Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing DOI Creative Commons
Brett Emery, Kelsey L. Snapp, Daniel Revier

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

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

Language: Английский

A feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors DOI Creative Commons
Nick Willemstein, Saivimal Sridar, Herman van der Kooij

et al.

Wearable Technologies, Journal Year: 2025, Volume and Issue: 6

Published: Jan. 1, 2025

Abstract Sensorized insoles provide a tool for gait studies and health monitoring during daily life. For users to accept such insoles, they need be comfortable lightweight. Previous research has demonstrated that sensorized can estimate ground reaction forces (GRFs). However, these often assemble commercial components restricting design freedom customization. Within this work, we incorporated four 3D-printed soft foam-like sensors sensorize an insole. To test the had nine participants walk on instrumented treadmill. The behaved in line with expected change pressure distribution cycle. A subset of data was used identify personalized Hammerstein–Wiener (HW) models 3D GRFs while others were validation. In addition, identified HW showed best estimation performance (on average root mean squared (RMS) error 9.3%, $ {R}^2 =0.85 absolute (MAE) 7%) vertical, mediolateral, anteroposterior GRFs, thereby showing resulting force reasonably well. These results comparable or outperformed other works force-sensing resistors machine learning. Four participated three trials over week, which decrease time but stayed 11.35% RMS 8.6% MAE after week seeming consistent between days two seven. show promise using piezoresistive system identification regarding viability applications require softness, lightweight, customization as wearable (force) sensors.

Language: Английский

Citations

0
A novel 3D food printing technique: Achieving tunable porosity and fracture properties via liquid rope coiling DOI Creative Commons
Aref Ghorbani,

Sophia Jennie Giancoli,

Seyed Ali Ghoreishy

et al.

Innovative Food Science & Emerging Technologies, Journal Year: 2025, Volume and Issue: unknown, P. 104022 - 104022

Published: April 1, 2025

Language: Английский

Citations

0
Foams with 3D Spatially Programmed Mechanics Enabled by Autonomous Active Learning on Viscous Thread Printing DOI Creative Commons
Brett Emery, Kelsey L. Snapp, Daniel Revier

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

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

Language: Английский

Citations

0