Evidence map›Paper›PMID 39331854›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

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

Brett Emery, Kelsey L Snapp, Daniel Revier, Vivek Sarkar, Masa Nakura, Keith A Brown, Jeffrey Ian Lipton

Abstract read
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In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Brett EmeryDepartment of Mechanical and Industrial Engineering, Northeastern University, 815 Columbus Ave, Boston, MA, 02120, USA.ORCID https://orcid.org/0000-0002-3163-225X
Kelsey L SnappDepartment of Mechanical Engineering, Boston University, 110 Cummington Mall College of Engineering, Boston, MA, 02215, USA.
Daniel RevierDepartment of Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA, 98195, USA.ORCID https://orcid.org/0000-0001-6246-7819
Vivek SarkarDepartment of Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA, 98195, USA.ORCID https://orcid.org/0009-0000-4120-8973
Masa NakuraDepartment of Computer Science and Engineering, University of Washington, 185 E Stevens Way NE, Seattle, WA, 98195, USA.
Keith A BrownDepartment of Mechanical Engineering, Boston University, 110 Cummington Mall College of Engineering, Boston, MA, 02215, USA.ORCID https://orcid.org/0000-0002-2379-2018
Jeffrey Ian LiptonDepartment of Mechanical and Industrial Engineering, Northeastern University, 815 Columbus Ave, Boston, MA, 02120, USA.ORCID https://orcid.org/0000-0003-0843-0999

Funding

National Defense Science and Engineering GraduateNational Science Foundation DMR-2323728NSF IIS-2212049Rafik B. Hariri Institute for Computing and Computational Science and Engineering #2024-07-001
6 · The paper itself

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.

Indexed as

3d printingcellular structuresfoamsself‐driving labsviscous thread instability

Identifiers

PMID39331854
PMCPMC11600193

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.