the entire foam volume while modulating the viscous thread’s self-interactions along this path to program stiffness. The slicer integrates multiple meshes into a unified print space and interpolates the print speed and height based on specified mesh parameters to program the desired stiffness variations. Using both qualitative samples and quantitative compression tests, we demonstrate that our slicer can (1) generate foam stiffnesses spanning an order of magnitude, (2) achieve millimeter precision in stiffness control, and (3) continuously vary stiffness between regions of constant stiffness using arbitrary functional forms. 

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, a self‐driving lab is utilized 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, an underlying self‐stabilizing characteristic of VTP layer thickness is discovered as 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.